A new (behavioral) perspective to product interaction

January 19, 2020, Comment off

A new (behavioral) perspective to product interaction

Product categories are a key unit of analysis and as such they are constantly tracked: from sales data, P&L, technological advancement to social and market trends. But when it comes to behavioral economics and behavioral psychology, we don’t see much scientific analysis to help us understand categories. Everyone talks about #ImpulseShopping and #Autopilot, yet there is little analysis going around. Apart from the (in)famous “70% of purchase decisions are made in store”, of course!

This article is the first in line to address behavioral aspects of shopping for different product categories. Shopper Vision, partnered with Shopper Intelligence www.shopperintelligence.com, is a unique provider of retail analytics, as our findings are built upon a database of 1 million in store interactions and 10.000+ shopping trips. Interactions like: focusing on a product, seeing a POSM, taking the product in hand, returning to shelf vs putting in cart etc. 

The very Interaction preceding the act of purchase

As a retail or marketing professional, have you ever wondered what sort of interaction happens just before the product is put in cart? Do your shoppers typically make the purchase decision while holding the product or just by browsing the shelf? And if you have, what were the implications on your category/brand execution?

Using a k-means cluster analyses, we have segmented approximately 240 product categories per two criteria:

  1. Time spent looking at the category fixture
  2. Time spent observing the product in hand before it is put in cart

…to get quite interesting results! Four distinctive category segments have been identified based on the way products were bought.

Before we dig deeper into results, a small reminder on the logic behind clustering – it’s a machine learning process that groups a set of objects in such a way that objects in the same group (called a cluster) are more similar to each other than to those in other groups. In our case, we have grouped 240 product categories into four distinctive category clusters. Product categories in one cluster are similar to each other in terms of being looked at the shelf/taken in hand.

The Segments

Looking at the category map below, four segments are placed onto the continuum of 1. time spent looking at the fixture (Y axis) and 2. time spent inspecting in hand (X axis).

  1. Segment 1 (S1, orange map area) i.e. the Autopilot segment are categories bought automatically (fast) – after a quick scan of the shelf, with the only hand movement being to pick the product and drop in cart. This segment is made of impulse drinks & snacks categories, some “generic” food items like rice, oil, mayo etc. To illustrate, chilled drinks are bought in 6 seconds on average: three seconds being browsing time and three more seconds to put in cart.
  2. Segment 2 (S2, blue map area) are Actively browsed categories – shoppers more actively browse the offer but don’t inspect products in hand. After the right product is spotted out of a set of considered products, it gets put in the cart without inspecting it in hand. Dairy categories, many pantry categories and some frozen products are in this segment.
  3. Segment 3 (S3, purple map area), i.e. Deli and meat categories are browsed for longest.
  4. Segment 4 (S4, red map area) are categories Inspected in hand. While the shelf itself doesn’t attract as much attention, these products are thoroughly inspected in hand. They are held in hand three times longer than an average supermarket item. This segment is made of some Health, Beauty & Baby categories, some Homecare and almost all General Merchandise. Cosmetics are on the far end of the spectrum as a category held in hand the longest (by far).

Implications on Category Management and Marketing

What do the four distinctive segments have to do with your category/brand execution?

Behavioral targeting is used more and more in the digital world, and there are no more barriers to use it in the brick & mortar planning as well. Behavioral segmentation can help you allocate category investment to maximize ROI. In a nutshell:

  1. Autopilot categories tend to benefit most from activities aimed to increase the chance to see the category. Proper secondary location planning and traffic flow is key. Also, these categories benefit from investing in digital signage/displays.
  2. Actively browsed categories are a lot about shelf marketing & signage and shopper centric planogram layouts. SRP can contribute to increased product shelf visibility as well.   
  3. For categories inspected in hand it’s more about what happens outside the supermarket, as package design and communication of functional benefits play a major role in shopper decision making.

Behavioral category segments were made atop a database of 10.000 shopping trips in Australia, UK and continental Europe, developed in partnership with Shopper Intelligence www.shopperintelligence.com. Next to product interaction, Shopper Vision provides behavioral data to optimize POSM, layouts and secondary location planning.

Do you want to understand how shoppers interact with your category in any country? We would be more than happy to talk! info@smartshoppervision.com