SAI Groups

Average Time in Store

Revisit Alert

Detailed view

Average Time in Store is a core analytics feature of SAI Group’s Visual AI platform that helps retailers understand how long customers spend inside a store during a visit. By using computer vision and AI-driven tracking, the feature transforms raw video data into actionable insights about customer engagement, store performance, and operational efficiency.

Rather than relying on assumptions or manual observation, retailers gain a consistent, data-driven view of customer dwell behavior across different times of day, store zones, and formats. These insights help retail teams make better decisions around store layout, staffing, merchandising, and customer experience optimization—directly linking time spent in-store to business outcomes such as conversion, satisfaction, and sales uplift.

Why the Average Time in Store feature is important for retail stores

In physical retail, time is a strong proxy for intent and engagement. How long a customer stays in a store often reflects how comfortable, interested, or successful they are in finding what they need.

Key reasons this metric matters include:

  • Customer engagement insight
    Longer visits may indicate strong product interest or effective visual merchandising, while very short visits can signal confusion, poor layout, or unmet expectations.
  • Experience measurement beyond footfall
    Footfall alone shows how many people enter a store, but average time in store reveals the quality of those visits.
  • Operational decision-making
    Understanding visit duration helps retailers align staffing levels, improve queue management, and reduce friction points that cause early exits.
  • Performance benchmarking
    Comparing average time in store across locations, days, or campaigns allows retailers to identify best-performing stores and replicate successful practices.

    • In an era where online retail offers rich behavioral data, Average Time in Store helps physical retail close the insight gap.

    How does the Average Time in Store feature work?

    The Average Time in Store feature is powered by Visual AI and computer vision models that analyze video feeds from in-store cameras.

    At a high level, the process works as follows:

    1. Customer detection and entry identification

    The system identifies individuals entering the store using visual cues, without relying on personal identity information.

    2. Anonymous movement tracking

    Each detected customer is tracked anonymously as they move through the store, from entry to exit.

    3. Visit duration calculation

    The platform calculates the time difference between store entry and exit to determine the duration of each visit.

    4. Aggregation and analysis

    Individual visit durations are aggregated to generate:

  • Average time in store
  • Time-based trends (by hour, day, or period)
  • Comparisons across stores or zones (if enabled)

    • 5. Dashboard visualization and reporting

    Insights are presented through dashboards or reports that retail teams can use for monitoring and decision-making.

    The entire workflow is designed to be automated, scalable, and privacy-conscious, ensuring reliable insights without operational overhead.

    Benefits of using the Average Time in Store feature

    Implementing the Average Time in Store feature delivers value across multiple retail functions:

    Improved Store Layout and Merchandising

    By correlating visit duration with layouts or displays, retailers can identify which configurations encourage customers to spend more time browsing and engaging.

    Better Staffing and Operations Planning

    Understanding when customers tend to stay longer helps optimize staffing schedules, especially during peak engagement periods.

    Enhanced Customer Experience

    Short or declining visit times can act as early warning signals for issues such as poor navigation, stock placement challenges, or long checkout waits.

    Campaign and Promotion Effectiveness

    Retailers can assess whether in-store campaigns, promotions, or seasonal changes are increasing customer engagement by tracking changes in average visit duration.

    Data-Driven Store Comparisons

    The feature enables objective comparisons across stores or regions, supporting data-backed decisions instead of anecdotal feedback.

    FAQ

    Is Average Time in Store the same as dwell time?

    Not exactly. Average Time in Store measures the total duration of a customer’s visit from entry to exit, while dwell time typically refers to time spent in a specific zone or area.

    Does the feature identify or recognize individual customers?

    No. The feature is designed to work with anonymous visual tracking and does not rely on personal identification.

    Can this metric be viewed over different time periods?

    Yes. Average Time in Store can be analyzed across hours, days, weeks, or custom time ranges to identify trends and patterns.

    How can retailers use this insight in daily operations?

    Retail teams can use it to adjust staffing, improve layouts, evaluate promotions, and detect experience issues that may be causing customers to leave early.

    Is this feature suitable for all store formats?

    Yes. Average Time in Store is applicable to a wide range of formats, including supermarkets, specialty retail, convenience stores, and large-format stores.