Relationship between footfall and productivity
At Shopworks we frequently ask clients to provide information to help us develop our understanding of their stores' productivity. We often receive an impressive amount of sales and market information, but store footfall data is much more scarce.
Yet understanding footfall - and crucially, the walk-out rate (number of people leaving without purchasing) - is critical to evaluating store productivity.
Understanding store footfall, conversion and walk-out rates
To understand the full potential of the sales opportunity, retailers need to understand three things regarding the footfall into their store:
1. How many customers are walking into the store?
2. What % of the total footfall is actually purchasing (conversion ratio)?
3. What % of the total footfall is leaving the store without purchasing anything (walk-out rate)?
It sounds simple enough, but understanding conversion ratio and walk-out rate is not always an easy task. Several variables need to be considered before footfall data can be properly evaluated and understood - and become pertinent to investment and marketing decisions.
The use and limitations of footfall counters
Many retailers use footfall counters, which track people entering a store. To calculate the conversion ratio, retailers can use counters to combine the number of people entering a store in a given period, with the number of transactions made over the same timeframe.
Whilst this method certainly provides a first indication of conversion ratio and walk out rate, it doesn't take into account everything that needs to be considered for an accurate evaluation of store profitability.
Footfall counters are undoubtedly helpful, but retailers need to adjust the data gathered from them in order to make it relevant and accurate. Areas to watch when using footfall counters alone to analyse profitability include:
- Group composition - if a family enters a store as part of a group, they may yield only one transaction from their visit (although each member may have acquired something as part of this transaction). So whilst a footfall counter will have counted four people in, only one transaction will have been recorded; this may lead the retailer to incorrectly assume a 75 per cent walk-out rate. The scenario is even more likely at weekends, when families tend to shop together
- Staff movement - unless there is a separate staff entrance, it's likely that staff will use the same entry and exit points as customers; so their movement may be tracked by the footfall counter. This could lead to an inflated footfall count at the start of the day, break times and close of day - so data calculations from these periods would need to be adjusted to avoid assumptions of higher walk-out rates
- Exit channels - some stores may have numerous entrance and exit points. Without all points being covered by footfall counters, it will be impossible to gauge store traffic volumes accurately
- Visitor segmentation filters - imagine we need to evaluate footfall in a DIY hardware store; it's reasonable to assume that an adult may enter the store accompanied by a child. The footfall counter may record two store visitors and one transaction (say for a chainsaw) for the period of the visit. Taken at face value, this retailer could assume a 50% walk-out rate. Yet in reality, a child would never be a true candidate for a chainsaw purchase; so the true position may be a 100% conversion rate and 0% walk-out rate. So visitor segmentation filters need applying to footfall data to explore the actual retail potential
- Benchmarking walk-out rates - in normal circumstances, destination stores should have a much lower walk-out rate than non-destination stores; retailers need to compare themselves with the same store cluster and industry. For example, if we compare a traditional neighbourhood pharmacy with a 20% walk-out rate against a mall fashion store with a 35% walk-out rate, we should conclude that the pharmacy is the poor performer; that's because many more people will have gone there with the intent to purchase something
- Evaluating partial walk-out rates - in big store formats (such as hypermarkets) it's often more useful to evaluate walk-outs from a specific store section rather than the total store walk-out rate. That's because the total store walk-out rate is typically quite low. With partial walk-out evaluation, analysis is focused on the number of people passing a certain section of the store, and the number of purchases made from that section by relevant customer segments. This research helps to relate section productivity to both total store footfall and also the footfall generated in the specific section.
Measuring wasted footfall
The best way to quantify footfall is to use observational research and create standard measurable patterns, known as algorithms. By tracking footfall patterns such as visitor segments and group composition, you can understand how effective your footfall really is. (For example, you may be in a shopping mall with very high footfall - but if only a very small proportion of visitors enter your store, then much of that traffic is wasted.)
These algorithms should be created initially using observation and traffic flow research techniques along a certain period of time, until a robust sample is achieved. The store will then have a more accurate evaluation of the visitor group composition and relevant percentage of visitor segments.
Periodically, observation and traffic flow analysis should be repeated to refresh the calculations. Seasonality (the likelihood of visits at different times of year) should also be considered.
Understanding footfall - and in particular walk-out rates - is important when measuring store productivity. It's also fundamental to the evaluation of investment such as in-store marketing activity.
Measuring walk-out rate is a difficult task, that should be done with the support of algorithms obtained through observing traffic flow. This approach enriches footfall data and will provide more accurate walk-out calculations.
Shopworks has developed traffic flow research methodologies to add insights to footfall and walk-out rates, browse intensity and layout effectiveness.