Albert chose StringData to system check the graphic interface of its new self-service checkouts. Clear heatmaps of checkouts showed the movements of customers.
Albert Czech Republic operates a network of more than 300 hypermarkets and supermarkets under the Albert brand. It employs 17,000 people and is one of the largest private employers in the Czech Republic. Size goes hand in hand with increased demands on the process of recruiting new staff to man its checkouts. This was why Albert decided to introduce self-service checkouts.
After introducing self-service checkouts, Albert found that they were increasingly popular with customers. This was why the company created a more user-friendly graphic interface, which they tested. Once the new graphic interface was deployed in supermarkets in pilot operation, they asked us to monitor customersโ movements on individual screens. We needed to obtain feedback based on accurate data.
The whole project brought excellent results. Self-service checkouts work in line with the end customerโs requirements. This was confirmed by our measurements and an independent test of self-service checkouts by the IDNES news portal.
Self-service checkouts have proven to be very popular. However, we had to check how well the new interface worked from the customerโs perspective.
Customer interest in self-service terminals is growing and Ahold approached us with a request to test a new graphic interface for self-service checkouts in Albert supermarkets. In view of the new graphic interface, we had to find a suitable solution and monitor customersโ movements on individual screens in pilot operation. We needed to find out how the new design and interface works from the customerโs perspective.
We created clear dashboards with a heatmap of individual steps.
As soon as we were familiar with purchasing options, we designed a measurement methodology using special measuring probes. We deployed probes on 10 checkouts, which we jointly selected for pilot operation. We collected and analysed data from these checkouts over a period of 6 working days.
In order to ensure the most accurate analysis possible, we used probes for automatic process analysis (task mining technology from UltimateSuite). Thanks to accurate measurement and the high-quality data we collected, we performed a qualified and objective analysis of customer behaviour.
We connected the probe to the system to record what was happening at checkouts. It gave a detailed picture of the user interface of the checkout operated by a regular customer. We then processed all the data into a clear visualisation.
We clearly visualised the behaviour of customers using checkouts for the client.
The project brought excellent results. Self-service checkouts work exactly the way the end customer needs. The new graphic interface was proven to work excellently.
Data about customer movements on individual screens showed that customers found the new design easy to use. Measurement in the live production environment took place from 16 December to 21 December 2020 on a total of 10 self-service checkouts at the Arkรกdy Pankrรกc Shopping Centre. A total of 38,896 customers clicked on the screens of self-service terminals.
The StringData internal team worked on the project. They worked closely with representatives of the Ahold IT and marketing departments. We used UltimateSuite technology for measurements.
We began the project virtually right after the client contacted us. The project took a total of 18 working days. The client provided checkouts for test measurements and installed special probes on 10 checkouts.
Would you like to find out how processes work in your organisation or department too? We can help you analyse and visualise your processes on dashboards. You can then make an informed decision based on accurate and objective data. We can help you increase the efficiency of your teams and save you time and money.
Our solution is flexible, which allows you to analyse a large number of processes at your company, regardless of the department. You can evaluate the efficiency of employees working from home, at different off-site workplaces and at branches.
