The Challenge
Database Qualification
Qualifying the customer base through interactive data collection mechanics. Progressive profiling allows building richer customer profiles without requiring a single large form. Each interaction is an opportunity to learn something new about the customer.Connected Use Cases: 1
Why Data Qualification Matters
Most CRM databases contain transactional data (what people bought) but lack declarative data (what people want, prefer, or intend). This gap limits personalisation to historical behaviour rather than current intent. Data qualification through personalised Content:- Collects preferences organically within existing communications
- Enriches profiles progressively (no lengthy forms)
- Feeds all other Use Cases with richer targeting data
- Improves recommendation accuracy and lifecycle relevance
Connected Use Cases
| Use Case | Role in data qualification |
|---|---|
| Live Polling | Primary mechanic for interactive data collection within emails |
| Enriched Product Recommendation | Benefits from enriched preference data for better targeting |
Required Datasources
| Datasource | Usage |
|---|---|
| CRM 360 | Existing customer attributes, data completeness status |
| Workflow Events | Live Polling responses stored as events |
Connected Capabilities
| Capability | Role |
|---|---|
| Live Polling | Interactive surveys adapted to profile gaps |
| Feedback Collection | Systematic review and NPS collection |
Measuring Impact
Data qualification is measured through:- Profile completeness rate: % of profiles with key attributes filled
- Downstream impact: Improvement in recommendation click-through after profile enrichment
- Response rate: % of contacts who interact with qualification mechanics