entries ÷ people passing by
15.0%
of people passing by decided to enter
The conversion funnel that was invisible is now measurable
See how many people pass by, how many walk in, and where conversion breaks down.
Sentinela connects traffic outside your store to the journey inside, showing where opportunities are captured, advanced, or lost.
Estimated purchase is an experimental journey inference and does not confirm a sale, revenue, or financial transaction.
Sentinela
Conversion funnel
- People passing bytraffic in front of the store12,840100.0% funnel baseline
- Entriescrossed the entrance1,92615.0% from previous stage. 15.0% from first stage
- Estimated purchasesestimated inference36418.9% from previous stage. 2.8% from first stage
Visitor trends
Top zones
Visit duration
Visits without engagement
Physical retail's blind spot
Revenue shows the result. It does not translate conversion or efficiency.
Without separating external traffic from internal performance, a decline can look like weak demand when the storefront is the issue—or look operational when the corridor simply got quieter.
- 01
External traffic
How many people passed in front of the store?
- 02
Store attraction
What share of that traffic decided to enter?
- 03
Internal conversion
What share of entries progressed to an estimated purchase?
Sentinela separates traffic effects from operational effects.
The metric that changes the reading
Attraction rate: how much corridor traffic your store turns into opportunity.
Volume shows the size of the traffic. The rate shows the effectiveness of the storefront, campaign, and proposition in that context.
Volume
- People passing by
- available demand
- 12,840
- Entries
- captured opportunities
- 1,926
- Estimated purchases
- estimated inference
- 364
Efficiency
- Attraction rate
- entries over passers
- 15.0%
- Estimated internal conversion
- estimated purchase over entries
- 18.9%
- Estimated total conversion
- estimated purchase over passers
- 2.8%
The same number of entries can represent very different performance when corridor traffic changes.
Two rates, two questions
First identify which transition lost efficiency. Then investigate the operational causes that are compatible with it.
Passers → Entry
15.0% attraction rate
Is the store turning available corridor traffic into entries?
Hypotheses to compare for attraction
- Storefront
- New collection
- Campaign
- Facade
- Appeal
Entry → Estimated purchase
18.9% estimated internal conversion
Are visits progressing through the store toward an estimated purchase?
Hypotheses to compare inside the store
- Service
- Layout
- Queue
- Availability
- Experience
Estimated purchase is an experimental journey inference and does not confirm a sale, revenue, or financial transaction.
Compare what actually changed
The new collection attracted more people, even with less corridor traffic.
Normalizing by available traffic reveals the response to the storefront and collection without confusing efficiency with volume.
Before the new collection
- People passing by
- corridor traffic
- 8,420
- Entries
- attracted by the store
- 842
- Attraction rate
- baseline
- 10.0%
After the new collection
- People passing by
- corridor traffic
- 6,900
- Entries
- attracted by the store
- 1,035
- Attraction rate
- with new collection
- 15.0%
+50%
Attraction-rate lift
-18% corridor traffic
Even with about 18% fewer people passing by, the storefront converted a 50% larger share of traffic into entries.
Where to investigate first
The change appears in the attraction rate. That directs the first analysis toward the store's external elements, without concluding that any one of them caused the result.
- Storefront
- New collection
- Campaign
- Facade
What happens after entry
See what holds attention — and what ends early.
Attraction shows who entered. Trends, zones, and duration reveal how those visits behaved inside the store and help direct the next investigation.
Illustrative data
Previous periodNew collection
Visitor trends
Daily entries across equivalent periods.
- Mon: 120 entries in the previous period and 143 with the new collection
- Tue: 118 entries in the previous period and 147 with the new collection
- Wed: 121 entries in the previous period and 150 with the new collection
- Thu: 119 entries in the previous period and 153 with the new collection
- Fri: 123 entries in the previous period and 149 with the new collection
- Sat: 120 entries in the previous period and 145 with the new collection
- Sun: 121 entries in the previous period and 148 with the new collection
Average time in store
24 min
+33% vs 18 min
Visits without engagement
18.0%
-9.0 pp vs 27.0%
Bounce rate: completed visits that did not qualify dwell in an engagement zone or at checkout.
Store heatmap
Locate where circulation and dwell concentrate across the store floor plan.
Illustrative occupancy map: Shoes accounts for 22% of time, Shirts 18%, and Trousers 15%.
Top zones
Share of total occupancy time recorded across zones.
- 1Shoesof total occupancy time22%+4 pp
- 2Shirtsof total occupancy time18%+1 pp
- 3Trousersof total occupancy time15%-3 pp
Visit duration
Distribution of completed visits.
- < 5 min
- 5–15 min
- 15–30 min
- 30–60 min
- 60+ min
Shoes accounts for 22% of occupancy time and gained +4 pp. The signal indicates greater attention; compare merchandising, availability, and service before attributing a cause.
Compare equivalent contexts
- Periods
- Stores
- Campaigns
- Collections
- Dayparts
Confidence in every reading
New technology in the background. With the camera infrastructure you already have.
Sentinela monitors your local cameras, processes data locally, and turns movement into clear information for your operation.
Local processing
Data is processed locally in the store. During normal operation, analytics receives events instead of full videos.
Information for action
Track the funnel, floor flow, zones, visit duration, and operational health in one management view.
What you need to know
Does estimated purchase confirm a sale?+
No. It is an experimental journey inference after a checkout-qualified visit. It does not confirm a transaction, revenue, or POS data.
Do I need to replace my cameras?+
Not always. A qualified technician assesses compatibility, installs, certifies, and calibrates the system. The store team receives the solution ready to monitor.
Does the platform identify customers?+
It does not create cross-day profiles. Cross-camera continuity is limited to the active visit, with bounded crops and expiring embeddings.
Can I compare multiple stores?+
Yes. Each store keeps its own configuration and can be compared using rates, equivalent periods, and comparable contexts.
Estimated purchase is an experimental journey inference and does not confirm a sale, revenue, or financial transaction.
Turn passers-by into an operational metric
Discover how much traffic in front of your store truly becomes a sales opportunity.
See how Sentinela can measure your operation's physical funnel in a demo tailored to your scenario.