Flying blind without In-store Path-to-Purchase data
Rajeev-sharma-1
Rajeev Sharma
July 8, 2021

Flying blind without In-store Path-to-Purchase data

This may seem like a bold statement given the wide array of data and analytics employed in Category Management and Shopper Marketing today. But the fact remains that without data on actual in-store shopper behaviors, there is a huge “blind spot” that cannot be filled by any amount of SALES or SURVEY data.

SALES data provides necessary insights on market share; linked with loyalty card or household panel, it reveals buying trends for different household segments and enables very useful targeting. But fundamentally, it’s all about the BUYER.

SURVEY data provides useful data on CONSUMER attitudes and preferences but very little about the actual in-store shopping process.

The big gap so far has been in the quantitative data on an actual SHOPPER’s in-store “path-to-purchase” and all the factors that influence in-store purchase decisions. This insights gap can now be filled by in-store behavior data powered by advances in AI technology and shopper science.

It is the HOW dimension of the shopper. How they navigate the store, how they engage with different elements of the store, how they shop at the shelf, how they make their decisions. It’s also how you can adapt your strategy and tactics to win in-store!

Anyone responsible for improving the performance of a category or brand cannot overlook the importance of in-store “path to purchase” data. Does your category or brand get the right “exposure”? What strategies can lead to better “engagement”? Do you know the “closure rate” for your category or brand? What can be done to improve the conversions at different stages of the sales funnel?

Our data from analysis of billions of in-store shopper journeys shows that a majority of grocery categories have a very high “walk away” rate. That is, shoppers who engage with a category or brand then walk away without buying!

In fact, the average closure rate for center store categories in Grocery stores in Q1 of 2021 was only 52.8%! That means, 47.4% of all shoppers who stopped to shop a category ended up not buying anything from that category on that trip. Identifying and reversing this “shopper leakage” is one of the lower hanging fruits in Category Management and Shopper Marketing.

Unfortunately, without Path-to-Purchase data you don’t have an objective basis to identify and diagnose the opportunity for a given category or brand. Here are just a few issues:

  • Is there an issue in pricing that is leading to a systematic leakage?

  • Is “out of stock” issues during promotions causing loss in sales performance?

  • Is there a shoppability issue due to a planogram that is mismatched to the shopper decision process?

  • Is the aisle flow not helping with the right exposure or cross-purchasing

  • Has the shopper base shifted and the new segments (trip missions, demographics) not responding as well to the category or brand?

  • Is the promotional display actually stealing share from the main aisle?

  • Why did a marketing campaign not move the sales needle as expected while the other one hit the ball out of the park?

With a systematic data on each stage of the in-store purchase funnel and detailed analysis of at-shelf behaviors, a lot of guesswork goes away in understanding and matching shopper needs/wants as articulated by their actual behavior. After all, action speaks louder than words!

Given the pressures for getting better financial ROI on trade and shopper marketing dollars, a fact-based approach for optimizing any marketing investment is clearly a high value.

There is an even greater urgency in applying behavioral shopper insights to guide your category and brand strategy today as we emerge from the pandemic era which has caused major shifts in shopper behaviors and accelerated changes in retail practices.

AI technology now provides the same visibility to path-purchase in physical retail that the online retail has been enjoying for over decade. It’s time to remove the blindfold and make decisions based on what’s actually happening in the store and at the shelf. 

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