Fused ThinkingFused Thinking represents our latest thinking in how retailers can grow their sales and profits through better data, enhanced decision-making processes and analytics. Feel free to scan our posts for the particular topic most relative to you, and then reach out to us when you are ready to talk more.
2014 Canadian Retail Sales to Come in at 2.7%
Canadian retail sales are expected to continue their slow but steady climb next year, rising from 2.0% growth in 2013 to 2.7% in 2014.
Prior to the recession in 2008, Canadian retail sales grew at 6.5% year-over-year. However, from post-recession 2009 to 2012, retail sales only grew on average 2.5%. When looking at the underlying drivers of retail sales - the trends, oscillations, LY performance, lag impacts and tipping points - it is unlikely that retail sales growth will return to 6.5% until we see a dramatic rise in Discretionary Income (the amount of money Canadians have left after paying their taxes and cost of living expenses).
Discretionary Income growth is expected to stabilize near the historic growth rate in 2014 due to two offsetting trends. First, inflation will approach its norm of 0.8% in 2014; this higher inflation rate will dampen Discretionary Income growth. Second, the slow decline of the unemployment rate will continue into 2014, increasing discretionary income so that the net impact will be stable Discretionary Income growth.
If the unemployment rate drops below its tipping point of 6.5%, Canada will experience accelerated wage gains, which will increase consumers' discretionary income and boost retail sales. The good news is that despite the statistical noise in monthly unemployment data, there is a clear trend that the unemployment rate is slowly falling. At the current rate, unemployment is not expected to hit the 6.5% threshold until spring 2015.
Cross-border shopping has been a major concern among Canadian retailers for it now siphons off 2.9% of the market each year. With Bay Street forecasting depreciation in the Canadian Dollar next year, cross-border shopping growth will be flat to negative (at 2.9% of retail spend or less), causing no further erosion to Canadian retail sales.
E-commerce has been hailed the "next big thing in retail" for the last twenty years. However, it represents only 3.5% of total retail sales and will grow to 3.9% in 2014. This news will likely be reported as 10% growth (3.9/3.5 -1 = 10%), but this is just a denominator trick. The right way to look at e-commerce growth is to consider it as a percent of total retail, which is 0.4% growth. To put it another way, if the rest of retail is worth $96 and e-commerce is worth $4, and if regular retail grows by 3.5% next year but e-commerce grows by 10%, which is going to contribute more to a retailers bottom-line in 2014? The answer is good old fashion bricks and mortar. Here is the math: $96 x 3.5% = $3, but $4 x 10% = $0.4.
Warmer temperatures can jumpstart the spring season, a key sales period for many retailers. This is exactly what happened in 2012, which experienced the warmest March in over 10 years, pulling seasonal sales forward and kicking off a warmer-than-average year. 2013 retail sales were therefore disappointing as it not only had to roll over fantastic 2012 numbers but also had to contend with below-norm temperatures. However, what is bad news one year is good news in the next. Retailers will see year-over-year temperature increases in 2014, which translates to higher sales for most retail categories.
Consumers who have recently moved houses purchase from more categories and spend more than other retail consumers, making home turnover - a measure of recent movers - a major factor in overall retail sales growth. Due to a very soft roll over in 2013, home turnover will be positive throughout all of 2014. Although there is a potential housing bubble in Canada, there has been no bubble in home turnover since 2009, and so we expect home turnover in 2014 to follow its long-term trend.
Consumer debt continues to rise in Canada, but in many ways this is a misleading metric. While debt is increasing, income is also rising. When coupled with historically low interest rates, debt payment as a percent of income has fallen considerably since 2008 and shows no sign of increasing in any material way in 2014.
The population of Canada will grow 1.3% in 2014, but the working age population will grow just 0.7%. The % of visible minorities in Canada continues to grow but beyond food and beauty products, we find there is no material difference between the buying habits of visible minorities and other Canadians.
Barring the occurrence of an unexpected international economic crisis (e.g. China's property bubble, US debt default), Canadian retail sales growth will increase from 2.0% to 2.7% in 2014 and march to 4.0% in 2015.
Stop Cleaning The Potatoes
Retailers are already drowning in data, most of it noisy, inaccurate, blind to opportunity, and therefore wrong. The solution isn't more data, but a whole new approach to analytics.
Decades ago, Japanese scientists studying Macaque monkeys on Koshima island would bring dirty sweet potatoes to try to bond with the animals. On one visit an amazing thing happened; the scientists saw one Macaque starting to use water to clean off the dirt. Soon, other Macaques learned to wash their potatoes too.
Year after year the Macaque monkeys would wash their potatoes. It even became a tourist attraction. It has been years since the scientists brought dirty potatoes to Koshima (today, you can only buy cleaned potatoes in Japan). But what do the monkeys do? These creatures of habit still keep washing their potatoes, doing things as they have always done them, even if it no longer makes sense. Canadian retail experiences the same phenomenon today.
The need for Canadian retailers to adopt a new school of thinking has never been more urgent. The simple truth is that retailers in Canada don't grow like they used to. The times of a rising tide raising all ships is over, and it's not likely to come back any soon. Yet the same approach to business planning persists many retailers continue to wash the potatoes. They aren't looking to do things differently; they are looking to do more of the same at a faster pace.
Many traditional retailers are shrinking or closing. They used to increase same-store-sales-growth at an average rate of 6 to 8 percent per year. In the past few years, though, growth has been reduced to between zero and 2 percent. Most retailers have grown by opening more and more stores, to the point where they can't count on retail expansion as their main source for growth. With easy growth gone, they need to focus on stealing share from others.
Why is growth slowing? There are many larger economic reasons beyond the control of individual retail CEOs. But a lot of the problems are homegrown, and failure to apply a proper strategic approach to decision making leads the charge.
The solution to date has been "do even more of the same, and do it faster". One example is today's infatuation with "big data". Today the world produces data in the range of one Exabyte every single day. Five Exabytes equals "all words ever spoken by human beings", according to a 2003 research study . Software vendors are pitching retailers on the incredible potential of tapping into this massive amount of data, and the ability to slice and dice more data in more ways. But more insight does not necessarily equal meaningful insight.
Ever since computers have been introduced to businesses, retailers have tracked sales data and come up with innovative ways to crunch more numbers more quickly. Since then, the amount of data has increased by leaps and bounds, but the strategic thinking and the habits of data analysis have largely stayed the same. Retailers install new information technology and hire more analysts to "better wash the potatoes", yet nobody steps back to ask whether the potatoes are already clean, need to be washed differently, or should be replaced by another edible plant.
The outcome is that many retailers are spending vast amounts of money and resources answering the wrong questions, doing even more of the same, and doing it faster. All the while CEOs are left without the information they need to find new growth opportunities.
Many retail executives we talk to feel they are just going through the motions. They go to the weekly Monday sales review meetings because they have to, and they know they are just reviewing the "noise" from last week. In this type of analysis, a merchant is a hero one week for hitting the numbers due to a money-losing promotion, and a loser the next week as they roll over a strong number from last year. Many executives view these reviews as a game they have to play while drowning in a sea of noise and meetings.
But it goes beyond the weekly sales meeting ritual. Strategic planning itself is not effective. Many managers are asking themselves "Why does strategic planning consume so much time and have so little impact on company actions?" . This issue needs to be addressed, so that the CEO and the executive team get the right information: analysis that is not limited by departmental bias or the tendency to be "precisely wrong" instead of "directionally right".
We are not arguing that retailers shouldn't strive to make decisions based on data, logic and analytics; on the contrary. It is the approach of how data and analytics are used that is flawed. As long as retailers don't restructure their organizations for better decision making, they have little need for detailed loyalty data and customer insights, or for more and faster data of any kind. The retail landscape in Canada is in the middle of dramatic change, and only a few retailers have embraced the proper usage of analytics in a way that truly gives them a competitive edge.
If retailers do only one thing in 2014, they must shift their thinking on how they make decisions. They must learn to stop cleaning the potatoes and change their approach to how decisions are made.
My Analytical Story
I have loved business my whole life. When I was a little boy, my grandma would take me down to the local toy store and I would buy Monopoly money (you didn't have to buy the board game, you could just buy extra money). Then, I would put on the suit my parents bought for me and walk around with the monopoly money in my pockets feeling like I was a businessman. The first business I was part of was selling corn on the side of the road with my Aunt Coreen. It was just so amazing - we would have to decide the price of the corn, make signs, pick the corn, find the perfect spot on the road to sell it and work hard all afternoon servicing our customers. I loved every part of it, and I can't wait to run a small business like this with my kids. My second business was in Junior high school. My Dad would travel to the US for business and buy a case of peanut M&Ms at the US Costco. I would buy them off my dad for $0.30 each and sell them for $0.50 a pack. It was tons of fun and I would sell out my supply in a few days and then have to wait until my Dad went down to the US again. In grade 10 I read my first business book, "What they don't teach you at Harvard Business School", which kicked off my love of reading. I went to University to major in marketing but quickly also fell in love with economics, management accounting, financial analysis and statistics.
My second job after University was at a small financial institution in Alberta where I was in the marketing department. It took me a while but eventually I realized a great business is founded on insight, and insight is best built off analysis, which means mastering accounting, finance, statistics and consumer insights. This path eventually let me to the conclusion that the very best insights come from fusing multiple disciplines together. The first campaign I had to put together was for mortgage season. Every bank, including ours, would advertise mortgages using mass-media and targeted direct mail to existing customers. To kick off my thinking of how to do the campaign I went down and talked to a few mortgage managers and asked "how do consumers decide where to get a mortgage?". One manager's response was the nugget of insight I was looking for - that all our customers look around for the best rate and then use that as negotiating power to chew us down on our rate and it was almost our decision to match that rate or let the customer go. All our competitor's customers were doing the same, phoning us to find a better rate but their current bank would often match it and keep the business. So the key to getting a mortgage was not building a marketing campaign around a great rate unless we had a fundamentally different business model that had a lower cost structure to allow us to quote a rate the competitors won't match. The key had to be something different - you had to get them as a customer first, then you had the chance to win their mortgage. So how do consumers decide which bank is their main bank? Whose customer is whose? That I had to research.
So I went back to my office and dove into our CRM and what I found formed the initial foundation of what today Fusion calls the Revenue Opportunity Chain: that the most important way to segment a customer is not by demographics, or life-stage, or psychographics, but by their orientation to the business. We needed to determine whether they were an existing core customer, marginal customer or non-customer, and this classification would drive our marketing, messaging and pricing. In our trade area (where we had branches) approximately 10% of the population had a chequing account with us, and during a mortgage campaign we would advertise to the whole population. However, 95% of our business would come from the 10% of the population that already had a chequing account with us. The same for RRSPs. The same for any campaign. And the real kicker was we had no strategy actually aimed at getting more chequing accounts, even though that was by far the determinate factor driving the business' success. This insight was transformational; the way to get more mortgages wasn't to advertise mortgages more but to advertise chequing accounts more. The way to get mortgages wasn't through a lower interest rate campaign (though we did need to aggressively match rates) but through more tellers, higher chequing overdrafts, more ATMs throughout town. If you remember back to the 90s all the retail banks were cutting hours, having branches with no tellers or even closing branches. But what were we doing? Adding hours, adding tellers, adding branches and improving our chequing services. And all of this success started with insight, it started with me walking down to the branch floor and listening to the front line staff and then fusing this insight with data-mining, market research and financial analysis to uncover something nobody else knew in retail banking 15 years ago.
Years later when we were proven right in our strategy I started to wonder "why did we uncover this and not the banks? Why was their strategy so flawed when they had access to way more information, analysis, insight and consultants?" What I began to realize is most decisions are first made not with insight, but with an idea. The idea could be the "internet is the next big thing" or "ethnic consumers are growing" or "we need a loyalty program." Usually these ideas are smart and hard to argue with. Everyone knows the internet is the future. It is what Nobel Prize winner Paul Krugman calls "what the Very Serious People know." All the Very Serious People know the internet is the future, and if you don't understand this you are not Very Serious. And they are right, but in important ways they are also wrong. This approach of coming up with an idea and then testing if it is true, what you could call developing a hypothesis and then doing analytics to validate it, is extremely blinding, for often something can be true but still not be the best path. Today our firm Fusion Retail Analytics calls this "bottom-up thinking" vs. the better approach of "top-down thinking." When faced with a problem or decision, we believe the key is to start the conversation not with "how did X do?", or "how do we do X better?" or even "should we do X?". At Fusion we believe that instead of starting with the "X", the resource, we start with the issue, asking "what is the best way in the next 12 months to grow market share?", and working our way down. This sounds simple but it is transformational. I believe it is the main reason we uncovered the right way to do retail banking, and is the main reason Fusion is able to bring new thinking to the table and really take retail analytics to a whole new level.
After the financial institution I went for my MBA in London Ontario at Ivey. During my summer internship at a major fast food chain, I learned another valuable lesson: that business is about making the right decisions, and decisions are based on how we view the world, the results we see from the actions we have taken in the past, how we size up competitors, how we see consumers and how we understand our financials. All of this means we need the right data, the right analysis, the right metrics and ultimately the right questions. Nobody at the company was spending any time worrying about collecting the right data, data quality, building better analytical formulas, understanding how the metrics worked or if we had the right metrics at all. One metric that was beyond the pale was the percent mix of sales of a new product. This was basically the only metric the company used to measure a new product's success: the higher the mix the better. But here is the rub - almost all new products had a lower profitability than the existing mix, so if the new product was just cannibalizing other products then the company was worse off, but if the new product sales were incremental sales then it was winning. So, my view at the time was why wasn't the metric incremental sales instead of product mix?
After thinking about this for eight years I have decided the best answer to why these horrible metrics won't die is because most people shy away from ambiguity. People prefer a hard, cold precise number over a fuzzy approximation, even in situations where a fuzzy number like incremental sales is clearly more accurate (i.e. will lead to a better insight and action) than using the more precise but at best useless, and at worst counterproductive, metrics like sales mix and flyer sales.
When I started to notice these poor metrics I looked around and what I saw got me extremely depressed. We live in a sea of horrible metrics. Retailers ask their customers if they were satisfied with their last purchase but (and this is a very large but) we only ask it to those that were satisfied enough to make a purchase. We are literally only asking satisfied people if they are satisfied. For flyer performance we measure flyer sales, or flyer sales as a percent of total sales or flyer sales per square inch. But if all you are doing is putting something on sale that you would have sold anyway, and you're not driving incremental sales, then you are not helping your top line and you are killing your bottom line. Despite these inherent flaws, it is almost impossible to get rid of these horrible metrics.
But metrics are the fundamental way we learn at companies; they form the information that builds decisions. In my view nothing is more important than fixing them. My mission in life, the mission of Fusion, is to bring better questions, metrics, formulas and data to retailers. When I started Fusion six years ago I had two concepts in mind:
- That retailers would benefit from better metrics across all disciplines, from marketing to merchandising to pricing, real estate, operations, inventory, etc.
- That retailers have a need for historical tracking, but there is a blind spot regarding the equivalent need for forward-looking opportunity modeling. Regardless of the discipline, be it price elasticity tests, econometric-based modeling for media mix or run-rates over LY for inventory demand forecasting, almost all analysis is focused around just historical measures, which is important but just half of the puzzle. There is a big win to adding opportunity modeling to the mix.
It also seems to me that over the last six years the retailers that are growing dramatically are the ones who grab onto this kind of thinking and embrace it. The future of retail analytics is bright, if retailers have the knowledge and motivation to ask the right questions and seek the right insights. The good news is we are starting to make a difference; we now have a dozen retailers working with Fusion on a whole new and better way to use information to make decisions. One of our clients calls it "taking the red pill." In the movie "The Matrix", the main character Neo is offered the choice between a red pill and a blue pill. The blue pill would allow him to remain in the fabricated reality of the Matrix. The red pill would lead to his escape from the Matrix and into the real world. For Fusion the red pill means beginning to think less about tracking and more about opportunity modeling, less about bottom-up thinking and more about top-down analysis and worrying less about being precise and more about being accurate. For 2014 we hope even more retailers take the red pill.
The Faster He Mows The Faster It Grows
Moving from more noise to finding the opportunity
Two years ago I met a retail analyst who told me "I feel like Ali Sard from that Dr. Seuss story." I had to head home and dig through my son's collection of Dr. Seuss books before I understood the analogy.
Ali Sard is a character from the book "Did I ever tell you how lucky you are" . The book is about horrible jobs, and Ali's is to mow his uncle's yard. The problem is that the yard is so huge that by the time Ali is done, the grass has grown so much that he needs start over, right away. As the story goes, "the faster he mows, the faster it grows." This is exactly what this retail analyst is going through every day of his work life.
He suffers because his bosses (and many other retail executives) look at analytics the same way that Ali's uncle looks at his lawn in this children's story. They are trying to mow acres of grass with faster and bigger mowers. But as the speed and power of our computers increases, so does the desire to look at more data, in greater detail. Retailers used to create monthly sales reports, then weekly, and now they are daily! Yet sales growth still comes in at two to three per cent. All of this noise is hurting, not helping.
Many retailers have large teams of analysts creating detailed sales reports that keep the CEO and executive team up to speed on everything that goes on in every store across the country. An analyst's job boils down to answering one key question with precision: how did we do against plan last week?
Sure enough, after every Monday sales review meeting, there is a question. For example, why were we down in the West? And the analysts have to drill down into the available data. Unfortunately they don't have easy access to industry data which would explain almost all of the week-to-week noise. At Fusion, we have recently completed an extensive study of eight major retailers in North America and found that on average 96% of their weekly sales variances were due to either industry changes or shifts in the retailer's promotional calendar from one week to another. Only 4% were due to anything that could be considered insight. These retailers are being completely blinded by data.
Worse yet, it is a dirty little secret among analysts that changing the pre-period in a formula by just one week - let's say from a six week pre-period to seven weeks - allows them to swing the results by one to two per cent. Most programs are lucky to lift sales by two to three per cent, but the noise in most analytical models, even under test and control, is more than three per cent! This makes the whole measurement exercise unusable at best, and leads to the wrong decisions at worst. Most of the time it is just multi-million dollar retail voodoo, or what derivatives-trader-turned-essayist Nassim Taleb would call being "fooled by randomness."
Even if the analyst does manage to filter out the 96% noise, they are only doing analysis with one dataset: sales, transactions and unit analysis. Great surgeons don't use just one kind of surgical tool, and great retail analysis is the same; it requires access to a complete set of analytical tools. However, for most retailers each department only gets one tool, and for the weekly Monday meeting the only tool allowed is sales analysis. So the analysts try their best to uncover the story by looking at the available point-of-sale data, diving deeper into sub-categories and geographical pockets. Just when they think they have found the answer, the topic of the week is over and a new week begins with a new set of reports and questions.
If the analysts can't keep up with demand, retail executives hire more analysts or approve better software. As a result, the teams of analysts get bigger which leads to more reports which leads to more questions. Around and around they go, mowing more and more grass. Too often retail decision makers pursue non-critical information - information that appears relevant but isn't. That's why today's retail CEOs receive more and more data and invest more and more in analytics. It also explains why their decisions don't get any smarter. Retailers need to stop trying to do things better and faster, and start to do things differently.
The solution is to move away from cult of tracking and daily/weekly performance through a fog of noise, and instead embrace "Opportunity Modelling" which seeks out clarity on where the largest investment opportunities are for the company and aggressively align resources to them.
There is little planning action to be taken from tracking. Retail executives already intuitively know the data they are seeing is not working, but only a few have started to rethink the whole approach, that it isn't about more data, but about moving from tracking to opportunity. Then, and only then, can you track to measure how well you executed on the plan. Tracking data in of itself, be it sales data or consumer insight data, can never tell you what to do.
Opportunity modeling means we don't look at historical performance, we look at future opportunity. Let's use measure a TV brand campaign as an example. How can a retailer know what the results of a TV branding campaign should look like if they have never done one? How can they know if a brand campaign focusing on Category A is better than the campaign they never ran on Category B? There is no historical tracking data for most of the questions that matter in retail. This is why tracking leads to incremental improvement on what you are already doing best; it keeps you at the industry growth rate of 3%, but doesn't create transformational growth at 10%. Tracking simply does not identify insight that will have a material impact on your business.
Nate Silver, the famous American political statistician, likes to use the analogy of poker. You can actually play the best strategy given your cards but lose the round, and you can play the wrong strategy given your hand but win the round. The short-term can lie to you, as it is full of noise. A TV campaign might have worked or not worked for dozens of reasons and noise, but if you really want to drive your business forward you shouldn't care about the short-term noise, you want to be focused on the right opportunity. This means aligning to the safest bet, which in TV is focused on categories with large market size, high margins, high brand upside and good flow-through from visits to conversion. Consistently doing this will win you the game.
By moving analysis from mowing more backward looking data ("how did we do?") to a forward-looking formula ("where is the opportunity?"), you will enable the right conversation, and insight.
What Is The Purpose Of Tracking
"Scanning for insight" is the most common reason we hear from retailers on why they use a tracking dashboard. Many retailers scan inventory, margin, labour, market share, visit counters, customer complaint reports, brand equity reports and so forth every week, looking for insight. But with so much data it isn't easy to get it all on one dashboard. We have seen some dashboards get down to 6-point font in order to slam it all in. Yet still there is more data to scan, and so the next step is another dashboard - a "dashboard of dashboards".
Why do retailers spend so much time and money on tracking and dashboards? It is not uncommon for a major retailer to spend tens of millions on IT software with the promise of faster access to data and the ability to generate daily sales by subcategory, broken down by region. There is no lack of want for more and more tracking, with the push towards "Big Data" driving this even further. Yet even with all this data and all these dashboards, Executives keep saying "this is very interesting, but what can I do with this information? What action can I take?" I guarantee you the solution is not more dashboards, nor more data or software. The whole approach needs to change.
A typical monthly dashboard can be wrong due to shifts in the underlying classification of products, shifts in holidays, one-off shifts in budgeting (e.g. moving a major promotion from one month to another), noise in the last year line, blindness to industry trends vs. retailer-controllable performance, and on and on. In most of these reports retailers are seeing one-off movement in the numbers (i.e. the noise) and trying to explain it. You will hear comments like "Oh, sales went up here because we ran that Scratch and Save event." But shouldn't we look at the other weeks that ran Scratch and Save before jumping to conclusions? Could this have been a one-off? Could it have been due to something else that week? The noise is bad enough, but layered on top of it is anecdotal soothsaying. Tracking as it is done today isn't about seeing the actual trends at all, for actual scanning requires factoring in the nuance (e.g. the shift in holidays); it requires seeing a consistent pattern over time and integrating multiple sources of information to give a reconciled understanding. In other words, scanning can never be a templated report.
But noise isn't the only issue. A dashboard is not only used for scanning for insight but can also be used to track performance. If the objective is to move brand awareness from A to B, then you obviously need a scorecard to see if the objective is met. But what is obvious is not that easy. For example, let's say the CMO's objective from the CEO is to increase brand awareness from 40% to 45%. Sounds simple enough to measure. But one of the largest drivers of brand awareness is store coverage area, and as the retailer builds out its network of stores, regardless of marketing activity, brand awareness will go up. Now we need more analysis to figure out how much of the gain is due to marketing and how much can be credited to simply opening stores. And this is just factoring in one variable, store expansion. What about a new competitor entering the market? That will create share of voice issues which can impact awareness, so even if marketing has the most appropriate messaging and media mix the retailer can still lose awareness. Shouldn't that be factored into the objective? What good is an objective that isn't controllable? Retailers end up with simplistic objectives that are meaningless garbage, or metrics so complex nobody understands them. When you add it all up, tracking is mostly fog and noise. You end up being blinded by the data. It is what Nassim Taleb calls "being fooled by randomness."
There is another way: what we have been working on now for fifteen years and call Opportunity Modeling. The essence of Opportunity Modeling is to be able to cross-compare multiple activities and resources across disciplines, categories and geographies to pick what will have the highest ROI going forward. It is grounded not just in what you are doing, but what you could be doing to remove any blind spots. This includes categories you compete in today vs. categories you could enter, media you are using today vs. media you could be using, and so forth across all possible resources and activities.
This insight is achieved by understanding the inputs, conditions and variables that drive each activity's long-term success, what we at Fusion call Thinking-in-Formulas. For example, documenting the eight variables that drive TV ROI, then putting these variables together into a quantifiable formula that fully captures the complexity of the situation and points to where to spend time and resources to improve.
The process fuses several analytical disciplines together (it is where the name of our company comes from) including financial analysis, loyalty data, econometrics, GIS, consumer surveys and ethnographics in order to give a complete picture of understanding. Opportunity Modelling reconciles multiple data points to give one-truth throughout the organization, so that sales growth and industry growth mathematically tie out with market share growth, which then links to traffic counts and brand health and all metrics across all disciplines so they never conflict in their insights but instead talk a common language to build clarity.
The process is top-down, unlike the typical bottom-up approach. A bottom-up analysis starts with an idea, for example launching a loyalty program, and works backwards to why it should be done and then how it rolls up to an overall company issue of sales or profits. A top-down process is the exact opposite; it starts with identifying an opportunity, e.g. increasing conversion, then drills down into why conversion isn't higher, getting deeper into the insight until the appropriate tools and resources are finally identified.
Lastly, Opportunity Modeling creates a positive feedback loop to build long-term momentum and a sustainable competitive advantage, allowing one investment to build on the next in a process we call Profit Recycling. The final result is a complete picture on where to budget resources both strategically and tactically to drive maximum top-line and bottom-line growth.
Everyone Wants To Know ROI...
Everyone wants to know the "ROI". But inevitably, once they get this number the next question is "what am I supposed to do with this?" (or, maybe, a slightly more polite question of "how can we get our ROI higher?"). The problem with ROI, or any other output-based metric like market share or sales, is that they don't necessarily give you a clear direction. If your flyer is ROI-negative, does that mean you shouldn't have a flyer? If your flyer is ROI-positive, does that mean you can't improve it further? Is the more important question not "how can we get the flyer better, period?" At Fusion we would argue that ROI as a raw metric is not only meaningless, but is actually very distracting and counter-productive. The more important question of "how to get better" is actually very different and requires very different thinking, analysis and data. As we invest more time and resources into determining ROI, the less we have to get to the heart of the matter, which is how to improve.
The Future of Retail Is A Formula
Great analytics is not about measuring results. Results are full of noise, are blind to opportunity and don't actually tell you why something worked or where it will work. The best analytics is therefore not about tracking, but about opportunity modelling - finding out where to invest and why, which starts with learning about the underlying variables that drive an activity's success. Take TV advertising as an example. It is not helpful for an executive to say "TV doesn't work" or "TV is dying" or "People just PVR". All of these situations are in some cases true, although not in all cases, and learning that nuance is a huge win for a retailer. The fact that TV is dying and more people are using a PVR should be pieces of the puzzle to understand TV, but not the entire picture itself. Great analytics is identifying all of the variables that should drive a decision to invest in a resource or activity, like TV, by capturing the richness of the situation and the nuances of and interrelationships between variables. This is best done by putting your thinking into a formula. The retailers of the future will think in formulas and compete on having the best formula. Just as Google and Bing compete for the best formula for search, retailers will compete for the best formula on how to use TV, how to set pricing, how to manage inventory, or how to schedule labour. May the best formula win.
Where Is Your Longbow?
Moving from bottom-up silos to top-down thinking
Thinking about opportunity and not tracking is the key shift that leads retail organizations to the right questions to drive growth. But why doesn't this shift in thinking occur naturally?
It's because some retailers are a little like the French nobility during the Hundred Year War with England. In the 1346 battle of Crécy, the very first battle in this long war, an outnumbered English peasant army of approximately 8,000 armed men, including 5,000 archers, did the unthinkable. They took on a French army that, depending on sources, was anywhere between 12,000 and 120,000 men strong, and included Europe's most powerful cavalry. And the English won.
The supposedly superior French cavalry charged the English line with full force but the noble French knights were mowed down for the first time in history by the arrows from English peasant archers with longbows. Underestimating the range and power of the arrows, the knights fell from their horses, and became an easy target for English foot soldiers with axes. King Edward was told the next day that his forces suffered only 300 casualties while 16,000 of his enemies died on the battle field.
What would most people do after losing so badly? From the outside it is obvious that the French better learn how to use a longbow! But, like the French army at Crécy, most organizations don't think that way. Companies are not organized to discover an opportunity (i.e. the need for longbows) and then allocate resources, budgets and organizational structure accordingly. In fact, companies work the opposite way, from the bottom-up. Each element within the French army looked through the prism of what they know and what they are in charge of. The blacksmith viewed the battle through their own lens and so what they saw was very clear to them: the need for thicker plates of armour. And on it went through each element of the French army.
Everyone was doing their job, following the patterns they were used to, and focusing on how to get better at their job - with improvements and ideas flowing up to the top of the command. The result: Ten years later the English army, outnumbered and close to starvation, defeated the French again at the Battle of Agincourt. It took the French a hundred years before they were able to outsmart the English and regain almost all of France.
Retailers don't have a hundred years to change their strategy - a new process is required. Retailers need to move away from their current structure of having each department build its own plan, and then rolling these up into a disjointed list of "to-dos" rather than a coherent strategy. Instead, a top-down approach should be used, where the Executive team collectively identifies the largest profit opportunity and how to align resources, organizational structure, and metrics to capture that opportunity.
Today everyone is pitching the CEO for resources using their own team of analysts and their own data. Each week, month and quarter, departments look back on how they did, and submit their ideas on how to improve. Just like the French in the 14th century made their swords sharper and the armour thicker, marketers fire their advertising agency and bring in new creative people when sales are down. They try social media. Merchandisers submit ideas on a critical need for more promotions. If one promotion was a good idea last year, two will do the trick this year.
Each department thinks in its own paradigm, with its own metrics and reports, its own vendors feeding them the latest fads. All of this is then supposed to roll up into a master plan for the CEO. I have had many retail CEO's tell me "I have yet to see a negative ROI proposal; everything is going to make money." The result is that the CEO is stuck with hundreds of pages of ideas but no clear idea on what the actual company priorities should be.
No marketer requests their budget be cut, even when it should be. Everyone in the loyalty department believes loyalty is key to the company, otherwise they would have chosen a different career. So what's a CEO to do?
There are dozens of tools that can help CEOs implement a loyalty program, improve the company's approach to social media, price better and manage inventory, or engage staff with customer service. But where is the strategic thinking, and the associated data, tools and processes, to decide whether they need to invest in labour hours versus inventory versus loyalty? If the company is optimizing loyalty but the competition is fixing the real issue, they act like the French, fixing their armour as the English win the war with longbows.
The solution is a top-down analytical approach. Strategy needs to start with opportunities, and opportunity modelling starts at the top. It means skipping the departmental level with its merchandising data and its marketing metrics, and applying company-wide thinking first. Then, and only then, should CEOs work towards bringing in the right resources, budgets, organizational structure and metrics.
Here is an example from a client I worked with: a US retailer whose director of merchandising was convinced that the company needed to invest in a capital refresh of his department. He had made up his mind based on gut feeling and fifteen years of business experience, and was now looking for ways to get what he wanted.
He knew the idea involved capital approval from the CFO and CEO, but he couldn't just pitch the idea with no data; retailers would much prefer half-baked, siloed, noisy assumptions dressed up as analytics to having no data at all. So the next step was to prove the worth of his proposed project with the help of a capital ROI analysis from the company's finance department. He worked closely with a young analyst, not yet a manager, and was "kind" enough to even help review the analysis model with the analyst, sitting on their desk as they worked. They tested different assumptions, pre-periods and control groups, until the results showed the truth: that the project is great and should get approved. Of course, the proposal gets approved. And it fails.
Why did it fail? Because the corporate culture wasn't about "learning", it was about proving what people already think they know and selling it to their peers. Part of the issue is starting with a hypothesis, in this case the need for a capital refresh, and then working to find any supporting data that supports the argument. It is using analytics to support a decision, and then rolling this decision bottom-up to the CFO and CEO for approval.
Did anybody question whether the issue was related to capital, and if it was, whether the specific project proposal was the best one? No, because retailer analytics don't work this way today. They are case-by-case analyses of supporting solutions, typically through tracking results in each departmental silo, culminating in several pitches to the CEO until the company has two dozen strategic initiatives to implement, none of which might actually align to key growth opportunities.
The company failed to provide sufficient check points on the proposal's way to CFO and CEO approval. But would the analyst's manager have stopped the project? Can any additional management layer really make a difference in the decision process? Or should the CEO first ask herself if the issue is really how analytics is used. Is the goal to track and prove? Or is the goal to uncover opportunity? The former is all about "I know this activity is worthwhile so let's see the results" and the latter is all about "where should I direct my limited time and resources?"
The outlined experience is not an exception. It is typical of retailers where big business decisions are made bottom-up, with everyone pitching the CEO for resources using their own team of analytics and their own data.
The power of analytics begins when decisions start not with a solution in mind, but instead with the top-down, company-wide opportunity (finding your long bow). It starts by identifying the fundamental issue of why the company is not growing faster and works its way downwards, finally landing on the right resources and initiatives.
Build A Tower Of Babel
How a new structure is required to improve strategic planning
In the Old Testament, God created distinct languages so that the people of the Earth could not talk to each other and build a tower to heaven. The biblical Tower of Babel was never completed because of it.
Today's retail executives have the opposite problem: every department in their company has its own metrics, its own way of thinking and its own "language". This makes it difficult for CEOs to make key strategic decisions that will transform retail growth. Retail executives need to embark on a mission to build their own Tower of Babel. The first step is getting everyone to talk the same language.
In most corporate environments today, each department is in its own "silo", making use of only its own reports and data:
- The marketers use consumer survey data
- The finance analysts are comfortable only with point-of-sale data
- The real-estate analysts use geographical modeling tools
- The stores have a mystery shop program
This approach invariably creates blind spots. Each unit has its own data sources, tools, metrics and reports, and they are all perfectly designed for incremental improvement. The lack of smart integration makes it impossible to get to the fundamental knowledge of where to shift the company's overall resources, time and energy.
But what if all were to talk the same language? What if you had your own Tower of Babel? If retailers were able to easily make decisions across different departments, resources and regions? A common business language allows retailers to move from tracking performance and starting with solutions to a focus on opportunity and finding the right tool for the job. Opportunities start company-wide and then go top-down into resources, departments, and finally into meetings, metrics and reports.
Let's look at just one example of many. It starts years before my company was called in to consult. This particular retailer kept opening store after store each year because management followed the vision that expansion through store launches was the best strategy for growth. The challenge with the strategy was that they were unknowingly adding very little net revenue. Despite having all the necessary data available, they were blind to the real profitability. Why?
Data didn't cause the breakdown - not having a common, integrated analytical approach did. All the necessary data was at the executives' fingertips but broken out into different data siloes within different departments. What was missing was their own Tower of Babel with all of the key information put together into the same language.
Here is what was happening. The real estate department received a projection of the sales for every future store. If the sales projection for each store hit a key threshold where the store's income statement looked good, it got built. The average new store was projected to do $20 million in sales and $2 million in profit. The finance operations department, located in a different corner of a different floor at head office, knew that when a new store is being built, existing stores in its vicinity need to adjust their sales plans. How did the finance department know? Because local store managers would call head office, screaming that they need a plan adjustment. They know a new store will cannibalize their sales and thus demand a reduction in sales goals for next year. As a result, the finance department built a model for new openings that predicted the sales impact on existing stores and adjusted each store's sales plan accordingly, in this case by 15%.
15% cannibalization doesn't sound too bad, does it? However, there is an unintentional lie in this data. The 15% cannibalization isn't based on the new store's sales, but the existing stores' sales, and this innocent switch in denominators blinded the retailer to what is really happening. Each new store impacted three existing stores, with each existing store doing $30 million in volume. Though the new store will do $20 million, an existing store does $30 million. So 15% x 3 stores x $30 million = $13.5 million in cannibalization. Notice how the 15% hides this number? Working in percentages, which is how most retailers think and talk, creates huge issues with changing denominators. But here is the real kicker; the $13.5 million shouldn't be compared to the existing stores, but to the proportion of the new store's sales. The real insight is that 68% (13.5/20 = 68%) of the new store's sales are cannibalized. So when the CEO asks for the cannibalization and was told it was 15%, the true impact and more accurate figure of 68% was lost in translation. You only get to the truth by having the right metric and a common Tower of Babel language which everyone speaks. When I asked the CEO if he would have ever approved a store opening with 68% of its sales coming from existing stores, what do you think he said? Analytics is not easy. The data matters, the analysis matters, and having an integrated approach (a Tower of Babel) matters.
So why were we able to figure this out, while the client was misled? The math seems straight forward. The reason is it actually really isn't about being smart enough to do math, but having the right process in place. The finance department is working on their task of adjusting existing store sales; it isn't even on their radar to think about the validity of opening the new store and so they don't even share their knowledge with the real estate department. And even if finance did tell real estate about the 15% cannibalization, without adjusting it into a common language (i.e. 68% of the new store's sales instead of the finance language of 15% impact to existing stores) the true insight would be lost.
And it's not like it's in the real estate department's best interest to uncover that the stores they proposed opening are at 68% cannibalization. It is in the interest of the real estate department to open stores, and any knowledge that opening stores cannibalize sales would work against the size of the budget and team. They may not have wanted to be blind on purpose, but why would they aggressively pursue something that is not in their interest? So they focused on opening one store after another.
If finance is working on their job of sales adjustments, and real estate isn't really in the job of making themselves look bad, then who is looking out for the company? Even if you were an analyst working in operations, merchandising or marketing and you uncovered this issue, who would you share it with? The VP of Marketing isn't going to pick a fight with real estate on this because it isn't their battle. There is simply no incentive to integrate insights together into a common language, no structure to do so and nobody in charge.
Later the retailer asked my team to come in and help them improve business performance. We approached the situation with fresh, analytical eyes and put the math together. In other words, we started to work with them to build a Tower of Babel. We looked at new store sales minus cannibalization, and adjusted the profit and loss statement accordingly. It turned out that over 20 of the new stores built in the last three years were losing money.
Here is what the real estate department, finance department and CEO saw, and what they should have seen:
|What the CEO
needs to see
|Net profit of $2M||Net LOSS of $2.05M|
Data was available but spread over two different departments. The result: Nobody knew that by opening a new store, net profit for the whole system goes down $2M for each store opened. The real estate department excels at picking store locations and managing capital projects. But strategic business analytics is not part of this department's core expertise and putting it there may be a source of conflict since more comprehensive models may directly impact the department itself, including asking real estate to shrink its budgets and teams.
The finance department runs the books and just wants to know what each store's sales plan is next year and get it into the system. The CEO and CFO may ask for more data, but all of the necessary data was there already, sitting pretty in different siloes. The missing piece of the puzzle was somebody tasked with picking the right data and putting it together in the right way.
In Canadian retail, this type of scenario happens every day because nobody is in charge of best-in-class, integrated opportunity analytics across the whole organization. In these old-style organizations, data and analytics are all about tracking, not opportunity. Executives receive bottom-up, "siloed" solutions instead of top-down insights from opportunity to resource allocation. The system fosters a culture of being precisely wrong and not one that leads to accurate decisions. It is full of isolated, incompatible metrics. This is a fundamental challenge that prevents growth in many retail organizations.
In my nearly two decades of working as a retail analyst, I've often seen decision-making like the following: "Our margins were 35% last year, so this year the goal is 35.5%. Last year we spent $40 million on marketing, so this year we'll spend $42 million."
Why is this normal, and not what the best-in-class retailers do? Typically, departments are asked for their plans first. But it is from identifying opportunities that a conversation begins on which departments align best to that opportunity and need more resources. And, just as importantly, which departments align least to the opportunities and need to rationalize their resources. A process focused on getting to the right questions and finding the right data requires department-agnostic analytics.
Why don't many Canadian retailers plan this way? It's because there is no Chief Analytics Officer (CAO) with one integrated view of the business. The CAO role would consolidate all forms of analytics currently spread across the retailer (e.g. consumer insights in marketing, financial analysts in Finance) under one department, to build one common company-wide language so the CEO would have the "one truth" and knowledge to make the most profitable decisions for their business...
The Benefit of Converting Your Business To A Common Denominator
A key step to better analytics is move more of your reports/metrics to a common language so different opportunities are cross-comparable. A quick way to get started is to use a common denominator in your monthly category reports. For example, here is what a typical monthly report looks like for a retailer:
This report tells retail executives that the furniture merchant is doing a great job. In fact, her category is the only one performing above the company's total growth rate. While this may be true, there are typically two common errors that make this implied assumption wrong.
First, what is the industry growth rate for each category? Most of the story here could just be due to different industry dynamics (however, let's put that aside for now and focus on second issue for this blog post).
The second most common error is caused by different denominators. Different denominators prevent you from correctly comparing performances across categories. While it is fair to measure furniture's performance over time to see if the 20 percent is higher or lower than last year's, comparing furniture with any other category is highly misleading. In fact, it is voodoo math. Here is what is really happening:
|Last Year Sales||Growth Rate||Absolute Growth||% of Company Growth|
Although furniture has the highest category growth rate, its small denominator means that its absolute growth in dollars is also the smallest. After converting all category growth rates to absolute terms, it becomes clear that appliances are driving the bulk (41%) of total growth. Though the concept is simple, misleading metrics due to different denominators are rampant in the retail industry.
We have recently seen this firsthand with a retailer that was debating whether it should focus on Category A or Category B (actual categories are masked due to client confidentiality). The retailer was convinced that their future lay with Category A. Why? The main reason was because it had a higher percentage growth rate. Consequently, they ran an advertising campaign that resulted in 10% growth in Category A and only 8% growth in Category B. The hallways quickly filled with chatter that Category A is driving more business.
Here is the issue: Category A and B have completely different denominators. This retailer sells $4 in Category B for every $1 they sell in Category A. You simply can't compare the percentage increase over different denominators when discussing overall impact on the business. In school, we learn to not add fractions with different denominators, and this rule still applies today. You must first convert numbers to a common denominator before comparisons can be made.
The easiest way to get to a common denominator is to convert metrics to absolute sales growth. For the retailer above, Category A lifted sales by $300K, while Category B lifted sales by $950K. This works out to 76% [$950 / ($300+ $950) = 76%] of the total lift coming from Category B. Comparing metrics with different denominators led to the wrong initial insight that it was Category A that contributed to overall success, which led to misguided implications for the retailer. With a more accurate report using one common denominator across categories, the retailer now has a clear view on what is driving their business and has since corrected its course.
How Indexes Can Be Deceiving
One of the most common methods for making comparisons in business is to use division to create ratios. However, these ratio metrics can be implicitly confusing, for division often involves different denominators that make cross-comparisons extremely misleading.
One form of these misleading metrics is the index. Often, advertising agencies will say "this segment over-indexes by 1.6 on X", of which "X" could be valuing environmental product, reading golf magazines, or having fish as pets. What is not shown, however, is the size of the overall population who actually cares about "X". For example, if only 5% of the overall population cares about "X", then an over-index of 1.6 means that 8% of this segment's population cares about "X", and 92% of the segment does not care. The use of an index here does not add any particular insight, and in fact hides the real takeaway.
To further complicate the issue, ratios like indexes are difficult to compare for they involve changing denominators. If the ad agency also says "this segment over-indexes by 1.3 on Y", most people's intuitive response is to compare 1.6 to 1.3 and conclude that of this particular segment, more people care about "X" than "Y". However, if 50% of the overall population cares about "Y", then an over-index of 1.3 means that 65% of this segment also cares for "Y", which is significantly higher than the 8% who cares for "X". The use of indexes here is not only misleading but is likely to lead the ad agency's client to wrong business implications.
An easy solution to create a better index is to switch from doing comparisons in multiples (division) to comparisons in absolute numbers (subtraction). Instead of showing the size of "X" and "Y" as multiples of 1.6 and 1.3, show the preference above overall population as absolute differences of 3% (8% - 5%) and 15% (65% - 50%). The difference between "X" and "Y" now becomes clear and the right conclusions can then be drawn.
Don't Hire An Accountant To Do Analytics
Why being accurate requires you to not be precise
Moving from tracking results and bottom-up, siloed planning towards top-down opportunity analytics requires someone to crunch the numbers. But who? My advice would be to avoid using accountants.
Why? My accountant does a fantastic job...at being precise. I once asked him if our accounting software costs about $80 per month. His answer: "No, it's $79.45 per month." Now that's precision.
But precision is a false god when it comes to strategic decision making. Often you can be highly precise and completely wrong. The goal in strategic decision making is not to get at the precise answer but to the right answer, or in other words, to be accurate. Accuracy and precision sound like the same thing but are in fact fundamentally different. Often they are at odds with each other, which can lead to poor decisions.
For example, most retailers measure their flyer performance using a metric called "flyer sales". This is literally the sales volume of all items featured in a particular flyer, and is the metric about 70% of retailers in Canada use. Besides being a completely useless metric, it has the added downfall of leading to incredibly bad decisions. For example, you can sell 100 units of an item the week before the flyer, and then 110 units during the flyer. What are your flyer sales in this case? The report will say 110 units. However, only 10 units are incremental, often with no mention of the "baseline" 100 units. A merchant that wants to look good can game the system by simply putting their top-selling SKUs in the flyer, as more units sold before the flyer will mean more sales in the flyer, leading to fantastic productivity metrics. All of this is very precise - precisely wrong.
The right metric, which is tough to measure, is the incremental sales lift. And while some retailers attempt to incorporate this in, it can cause some apprehension in its ambiguity. Do you use lift vs. the pre-period? How many weeks should the pre-period be? What if you have a flyer in the pre-period? How do you factor in cannibalization of non-promoted SKUs? What about the sales the promotion is stealing from future weeks? While all of these questions make the analysis more accurate, they hurt the precision for none of these questions can be answered definitively as they each have an element of error. The fact is people hate ambiguity, and would rather have a precise but wrong number than a directional but accurate number. Overcoming this fear is the key to great analytics.
Ambiguity drives people crazy, so they just start to hide it and as a result start to fool themselves. More often than not, they start plugging the numbers by just making them up. For example, the CFO might say "we need an ROI analysis on this". The intention is to get precise information on the return on investment of an initiative or campaign. But an ROI analysis consists of a number of inputs, many of which are not hard numbers. If there are ten inputs, eight of them may essentially be assumed, leaving only two based on hard facts. People start making stuff up just to get a precise number!
In some cases, they might even remove data points because they don't fit into a precise picture, even though those points need to be triangulated to get a better picture. So, in the process of trying to be as precise as possible, the comfort of black and white thinking actually leads to less accuracy. Allowing some grey to enter the black and white zone is the only way to get information that isn't misleading. This will require a change in thinking, such as when and how "estimates" should be applied. For many people dealing with financial analysis at retailers, the worst word in the English language is "estimate".
Let's take a weekly dashboard as an example. Even a simple report like a weekly dashboard is incomparable across departments. It's not a perfect example because it's a tracking report (and hence not about finding opportunity), but it is an example most retail executives can relate to. Here is what executives at a billion dollar retailer would typically see:
|Plan||Variance to Plan|
|Margin||35%||-0.5% below plan|
|Inventory||$175M||+10% above plan|
|Labour hours||$103K||+3.0% above plan|
All three departments are doing worse than they should, according to plan, and the inventory team is even off by 10 percent. Listening in at dozens of weekly sales meetings, my consulting team found that the retail team would typically focus on the biggest issue: inventory. Thus, the main message coming out of the weekly team call is that inventory is dramatically off plan.
But is this fair? The report is designed to talk in each department's "language." This works fine for optimization within each department. Merchandising know they are off margin plan by 0.5% and operations know they are off the hours plan by 3%. This works for their silos. But how does the executive team know where to focus their additional time?
Our recommended approach to a weekly report would look like this:
Get every department talking the same language. For this retailer it meant using costs.
Start with margin data, and convert the 0.5% variance into an absolute number. Percents almost always have different denominators which can cause significant misunderstanding, so we need to get the data into the common language of absolute costs. This retailer did $19.2 million in sales that week, so a variance of 0.5% in margin was a cost impact of $96,000.
Convert inventory into a cost number. For inventory that is a variance of $17.5 million inventory dollars multiplied by the estimated weekly holding cost of 0.3% to equal $50,000. Please note the use of the word "estimated". One of the major reasons why retailers don't put everything in costs each week is that the actual costs aren't precisely known. There is no precise way to measure holding costs, and they vary by category, season and geography.
This lack of precision has led to grave errors at many retailers. They give up relevancy and accuracy to gain precision. 17.5 million dollars is a nice, precise number. But it also prevents the executives from comparing costs from one department to another, making the executive team read the report the wrong way and leading to an incorrect decision. It's what we call "being precisely wrong instead of directionally accurate". This retailer can be extremely wrong in its holding cost estimates and still have a more relevant and helpful report than before. That fact is often lost.
Finally, we convert labour hours into cost. This retailer had planned for 100,000 in hours, but ran 103,000 hours, or an absolute variance of 3,000 hours. To get this into costs, we need to estimate again. Most retailers have a good idea of their hours per week but not their cost per hour. Hourly costs are always changing due to the mix of part time, full time and overtime.
On Monday morning, when everyone is rushing to get the reports out, there is no time to know for sure what the precise hourly costs are, so instead of estimating them, the analysts release the number of hours, which is the only precise number they have.
But there is another solution: to use an estimate of the labour costs, which will be only directional, not precise, but will lead to an overall view that is more accurate. In this case the client agreed to use $15 per hour, and so a 3,000 hour variance multiplied by $15 worked out to $45K.
Now, with all three departments using the same metric and talking the same language of absolute cost, the executive team can see what is actually driving performance:
|Plan||Variance to Plan|
|Margin||35%||$96K lost profit off plan|
|Inventory||$175M||$50K cost over plan|
|Labour hours||$103K||$45K cost over plan|
After going through the recommended approach, the insight is much clearer. Great analytics isn't about precision; it is about capturing a more accurate picture by using a wider set of different lens on the problem and using a common language, in this case by removing percentages and moving to a common denominator. The result is a great report for the executives to quickly see the true issues driving their business performance.
Should You Move To EDLP?
Adding facts and nuance to your pricing strategy
Over the last fifteen years, I have talked to hundreds of retail executives about pricing. Most of them say the same thing – their overall pricing strategy this year is essentially the same as it was last year (and the year before that, and the year before that…). If they were using high-low pricing five years ago, then they are high-low today, perhaps with some aggressive competitor monitoring and tactical experimentation on select offers.
Everyone recognizes there has to be a better way, but there is a surprising lack of sophisticated analytical tools to help. At most, retailers find vendors peddling econometric models to measure price elasticity, but these do not address the need for strategic pricing tools capable of taking retailers to the next level. Three years ago, a major department store chain in Canada came to this realization and asked my company, Fusion Retail Analytics, to start thinking about a better way of pricing.
Their biggest pricing question was “Should I move to everyday-low-pricing (EDLP)?” What we found is that the move to EDLP has to be made carefully, with four key considerations to help shape the conversation on whether or not this pricing strategy is appropriate.
- Need vs. Want
- Low absolute price
- Price segmentation
EDLP works best in categories that are predominantly “needs-oriented”. Consumers don't want to constantly invest the time to find the best deal for items they "need". A great example is Walmart, which predominantly sells everyday grocery items and commodities.
Where EDLP becomes less effective is for categories that are predominantly “wants”, like women’s outerwear. Most women already have five jackets and don’t really need another one, so pricing works as a powerful trigger to help push them towards an incremental purchase. Pricing isn’t the only trigger (a new style, trend or new feature can work as well), but pricing is one of the most powerful. Outside of women’s commodity apparel, you rarely see EDLP work in women’s fashion and it is fundamentally why JC Penny was bound to fail as they moved towards EDLP.
EDLP typically works better in categories with low absolute prices, while high-low pricing is most effective in higher-ticket categories. In price-elasticity tests involving discounting, Fusion found that a consumer needs to save at least $4 for them to switch from one retailer to another. For example, if a retailer has a business model that supports no more than 20% off an item, the implication is that the absolute price of the item has to be $20 or higher to get over the $4 discount threshold ($20 x 20% off = $4 savings). Below this threshold, a retailer is typically better off with EDLP to build price confidence as they will not be able to offer enough of a discount to change consumers’ shopping patterns.
This is not a universal rule; price-elasticity tests can, at times, disagree with this rule based on where the category lies on the scale of “need” versus “want.” It is, however, almost always beneficial to have absolute price as one of the criteria when deciding if you should move towards EDLP and how far to move.
The biggest benefit of high-low pricing is its ability to create price segmentation. When you have a consistent, "average" price, which is what EDLP really is, you end up with some consumers that would have been willing to pay more and some consumers who will not purchase because the “average price” is not low enough. This is the main reason why transitioning to EDLP is so difficult.
One hypothetical example is if a jacket is $100 at regular price and $60 on sale (40% off). Let’s assume that 20% of the jackets are sold at regular price and the remaining 80% are sold on sale. The weighted average, or equivalent EDLP, of these jackets is $68.
If this retailer were to move to EDLP, the 20% of customers who paid $100 are now happy since they only pay $68, but because they were going to buy it anyway at the higher price, the retailer missed out on a profit opportunity. The remaining 80% of customers that would have paid $60 are now being asked to pay $8 more at the EDLP of $68. Some will decide to pay up, but many will wait for a sale or hunt for a deal elsewhere. This is why transitioning to EDLP works best when the new price is as low as the previous promotional price.
Staying with this example, an EDLP of $60 or lower would only be possible under situations where there is room to lower gross margins in the category, or in situations where over 95% of the merchandise is sold on promotion. The lower your current promotional mix, the higher your new blended price and the less effective that transition to EDLP will become.
A pure EDLP strategy is rarely the most optimal strategy; having price segmentation is a big win. In fact, through our experience we have found that the biggest advantage of programs like loyalty is not the customer analytics, nor the ability to do one-on-one customized marketing, but the ability to create price segmentation through friction. For example, a brilliant retailer like Shoppers Drug Mart has the Optimum loyalty program, targeted at their price-sensitive customers. The registration process and the need to carry the card forms friction, and the remaining customers who are less oriented towards price don’t bother joining the program and therefore don’t get the discount. With their Optimum loyalty program, Shoppers has created price segmentation.
When used properly, the power of high-low is the power to price-discriminate based on willingness to pay.
Contrary to most opinion we have found consumers rarely expect a certain pricing strategy from an industry as a whole, but they do build up pricing knowledge and expectations for specific retailers. 10% off in apparel at one client can drive a 20% lift in sales, while 20% off at another client translates into minimal sales impact because consumers know that retailer has sales as good as 40% off. In other words, consumers get anchored to a certain price at specific retailers.
Anchoring is the reason why reducing promotions from 50% off to 40% off often backfires. Consumers will continue to wait for the 50% off deal because that is what they expect from the retailer. The key to transitioning away from heavy discount pricing is to make the new EDLP as low as the promotion price and/or de-anchor it. For example, introduce a new pricing and promotional strategy not on existing merchandise but under a new product label that consumers don’t have a discount baseline to compare to. Alternatively, maintain the discount at 50% off, but add friction like the need to use a loyalty card to qualify in order to reduce margin impact without impacting the “50% off” headline.
What about the competition?
What should you do when all your competitors are high-low or EDLP, and therefore consumers expect a certain pricing strategy? The typical gut answer is you need to copy them. What we have found is the larger win is to do the opposite. If everyone is high-low, there is space to be EDLP, assuming the underlying conditions exist (need-orientated category, lower absolute price points, extremely high mix of high-low). Or, if everyone is EDLP, there is a win to be the first to price-segment with a mild form of high-low.
There are two implicit assumptions you make when you follow the competition: 1) you are saying they are making the right move, which in our experience is not often the case, and 2) you are saying that move is so important that the fact that it will be crowded with competitors doesn’t matter.
My first boss used to always say, “you want to be a big fish in a small pond.” 15 years later, after doing analysis on over fifty retailers, I can say he is right. Most retailers are chasing what they feel are large ponds, but often turn out to be small ponds, full of lots of other fish. We were in a meeting with an AVP of Marketing, who said, “If this strategy is so smart, why is nobody else doing it?” But isn’t that the point? Carve out your own pond. If all of your competitors are going to the left with pricing, see if going to the right makes sense.
Should you move to EDLP?
The best strategy is never black & white with a proclamation that EDLP is better than high/low. Instead, the best strategy involves a nuanced discussion to uncover in which situations different pricing strategies work and why. By thinking through the main components that drive consumers’ pricing decisions you compete in you can best optimize your pricing strategy. As a starting point, consider building analytics around the criteria of need vs. want, absolute price, price segmentation and anchoring.
Is Your Real Estate Keeping Up With The Changing Landscape?
Moving from store centric to consumer centric analytics
Real estate is in flux in Canada. Target is opening 120+ stores in Canada, major competitors like Walmart continue to aggressively expand their store count, e-commerce and cross-border shopping have hit new levels and Canada continues to urbanize and become more ethnic.
The changing environment is producing challenges but also opportunities for smart, fast-moving retailers. Most successful retailers have always been about understanding the consumer, with street smarts on how consumers shop and the implications for store counts, locations and formats. The issue has been their analytical data and software has always let them down.
Most retailers would describe their real estate modeling as "drawing a circle around a store". The analytical lens is from the point of view of the store, not the consumer - it is "store-centric" modeling. The latest approach in real estate analytics is therefore a giant leap forward, moving from "store-centric" to "consumer-centric".
Consumer-centric analysis looks at decisions from the lens of the consumer. In real estate, this means taking a viewpoint from all 812,000 postal codes in Canada. Instead of just one drive time rule and a trade area for each store think of it as having 812,000 drive time rules - a 10,000% increase in analytical horsepower.
Each postal code’s drive time rule is uncovered by looking at the relative distance between that postal code and all nearby stores - not just your own. Consumer behaviour is not driven by absolute rules like "20 minute drive to the store". If a consumer lives in the countryside and the nearest Loblaws store is 50 minutes away, but the nearest Sobey’s is 60 minutes away, then all else being equal Loblaws will gain more share from that postal code. Compare this to a situation where a consumer is only 20 minutes away from a Loblaws store but 5 minutes away from a Sobey’s. In this case, even though Loblaws is very close, it is at a "drive time" disadvantage and will gain less share than Sobey’s from this postal code.
While this insight may be obvious when stated, many retailers continue to use absolute drive times to evaluate store opportunities. Here is what the absolute drive time looks like for RONA (see exhibit B below).
Here is what their relative distance drive time looks like (see exhibit C below). When looking at relative distance, we begin to see the true gaps in market share and can start to get to dramatically higher-ROI insights through analytics on store count, locations and formats.
Using relative distance, it becomes possible to determine your current market share with a particular postal code and how much higher it would be if you relocated a store 5 minutes closer, 10 minutes closer, 20 minutes closer and so forth. With relocations costing big box retailers several million dollars, it pays to be accurate.
To calculate the impact of relative distance you must first map the distance of each postal code to each of the stores in your network as well as the distance to each of your competitors’ stores. The relative distance is then determined by taking the distance between each postal code and your closest store, minus the distance of the closest competitor, for each major category you compete in. The analysis has to be done at the category level because different categories have different competitors and therefore different relative distances.
The calculation power required is immense. Let’s say you compete in 5 major categories with 80 stores, and your competitors have 420 locations between them. Across 812,000 postal codes that works out to more than 2 billion distance calculations. The end result is an extremely granular and accurate understanding of real estate locations, which was not possible to this extent even a few years ago.
After modeling all of the relative distance combinations, the next step is to uncover the market share for each postal code. There are a couple of ways to do this: through a postal code harvest, through loyalty data (assuming it is capturing at least 70% of your sales!) or through a robust survey.
This data provides the basic foundation needed to map out the market share decay curve, which will visually show the relationship between relative distance and market share. As you build your model you will be able to learn how your market share declines based on relative distance, by category, against different kinds of competitors, for different retail formats, giving you the tools to make incredibly powerful decisions on optimal store count, exact store locations and store format options. Today, most real estate analytics is done piecemeal, store by store - it’s like looking at a few trees at a time and never the whole forest. But with the model we just walked through, your team has the ability to look at your full real estate portfolio all at once and know precisely where the network has holes.
RONA is a textbook example of guessing vs. leveraging analytics when it comes to real estate planning. In 2011 RONA announced that they were downsizing their big-box stores in Ontario to a medium-box format - a mistake waiting to happen, based on what our models were saying. In postal codes where RONA is relatively closer than a competitor, they get 24% market share, regardless of whether it is a big-box or medium- box store. However, in postal codes where RONA is 15 minutes farther away than the closest competitor, RONA’s market share decays by an additional 2% more in medium-box stores than in big-box stores. When RONA was more than 15 minutes away, the gap between big- and medium-box share increased to 4%.
Before RONA even made their move, the key insight that we uncovered for our clients was that a medium-box RONA store has dramatically less drawing power than a big-box store - they are walking into disaster. It took RONA 18 months and several million in lost sales before they realized their mistake. That is the power of consumer-centric analytics. I suppose that I’m a bit biased, but I have always struggled to understand why a retailer would rather waste several million to learn what $100K in great analytics already knows. What I do know is that retailers with access to our analytics love the fact they know more about the competition than the competition knows about themselves.
Think Like A Scientist But Act Like An Artist
People often talk about retail being both an art and a science, but what exactly do they mean by this? When I talk to the best retail Executives in North America - the ones who consistently outperform their competitors - I always find they think like scientists, but act like artists.
A scientist assumes everything is wrong. They are careful, detail-oriented, never jump to conclusions, check the gauges five times and then check them again. A scientist will lock the door to their house, get in their car, and then worry that they didn't lock the door and go back to check it. A scientist is basically paranoid! A scientist never believes the first answer they see, they don't jump to conclusions, they don't "know the answer" and then try to prove it; they always start with questions and slowly let the answers build.
An artist, on the other hand, sees the big picture. They see the integration - the fusing of data into one big picture and story. They are not frozen by ambiguity, complexity or the fog of war. They talk with conviction and emotion and are bold in their actions. In our boardroom, we have a picture painted by a Japanese Shodo Artist, and I guarantee that when you look at it all you will see is a circle and think "why the hell did Fusion spend $3K on a circle and put it on their wall?". When the artist first showed us the painting she talked for 10 minutes about that circle - how it is called an Enso, the meaning of circles, the link to Zen Buddhism, how the brush strokes turn outward to show action, how the brush strokes are strong and bold to show strength, the kind of brush she used and why, the kind of paper she chose and why and where the paper came from. This is how Artists think. And this, too, is how great retailers make decisions. They see how all the analysis comes together to form a story, something beautiful, something great, something that will transform their business.
All great retailers find the balance between the scientist and the artist, but most struggle to find the right mix. Many retailers are very certain in their thinking, jumping to conclusions because they are confident that they know why sales are down and what they need to do to lift them. However, when it comes time to take action, they just keep department budgets the same as always, invest in the same media, support the same categories, and run the same promotions they have always run. Maybe they'll do it a little better or a little faster this time, but ultimately they are making small, immaterial steps toward their goal, sticking to what they know and never pushing themselves to take a risk. They think boldly like artists but act timidly like scientists, but greatness comes when we do the reverse: think carefully like a scientist and then act boldly like an artist.
The secret to being a successful retailer and individual in general is to act like a scientist - careful with your thoughts, triangulating your data points, fusing your insights - and then not be afraid to jump and take a risk like an artist, embracing ambiguity and the unknown. The retailers doing this are the ones winning the war.
Canadian Business magazine recently asked us our opinion on Lululemon's strategic move into men's activewear. Lululemon is a Canadian business success story. The company is a household name and their famous yoga pants are a staple in women's yoga classes everywhere. Now, Lululemon has its eye on the men's activewear market - a large, profitable industry in which the company has just begun to scratch the surface. But can Lululemon translate their successful, female-friendly brand image in women's wear into the rough-and-tough world of men's sportswear?
With Lululemon's strong presence in women's activewear (they are the #1 player in Canada) it makes strategic sense for them to begin to expand into adjacent categories to maintain growth. Picking the right adjacent category is absolutely critical for success. We have seen many retailers struggle to translate their success in one market to another. What makes a retailer extremely popular with one consumer segment can turn-off another segment. Lululemon's soft, comfortable, self-empowering brand image - which resonated strongly with female shoppers - contradicts the competitive, rugged image men typically look for in activewear.
Lululemon would most likely have an easier time focusing on categories that have better brand synergy with their core offering, a large profit pool, and fewer entrenched competitors. Women's outerwear, shoes or fitness accessories all potentially meet this criteria. Lululemon already dabbles in most of these categories, but they only sit at around 3-5% total awareness and so have significant upside for growth.
While we think there is certainly a possible opportunity for Lulule to grow their share of the men's activewear market, there are lower-hanging fruit that fit better into their strong, established brand identity and so will provide much higher ROI.
You can read the Canadian Business article here.
Consumer Confidence At 0.4% In Feb. '13, Stabilizing Around A New Norm
Consumer confidence was 0.4% in Feb. '13. Prior to 2008 consumer confidence in Canada was stable at around +4% due to a much stronger economy and the housing wealth bubble. The recession in 2008 caused a major crash in confidence, but since then it has oscillated around a new norm of +1% as consumers have become more wary of swings in the economy. Noise such as weather and news headlines will continue to cause consumer confidence to fluctuate, but consumer confidence is expected to stabilize and oscillate around the norm of about 1%.
How Successful Will Target Be In Canada?
Wall Street Journal recently asked us our opinion on Target's entry into Canada. Target Canada will be competing in a diverse set of categories worth a combined $175B. New stores typically take 6-9 months to hit their full stride, so Target is expected to capture $1.78 billion in sales by the end of 2013. Once fully open, their network of 122 stores will generate $3.45 billion in revenue according to our analysis - approximately $28.3M per store.
Target's challenge will be lifting its sales above our estimated threshold of $3.45B. Even with a 122 store foot-print and an established brand, we expect Target to have a tougher time cracking the Canadian retail market than most would expect. While Target is making quite an entrance into the market, generating plenty of buzz and focusing their messaging around style - a key factor for building brand - the challenge they will face is not brand building, but converting this strong brand image into steady traffic and sales.
Canadian consumers already have a large array of retailer options when it comes to their weekly shopping trips. Busy Canadian families often choose to shop at general merchandisers like Target, Walmart, Costco, Canadian Tire and Real Canadian Superstore, predominantly for three reasons:
- Convenient location
- One-stop shopping
- Low prices
If Target is to beat sales expectations in Canada, at minimum it has to be on-par with entrenched competitors, and ideally over-perform and win on these attributes.
As of March 2013, Target will begin opening locations across the country. By the end of the year they will have 122 locations and 8 million Canadian households will be within a 10km driving distance of a Target store. However, those 8 million households are also within 10km of a Walmart, Costco and Canadian Tire. Even worse, 6.6 million households (83% of Target's store coverage) are near all three direct competitors.
The story is the same for one-stop shopping. Most Canadians will need either strong justification to make an additional trip to Target, or be convinced that they should replace their typical trip to a Walmart or Costco with a Target location instead. To achieve the latter, Target needs to out-perform its direct competitors on one-stop shopping despite on average smaller stores and narrower category breadth. This will become especially important in key traffic-driving categories: household consumables, grocery, and pharmacy.
The third key driver of visits is owning the attribute of low price, and with four retailers (Walmart, Costco, Canadian Tire & Real Canadian Superstore) already well-established as low-price destinations in the minds of Canadian consumers, we wonder whether there is room for a fifth competitor.
Successful market entry strategies do not involve a direct challenge to entrenched competitors - but to find untapped ground the entrant can win on. This could involve defining a unique positioning that gives consumers "reasons to visit", or finding highly profitable but less competitive categories. Target is doing some of this right, but we don't see early evidence it is doing enough to move much beyond an expected 2% market share on a 122 store foot-print.
What would impress us is if Target begins to develop a hybrid strategy of being a general merchandizer as well as a category killer, similar to what Canadian Tire does by finding mid-size categories with high profit-pool- to-competitive-intensity ratios. Without owning key profitable categories the odds of success are stacked highly against them.
It would also be interesting to see if they are able to bring innovative tactics to drive weekend visits beyond the well-worn ideas of owning grocery, photo development, gas stations or pharmacy, to give Canadians a reason to replace a visit to Walmart with a visit to Target.
Until then, we expect Target to perform much more conservatively following their market entry than other retail observers.
Read more about Fusion's expectations of Target's entrance into the Canadian retail environment in the Wall Street Journal PDF version here.
6 Flyer Creative Elements Every Retailer Should Ask Themselves
How many flyers do you receive each week? Which ones do you take the time to read and which ones head straight for the recycling bin? One factor that will affect whether consumers will look at your flyer is the ease of reading the flyer. The average consumer receives 18 flyers every week and has to sort through what is relevant to them and what is easy to read. This is where flyer creative plays a role.
These are some of the questions Fusion is often asked by clients when helping them develop their flyer creative:
- Flyer format - The format of the flyer is a major factor of how the promotions will be laid out and can affect the readability of the flyer.
- Should the flyer be a broadsheet or a tablet-sized flyer?
- What shape should the flyer be?
- What type of paper quality should be used? Would it have an impact on consumer appeal?
- How should it be assembled? Should it be stapled or glued, or left unattached?
- Flyer covers - The cover of the flyer is the first impression a consumer gets, which is the key to the success of the flyer.
- How many promotions can be placed on the front and back covers that would (1) maximize consumer targeting and (2) still be easy to read?
- What format should be used on the cover? (e.g. even spacing for each promotion or asymmetrical layout)
- What logo/masthead design can best catch the consumer's attention?
- What other supporting information do consumers find important to have on the covers?
- Flyer inside pages - The inside pages of the flyer are where the bulk of the promotions lay.
- How many promotions should there be per page to best showcase promo selection without making it overly crowded to read?
- Which creative elements should be incorporated to enhance its "easy-to-read" quality? Which elements should be avoided?
- What should the format be for the inside pages?
- Should the promotions be grouped by product type?
- How can the product description/copy standout better to consumers, providing all the key information while making it easy-to-read?
- Flyer flaps and pop-ups - Flaps and pop-ups are mainly used to communicate a unique message for that flyer.
- Would our consumers find these flaps and pop-ups relevant?
- What are the key rules of thumb when deciding if pop-ups and flaps should be used?
- Promotion design - With the overall layouts decided, each promotion is now looking for the consumer's attention.
- What information is a "must-know" and what is a "nice-to-know"? (e.g. pricing information, brand logos, colour, size, product description, etc.)
- Is it important to demonstrate the product in action through the use of a professional model?
- What type of background is appropriate: A studio or real-life?
- What is the best way to showcase the different colours or styles for the promoted product within the limited space?
- What is the best way to call out the price and discount?
- Supporting non-price information
- How prominently should we showcase flyer effective dates?
- Would showing customers' reviews be beneficial to consumers?
- How much product description and facts should be shown?
- Should we show our retailer's web address? How strongly should we push consumers to go online for more information?
- Should we show our Facebook, Twitter, etc. addresses?
The flyer is one of the most important ways to attract incremental customers who like you but might not be coming to your store frequently. By employing flyer creative in your format, covers, inside pages, pop-ups and flaps, promotion design, and other supporting information; you have the potential to gain higher ROI on a limited flyer budget. Please reach out to Fusion to learn more on how we can help you optimize your flyer creative.
Can Retailers Again Expect Strong Performance In March?
Most retailers can expect pretty soft sales numbers in March simply due to weather. March 2012 had an average maximum temperature 3.8 degrees above normal (click graph to enlarge). These warm conditions jump-started the spring season, pushing overall retail sales in March 2012 up to 5.3% YOY (unrolled).
The key to understanding weather is that you can't forecast the weather out more than two weeks, and you don't have to. What you can do very accurately is analyze the trends to determine the most likely scenario. The most likely scenario for March is that it will be cooler than last year; in fact, statistically, if you look at over 10 years of data, there is only a 5% chance of weather being warmer in March 2013 than it was last year, and a 95% chance March 2013 will be colder than last year, leading to weaker sales.
Today, most retailers get - not only weekly sales - but daily sales reports, providing them a daily dose of voodoo analysis to remove the chance of seeing true clarity in their business. The reason this analysis doesn't work is that over 90%, and often 100%, of short-term sales variances are simply due to external industry influences like the impact weather will have this March, or due to simple shifts in a retailer's resource spending from one week to another (e.g. moving a promotion forward one week). With this much noise the trend in the data is completely lost.
Without making adjustments in their sales reports for these uncontrollable industry impacts or internal resource shifts, a retailer is more often staring at a report that is more wrong than right, leading to poor sales forecasting, incorrect inventory ordering, inaccurate assessments of marketing activity and so forth.
Smart retailers take one of two options to solve this significant problem:
- They acknowledge that their daily sales reports are not the best way to judge performance, and instead they focus more on what Fusion has coined as "Opportunity Analysis". Opportunity Analysis avoids looking backwards at the noise; it looks forward at where the largest opportunities are and aligns resources to them.
- They build advanced econometric models to make adjustments for industry impacts and internal resource shifts. At Fusion, we work with our clients to improve their reporting by separating the trend from the noise. Weather is just the beginning - we can truly tell you how your business is performing vs. the industry and specifically why.
Fusion in Canadian Business
Fusion was interviewed about Le Château's brand re-positioning to extend beyond the younger generation. "While it may not be the sexy choice to walk away from young female shoppers, moving against the grain could mean big things for Le Château-and their bottom line," says Sasha Poljsak of Fusion Retail Analytics. Read more about Le Chateau's turnaround strategy and why Fusion thinks it's making a profitable choice in Canadian Business here.
What You Need To Know About January's Job Losses
The unemployment rate for January 2013 jumped up to 7.4%, 0.9% higher than the previous month, with the headline in all the newspapers being about 21K job losses and 58K people leaving the workforce.
Here is what you need to know: Canadian unemployment is very seasonal as employers always shed jobs in January. Table 1 outlines what happened to unemployment rate during the last four seasons:
Table 1 - Unemployment rate in Dec. & Jan. over the last four years
Every January you will read articles about how Canada lost jobs! This is not a signal of the economy hitting a rough patch or a potential slowdown, just regular seasonal fluctuation. At Fusion we analyze data and separate knowledge from noise, and the real knowledge is that every January we see an uptick in unemployment. When we remove this noise the real pattern is a slow but consistent improvement in Canada's economic picture.
The two most important numbers that relate to the unemployment rate are 8% and 6.5% as these are key thresholds that help drive discretionary income and hence retail sales. When the 4-month rolling unemployment rate is above 8% it is typical to see no household income gains. When the unemployment rate drops between 8% and 6.5%, household income gains begin to rise in the range of 2.5% and 3% YOY. As the unemployment rate drops below 6.5%, household income gains typically rise above 5.0%. These strong household income gains flow nicely into higher retail sales.
Conclusion: Canada's unemployment rate went up in January exactly as expected and is in line with Fusion's 2013 forecast. The unemployment rate is slowly inching lower, and if this trend continues it will pass the key threshold of 6.5% in late 2014 or early 2015, which will mark the return of stronger wage gains and therefore a much stronger retail environment.
Is Social Media Working For Retailers In Canada?
Social media is popular among Canadians. In a recent Fusion Retail Analytics study of over 1,000 adult Canadians 56% of retail consumers in Canada stated that they had recently used Facebook, 38% said they used YouTube and 9% said they used Twitter. Facebook was the most frequently used social media, with 33% of Canadians checking Facebook every day. The survey was conducted online using Fusion Retail Analytics' national household panel between Nov. 1st and Dec. 31st, 2012 and is balanced to reflect the overall make up of Canadian retail consumers.
But can advertisers benefit from this trend? In retail the results so far have been mixed. There is no doubt that the digital revolution is here, with online TV usage growing rapidly: 40% of young adults (aged 17 to 24) stated to us that they now watch more online TV than cable TV and 48% of Canadians have gone to at least one retailer's website in the last month.
In the media effectiveness models Fusion runs for over a dozen retailer clients we find that a retailer's website can be a high return-on-investment (ROI) tool. So we don't have issue with all kinds of digital media, there is lots of buzz specifically on social media and it is here where the results are concerning for three key reasons.
First, at least for 2013, consumers show little interest in engaging with most companies via social media. In our study we found that for every 100 consumers interested in going to a retailer's website only 15 are interested in going to a retailer's Facebook page.
In a follow-up series of focus groups we heard the same comments from consumers over and over - "I use my FB ONLY to connect with friends and family who do not live near me. I do not use it for shopping." In consumers' minds media have different roles. Consumers read fashion magazines as much for the advertising as for the articles, consumers read flyers to look for deals, they go to a retailer's website to get more product information. The role of social media on the other hand, particularly for retailers (the focus of our study), is very limited in consumers' minds and this is causing difficulties for advertisers.
Second, a key driver of media ROI is total unduplicated reach & frequency (TURF). Most statistics today will be able to give you usage data for TV viewership, website clicks, Facebook likes and so forth. More exposure is good, but exposure comes in two forms, either the same person sees your advertising many times (frequency), which is effective for re-enforcing a message, or many different people see the advertisement only a few times each (reach), which is effective for building awareness. Both reach and frequency are important and deciding how to tilt between the two depends on your current opportunities. The key regarding social media is that it is very good at frequency, but poor at expanding reach. For example, if a retailer had only a website, with no social media presence, it would on average reach 48% of consumers. Adding a Facebook page would then expand the retailer's reach by just 1%, to 49%.
Third, social media is poor at reaching out to new customers. At Fusion we measure the ability of all media types to reach consumers based on the consumer's orientation to a retailer (what we call TURF 2.0). For example, how well flyer, radio, out of home, TV and social media reach and are consumed by existing loyal customers, versus how well each reaches out to consumers who don't know of or like a retailer. This leads to a natural division of media into two broad categories. In one category are active media, where the consumer has to take action to consume the media. For instance, to read your flyer consumers must take it out of their mail box and sort through 16 other flyers to find yours, making it a very active media. In general flyers, catalogues, websites, in-store brochures are all active media.
The other broad category is passive media that requires no effort from the consumer. For example, a consumer watching TV when your commercial comes on automatically is exposed to the commercial without having to take any action, in fact they have to take action to not watch! They have to look away or flip the channel. Magazines also work this way, a consumer reads a magazine and their eye naturally will land on your page. This is passive.
So what is Social Media? It is active and that is bad news because it ruins its effectiveness. On Facebook a consumer has to go to your page, and for you to get on their wall they have to click "like." This works well for your existing core customers. In fact when done right, social media can do reasonably well at deepening a relationship with your existing customers and can even reduce the cost of marketing to them. But the problem is that most retailers already have strong contact with their existing customers: they make up most of the visits to a retailer's website, they make up the vast bulk of a retailer's email database, they read the retailer's flyer, they get exposed to in-store signage/brochures and they talk to associates on the sales floor. It is the potential customers, those outside of your consideration set, that are under-communicated to and it is only through passive media that they get exposed to a retailer's brand. However, many retailers have almost no passive media budget, they often end up allocating 100% of their media to core customers!
You don't want to forget about your core, existing, loyal customer, but they can only contribute 2-3% sales growth a year. Explosive sales growth, growing your sales by more than 10% a year, requires growing your customer base, which means using passive media to capture incremental customers.
Conclusion: Many companies are expounding social media as the "next big thing" in advertising, when in fact so far it has been the "next small thing." It should be part of your tool-kit but shouldn't distract from much larger wins.
Is Your Customer Satisfaction Report Lying To You?
Most retailers leverage their POS receipts to have customers fill out satisfaction surveys. In fact, it is almost impossible to find a retailer that doesn't have some kind of survey contest on their receipts: from traditional retailers, to gas stations, to restaurants, these surveys are everywhere. The problem is that none of the data is useful; in fact, it is often counter-productive, analytical voodoo.
The reason is that these surveys inherently suffer from a huge bias. They are only filled out by customers that bought at the store, not the 40% to 65% of store visitors that did not buy and so didn't get a receipt with a survey link on it!
The CEO of one of our clients said it best when he asked "how come whenever a competitor's store opens up my customer satisfaction scores go up?" The reason is that his mix of customers changed. Marginal customers no longer make purchases at his store, they've moved on to the competitor, and therefore do not fill out surveys. The new mix of customers contains a higher proportion of core customers, those that were very happy with the retailer to begin with, and less marginal customers, making the average customer satisfaction score higher!
Table 1 is an illustration of what our client saw.The implication drawn from the report was that the initiatives to blunt the competition were working. But is it true? Did customer satisfaction go up due to activities the retailer did to blunt the new competitor opening? When we dived deeper here is what we found: Prior to the competitor opening, the store had 100 customers (results indexed to 100 customers), 50 of them were loyal customers and 50 of them were marginal customers. As a result, the overall customer satisfaction score was 7. Results shown in Table 2.
After the competitor opening, the 50 loyal customers remained loyal, but 25 of the marginal customers left to go to the competition. So the new mix of customers in the store filling out the POS surveys were 50 loyal and 25 marginal. As a result, the overall customer satisfaction score jumped to 7.3, but only because the store lost 25 customers! See Table 3.
In fact, customer satisfaction was flat, what was changing was the mix of customers, giving quite different implications. Without significant consideration customer satisfaction reports often imply the wrong insights. This situation is happening every day in retail. In many ways the move to more data in retail has reduced executive visibility, as it has created a fog of misinformation about what is really happening in their stores.
We have found that the opposite is also true, as a retailer improves their brand consideration there are more new customers in the stores. A new customer almost never scores a retailer as well on satisfaction as existing, loyal customers. New customers might eventually become loyal, but initially they are just testing your store. As a result, they still have many questions about you and are likely to default to rating your store a 5 or 6 out of 10 on a 10 point satisfaction scorecard. Thus, the new customers push the average blended customer satisfaction score lower, implying customer experience is in decline, when in fact the only thing changing is the new/loyal customer mix ratio.
Table 4 shows what we found with a client engaged in a highly successful brand campaign when they couldn't figure out why their POS survey was telling them customer satisfaction was dropping. They began to question their branding initiative - with dropping satisfaction scores, was now a good time to focus on bringing new customers to the store? They wondered if instead they should focus on fixing the decline in customer experience first, then advertise.
But is this report accurate? Did customer satisfaction go down because customers were becoming unhappy with their in-store experience? The results we found when we did the analysis properly are in Table 5.
After we put the new analysis in place the right insight became clear. The brand campaign was working, the in-store experience & satisfaction were unchanged and the mix of customers was changing as expected, don't panic and keep it up!
So what is the solution? We recommend three things:
- Turn off your POS led customer satisfaction survey today! There are great ways to leverage a POS survey but this isn't one of them. Customer satisfaction surveys need to sample everyone that enters your stores, not just those that buy.
- Fuse and triangulate your data sources. Insight is only correct if you are isolating each independent variable, revealing how each component of your business (store trade area, branding, traffic driving, in-store experience & conversion) is doing. Chances are, if you are examining the sales lift of your last flyer without factoring in your brand strength, or looking at your in-store experience scores in insolation of other variables, then all you are seeing is noise.
- Think deeply about each issue, many people think analysis is just crunching the numbers. Correct knowledge is much more nuanced. It is not about doing math (anyone can do math when the formula is given!), it is about asking the right questions, getting deep into the numbers and coming to considered conclusions.
TV Usage Changing Rapidly in Canada
Television viewership is not yet dying in Canada. What is changing is how people use their TVs, with PVR usage continuing to grow and many consumers opting to view TV programs online. In a recent study we conducted 26% of Canadian consumers told us they now typically skip over advertisements using a PVR. Another 11% told us that they mainly watch TV online, and although online TV can have commercials these ad slots are typically bought separately from cable TV slots. For young consumers (aged 17 to 24) almost 40% now mainly watch TV online.
Both of these trends come up frequently in conversations with our retail clients. Some are questioning if they should still do TV, and many retailers that have traditionally felt nervous about TV advertising are finding all the more reason to stay away. The key is to understand the exact role and ROI of each media, monitor changes in media usage and adjust your media mix each year accordingly.
One of the key services we provide for our clients is an annual media mix optimization exercise. Here we identify the most profitable marketing investments by examining growth opportunities by category, geography, consumer segment and by media. TV still scores very high under certain circumstances. Unfortunately, too often retailers apply rules of thumb that over simplify the situation. We sometimes hear an executive say "TV doesn't work" but this is a dangerous oversimplification that can result in poor investment decisions. Perhaps TV failed for this executive because it was used to target an inappropriate opportunity, the messaging was wrong or the execution was poor. When making TV investment decisions it is crucial that executives are able to accurately answer the question "under what circumstances does TV work and are those circumstances changing?" We have clients that have used TV to transform their sales growth, where their sales used to grow only 3% per year, they now grow 8%. At the same time, we have seen situations where we were able to make drastic cuts to a retailer's TV budget with no impact on sales.
Over the last 15 years of doing retail analytics, across 40 retailers, the most powerful method we have seen for understanding TV ROI is the following formula:
The formula requires a number of steps to fully capture the complexity of advertising, but in its essence it is very straightforward. TV is effective for categories where there is a large amount of profit dollars on the table and many consumers are in the retailer's trade area but few of them think of or consider visiting the retailer for the category. If those factors align, then the retailer needs to ensure that many consumers will see the ad and associate it with their brand, while maintaining the infrastructure to convert consumers with a newly formed opinion into sales. The more a retailer aligns their TV marketing spend to this formula the more impact their TV spend will have.
TV usage is changing, but notice that the changes are only impacting one piece of the formula, step 3 (the % of consumers expected to see the TV advertising). This step, and only this step, is now 37% less efficient than if nobody was using PVRs and everyone were watching televisions. The result is TV is less effective than it used to be. But, given the right situation, it still is extremely powerful. The key is to not make blanket statements such as "TV doesn't work anymore" but to consider the intricacies of the situation and determine where and how TV can work for you. Having done so it is possible to compare the results with other media and then have a robust conversation & working-session regarding the results.
Conclusion: Though TV usage is changing, in the right situations TV still has a powerful ROI. The key is to do the analysis.
Should Retailers Be Worried About The Home Turnover Bubble?
Canadians who have recently moved to a new home spend dramatically more than other retail consumers. They also purchase from more retail categories. This makes "home turnover" - a measure of recent movers - a major factor in overall retail sales growth. Looking at recent numbers, it could also be a significant worry for retail performance in 2013.
Home turnover growth in Canada has been negative for the past six months. In December 2012 the numbers even went down an eye-catching 17 per cent, giving heartburn to executives at furniture, mattress and home improvement retailers across Canada. So, should retailers be worried about a home turnover bubble? The short answer is "No".
At Fusion Retail Analytics we uncover the fundamental drivers of future retail growth by determining the "tipping points" and measuring the year-over-year roll-over impacts and oscillations. We therefore have a strong sense of the most likely scenario.
Let's look at home turnover over time: 390,000 homes were sold to new owners in 2001, and as the housing bubble picked up steam, turnover rose by 33 per cent to a high of 517,000 homes in 2007. It then fell to 430,000 homes in 2008 and picked up slightly again to reach 454,000 homes in 2012. Now let's set this number in context with population growth. Every year there are more Canadians, so more homes are needed for them. On average, 1.3 per cent more Canadians require homes each year. When we do the math, it shows that over the last eleven years the "normal" amount of home turnover has risen from 390,000 to 452,000 units. That number is only 2,000 less than the actuals of 454,000 units sold in 2012. In other words, population growth has caught up with home turnover!
That's good news. Does it mean turnover will be positive in 2013? No. Our model shows we will be looking at home turnover in the range of 444,000 homes or a year-over-year decrease of two per cent. Blame recent government policy changes and short-term trends for this development.
But here are two reasons for optimism. First, if we accept population growth as an "anchor" for home turnover developments, it means that the farther we get from the anchor, the stronger the pressure will be to swing back to the "normal" state. This alone makes a significant drop in home turnover in 2013 extremely unlikely.
A second reason is the roll-over. The worse home turnover was in 2012, the easier the roll-over will be in 2013! Due to low interest rates and a stable economy a fundamental crash seems highly unlikely. Once we hit June 2013, the most likely scenario is that we will be rolling over soft home turnover in Canada, thereby helping the year-over-year results.
Conclusion: when we look at the overall picture of home turnover and factor in all the relevant drivers, we see it go down by -2.2 per cent in 2013. Not a great year, but nothing to give retailers heartburn either.
Fusion in The Globe and Mail
Fusion is forecasting a 2.7% increase in retail sales for 2013 due to lower home turnover, cooler temperatures and increased cross-border shopping. Click here for the Globe and Mail article or contact us for the full report.
Fusion in The Globe and Mail
Fusion is proud to be associated with The Source's marketing transformation story. Through Fusion's Business Planning Model we assisted The Source in identifying their true business/market opportunity, growth category priority and the media mix investment and recommendation on how to fund it. Click here for the Globe and Mail article.
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