In-store Testing with a Behavioral Coach
Rajeev Sharma
February 28, 2023

Behavioral data helps “coach” in-store tests to rapidly evaluate and improve new concepts, powering smart innovation

Imagine coaching a football team without being on the sidelines during a game or ability to review the game “tapes”. Could you make good plays using only the scoreboard or by talking to the players after the game?

That’s exactly how most in-store tests are run today in retail, using only sales data from test/control stores with an optional shopper survey to evaluate new concepts!

To truly evaluate the worthiness of a new idea, you need to “observe” behavioral responses of shoppers. Action speaks louder than words. Insights through shopper surveys can be helpful but cannot accurately capture what they did or understand the factors that influenced their behaviors.

Skipping behavioral research for in-store tests can be extremely risky and ultimately time-consuming because of the need for (a) predicting performance to justify the investment in a wider rollout, (b) diagnostics to “tweak” an innovation before a rollout.

Testing in even a handful of “behavior lab” stores that are equipped to measure behavioral responses from shoppers creates a “smart” innovation process. In-store behavior analytics using video/AI enables reliable performance evaluation and rapid, actionable feedback to tweak ideas. Technology enables objective analysis of the detailed behavioral responses of a large sample of actual shoppers (typically tens of thousands within weeks).

The resulting feedback goes far beyond just sales data, pinpointing the performance along the path to purchase “funnel” and revealing specific “prescriptive” insights on improving engagement and conversion rates. Very often simple implementation tweaks (e.g. right position, signage) can turn around a struggling idea into a big commercial success.

Examples of Behavior Data “Coaching”

VideoMining has been involved with 100s of in-store tests, leveraging behavioral data to help evaluate and evolve new concepts: category resets, aisle reinvention, store re-design, new product launches, packaging changes, marketing campaigns, merchandising innovations, etc.

Below are three examples, showcasing the “coaching” power of behavioral data for testing different areas of innovation.

Product Innovation:

A major re-packaging for a popular food brand was launched after successful VR tests. After the re-branding, the sales dropped substantially. The insights from in-store behavior data revealed that the packaging was not engaging shoppers – even after they got over the initial confusion. In fact, there was barely any interaction with the new features highlighted in the packaging. In-store testing with the behavioral research before the launch would have helped in avoiding the costly “re-launch” from the initial re-design flop.

Merchandising Innovation:

A major CPG company approached a grocery retailer with an innovative merchandising concept that involved creating a “lunch destination”. It was designed to meet the convenience and value needs of shoppers by putting together all the key components of a lunch in a multi-product display that would also include refrigeration. The goal was to attract shoppers to the convenient lunch “solution” and encourage cross-purchasing.
While testing the solution merchandising concept, in accompanying intercept surveys, 73% of shoppers stated that they use the display regularly, yet the behavioral data showed that only 7% of shoppers actually picked up items from the end-cap! Behavioral data also showed that those who engaged with the end-cap just picked up an individual item rather than the intended meal combo. Behavioral insights was used to modify the messaging and also change the location of the end-cap to provide the right exposure that eventually made the concept successful.

Marketing Innovation:

A digital kiosk was being tested by a mass retailer to help with the beauty products aisle. Behavioral data showed poor usage and impact. But along with the evaluation, our diagnostics showed that even though the kiosk was in a high traffic area, it was being encountered after shoppers visited the relevant categories. Changing the position to a less busy area but the right side of the aisle dramatically improved the performance with overall lift of 10% in sales for the represented products providing the ROI to justify the kiosks. This led to a wider rollout of the kiosks while also incorporated other specific improvements from the insights on the demographics and behaviors of kiosk users.

There are numerous other examples of behavioral research quickly uncovering insights about an in-store innovation that could not have been captured by sales data or surveys.

Speeding Innovation & Reducing Failures

It is reported that as much as 95% of new CPG product introductions fail, despite substantial investments in consumer research to predict demand. To get reliable evaluation of any new product or concept, it is important to conduct real-world tests with behavioral feedback from real shoppers. Relying only on survey-based research or sales data is too risky.

Behavioral feedback provides the objectivity and precision that is not possible from survey data or only sales data. Behavioral research also shows that sometimes even great ideas may appear to fail due to unexpected operational issues or factors that were not accounted for in the launch plan.

In today’s fast paced retail environment, it may be tempting to bypass a thorough in-store testing phase with behavioral research before rolling out an innovation. Unfortunately skipping behavioral research could be risky and costly both in terms of dollars and time.

Behavior Labs help leverage AI technology to provide rapid predictive and prescriptive insights, greatly improving the odds of success. Given the critical role of innovation in today’s fluid retail environment, behavioral research is simply a smart way to unlock the true potential of any in-store “play”.

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