Why test in the stores?

Screen Shot 2015-10-05 at 11.05.38 PMUnderstanding and managing category performance through indirect means, limiting tuning and optimization to early phases of development, and based on incomplete information, no longer makes sense.

Automated in-store passive observation, facilitated by technology, has created opportunities to maximize positive outcomes of changes to a category, while minimizing the risk of negative impact on its performance.

Optimizing changes, choosing from a set of final candidates, validating learnings and assumptions with real consumers, are common practices that have greatly benefited ecommerce as well as other industries.

Off-line retail adoption of testing in stores with real shoppers, by leveraging automated and passive observation, continues to lag behind ecommerce, no longer because of the lack of viable and effective means of testing and observing, but due to slow adoption.

Location and proximity technology solutions, based on beacons and wifi, facilitate the testing and tuning of investments in improving store performance aimed at increasing the number of people coming to the store, and potentially the time they spend in it.

2D and 3D sensor based solutions, such as the ones Shopperception uses to provide its category assessment and testing services, enable the validation and optimization of investments in category improvements, such as planogram changes, POP messaging and activation, in-store signage, display positioning, product launches, category reinventions, etc.

Planogram changes, for instance, are the most complicated to understand and tune in terms of tracking their effectiveness, as validation is done primarily through sales numbers, which provide very indirect indications of the effectiveness of the new planogram, and often much later than needed.

How were category hot zones impacted by the planogram change?
Did the change move the first points of engagement or interaction?
Was navigability improved by the new planogram?
How were cannibalization and substitution affected by the change?
How does the conversion funnel look like after the change?

Another good example is tuning performance of additional displays. While sale increases can be perceived through traditional sales number analysis, conversions through additional displays can only be optimized by understanding which display is generating the additional conversions, which configuration of that display yields the best conversion funnel and navigability, which positions in the store drive conversions versus awareness versus additional traffic to the category versus just additional exposure.

Testing in real stores, capturing data from real shoppers, not wearing devices, in a totally passive and automated observation setting is what separates good from great.

Why settle for good when you can have great?

Also, allow me to invite you to take a look at our Category Assessment sample, which is a deliverable from our in-depth, in-store category observation turn-key service.

See you in the store!


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