TL;DR Comparing store performance isn’t about raw numbers — it’s about normalising for footfall, format, and context so you can identify real winners and losers. Book a 20-min call to build your store comparison framework.
The problem with raw store comparisons
You run a campaign across 80 stores. The Oxford Street flagship reports 400 engagements. A rural Tesco Express reports 35. The flagship “wins.”
But does it?
The flagship has 50,000 daily visitors. The Tesco Express has 800. When you normalise for footfall, the Tesco Express converted at 4.4% of visitors. Oxford Street converted at 0.8%.
Raw numbers lie. Normalised metrics tell the truth.
Every field marketing agency running multi-location campaigns faces this problem. If you’re comparing stores without accounting for context, you’re making bad decisions about where to deploy resources.
The normalisation framework
Fair store comparison requires normalising for factors that the campaign didn’t control. Here are the key variables.
Footfall
The most important normalisation factor. A store with 10x the visitors should generate roughly 10x the engagements. If it doesn’t, something interesting is happening.
Metric: Engagements per 1,000 visitors (or per 100 in low-traffic stores).
Data source: Retailer footfall data, third-party providers (ShopperTrak, Springboard), or your own estimates based on store tiering.
Store format
A hypermarket and a convenience store are fundamentally different environments. Comparing them directly is meaningless.
Approach: Group stores by format before comparing.
| Format | Typical Characteristics | Comparison Group |
|---|---|---|
| Hypermarket / Superstore | High footfall, wide aisles, dwell time | Compare within group |
| Standard supermarket | Medium footfall, moderate dwell | Compare within group |
| Convenience / Express | Low footfall, quick trips | Compare within group |
| Department store | High footfall, browsing mindset | Compare within group |
| Specialist retail | Targeted footfall, high intent | Compare within group |
Day and time
Weekend activations typically outperform weekday. Morning slots differ from afternoon. If one store got Saturday and another got Tuesday, the comparison is skewed.
Approach: Normalise by day-of-week and time-of-day. Or compare stores that had identical scheduling.
Ambassador experience
A veteran ambassador with 50 activations under their belt will outperform a first-timer. If your best person worked Oxford Street and your newest worked the Tesco Express, the “store comparison” is really an ambassador comparison.
Approach: Factor in ambassador experience level. Or rotate ambassadors across stores to neutralise this variable.
Building the comparison dashboard
Once you’ve normalised the data, you need a way to visualise it that makes patterns obvious.
The store performance table
Start with a sortable table showing every location with normalised metrics.
| Store | Format | Footfall | Engagements | Eng. Rate | Conversions | Conv. Rate | Sales Lift |
|---|---|---|---|---|---|---|---|
| Store A | Hyper | 12,000 | 380 | 3.2% | 95 | 25% | +32% |
| Store B | Standard | 5,500 | 210 | 3.8% | 58 | 28% | +27% |
| Store C | Express | 800 | 35 | 4.4% | 12 | 34% | +41% |
| Store D | Hyper | 14,000 | 290 | 2.1% | 44 | 15% | +11% |
Sort by engagement rate or conversion rate — not raw engagements — and the real picture emerges. Store C is your star. Store D needs attention despite having the highest footfall.
The heatmap view
A geographic heatmap shows performance by region at a glance. Colour-code by your primary KPI (engagement rate, conversion rate, or sales lift).
This view is powerful for brand clients. They can see immediately which regions are performing and which aren’t. It drives resource allocation conversations far more effectively than a spreadsheet.
The quadrant chart
Plot stores on two axes: engagement rate (x-axis) and conversion rate (y-axis). This creates four quadrants.
Top right: Stars. High engagement, high conversion. These stores are working. Protect them.
Top left: Converters. Low engagement but high conversion. The ambassador is effective — they just need more traffic. Consider moving to a higher-footfall time slot.
Bottom right: Attractors. High engagement but low conversion. Something draws people in but doesn’t close. Investigate messaging, product fit, or ambassador training.
Bottom left: Underperformers. Low engagement, low conversion. Either the store isn’t right for this campaign, or something fundamental needs changing.
💡 This is what we do. We build multi-location comparison dashboards with normalised metrics, heatmaps, and quadrant analysis — so you can see which stores work and why. Book a 20-minute discovery call — no pitch, just scoping.
Identifying top and bottom performers
Once you have normalised data, create a simple ranking system.
Top 10%: Your best-performing stores. Understand why. Is it the location, the ambassador, the time slot, or the store manager relationship? Document the factors and replicate them.
Bottom 10%: Your worst performers. Don’t just flag them — diagnose them. Low footfall stores in the bottom 10% might just be wrong for the campaign. High footfall stores in the bottom 10% have a problem worth solving.
The middle 80%: This is where optimisation lives. Small improvements across 80% of your stores compound into massive overall gains. A 2% improvement in conversion rate across 60 stores matters more than fixing one underperformer.
Using store analytics for planning
Historical store performance should drive future campaign planning.
Store selection: Use past data to pick stores most likely to deliver results. Don’t just accept the retailer’s default list.
Scheduling optimisation: If Tuesday mornings consistently underperform Saturday afternoons at a given store, schedule accordingly.
Ambassador matching: Some ambassadors perform better in certain formats or regions. Use the data to match them.
Resource allocation: Put more hours and more experienced ambassadors in stores with the highest conversion potential. Don’t spread resources evenly when performance isn’t even.
Making it operational
At Chartica, we build store-level analytics dashboards for field marketing agencies using Looker Studio and BigQuery. Field data from your apps (Repsly, Reapp, Skout, or custom tools) gets piped via Fivetran into BigQuery, joined with retailer footfall and sales data.
The result: a live dashboard showing normalised performance across every location. Filterable by region, format, campaign, ambassador, and time period. Updated automatically.
We typically deliver in around three weeks and manage everything in our cloud. Over 20 teams use our Analytics as a Service approach — fully managed, monthly retainer, nothing for your team to maintain.
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→ See all our field marketing analytics resources: Field Marketing Reporting & Dashboards
If this sounds like more work than you want to take on, that’s what we do at Chartica. Book a 20-minute discovery call — we’ll scope it out, no pitch.