Good question! Unfortunately, it is not quite so easy to answer, not even by scanned data from the retailers or market research companies- that is, we do not know anything about the final result and we know even less about the shopper and how he/she arrived at that particular product.
Can we discover anything else through loyalty cards? Yes and no … or rather, it depends: the card is for personal use, but in fact, it can be used by any other member of the owner’s family. In a recent case study, we compared the registered cards in relation to the gender of the buyers registered during the Check-Out and the gender detected through video analytics technology (Shopper Reality Mining) placed on the shelves. When we compared the data obtained from the shelves to the numbers indicated by the loyalty card modules, the latter tended to overestimate the female targets (face detection: 42%, loyalty cards: 52%).
Well, at this point, you may wonder what video analytics systems are being deployed. Let’s briefly reconstruct their story: they appeared at the beginning of the 21st century, for military and security purposes, and these systems have been adapted to other areas, especially for marketing and communication. They enable audience estimation in public places (even if it is regarding digital out-of-home, supermarkets, shops, exhibitions or events) and specifically to count the passers-by, the viewers (those who actually noticed a screen, a shelf, a product) and to measure the average stop time (in front of a certain product) and attention. All data is then segmented by gender and age.
The combined link between this data and that obtained by a Retailer (i.e. data scanner and planogram information) allows for the reconstruction of the “path to purchase”, the understanding of consumer behaviour in-store and the verification of the effectiveness of planograms and exposures for the purpose of acquisition. Now, to answer the question initially posed by the article: How many buyers actually buy my product?
On our platform, Dianalytics™, we link these different data sources (loyalty cards, web analytics, planograms, advertising etc.), in order to “capture” the buyer from the moment they entered the store up to when they reach the shelf. We measure:
Store traffic: people entering the store
Aisle traffic: people who reach the aisle
Shelf traffic: people who reach the category area
Potential shoppers: people in front of the shelf and observing the category
Actual shoppers: People who actually buy one or more products
We had one case, in a high awareness brand, monitored by us for a period of 6 months in a panel of a hypermarket in Italy. With this client, we were able to collect the following data: more than 470,000 shoppers entered the store; 37,931 people, meaning that 7.4% of them noticed the brand in question and “only” 794 bought it – that is 1,4% of the potential shoppers. If it were to raise the sale conversion by 1.7%, we would have a total sales increase of 13%.
And now our question focus turns to: how can potential buyers change to actual buyers?
Read upcoming articles to find the answer.