As most brand marketers know, not all stores are created equal, and to realize the best possible business results, they shouldn’t all be treated equally. But how do you know when Store A should get more marketing support than Store B? And how do you know what levers to pull for Store C versus Store D to optimize success in each location?
Too often, analytics are only used on the back-end of a campaign to measure marketing success. At Callahan, we embrace and emphasize the benefits of front-end data analysis to point the way to desired business outcomes. Front-end analytics, when applied to multi-unit marketing (whether store chains or multi-unit retailer distribution) can illuminate three key factors: what business opportunities exist, where they are and the size of the opportunities.
The specifics of implementing front-end analytics for different types of businesses can vary greatly. For example, a regional multi-unit chain of company-owned stores will have access to different data points than a national packaged goods company selling through third-party retailers. Regardless of the type of business, however, once front-end analytics are put into practice, the benefits in each case will be similar: understanding what, where and the size of the opportunities.
Despite any difference in business type, we always start in the same place: store-level sales information. Then, the secret is to overlay sales data with demographic information and as much other relevant data as possible. For example, for a dog food brand, we would look at the geographic penetration of dogs per household in a certain radius around each store or distribution point. In the case of a lawn and garden product, we would overlay weather data. For a restaurant chain we could look at the density of millennials or another key demographic segment within each local store’s trade area. Typically, we use multiple layers of data to develop the richest view of the business situation.
When all inputs are compiled and visualized in our system, we see that correlations and patterns start to emerge that weren’t previously seen. We can begin to understand how the stores are different or similar, and which combinations of data illuminate the path to the biggest business opportunities.
Once sales and overlaid data can be analyzed on a store-level basis, it’s a huge milestone. We can start to understand which markets, or which store locations, represent the greatest opportunity for growth and which ones are not performing as well. While many brands already know that, what they often lack is an understanding of the specific triggers or levers that can influence sales (up or down). This is where overlaying other relevant information comes into play. For example, by comparing retail foot traffic or the square footage of a store on top of sales, we see patterns emerge that identify untapped growth opportunities (what, where and the size).
For example, consider a restaurant chain where Stores A and B have the same square footage. Assume that in this case, square footage can be correlated to potential sales volume. When we analyze the data we see that Store A outperforms Store B in sales, so we could say that there is more opportunity for growth in Store B. And, if Store A is performing in the top tier, chain-wide in sales per square foot, it may be unwise to try to push more traffic there because it could result in poor customer experience. This simple example shows that Store A and B should be treated differently from a marketing standpoint, and it starts to illuminate the path of what the restaurant chain should do.
All this enables us to create specific marketing activities at the store or market level, or at granular points of distribution. Once the opportunities come into focus, it’s possible to establish a budgeting model for media dollars or marketing resources that allow a brand to spend to the opportunity. Marketing investments can be made in specific geographic areas or against certain stores or distribution partners. Where the greatest opportunity for growth exists, more spending is justified. Conversely, we can take the care to not over-invest in areas where there’s limited opportunity for growth. So, when you identify underperforming stores, how do we know whether there is opportunity worth investing in? Since we’ve already identified the characteristics of the better performing stores based on combinations of data points, we can better assess the growth opportunities of the underperforming stores.
When you run a marketing campaign based on strategy that’s informed by that kind of front-end data analysis, you’re already prepared for the inevitable question: “Did it work?” The front-end preparation serves as a great statistical benchmark to measure performance on the back-end. For example, when you make an investment in stores that are underperforming relative to the opportunity, the mechanisms and benchmarks from the front-end data analysis are already in place to measure success on the back-end. Media and marketing spend can be compared relative to increased sales, and ROI can be calculated on a store-by-store basis.
We expect the ROI to be higher when there is a bigger opportunity. And that ROI calculation can include variables in factors such as media costs across different markets (e.g., local media in Tampa is a greater expense than local media in Topeka). Variations in media cost is just one factor that can be used to understand how much it’s going to cost to pursue the business opportunity in different markets. Media spend and traditional marketing metrics (impressions, CTR, etc.) are yet more data sources that can be overlaid on a store-level basis to complete the picture that started in the front-end data analysis. So, on the back end, not only are we able to determine if we moved the business in a positive direction based on KPIs, but we can verify if we did so efficiently.
The bottom line? By using front-end analytics we can identify true business opportunities at a local store or market level (what, where, and the size), which allows us to spend to the opportunity (rather than treating all stores equally). This sets the stage for more effective and efficient marketing, and the ability to show measurable results. And that’s every marketer’s dream.