One of the most basic tools CPG brands use to understand their position in the market is point-of-sale data. From the very first time a shopper put your product in their cart to the nationwide sales you may enjoy today, POS data paints a picture of when and where your products -- and your competitors’ -- are scanned for purchase. Armed with this intel, brands can identify new shopping trends, opportunities for growth, competitive threats, and more, and use that insight to build an intelligent strategy for where and how to invest at retail.
But as CPGs build a deeper understanding of not just where and when they’re sold, but what field activities and in-store conditions are influencing those sales, they’re able to use POS data to inform their retail execution strategy as well. From optimizing store position and display execution to improving partnerships with retail managers, CPG brands are using point-of-sale data to radically improve execution in the store to drive sales velocity.
In this blog post, we’ll outline how CPG brands can use their point-of-sale data alongside field activity and in-store observational data to supercharge their retail execution strategy.
What is POS Data?
There are four major types of retail point-of-sale data, which are defined by both their source and their focus. Let’s talk about data source first.
The simplest form of point-of-sale data comes directly from a single retail chain, like Whole Foods or Kroger. Not surprisingly, this is known as retailer direct data. For brands looking for a wider picture of the market, syndicated data contains sales metrics from multiple retailers, which are gathered together by a syndicated partner like Nielsen, IRI, or SPINS.
Separate from source, point-of-sale data can either focus on overall sales by store, or it can focus on the behavior of individual shoppers. Through these two lenses, CPGs interested in store-level data should focus their attention on syndicated store data or retailer direct store data. Brands seeking to understand broader consumer behavior will have more luck with syndicated shopper data or retailer direct shopper data.
Retail execution teams should consider tracking store-level data (either syndicated or retailer direct) rather than shopper data, since retail campaigns are targeted at individual stores or chains. Shopper data, on the other hand, may be used to measure the effect of social media advertising, or other campaigns that reach shoppers beyond the four walls of the store. For the purposes of this blog, we’ll use “point-of-sale data” to refer to syndicated store data or retailer direct store data.
Below, we’ll outline the three ways brands are using POS data alongside field activity and in-store observational data to improve store-level retail execution:
- Execution Tracking
- Retailer Partnerships
- Resource Planning
Promotions are the lifeblood of so many successful retail strategies. When executed properly they fuel predictable sales lifts, and can be targeted to support growth in strategic regions or chains. However, poor execution plagues even the best planned planned promotions. As much as 50% of CPG displays are not executed as planned, contributing to an average 25% loss in sales due to poor retail execution.
One of the reasons incorrect displays have such a big impact on sales is that they’re traditionally not corrected until weeks after implementation -- if they’re noticed at all. Brands have typically had to wait until their sales reps visited each offending retailer to get a sense of where a promotion had failed to get off the ground. Now brands are using point-of-sale data to spot missed execution in a matter of days, and can deploy their teams to take corrective action before it’s too late.
Brands that keep track of the activity their teams complete in the field can see at a glance which stores they’ve visited to kick off the promotion (by delivering posters, setting up displays, etc.). Then, they can monitor the POS data of that account list to see which stores aren’t getting the lift they expected -- or where the expected spike in sales is short lived. Chances are, promotions at those retailers are out of compliance.
Retail execution software, especially those focused on field data, empowers brands to track execution and take action faster than ever, pairing field activities and sales data together in clear reports and sending alerts when sales lag behind expectations in targeted accounts.
When promotions fail, one of the easiest scapegoats for CPG brands are the retail store staff in charge of setting up and maintaining displays. While research shows they’re not much more effective than brokers, on average, the truth is store personnel only execute displays properly 37% of the time. With that in mind, getting retailers to care about the success of your brand is key.
That’s easier said than done, and not because retailers are negligent -- they simply have a lot more on their plates than overseeing any single promotion. That said, innovative CPG brands are using their point-of-sale data to generate buy-in for their promotions with retail staff, helping improve execution rates with store personnel.
Even for brands that actively use point-of-sale data to measure and plan promotions, that data is typically hard to access. It’s useful for campaign retrospectives, periodic strategy sessions, and sales reviews, but likely isn’t readily available for use in the field. When CPG brands unlock their POS data and make it available to their field sales team they’re empower their reps to engage in fact-based selling, showing their retail partners just how much incremental sales they stand to gain from a successful promotion.
With POS data at their fingertips, your sales reps are in a position present themselves as true partners to retailers, presenting ideas that will not only help your brand but improve the category as a whole. Faced with that opportunity, retailer managers and store staff are far more likely to prioritize your promotions in their daily plans.
Whether you have a dedicated retail execution team or your sales staff pulls all the weight in the store, your reps in the field are the ones making a difference for your brand in your key accounts. As you grow, however, you’ve likely found there are far more opportunities for your team to cover than they can get to in a week, or even in a month or quarter.
With such a huge opportunity to influence sales through retail execution, how do you prioritize accounts? Should your reps create multiple touch points at your highest volume accounts at the expense of having any face-time at smaller stores? What’s more important to your bottom line, adding new facings at high traffic stores or expanding your set in new territories? Answering questions like this is central to making the most of your investment in a field team. Adding point-of-sale trends to a data-driven field management plan can more clearly connect field activities with sales lift, helping retail execution teams prioritize both daily objectives and quarterly goals.
As we talked about earlier, viewing field activity data alongside point-of-sale data gives field teams insight into how individual actions in the store affect sales. Plotting something as simple as store visits, for example, against sales lift allows teams to quantify the business impact of every call, by store location. Or, securing new facings might emerge as your most valuable in-store activity, leading you to prioritize under-penetrated accounts above your top performers.
Reinforcing point-of-sale data with field activity data empowers teams to answer not just where, but why, fueling smarter resource planning for teams to have a bigger impact on sales.