This article was originally written by one of Pygmalios’ big telecom clients O2 and published on their blog. Since we think it very well explained how retailers can maximize revenue by using in-store data, we decided to publish it on our blog.
“The store is currently filled with customers at 34%.”
You must have heard a similar announcement a myriad of times. Since 2020, retailers and some service providers have had to monitor (and regulate) the number of customers inside their stores. And so similar messages placed on digital screens at the entrance are nothing special. But counting incoming and outgoing customers is just the tip of the iceberg in the capabilities of in-store data. And data certainly doesn't just have to regulate visitors. They can help save costs or, even more likely, make money.
While data from internal CRM or sales data describes "only" the end of the customer journey, ie the purchase itself, or any other conversion performed in the store, data from in-store analytics show almost everything that happens before the customer buys (or not). This can even include events in front of the store.
By the way, according to data from Pygmalios experts, with whom we collect and evaluate data from stores and branches for our clients, retailers are unable to capture and evaluate more than 90% of customer behavior in stores.
Often they only know how many customers they have bought something, but they no longer know how many of them have passed the store and how many have subsequently entered.
They don’t have information about how they browse the store, how long they wait for the staff, and how much time they spend in queues or talking to the staff.
Most importantly, they don’t know whether they’re not losing profits because they are not able to reach their full potential.
Smart systems can change all of this. And not only that - they connect the collected data with information about sales or, for example, what the weather was like on that day. The result is a truly plastic picture of what is happening at the branches and what should be changed in the future.
After all, let's see what kind of questions the collected, evaluated and correctly interpreted in-store data can answer.
Who comes to our stores and when?
The system (even with the help of AI) can turn information from sensors into information about how many people are at the branch at what time of day, how much time they spend here, and where specifically they spend that time. The system can determine whether it is a new or returning customer, recognize their gender, or even detect whether they’re wearing proper face coverage if needed. At the same time, it evaluates all data over time and can track long-term trends, but also local anomalies.
For example, O2 strengthens the teams of consultants in stores at the times when most customers are predicted to come. Or we increase the number of consultants in the morning, even if self-service kiosks would be enough to handle the traffic - because the majority of customers are seniors who can't work with them.