Summer is over, which means it’s not long until this year’s Christmas adverts are released and we can all relax again. The disposable barbecues have also come down from the shelves, and Christmas decorations looms over shoppers. It’s not just an important time for people looking to buy gift sets of candy though, for many retailers the run-up to Christmas defines whether or not their year has been a success.
Retailers can now employ a variety of methods to optimize sales, with the psychology behind making a purchase heavily influencing where goods are placed in store, a mass of data about consumers’ backgrounds available, and their every move monitored as they walk around shops. Among the most important metrics that retailers are looking at is ‘footfall’, which literally means the number of people who have visited the store over a given period.
Footfall analytics enables shop owners to look at the number of people entering their store, and then compare it to sales made. They can then see how much footfall is converting into purchases. A number of external and internal factors are then monitored that could be accounting for this rate and at what time it’s highest, examining CCTV and time stamps to establish what the cause could be. For example, if it’s raining, more people may come in simply to shelter from the weather. You can also see what demographics are in the store at that time, pinpointing the more casual strollers, which tend to be youths, and those who are shopping with clearer purpose. This data can be leveraged by shop managers to plan and re-organize store layouts and merchandise displays to attract the casual strollers who are seen to hover around the entrance/central area, and translate these casual footfalls into sales.
Footfall analytics are also useful for plotting staff rotas, allowing you to see where various highs and lows of visitor numbers are, right down to an hourly basis, so you can assign staff accordingly - rather than just having members of staff standing around during non-peak times.
There are issues with footfall analytics. On the whole, consumers - especially millennials - are comfortable with retailers collecting a certain amount of data about them. However, certain collection models are viewed with more suspicion than others, with people reacting better to opt-in models such as loyalty cards as opposed to a model where they do not have a choice about the data being taken, such as monitoring footfall or facial recognition. Some companies are targeting people’s feet to measure footfall so as not to show their faces, and these have also proven useful for seeing peoples’ genders to a 75-80% level of accuracy. However, it is still important for retailers to be as transparent as possible if they are to gather information about their customers, or they stand a great danger of putting them off. And then there won’t be any data to collect.