Targeting Hypothesis and Qualification Sprint (10-15 days)
A fixed-scope engagement that converts a client's vague ICP into a tested targeting hypothesis, a scored qualification model, and a clean outbound-ready list, so the agency owns the strategy layer rather than reselling seat licenses. Time: 10-15 days.
By InnovaAI ResearchPublished
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Targeting Hypothesis and Qualification Sprint (10-15 days)
A fixed-scope engagement that converts a client's vague ICP into a tested targeting hypothesis, a scored qualification model, and a clean outbound-ready list, so the agency owns the strategy layer rather than reselling seat licenses.
- Signed SOW that separates strategy fees from any platform pass-through costs. Access to the client's CRM, closed-won and closed-lost records for the last 12 months. A named client-side owner who can approve ICP definitions within 48 hours. Agreed definition of a qualified lead, written down before any list is built.
- 1.Pull 12 months of closed-won and closed-lost records and tag them by source
- 2.Interview the client's two best sales reps on what a good-fit account looks like
- 3.Document the current lead flow from list build to first meeting
- 1.Segment won accounts by firmographic cluster and identify the top three
- 2.Map the trigger events that preceded each cluster's first touch
- 3.Draft the targeting hypothesis as a one-page statement with named exclusions
- 1.Review the hypothesis with the client owner and record every objection
- 2.Convert objections into testable filters for the chosen platform
- 3.Freeze version one of the ICP definition for the sprint
- 1.Configure the chosen platform with the frozen filters and exclusion lists
- 2.Run a 200-record sample pull and check field completeness
- 3.Flag records missing decision-maker contact data for enrichment
- 1.Enrich the sample through the automation stack and dedupe against the CRM
- 2.Score each record against the draft qualification model
- 3.Send the scored sample to the client for blind review
- 1.Compare client review scores against the model's scores and calculate agreement
- 2.Rewrite scoring weights where the gap exceeds 20 percent
- 3.Document the revised model with worked examples
- 1.Build the full list at agreed volume using the validated filters
- 2.Run email and phone validation and remove bounced or unreachable records
- 3.Export in the CRM's import format with source and score fields attached
- 1.Load the list into a staging environment and run a dry-run import
- 2.Confirm routing rules send scored leads to the right owner
- 3.Set up the weekly refresh job on the automation stack
- 1.Draft the first outbound sequence against the hypothesis, one variant per cluster
- 2.Define the reply-handling rules and the disqualification criteria
- 3.Agree the 30-day measurement window with the client
- 1.Present the targeting framework, scoring model, and list to the client
- 2.Hand over the refresh runbook and the qualification scoring sheet
- 3.Book the 30-day review to compare pipeline against the hypothesis
The platform layer is commodity infrastructure, so a resold seat earns reseller margin while a documented targeting framework earns partner rates. Clients pay for the hypothesis and the scoring model because those assets survive a platform switch and directly change reply-to-meeting conversion. The sprint also creates a recurring refresh retainer that is priced on judgment, not on data volume.
- One-page targeting hypothesis with named clusters, triggers, and exclusions
- Qualification scoring model with weights, worked examples, and a client-agreed threshold
- Validated outbound-ready list with source, score, and enrichment fields populated
- Refresh runbook covering the automation stack, dedupe rules, and weekly schedule
- 30-day measurement plan tying pipeline outcomes back to the hypothesis
The client signs off on the targeting hypothesis and scoring model, and the first scored list imports into their CRM with routing rules firing correctly on a live test record.