Household Panel Intelligence
✓ Cloud-First
✓ API
✓ MCP
✓ Vendor-Agnostic
✓ No Data Lock-In
✓ ISO 27001:2022 Certified
✓ GDPR Compliant
Panel data isn't missing. It's just not talking to the rest of your commercial data.
Household Panel Intelligence is ScanmarQED's approach to harmonizing household panel data, penetration, trip frequency, loyalty, and basket value, onto the same governed layer as sell-out and promotional data, so shopper behavior and commercial performance can be analyzed together instead of as two separate datasets from two separate systems.
Before
and why”
and when”
Two stories. Two systems.
Manua lcleansing and processing - if at all.
After
Household Panel Intelligence
Penetration · trip frequency · loyalty · basket - on one layer with sell-out and promo
Analyzed together - not as two separate datasets
One layer. One version of truth.
Actionable from day one.
Household Panel Intelligence is our approach to harmonizing household panel data, penetration, trip frequency, loyalty, and basket value, onto the same governed layer as sell-out and promotional data, so shopper behavior and commercial performance can be analyzed together instead of as two separate datasets from two separate systems.
Connect PulseQED to the AI assistant you already use
Powered by MCP, the open protocol for AI tool connectivity. Bring your marketing and sales data directly into Claude, Gemini, Cursor, or any MCP-compatible tool. No exports. No context-switching. Your data where your thinking happens.
Works with:
.webp)
How is household panel data connected to sell-out and promotional data?
PulseQED harmonizes household panel data onto the same governed data layer as sell-in, sell-out, and promotional calendar data. Once connected, penetration, trip frequency, loyalty, and basket value sit alongside the sell-out and promotional records for the same period and category, so a shift in either dataset can be checked against the other without exporting from one system and reconciling it by hand against the second.
Can panel metrics support promotional ROI analysis, not just shopper reporting?
Yes. Once panel data is harmonized alongside sell-out and promotion calendar data, it feeds directly into base-incremental analysis: separating the volume a promotion would have sold anyway from the volume genuinely driven by the activity. Trip frequency and loyalty data show whether a promotion pulled in new buyers or simply pulled forward purchases from existing ones, the difference between real incremental growth and short-term stock-loading.
How this fits the platform
Household Panel Intelligence runs on PulseQED's harmonized data foundation, the same layer that feeds both PulseQED's Market & Consumer Insights solution area (shopper and brand understanding) and its Price & Promotions Analytics solution area (promotional ROI and base-incremental split). Panel data isn't a side dataset here; it's a direct input into both.
Proof
- 4–8 weeks, for structured onboarding to first unified dashboards.
- Vendor-agnostic, no lock-in to any single panel or syndicated data provider; PulseQED connects to whichever panel supplier your markets already use.
After a promotion we can see straight away whether it brought in new households or just got existing buyers in more often, and split that by any consumer group we want without going back to the panel supplier.
Panel data alone vs. panel data harmonized in PulseQED
| Panel data alone | Panel data harmonized in PulseQED | |
|---|---|---|
| Сleansing and processing speed | Reconciled manually against sell-out and promo data, typically taking weeks per period | Connected automatically to the same governed layer as sell-out and promotional data |
| Connection to promo / sell-out data | Lives in a separate system on a separate cadence; merged by hand if merged at all | Sits alongside sell-out and promotional records for the same period and category |
| Base-incremental visibility | Panel view alone can't isolate promotional lift from base shopper behavior | Connected to the promotional calendar and sell-out data, enabling a full base-incremental split |
Platform architecture
See how your panel data connects to promotional performance
If your panel data and your promotional data currently live in separate systems that nobody's had time to reconcile properly, talk to us about what a connected view would show for your own markets and panel supplier.
Household panel
Penetration · loyalty · basketSell-out data
Volume · distributionPromotional calendar
Mechanics · timingSell-in data
Trade supply · pipelineYour panel supplier
Vendor-agnosticPulseQED
Household Panel
Intelligence
Single governed data layer
Switching behaviour
Brand gains & losses by sourceNew vs repeat buyers
Acquisition & retention splitBrand penetration
Buyer reach & frequencyShare of wallet
Category share & loyalty depthMore brand KPIs
Measurement & planningTalk to us about what a connected view would show for your own markets and panel supplier
Common questions about PulseQED's Household Panel Intelligence.
Household panel data, penetration, trip frequency, loyalty, and basket value, is harmonized onto PulseQED's governed data layer alongside sell-in, sell-out, and promotional calendar data, so shopper behavior can be analyzed against commercial performance in one place.
No. PulseQED is vendor-agnostic. It connects to whichever panel supplier your markets already work with rather than requiring a switch or replacing the panel relationship.
Yes. Harmonized panel data feeds into base-incremental analysis alongside sell-out and promotion calendar data, helping separate volume a promotion would have sold anyway from volume genuinely driven by the activity.
Core panel metrics including penetration, trip frequency, loyalty, and basket value, harmonized onto the same layer as sell-in, sell-out, and promotional calendar data.
Panel data is harmonized onto the same governed layer as sell-out and promotional calendar data, so records from both can be analyzed together for the same period and category without manual cleansing and processing between systems.
Structured onboarding to first unified dashboards typically takes 4–8 weeks.
Connected Data
Better Decisions
ScanmarQED Group, 2026