FootfallCam’s Staff Planning App empowers retailers to optimise labour allocation using real-time data and AI-driven demand forecasting. Moving beyond outdated staffing methods based on sales or raw footfall, the app identifies high-intent shopper groups and predicts when and where staffing is truly needed. It integrates with existing WFM systems to provide data-backed roster suggestions, while post-operation reviews quantify unmet demand and missed sales. By aligning staff supply with actual customer demand, retailers can maximise conversion, reduce wasted hours, and improve in-store experience—without increasing payroll. It’s data turned into action—simple, scalable, and immediately impactful.
Watch the Webinar Recording Here: FootfallCam Webinar – Staff Planning App
Download the Slides Here: Software App Workshop – Staff Planning App
The Problem: Missed Sales = Missed Demand
Retailers often lose sales quietly—not because their product was wrong, but because no one was available to serve a willing customer. These missed sales are invisible in most traditional metrics. Sales figures only show fulfilled demand. They don’t reveal what could have been sold had more staff been available.
FootfallCam’s insight? You can’t plan staffing effectively without understanding unmet demand. And unmet demand comes from one core issue: retailers are forecasting staff needs based on the wrong signals—footfall and past sales—instead of real purchase intent.
What the Webinar Covered
Our session introduced a suite of apps designed to help retailers operationalise the deep behavioural data already captured by FootfallCam sensors. The centrepiece was the Staff Planning App—a tool that closes the gap between data abundance and business action.
We walked through:
- How the app identifies intended buying groups (not just visitors)
- How to measure unmet demand—the people who wanted to buy but walked out
- A complete staff planning lifecycle: Predict → Plan → Execute → Review
- How to align staffing to actual demand, store by store, hour by hour
The Staff Planning App introduces a third, more accurate metric: intent-based modelling. It uses AI to predict staffing needs based on behaviour patterns—identifying who is likely to convert, and when.
This lets planners make smarter decisions, supported by:
- Real-time supply-demand curves
- Predictive staffing rosters
- Automated feedback loops that measure how well the plan worked
Closing the Loop with Real Metrics
Most planning models break down in two places:
- Prediction: Without measuring intent, forecasts are built on shaky assumptions.
- Review: Without tracking unmet demand, there’s no feedback to improve.
Our app tackles both. After execution, it compares:
- What you planned
- Who actually turned up to work
- What should have been the right staff count, based on observed demand
The result? A retrospective view of your staffing accuracy and the sales opportunity lost due to over- or understaffing. This closes the loop and allows the AI model to learn and adapt each week.
Why This Wasn’t Possible Before
Why is this a breakthrough now? Because until recently, it was technically and economically impossible to measure:
- Who walked into the store with real buying intent
- Which of them got served—and which didn’t
Now, with the latest generation of FootfallCam sensors (Pro2, Pro1, Centroid), we can track:
- Customer group size and demographics
- Staff availability in real time
- Queue build-up, dwell zones, and unmet demand
We’ve turned these raw signals into powerful metrics that inform every stage of the staffing process.
What’s Next: A/B Testing It In Your Stores
If you’re a FootfallCam client with sensors already installed, you’re just a step away from running a real-world trial. We recommend a structured A/B test:
- Run 5 stores using the new staff planning model
- Run 5 stores the old way
- Compare sales uplift, unmet demand, and staffing efficiency
Then swap the groups, and compare again.
We’ll support you with onboarding, training, system integration, and review dashboards—so you see the results for yourself.
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