Targeting Hypothesis and Qualification Scoring Build (10-14 days)
A productized engagement that turns commodity prospect data into a documented targeting thesis and a scored qualification layer the client can run without the agency in the loop. The agency sells the framework, not the seat license. Time: 10-14 days.
By InnovaAI ResearchPublished
How do you implement it?
Targeting Hypothesis and Qualification Scoring Build (10-14 days)
A productized engagement that turns commodity prospect data into a documented targeting thesis and a scored qualification layer the client can run without the agency in the loop. The agency sells the framework, not the seat license.
- Signed scope that separates data subscription cost from agency strategy fee, so the client sees what they are paying for beyond list access. Access to the client's closed-won and closed-lost history for at least the last four quarters, plus current CRM stage definitions. A named client-side owner for ICP approval and a second approver for scoring thresholds. Written agreement on the disqualification criteria the client will actually honor, since a scoring model nobody enforces is shelfware. Baseline numbers for current reply rate, meeting rate, and cost per qualified meeting.
- 1.Pull the last four quarters of closed-won and closed-lost records into one working file
- 2.Tag each record with source, entry trigger, and time from first touch to meeting
- 3.Interview two sales reps on which inbound leads they ignore and why
- 1.Draft the targeting hypothesis as a single sentence: who, with what trigger, at what moment
- 2.List the three firmographic filters that actually correlate with closed-won in the client's data
- 3.Identify the two filters the client assumes matter but the data does not support
- 1.Build the first prospect list against the draft hypothesis using the chosen platform
- 2.Run the same query against a control segment using the client's current targeting
- 3.Log record counts, match rates, and enrichment gaps for both segments
- 1.Define the qualification scoring model with weighted criteria and explicit thresholds
- 2.Set the disqualification rules that remove a record before it reaches a rep
- 3.Map each score band to a routing action: book, nurture, or suppress
- 1.Wire the enrichment and scoring steps into the automation stack
- 2.Test the handoff from list export to CRM with ten sample records
- 3.Confirm field mapping so scores land in a reportable property, not a note field
- 1.Run a 100-record dry pass through the full pipeline end to end
- 2.Time each stage and record where manual intervention is still required
- 3.Fix the two slowest handoffs before the pilot goes live
- 1.Launch the pilot against the hypothesis segment and the control segment simultaneously
- 2.Hold outreach copy constant so the targeting difference is the only variable
- 3.Set the daily monitoring view the client will see for the pilot window
- 1.Review day-one reply and bounce data for deliverability problems
- 2.Check whether scored-high records are actually getting replies
- 3.Adjust send volume if bounce rate exceeds the agreed ceiling
- 1.Compare meeting rate between hypothesis segment and control segment
- 2.Interview the reps on the quality of the first booked meetings
- 3.Document which scoring criteria predicted a real conversation and which did not
- 1.Recalibrate scoring weights using the pilot outcomes
- 2.Rewrite the targeting hypothesis to reflect what the data showed
- 3.Freeze the version that will go into the client-facing playbook
- 1.Write the targeting framework document with the hypothesis, filters, and trigger definitions
- 2.Record a walkthrough of the scoring model and its thresholds
- 3.Package the disqualification rules as a one-page reference for the client's reps
- 1.Hand over the pipeline with the client's team driving and the agency observing
- 2.Run a live troubleshooting session on the first client-operated batch
- 3.Agree the monthly review cadence and the metrics reported at each review
The platform layer is priced per seat and per credit, so any agency reselling access competes on the same spreadsheet as every other reseller and gets squeezed to a markup. The margin sits in the targeting hypothesis and the scoring thresholds, which are built from the client's own closed-won history and cannot be copied from a vendor's onboarding call. Once the client's meeting rate depends on a model the agency calibrated, the retainer survives tool-switching conversations because replacing the platform does not replace the framework.
- A written targeting hypothesis with named triggers and timing windows. A weighted qualification scoring model with thresholds and routing rules. A documented disqualification list the client's reps have agreed to enforce. A pilot readout comparing hypothesis segment against control segment on reply and meeting rate. A monthly recalibration template with the metrics tracked at each review.
The client's team runs the full pipeline unaided for one week, and the pilot readout shows a measurable meeting-rate difference between the hypothesis segment and the control segment.