---
title: How to qualify solo, group or family visitors in-store?
description: Discover how video analytics is transforming in-store visitor counting by identifying whether visitors are individuals, couples, families, or groups—all to help you optimize your sales strategies.
---

[XXII | Blog ](https://xxiiai.com/en/le-blog)

# [How to qualify solo, group or family visitors in-store?](https://xxiiai.com/en/le-blog/comment-qualifier-solo-groupe-ou-famille-en-magasin)

 Written by [Romane](https://xxiiai.com/en/le-blog/author/romane) | Jul 10, 2026 9:33:00 AM

### Key Points

Video analytics automatically classify a visitor as a solo shopper, a couple, a family, or a group by observing whether they enter and exit the store within the same time window as other people. This group detection relies on entry/exit re-identification (ReID), a technique already used to measure dwell time, without requiring additional cameras or models. It transforms a simple foot traffic count into data that qualifies purchasing potential, which can be used to optimize merchandising, staffing, and in-store promotions.

### Why does distinguishing between solo shoppers, couples, and families change how you interpret your counts?

In brick-and-mortar retail, the unit of measurement has long been the individual: we counted entries, segmented profiles, and measured dwell times. But a customer shopping alone behaves differently than one shopping with others. A family with children does not browse the aisles the same way a couple does, and a couple does not shop the same way a group of friends does.

The composition of the group is a key determinant of purchasing behavior, and until now, it has remained invisible in foot traffic data. A raw count of 500 visitors says nothing about actual purchasing potential: this figure could represent 500 solo shoppers or 150 families of 3 to 4 people—two completely different commercial realities.

### How does computer vision identify a group in a store?

Group detection is based on a simple principle: if people enter the store together and leave together within the same time frame, they are considered to belong to the same group.

Technically, a group is characterized by three dimensions:

- **Its size**: the number of individuals in the group
- **Its composition**: the marketing segmentation of its members (age, gender, profile)
- **Its typology**: four categories defined by the system

This approach relies directly on the entry and exit timestamps for each individual, which are already generated by the non-biometric re-identification engine. No biometric data, no facial recognition: only anonymized visual characteristics.

### What are the four detected group typologies?

| Typology | Technical Definition | What this reveals about purchasing behavior |
| --- | --- | --- |
| **Solo** | A group of one person, regardless of its composition | Quick shopping trip, individual decision-making, less influenced by family dynamics |
| **Couple** | Consisting of two adults | Joint decision-making, often longer visit duration |
| **Family** | At least one child under 18 and at least one adult | Itinerary slowed down by the children; high sensitivity to dedicated spaces and activities |
| **Other** | Any group that does not fit into any of the three previous categories (e.g., group of friends, coworkers) | Group behavior, potential for cross-selling |

### Is additional infrastructure required to enable this feature?

No. For stores already equipped with ReID Entry/Exit, group detection is activated without any infrastructure changes, additional cameras, or specific configuration. It is a native extension of the existing module: clustering is performed directly on the pre-calculated entry and exit time windows.

### What specific data does this feature generate?

Once enabled, the feature enriches existing dashboards with:

- the distribution of demographic types (share of singles / couples / families / other) over a given period
- the average group size
- the breakdown by marketing profile (age, gender, profile) cross-referenced with household type

In practical terms, a retail operations manager is no longer satisfied with simply knowing how many people entered the store: they also know who they came with, and can link this count to actual purchasing potential.

### What is the operational impact for retail teams?

Knowing the breakdown of solo customers versus couples versus families over a given period allows for concrete action on several fronts:

- **Merchandising**: redesigning the layout of the aisles based on the proportion of families or solo shoppers
- **Staffing**: Adjust sales associate staffing levels during times when groups with companions are predominant
- **Sales promotions**: schedule family- or couple-focused promotions during the most relevant time slots

This customer insight—which until now was only accessible through in-store surveys or panels, with the delays and biases that entailed—is now generated automatically every night using existing video feeds.

[View full post](https://xxiiai.com/en/le-blog/comment-qualifier-solo-groupe-ou-famille-en-magasin)

```json
{
  "@context" : "http://schema.org",
  "@type" : "BlogPosting",
  "author" : {
    "@type" : "Person",
    "name" : "Romane"
  },
  "dateModified" : "2026-07-10T09:33:00.398Z",
  "datePublished" : "2026-07-10T09:33:00Z",
  "headline" : "How to qualify solo, group or family visitors in-store?",
  "image" : {
    "@type" : "ImageObject",
    "height" : 982,
    "url" : "https://145145411.fs1.hubspotusercontent-eu1.net/hubfs/145145411/IMAGE%20-%20DESIGN/BLOG/image.png",
    "width" : 1860
  },
  "mainEntityOfPage" : "https://xxiiai.com/en/le-blog/comment-qualifier-solo-groupe-ou-famille-en-magasin",
  "publisher" : {
    "@type" : "Organization",
    "logo" : {
      "@type" : "ImageObject",
      "height" : 60,
      "url" : "/hs/hsstatic/content_shared_assets/static-1.4092/img/default-amp-logo.png",
      "width" : 60
    },
    "name" : "Actualités "
  }
}
```