Addressing the Issue of Falling Sales
One of the most common ways footfall data is used is to understand and forecast changes in sales volumes. When revenue is falling, footfall is often the first metric to look at for clues as to why this is happening.
The first thing to consider is: what else could be impacting sales? Are in-store purchases falling, or the number of people passing by? Foot traffic can be affected by a range of things – some of which are unavoidable. This may include weather, closures of surrounding stores, events or building and development works. All of which can potentially block access to the unit or divert people away. Flipping this on its head, footfall data can also be used to forecast changes in sales caused by some of the external factors mentioned above.
Understanding the correlation between changes in footfall and sales is valuable and can be utilised to make short, medium and long-term forecasts, which are not always linear. For example, a 5% reduction in passing foot traffic may cause a 10% dip in sales. Interpreting this information correctly, stores can plan and adapt. It’s an opportunity to implement initiatives such as marketing campaigns or new operational models.
It’s also important to gauge quality over quantity. You may enjoy a high density of passing footfall, but if sales are underwhelming, something obviously isn’t right. For many retailers, a proportion of passing footfall is simply ‘noise’ and not relevant to their store.
Identifying this 'noise' from the pool of potential customers can help to distinguish between low and high-quality footfall. For example, a clothing store may open next door to a busy bus stop. Therefore it is necessary to separate the bustle of the bus stop from customers associated with the clothing store. The ability to capture footfall data within a specific field of vision allows you to make this differentiation.
If footfall flows change course, your operational model should too - a shift in passing opportunity may require a change in operational models. Knowing the busiest point of each day is critical to ensure staffing and stock levels are appropriate. For example, if you are a coffee and breakfast retailer, changes to footfall density from 7am – 11am would have a much bigger impact on the top line than that of a cocktail bar.
This is also an effective way to identify the impact of opening and closing activity of competitors. Let’s say, for instance, a competitor has opened down the road and is running a promotion for its first week. At the same time, your footfall dips and customers temporarily dry up. Being able to identify a correlation enables you to make an informed assumption – that your customers love a promotion, and this is where they’re headed.
This data can also be used to identify when a retail cluster in a town centre moves, resulting in the prime pitch migrating over time. This can happen for a number of reasons including rising rental costs, surrounding opening and closure activity or significant changes to the pitch – such as an increase in vacancy, which can put new occupiers off opening in that area.
If your unit is no longer located in the best pitch for your brand, this can impact performance. Staying on top of changes to footfall can ensure that you don’t miss out on opportunities to be agile.
Finally, if there have been no changes to footfall but your business is still struggling, then the problem may be a little closer to home. If flows, trends and peaks remain the same, this could suggest the issue is in-store, rather than outside. If you are failing to convert footfall into customers then it’s time to assess the store layout, window display, product selection or staffing.
Determining How Well a Store is Performing
Like anything, it’s important to evaluate performance even when sales are strong. Footfall data can be used to compare conversion rates and sales volumes alongside stores with similar footfall levels. If store A is enjoying double that of store B’s footfall, but sales are only 10% higher, this could indicate a problem at store A. It may also suggest that store B, with lower sales volumes, is actually the better-performing site. Benchmarking is an effective way to help maximise the value of each and every site.
A common metric in retail is conversion – how many consumers are coming into a store vs the percentage making a purchase. Footfall data is often applied to a range of other data layers to provide forensic insight into every element of a store’s performance. Through this, retailers can assess relative strengths and weaknesses, including:
- Store size