Who are your customers? Detection of age, gender, and group size in retail

Key Facts:

• Demographic customer data contains age, gender, and group size.
• Measuring customer demographics in retail makes physical spaces similarly measurable, like e-commerce.
• Knowing visitor age and gender helps to measure and challenge in store and online marketing campaigns.
• Targeting for specific age or gender groups is now possible.
• Both detecting age, gender, and group size is completely anonymous and privacy compliant with video analytics.

‍What is demographic customer data?

‍Demographic customer data summarizes characteristics such as age, gender, and group size. This is not about identifying individual persons, but about recognizing anonymized patterns and clusters, e.g., "buyer group between 25 and 34 years old."

Why is demographic customer data so relevant for retailers?

‍Demographic information about the people moving across the sales floor is highly relevant for retailers because it bridges the gap between online and offline analytics and makes the sales floor measurable.

In e-commerce, a large part of clicks and interactions in the analysis dashboard can be directly assigned to specific age and gender groups. In brick-and-mortar retail, there is often still uncertainty at this point. Although most retailers and center managers know very precisely how many people enter their store thanks to classic footfall counting at the entrances. High-frequency retailers, such as supermarkets, can even precisely track what was ultimately purchased thanks to modern POS checkout systems and customer loyalty programs.

However, it becomes more difficult in retail segments where a purchase does not take place during every visit, such as in the furniture retail sector, where visitors often just want to look for inspiration. Here, pure checkout data does not help to get to know the customer base better. What has been missing across the entire retail sector until now is precise data on who was actually in the store. Which target groups leave the sales floor without buying anything? How are the age and gender distributed among visitors who stroll through the aisles but are not captured by previous systems? Intelligent video analytics closes exactly this data gap: the existing cameras capture the visitor structures on the floor in an anonymized form, making brick-and-mortar retail just as measurable as an online shop.

How do retailers benefit?

• A/B testing for storefronts & promotional areas:
  Which window display appeals to the young target group, and which to best agers? Marketing teams can test visual campaigns on the shop floor and read the success directly from the demographic composition of those who dwell there.
  ‍
• Measuring the success of marketing campaigns:
  If an expensive local social media campaign is running for women between 20 and 30 years old, the Isarsoft Perception dashboard can be used to check: Does the proportion of exactly this target group in the store increase significantly during the campaign period?
  ‍
• Target group-oriented digital signage:
  Linking real-time detection with digital advertising screens in the store. If a primarily male age group enters the department, the display automatically switches to the appropriate product advertisement.
  ‍
• Optimization of assortment & store layouts:
  In cooperation with shopfitting: Which products are placed in the zones where, according to the heatmap, the target group with the highest purchasing power dwells the longest?

How does the collection of age and gender work with video analytics?

‍In order to cluster people into age and gender categories without having to identify their faces or other personal data, Isarsoft Perception relies on computer vision and AI. The technology works as follows:

1. Capture in the video stream:
   ‍As soon as a person enters the field of view of a security camera, Isarsoft Perception detects an object of the "person" category and marks it with a so-called "bounding box".
2. Matching:
   ‍Based on anonymous features, the AI now recognizes which age group the person fits into and what gender they are.
   ‍

How does the collection of group size work with video analytics?

‍Video analytics technology with AI can also precisely capture groups of people and cluster them as a "group", "couple", or "individual".

1. Capture in the video stream:
   ‍As soon as people appear in the field of view of the security cameras, Isarsoft Perception detects objects of the "person" category, marks each one individually with a so-called "bounding box", and maps the individual paths, the so-called "trajectories".
2. Grouping:
   ‍If the trajectories are very close to one another, the software assumes that the people belong together. Depending on the number of people, categories such as "couple" or "individual" are created.

Is the collection of demographic customer data compliant with data protection regulations?

‍
‍With Isarsoft Perception, the age, gender, and group size of individuals are recognized without biometric data, using only abstract, visual patterns. The process is therefore 100% GDPR-compliant. The protection of identities is ensured by the technology at all times. At Isarsoft, we view data protection and information security as a fundamental building block of our software. We want to communicate transparently to our customers and partners how we handle these essential topics. We regularly undergo internal and external audits, and the ISO 27001 certification from TÜV Süd confirms our constant striving for data protection-compliant video analytics software.

Conclusion

‍Precise age and gender detection makes brick-and-mortar retail smarter and more competitive compared to online retail. It transforms inaccurate assumptions and subjective truths into a reliable database on which marketing teams and store managers can make well-founded decisions. Furthermore, the collection of demographic data complements the network of already existing data sources, such as footfall measurement, checkout data, customer surveys, and the master data of the customer loyalty system. And all of this happens in a data protection-compliant manner and without investments in new hardware.

‍

Explore the use cases of demographic customer data with video analytics for your retail store.