Sangeeta Jun 20, 2014 No Comments
How many people entered the store (A) during the peak summer month of May?
How did they behave?
What was the footfall to sales ratio for those 31 days?
What was the footfall per square feet during the period?
What does the effective sales per square feet tell about the store visitors?
Retailers who want to get ahead of the competition need to ask many such questions to analyse their store performance data. Such information is put together to understand visitor behaviour and demographics, identify trends and make suitable decisions.
Although organized retail represents only a 5 – 7 % of the total retail industry in India, it is increasingly adopting technology to drive sales. The “footfall” is one such tool used to estimate various consumer and sales metrics.
What is “footfall”?
As the word suggests, it literally means the number of people who have visited the store. Although conventionally, the number of footfalls was used by a retailer to translate into sales, it no longer holds true.
First, because a family of five is considered as ‘one’ footfall (one decision making entity) rather than five footfalls. Second, a visitor (a.k.a ‘footfall) may enter the store to check out what’s on display, but walk out without buying – because(i) the rates did not suit him, or (ii) the collar designs on the shirts were not what he wanted, or simply because (iii) he had entered to kill time, as he was early for his date!
Store A footfall analytics application
Take the above scenario, where the manager of Store A is checking why sales during May were poor, whereas data and video footage show a high footfall rate. When he correlates data of footfalls, video footage, time stamp and effective sales – he can conduct a suitable footfall analysis and arrive at several conclusions.
a) Most footfalls that did not convert into sales were during the peak afternoon hours.
b) When the visitor behaviour and demographics is studied from videos installed in the store, it reveals that most footfalls were casual strollers – either groups of youth or ‘repeat’ mothers.
c) It is analysed that these visitors had come in for the A/C cooling rather than purchases, and the mothers were probably marking time before picking up their children from the neighbourhood school.
Clearly, the store manager is disappointed. For mere footfall was not the true measure of business done.
Now what he will do with this insight depends upon supporting retail analytics, technology deployed and store policy. Using technology driven analytics, the store manager can convert a large part of the causal footfalls into sales and additionally focus on the serious shoppers by giving them more moving space and product visibility for higher purchases.
So what does the store manage do? He gathers cell phone data from people who enter the store and leverages this to send personalised, location-specific shopping alerts each time the cell phone user is in the vicinity.
This mobile-enabled foot traffic technology works by “tracking the movement of a customer’s phone” to gather data on how the owner moves around a particular location.
As this report from Software Advice elaborates, “Retailers use this technology to determine peak traffic behaviors, conversion rates and dwell times in the stores.” The data can be used to plan and re-configure store layouts and merchandise displays in order to attract the casual strollers (‘zero sales’ footfalls) who are seen to hover around the entrance /central area, and translate these casual footfalls into ‘real sales’.
The report details 4 Ways Retailers increase sales with mobile-enabled foot traffic analytics.
How to use this insight for furthering sales and store performance?
So if you seriously want to make your career in the fast-paced realm of retail analytics, what better way than to suggest your client to use innovative technology-driven analytics? Propose investments into such mobile-enabled foot traffic analytics to boost sales, enhance profits and earn that tap on your shoulder and bonus too?
Bottomline – Sales is equal to Footfall x Conversion ratio x Average Transaction Value. But to drive Sales > F x C x ATV – an analytic professional needs to get into the shoes of the customer and apply his thoughts as a businessman would do – applying innovative applications in the cutting edge retail landscape.