The Datasource described on this page is an illustrative example. The actual fields, structure, and naming will vary based on your brand’s specific data model and integration. Use this as a reference for the type of data needed, not as an exact specification.
Overview
The NPS Datasource contains Net Promoter Score survey responses and satisfaction data. This enables adapting communication strategy at an individual level — a promoter (score 9–10) and a detractor (score 0–6) should receive fundamentally different messaging.
Key Fields
| Field | Type | Description | Example values |
|---|
customer_id | String | Customer who responded | "C-2847391" |
nps_score | Integer | NPS response (0–10) | 8 |
nps_category | Enum | Derived category | promoter, passive, detractor |
survey_date | Date | Date of response | 2024-09-15 |
verbatim | String | Open-text feedback | "Love the quality but delivery was slow" |
touchpoint | String | Context of survey | "post-purchase", "post-support" |
Connected Use Cases
| Use Case | How NPS is used |
|---|
| Live Polling | NPS collection within email, follow-up based on score |
| Enriched Product Recommendation | Adapt recommendation aggressiveness to satisfaction level |
Data Requirements
| Requirement | Details |
|---|
| Update frequency | On survey response |
| Minimum fields | customer_id, nps_score, survey_date |
| Format | JSON via Datasources API |
| Volume | One record per survey response |
Integration Notes
NPS data is sourced from the brand’s survey platform (Qualtrics, Medallia, SatisMeter, or proprietary). A customer’s most recent NPS score is used for personalisation decisions.
NPS-based personalisation example: Detractors receive a “we’re sorry” message with a service recovery offer. Promoters receive a referral incentive or review request. Passives receive standard recommendations.
