3 Types of Retail Execution Data: What They Are, and Why They Matter
Mat Brogie, Part of founding team and CEO of Repsly, Inc.
In today’s fiercely competitive market, retailers are taxed with the responsibility of making sure their in-store presence gives them an edge over their e-commerce counterparts. Effective retail execution is absolutely essential to keeping store sales afloat, seeing as over half of purchase decisions are made within brick-and-mortar walls.
High-performing organizations incorporate three distinct data types into their retail execution strategy to keep store sales high and competitors at bay. Here we’ll discuss what those data types are, and how retailers and suppliers converge to best utilize them.
Activity data refers to the measure of outputs from brand representatives or store personnel. This encompassses metrics such as “visit frequency” or “number of shelf resets completed.”
These metrics have the power to reveal how team members are spending their time in-store. For example, retailers can rest assured that brand representatives are upholding their commitments by monitoring how often they visit the store and how much time is spent there. This data also proves useful in devising better schedules for employees based on how and when they interact with brand reps.
Managers from either party (retailer or supplier) can more easily identify top performers by comparing activity data to sales lifts. What’s more, transparency around this data promotes accountability for whichever party is responsible for various retail execution tasks.
Activity data sets the stage for what’s going on within the confines of a store, but it can’t paint the whole picture. The real value comes from analyzing it against observational and sales data.
Observational data includes qualitative data points that tell the story of what’s actually happening inside the store. Examples of this data type include notes on brand presence, photos of the shelf, or information on merchandising activities.
This data tells the story of what’s actually happening at the store level, and has several practical uses. Some include:
- Sharing photos to prove compliance with a display or promotion
- Monitoring photos and other data points pertaining to shelf presence in order to flag stockouts more quickly and keep track of damages that might become returns
- Brands building trust with a retailer by sharing competitive intelligence from other stores through photos and questionnaires (i.e. “What does the inside of their store look like?”, “Who is shopping in it?”, etc.)
- Recording information about foot traffic and customer sentiment during a promotion or demo event
Observational data is able to reveal key insights about in-store presence at a very granular level of detail. However it becomes even more powerful when used in conjunction with sales data.
Sales data is simply the sales generated over a specified time period. Perhaps the most straightforward of the three data types, it’s still extremely telling. What’s interesting to retailers is how the other two data types (observational and activity) are able to push the needle on sales.
Both retailers and brand suppliers can benefit from comparing historical sales data to improve forecasting, especially around the holiday season. When combined with activity and observational data, sales data reveals whether or not a promotion is effective or if a display is being properly set up.
Similarly, steady sales followed by a steep drop-off could signify a stockout or the consumer switching to a competitor. Taking a look at sales and observational data can also convey where cross-merchandising is present to help retailers and brands make decisions about where else to stock complimentary SKUs.
A close analysis of sales data can uncover a cannibalization that’s hurting other SKUs in a set. Conversely, brands can showcase which SKUs or promotions are the biggest sales drivers for retailers.
Putting It All Together
A sizable 25% of in-store sales are lost to poor retail execution. When used either separately or in conjunction, three types of retail execution data are a retailer’s secret weapon to winning over consumers. Forward-thinking organizations understand this reality, and apply the three types of data as part of a virtuous cycle of insights, planning, and action at retail.
About the Author
Mat Brogie is part of the founding team, and CEO of Repsly, Inc., the world’s leading solution for high performance retail execution teams. Mat has spent the past 15 years of his career focused on bringing technology enabled business solutions to the consumer goods industry, having implemented solutions for tens of thousands of field reps at companies such as Coca-Cola, Procter & Gamble, Pepperidge Farm and hundreds of others.
Repsly is a retail execution software that empowers CPG teams to achieve peak performance in the field. Repsly’s powerful manager’s dashboard equips teams with the data they need to uncover opportunities at retail, and the tools they need to deploy their team to take the right action in the store. Repsly’s best-in-class mobile app empowers retail execution specialists to have the biggest possible impact on sales, while equipping reps with the customizable data collection tools they need to report real-time insights from the field. Repsly is the only retail execution solution to centralize brands’ sales, field activity, and in-store data, connecting store-level activities with their impact on sales. More than 1,000 field teams in over 80 countries drive execution and sales in the field with Repsly.
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