Using Location-Based Analytics to Understand the Customer Journey

by Bill McCarthy on 04-06-15

The primary job of the retailer remains unchanged, but the ability to tap into how shoppers are behaving while webrooming or showrooming for example, are all pieces of the larger customer journey puzzle.

Technologies such as location-based analytics can help to put these pieces together and contribute to our understanding of the customer journey – from the moment the shopper walks into a store, counting them in using a variety of technologies from Wi-Fi to Bluetooth, BLE and video – to understanding once they are inside the store.

The technologies employed are determined by the data a retailer wishes to gather, which can be divided into either a one to many or one-to-one level of analysis.

One to many enables a retailer to anonymously gather trends about shopper behavior. This information can then be used to impact operational and marketing decisions. Retailer KPIs around one to many location-based analytics could include:

Draw rates – analyzing the number of shoppers walking past the store with those that actually enter, so that retailers can determine the total opportunity, and whether they are receiving their fair share of shoppers.

Abandonment rates – technologies such as Wi-Fi and Bluetooth monitor how shoppers are interacting in the retail space. How long do customers spend in the store? Do they turn around and leave in a few minutes? The parameters of this time limit can be determined by the retailer themselves.

Sales intercept – does a sales assistant greet the shopper when they enter the store? If so, after how long? And does this lead to an increase in turning shoppers into buyers? What is the optimum time for a sales person to engage with the shopper?

Zone level traffic – where are shoppers going within the store and how long are they spending there? Are they drawn to specific promotions for example?

Dwell time – how much time are shoppers spending in the store, and what is the average time it takes to make a purchase?

Take the following example. A shopper may enter the store on a busy Saturday afternoon in search of a new pair of jeans. There is a wide range of offers, but there is not an available sales assistant close by. The shopper becomes impatient and leaves the store.

Using location-based analytics, retailers can avoid this scenario. Understanding historical data also enables them to establish the average time spent by customers in each store zone – and to act upon this information to avoid drops in conversion rates once the “optimal experience time” is exceeded.

The closest sales assistant on the shop floor receives an alert on a handheld device to alert them to the fact that a shopper has been in the denim section for five minutes. The sales assistant is then able to attend to the shopper at the optimum time, assist them with sizes and take payment – all working to avoid the possibility of the shopper leaving the store without making a purchase.

This post was adapted from ShopperTrak’s contributed piece in The Retail Bulletin.

Read more posts by Bill McCarthy

Bill McCarthy is the General Manager of the Americas, in which he oversees regional efforts pertaining to the Traffic Insights business unit.