“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.