A large number of shoppers are entering stores and leaving empty-handed. POS and loyalty data offer some insights, but let’s be honest… it’s not enough to keep up with today’s constantly evolving shoppers and retail landscape. So, what can you do to adapt to the rapidly changing retail landscape? The answer lies in addressing 3 oversights created by in-store POS and loyalty data.

1) The Large Number of “Invisible” Shoppers

POS data is useful for providing insights on shoppers who have made purchases, but what about the shoppers that leave empty-handed? Likewise, loyalty data only provides insights for “known” shoppers—those who have downloaded an app or opted-in to a program, volunteering their data in exchange for some kind of reward. This “invisible” group of shoppers that both POS and loyalty data overlook represents your biggest opportunity of all, a chance to identify what’s working and what’s not to optimize sales.

In fact, this gap comprises a significant portion of shoppers: The 2018 Global Path to Purchase Survey indicates that 96% of shoppers have left a store without making a purchase at least once, stemming from reasons, including:

  • Inability to find the product they sought
  • The store didn’t carry the item they sought
  • Long checkout lines
  • Poor service

How do you know what factors are driving your shoppers away or acting as barriers to the purchase? POS and loyalty data alone won’t give you the answers. This is where in-store shopper behavior is key. But, shopper research and labs are costly, static and ill-equipped to handle a constantly shifting shopper interests and preferences.

2) Pre-Purchase Behavior

It’s always less expensive to keep existing customers than it is to acquire new ones. But, loyalty is an outcome of a good shopping experience. Another shortcoming of POS and loyalty data is that it focuses largely on the purchase. Very little time is spent on the shopper’s experience, including the precise mix of elements that incentivized them to purchase in the first place.

Additionally, shoppers are entering stores later in their buying journey, with more information and options at their fingertips than ever before. According to a recently released United States Ecommerce Country Report, 88% of shoppers research their buys online before making a purchase either online or in-store, so generic, one-size-fits-all messaging won’t have an impact on today’s knowledgeable shopper. To inspire these shoppers to visit your store again, you need to cultivate tailored experiences that resonate with them.

All in all, POS and loyalty data might give you insights into: which products are selling, a subset of demographic data, incremental impact of pricing and promotions, out-of-stock figures, forecasting, and same store sales. Yet, when it comes to what happened to influence the purchase, they leave significant gaps that only real-time shopper behavior can answer. For instance:

  • Why isn’t a brand or category performing—is it the wrong message, the wrong placement, the wrong product assortment or perhaps the wrong shoppers?
  • What gets shoppers to take notice, stop, and engage?
  • What’s the optimal level and types of promotions needed across varying shopper personas?
  • Do loyalty card holders behave differently than non-loyalty shoppers?
  • How are shoppers navigating the store?
  • Is it the location or the product assortment that’s affecting category sales?
  • What is the interest level of shoppers, and which products are they considering?
  • How does store associate availability and interactions affect purchasing?

Oh, and how are these different by gender, age, store location, time of day, etc? If you’re not investing in the journey prior to the checkout, there won’t even be any purchase data to analyze.

3) Personas

Loyalty data offers some insight into a subset of POS data to help establish high-value shoppers and key personas. However, these sources don’t offer insights needed to respond to these shoppers during their journey or create, high-impact, tailored experiences.

Take for example a running store with a failing women’s running top category. If a key persona, is a 30-year-old woman in a running store: How does she move through the store? What does she look at, and what products does she consider? How might messages or promotions about running tops by the sneakers influence her journey and basket? How would her journey and basket differ when she shops with her kids or husband versus alone? These kinds of in-store behaviorial insights are key for making store layout, merchandising, placement, or signage decisions, but are big gaps in POS and loyalty data.

In addition, in-store shopper behavior can also be used to react to shoppers while they are engaged. For example:

  • Instantly provide relevant information and recommendations about products to specific shoppers based on persona
  • Tailor your messages based on product interaction, gender/age, dwell time, and more
  • Alert associates with real-time insights on a shopper’s browsing, along with relevant product information or recommendations to better tailor the experience

Are you ready to go beyond POS and loyalty data to begin capturing actionable shopper behavior data? Learn more about how you can start unlocking powerful shopper insights with the help of our real-time analytics.

Image Credit: Shutterstock/Igor Kardasov