Why Measuring In-Store Shopper Behavior Matters
For any shopper marketer at a brand selling into retail brick & mortar, in-store programs and merchandising often yield high ROI but can be the most frustrating to execute, measure, and optimize effectively. That’s one reason why everyone from new entrants to some of the world’s largest CPGs (consumer product groups) are looking to collect and automate actionable insights from the most important part of the customer journey — that very last moment at the shelf.
There’s never been more data available to shopper marketers, but it still feels like a black hole when it comes to measuring customer behavior at the shelf in stores. In our shopper marketer and behavioral science toolkits, it’s been historically dominated by two types of in-store data sets: ‘report card’ and ‘forced.’
‘Report Card’ Data Sets
Transaction scan data tells you how you did, but it's a report card so you only get it at after the fact and it doesn’t tell you how you got there.
If report cards were really the whole picture, imagine all the hours and that could have been spared sitting parent teacher conferences! Scan data can be costly and some retailers just don't participate in sharing POS data. We can also miss key insights like what location in the store contributed to that sale. To clarify, scan data isn't bad and I'm an advocate for using it. It just doesn't tell the whole story.
‘Forced’ Data Sets
These are great scientific methods of understanding consumer preferences. However, they’re highly inorganic, often use small representative sample sets, and many agree there’s still no better lab than a real-life store shopping experience.
I might be BIAAS-ed (pun intended), but the next category pulls in some of the best data to augment traditional data sources and answer these shopper behavior questions.
‘Real-Time’ Data Sets
Observations - People / Cameras
MicroSensors (Retail Aware)
Observations such as sitting in a store and watching shoppers (which I’ve done my fair share of) and talking to frontline workers are strong qualitative data points. For example, I’ve both observed and had frontline employees divulge that merchandisers weren’t set up (which would explain 0 sales). Unfortunately, its the most manual and hardest data to collect at scale. Cameras are an alternative, but can be cost prohibitive and difficult to deploy for this use case. Let’s not mention the hurdle of getting a retailer onboard, and limitations on power and connectivity that arise in any retail environment.
And this brings me to MicroSensors. MicroSensors use motion, heat, light, vibration, distance, ambient signals, and more to measure footfall, dwell time, and characterize hand movements to tell how many times product is picked up. This combined with machine learning/AI (artificial intelligence) and real-time streaming into the cloud have shown early promise in making cost-effective, real-time shopper analytics at the shelf a reality.
Just think about what’s makes digital/direct to consumer teams so effective - it’s their ability to measure customer behavior and see what works and what doesn’t. Metrics like traffic, dwell time, clicks, conversions are proven staples in the digital world, so shouldn’t we look at brick and mortar channels in the same way?
Armed with actionable, in-store shopper behavior data, we can finally start the solve questions such as:
*What part of the store contributes to my sales?
*What shelf should we be located on?
*What are my conversion rates and engagement ratios by store, logo, region, country?
*Which displays deliver the best bang for our buck?
*What is my lift during a campaign such as a TPR or BOGO?
*What are problem stores and why are sales low at those locations?
*What is the profile of our best performing locations and how do we replicate?
About Retail Aware
Retail Aware’s award-winning, patent pending BiAAS platform leverages micro sensors to help brands, retailers, and beyond measure consumer behavior and retail execution in real time.