Success Story · Retail
How did a convenience store chain increase sales between USD 5,000 and USD 20,000 per store per month?
Learn how KSI helped increase the conversion of a convenience store chain by monitoring the operational efficiency of each location.
5-20K USD
increase per store per month
>100%
ROI - investment recovered in under a day
146
stores monitored with centralized architecture
- 01
The problem
The client began receiving indicators of very high-traffic moments in several stores, but sales conversion wasn't holding at the expected level. Reviewing real-time alerts, they detected that those peaks coincided with nearby students' free hours and, at the same time, with employee break times - leaving the stores understaffed during peak hours.
We had data on several stores that weren't selling similarly to the rest, but we didn't know why. By installing KSI to count the number of people entering and measure their dwell time, we realized there were hours with a lot of traffic where sales conversion dropped.
N.V.Head of Data Analytics - 02
How it was solved
The following changes were implemented:
- ✓Adjustment of staff break schedules.
- ✓Real-time monitoring and alerts for significant traffic increases.
- ✓Benchmarking between stores using objective count and conversion data.
- ✓Alerts for increased bounce rates and queue lengths.
We needed a fair way to compare the performance of our stores, and these indicators allowed us to do so objectively. We realized that by adjusting certain operations and the number of service points at specific hours, we would get substantial benefits we weren't receiving.
N.V.Head of Data Analytics - 03
Results
In the first month of implementation, conversion increased in several stores. The team can now monitor them in real time without needing to add staff unless the data warrants it.
- ✓146 stores incorporated into KSI with centralized architecture.
- ✓Revenue increase of USD 5,000 to USD 20,000 per store in the first month.
- ✓ROI above 100%: the investment was recovered in less than a day.
Already in the first month we noticed the increase in revenue thanks to the measures we had taken. The increase ranged from USD 5,000 to USD 20,000 in some stores - all thanks to the analytics we obtained with KSI. The investment paid for itself in less than a day.
N.V.Head of Data Analytics