Insight Engine Assembly (Onboarding)

Insight Engine Assembly (Onboarding)

1. 01

   Map the client's decision inventory before selecting any tool

   List every recurring decision your agency makes for the client, from campaign targeting to UX changes, and rank them by revenue impact. This inventory becomes the filter for which research tool earns a slot in the stack.

2. 02

   Pair self-reported tools with behavioral analytics for each decision

   For every survey or feedback widget you deploy, identify a complementary observational source such as session recordings or heatmaps. Self-reported data alone misses the gap between what clients say and what they do.

3. 03

   Define the evidence threshold that triggers a recommendation

   Agree with the client on the minimum sample size and confidence level before a research finding can drive a strategy change. Without this gate, anecdotal responses can masquerade as statistically sound direction.

4. 04

   Build a repeatable research template library for common client questions

   Create standardized survey and test templates for the top five recurring research needs, such as lead qualification or message testing. Tools like Typeform or Qualtrics allow you to clone and adapt these templates, cutting setup time from days to hours.

5. 05

   Establish a citation and source log for every insight delivered

   Record the exact source, date, and methodology behind each data point you present to the client. A 2026 study of 107 million AI-generated answers found citation gaps are a growing credibility risk, so your own reporting should model the rigor you expect from AI platforms.

6. 06

   Document the data flow and retention policy for each tool

   Map where client data enters, how it is processed, and where it is stored, then align that flow with the client's privacy obligations. Self-hosted LLM options are now viable for sensitive data, but the decision must be documented and signed off.

7. 07

   Run a pilot study on a low-stakes client question to validate the engine

   Test the full pipeline from data collection to insight delivery on a single, reversible decision. This surfaces integration gaps and calibration issues before the engine is applied to high-stakes strategy work.