Conversion rates are a key indicator for retailer businesses, which represent the % of buyers among the total amount of visitors. To obtain this indicator retailers install an automatic people counter at the entrance of the store and match its outputs with the POS results.


Conversion rates are a crucial KPI not only because it evaluates if customers found what they were looking for, but because it allows to objectively compare different stores and identify which ones have a better performance (be it in terms of sales teams effectiveness or product exhibition). If we only evaluated our performance on a results basis (that means sales) we would simply ignore many critical factors within the purchasing process (such as that ones just mentioned before) which definitely determine the last decision.


However, analysing an average conversion rate once per month is a complete waste of time. Monitoring this KPI along the time is what really counts. But actually not all retailers analyses it that way and, too often, get frustrated when can’t find out how to deal with it. So they go back to a results-driven analysis focusing only on data coming from POS and ignoring those powerful outputs generated from people counters.


Thus, what we suggest exactly?




We propose to measure all the conversion rates resulting from the purchasing process. In other words, we urge you to not focus exclusively on the traditional rate but going one step further: the more precise your are the easier you will come up with conclusions:


a) Acquisition rate: % of visitors among the amount of people passing-by the store
b) Activation rate: % of visitors with dwell time longer than X among the total amount of visitors
c) Conversion rate: % of buyers among the total amount of ‘activated’ visitors
d) Loyalty rate: % of visitors that have visited the store more than Y times  within a T period of time among the total amount of visitors




Now, we have to write down all those factors that we suspect can have caused these peaks and lows observed along the conversion rates evolution. Those factors can be, for instance, special promotions or unusual footfall traffic.




A/B testing are those experiments made to verify our hypotheses.


We will determine as best practices all those hypotheses that we verify are real triggers of change and are internal factors.




Now we have to extend these best practices along the whole activity, that means along the time and along all the stores of the chain. But when we talk about implementing improvements we also mean the following:


a) Align internal scheduling with external factors that affect conversion rates (weather, big city events,…)
b) Do this analysis on a daily basis
c) Set new goals and align rewards with these KPIs
d) Test new ideas and evaluate its impact
e) Share knowledge among different stores


We hope this post will help you to get the most of your people counters. Any questions you have, just let us know 🙂



We’re sure you’ll find these other pages interesting:

Infographic: increasing sales through analyzing ratios

Infographic: 6 months is the payback period of a people-counting project

How to Measure Retail Performance? 5 Essential Metrics