In-store campaign performance and ROI measurement
Retail campaign analytics measures the separate effect of an in-store campaign on visitor traffic, product engagement and conversion rate. EscherSense compares these three indicators before, during and after the campaign on the same metrics, and combines them with POS data to show the campaign's real return on investment by store and across the chain.
When revenue rises during a promotion, it is rarely clear how much of it the promotion actually drove. Traffic may have been rising anyway, the uplift may have come from another department, or the campaign may have pulled people in without converting them.
Campaign analytics splits the effect into three layers: how many people came, how many engaged with the campaign area and how many bought. Once you see which layer is leaking, the fix becomes obvious and marketing budget can follow real returns. To check you reached the right audience, demographic analytics sits on the same dashboard.
How it works
Baseline period
Traffic, engagement and conversion before the campaign starts are recorded as the comparison baseline.
Tracking during the campaign
Visitor numbers, engagement in the campaign area and transaction counts are followed daily and hourly on the live dashboard.
Impact and ROI
Before, during and after are compared on the same metrics; combined with POS data, the result shows the return.
Recommendations for next time
The layer where shoppers were lost is reported, and predictive recommendations are produced for future campaigns.
What it measures
Traffic change
Net footfall during the campaign compared with the baseline period.
Capture rate
How well a window campaign turns passers-by into visitors.
Campaign area engagement
The share of visitors who stop at the promo table or shelf and touch a product.
Conversion change
The campaign's effect on the rate at which visitors become buyers.
Audience fit
How closely the visitor profile during the campaign matches the targeted age and gender mix.
Campaign ROI
Campaign cost set against the sales value of the measured extra traffic and conversion.
In-store use cases
Did traffic rise, or conversion?
A discount campaign can bring in more people without changing conversion, or the other way round. Measuring each effect separately tells you whether to strengthen the window or the in-store experience next time.
The impact of a window change
Whether a new window display pulls more passers-by inside is compared with the previous display on the same metric. The return on visual merchandising becomes a number.
Where to put the promo table
The same campaign is tested in two locations with an A/B test. Differences in engagement and conversion settle the permanent placement.
Comparing a campaign across branches
The same campaign can work in some stores and fall flat in others. Measuring chain-wide and store-level impact separately lets you adapt it branch by branch.
Privacy and GDPR/KVKK
Campaign analytics works with aggregate indicators: visitor numbers, zone engagement and conversion rate. No facial recognition is used and no identity data is stored; audience fit is assessed only from anonymous age-band and gender distributions. By default, video is processed on a server inside the store and raw footage never leaves the premises; if your IT policy prefers, processing can run in the cloud instead. Retention periods are defined, and the setup is designed around KVKK and GDPR requirements.
Setup and integration
Campaign impact is measured from traffic, heatmap and engagement data produced from the RTSP streams of your existing security cameras; ONVIF-compatible IP cameras are enough. For ROI, visitor data is matched with your POS, and results flow via REST API and webhooks to ERP, CRM, marketing automation and BI tools. Reports are available as JSON and CSV.
Frequently asked questions
How do you measure the ROI of an in-store campaign?
In-store campaign ROI is calculated by setting the campaign's cost against the sales value of the extra traffic and conversion it generated. EscherSense compares before, during and after on the same metrics and matches visitor data with your POS. That separates how much of the uplift actually came from the campaign.
How can I tell whether a promotion raised traffic or conversion?
Because traffic, engagement and conversion are measured separately, you see directly which one moved. If traffic rose but conversion stayed flat, the campaign drew people in but did not persuade them in store. If conversion rose with flat traffic, it pushed existing visitors to buy.
Can the effect of a window campaign be measured?
Yes, by comparing the number of passers-by with the share who walk in. The share of people who look at the window is reported as well. Different window displays can be compared on the same measure.
Can I A/B test a campaign area?
Yes, the A/B testing module compares physical changes before and after on the same metrics. When the promo table or shelf layout is moved, differences in engagement and conversion are reported. The decision is made on measurement, not opinion.
How are campaigns compared across a multi-store chain?
All stores roll up into one dashboard, and campaign impact is measured both chain-wide and store by store. The benchmarking module compares stores on the same metrics. That shows exactly where the campaign worked.
How do I know a campaign reached the right audience?
The visitor profile during the campaign is compared with the target audience using anonymous age-band and gender distributions. If the profile does not match, the issue may lie in the communication channel. Read more on our blog or request a demo.
Related modules
People counting and store conversion rate, from your existing cameras
People counting is the automatic counting of visitors entering and leaving a store from camera video. EscherSense does it with AI on the security cameras you already have: it separates entries from exits, removes staff from the count and matches net footfall against POS transactions to give you the store's true conversion rate.
Explore module →Retail heatmaps and customer path analysis
A retail heatmap is a colour-coded view of how much time shoppers spend in each zone of the sales floor. EscherSense builds it anonymously from your existing ceiling cameras, and its path analysis reveals the most common routes from entrance to checkout, movement between aisles and the dead zones that almost nobody visits.
Explore module →Anonymous demographic analytics: visitor age and gender mix
Retail demographic analytics is the statistical estimation of visitors' age band and gender from camera video. EscherSense does this without identifying anyone, without storing face images and in line with KVKK and GDPR. The output is never a personal record, only hourly and daily aggregate distributions your store and marketing teams can act on.
Explore module →