Campaign-Based Footfall Analytics

Turning Events into Measurable Commercial Outcomes

In underground malls and MRT retail environments, events like Year-End Sales, Chinese New Year, or Holiday Promotions are assumed to drive traffic.

The problem:
No one can quantify how much they actually work—per location, per shop.

This is where a campaign framework becomes powerful.

1. Define Campaign as a Data Layer (Not Just a Label)

A campaign should not just be:

“Start date → End date”

That’s too basic and leads to weak insights.

Instead, define campaign with structure:

Campaign Object (Recommended)

1. Name
   • e.g. “CNY 2026”, “Year-End Sale 2025”
2. Time Window
   • Start date / End date
3. Campaign Type
   • Festive (CNY, Christmas)
   • Retail Promotion (Discounts)
   • Mall Event (Roadshow, exhibition)
4. Scope
   • Mall-wide
   • Zone-specific
   • Shop-specific
5. Baseline Period (Critical)
   • Automatically assign:
     • Previous 2–4 weeks (weekday matched)
     • Same period last year (if available)

👉 Without a baseline, your “comparison” is statistically weak.

2. What Data to Extract Per Campaign

From VS125 (passerby + entry), you should extract:

Core Metrics

1. Total Passersby
2. Total Entrants
3. Capture Rate (%)
   • Entrants / Passersby
4. Peak Hour Distribution
5. Dwell Time (if enabled)

3. The Comparison That Actually Matters

Most people will compare:

“Campaign A vs Campaign B”

That’s misleading unless normalized.

Instead, use this hierarchy:

1. Campaign vs Baseline (Most Important)

• Did traffic increase vs normal period?
• Did conversion improve?

Example:

• Passersby: +20%
• Entrants: +10%
  → Conversion dropped → campaign attracted traffic, but not buyers

2. Campaign vs Same Campaign Last Year

• Removes seasonality bias

Example:

• CNY 2026 vs CNY 2025
  → Real growth indicator

3. Cross-Location Comparison

• Which shop/location benefited most?

Example:

• Shop A: +30% entry
• Shop B: +5% entry

→ Location or storefront effectiveness issue

4. Underground Mall Insight (This is your edge)

Campaign impact behaves very differently underground:

Weekday Campaign Effect

• Traffic mostly fixed (commuters)
• Campaign must convert, not attract

Weekend / Holiday Effect

• Traffic is elastic
• Campaign can increase both passersby and entry

👉 This distinction is often missed—and it’s where your platform can differentiate.

5. Rental & Leasing Implications (High-value angle)

With campaign analytics, landlords can:

a. Measure “Event Sensitivity” per Location

• Some zones respond strongly to campaigns
• Others are “transit-only”

b. Justify Rental Premiums

• Not just based on location
• But based on campaign uplift performance

c. Sell Campaign Slots (New Revenue Stream)

• Charge tenants for:
  • High-performing campaign periods
  • High-conversion zones

6. F&B Operational Planning (Immediate ROI)

For restaurants:

Before Campaign

• Predict expected uplift using historical campaigns

During Campaign

• Adjust:
  • Inventory
  • Staffing
  • Prep timing

After Campaign

• Evaluate:
  • Did higher traffic translate to higher entry?
  • Was kitchen capacity sufficient?

7. What Most Systems Get Wrong (You Should Avoid)

Let’s be blunt—these are common failures:

1. No Baseline Comparison

→ Leads to false conclusions

2. Aggregated Mall-Level Data Only

→ Useless for tenants

3. Ignoring Passersby vs Entry

→ Cannot measure conversion

4. No Time Segmentation

→ Peak vs off-peak lost

8. Recommended Dashboard Structure

For each campaign:

Summary Panel

• Total Passersby
• Total Entrants
• Capture Rate
• % Change vs Baseline

Trend View

• Hourly traffic curves
• Weekday vs Weekend split

Location Comparison

• Shop-by-shop performance

Insight Layer (important for differentiation)

Auto-generated insights like:

• “Traffic increased, but conversion dropped”
• “Weekend uplift significantly higher than weekday”

 

Bottom Line

What you’re proposing is not just reporting—it’s:

A system to quantify the real commercial impact of events.

If implemented properly, it directly supports:

• Rental pricing decisions
• Tenant performance evaluation
• Campaign ROI measurement
• Operational planning (especially F&B)

Strategic Recommendation (Priority Order)

1. Build baseline comparison first
   → Without this, everything else is weak
2. Separate passersby vs entry clearly
   → This is your core differentiator
3. Standardize campaign templates
   → Avoid messy, inconsistent data
4. Add automated insights (not just charts)
   → This is what makes it “sellable”

Check out the dashboard for foot-traffic intelligence here: Click here