We’ve all had this experience: we’ve walked into a store intending to buy something, and walked out empty-handed. For simplicity, I’ll call this the empty-handed-exit experience. The reasons for the experience are many. Perhaps we change our mind in the store, because the product just didn’t meet our expectations once we saw it, or we decided it was better not to spend the money. But many times, the “empty-handed-exit” is a result of a poor in-store experience. The item is out-of-stock, hard to find, or too high to reach. The item is mislabeled, making it appear more expensive than we anticipated. Or, perhaps something about the display wasn’t right, like bad lighting, bad signage, dusty merchandise, something that just turns us off.
The list of things that can instantaneously confuse or turn-off a customer are numerous, and in today’s retail climate these occurrences simply cannot happen. So what can stores do to combat these operational issues on the sales floor?
The classic solution has been the store-walk. The store-walk is where management will comb the floor and scrutinize the in-store experience: signage, merchandising, shop-ability, and ensuring through a simple visual check that everything in the back-room is on the floor. While the store walk is holistic, it’s also time-consuming, not scalable, and prone to human biases and error.
A recent solution to this issue is retail surveillance. Many of these tools provide heat maps of the areas within the store where customers gravitate most. The problem with these heat maps is that they don’t isolate a problem down to a SKU level; they merely identify when overall sections and displays are underperforming. Furthermore, they require extensive hardware and setup.
CB4 is a solution that surpasses the store walk and video technology because we find and fix operational issues on the selling floor in a scalable and time-efficient manner without any hardware. How could this be? CB4’s approach is 100% data driven. We look for patterns within POS sales data and determines when a data anomaly is taking place at a certain store for a certain SKU.
For example, in the data matrix above, the 5 Hour Energy SKU in Store 7 is performing in an anomalous way. Given the pattern that exists between the four products, the 5 Hour Energy is acting peculiar. In CB4’s solution, Store 7 would receive a recommendation to check up on the 5 Hour Energy SKU in Store 7.
The above example is an illustrative example for concept purposes. The true CB4 software will comb thousands of patterns and evaluate statistical thresholds before proposing a recommendation.
Every store is different, and you know your customers and what they like to buy. CB4 helps operations and merchandizing teams by simply putting a spotlight on issues affecting your highest-selling products and helping you capture unmet demand on a store and SKU level.
To learn more about CB4 and how we drive between 0.8-3% same-store growth for our clients, contact us today.