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Campaigns aimed at a broad eligibility list spend most of their attention on customers who will not respond. A Response Propensity Modeling Playbook reads response history, behavioral signals, and offer fit to score each customer's probability of responding to the specific campaign at hand.
The Challenge
A campaign list built on eligibility (right segment, right tier, right product) treats every eligible customer as a candidate. Eligibility ignores the customer's actual interest and response history, so the campaign reaches many who will not respond and the conversion rate stays low.
Marketing automation logs which customers have responded to past campaigns and which have ignored them. The campaign planning process rarely joins this history to the next campaign, so the same customers who ignored the last three campaigns receive the fourth.
When the test cohort contains a mix of high-propensity and low-propensity customers, the result reflects the cohort mix rather than the offer itself. Without propensity normalization, the team draws the wrong conclusion about what worked and rolls out a campaign tuned to noise.
How eyko Solves It
A Response Propensity Modeling Playbook reads response history, behavioral signals, segment baselines, and offer fit (price point, product, message angle) to score every customer on probability of responding to the specific campaign. It ranks the eligible base by propensity, recommends a primary and secondary cohort to target, and surfaces the low-propensity portion of the eligibility list that should be excluded to protect campaign effectiveness.
The Playbook scored 18,400 eligible customers for an upcoming pricing-promotion campaign. The top decile (1,840 customers) shows a projected response rate of 24%, well above the historical campaign average of 4.2%. The bottom 40% (7,360 customers) shows a projected response rate below 1%. Targeting only the top three deciles delivers projected campaign volume with 30% of the spend.
| Metric | Current | Benchmark | Status |
|---|---|---|---|
| Primary indicator | Flagged | Target | Action needed |
| Secondary indicator | Monitoring | Within range | On track |
| Trend direction | Declining | Stable | Review required |
Response Propensity Modeling scores every eligible customer on probability of responding to the specific campaign at hand. The Playbook reads response history, behavioral signals, segment baselines, and offer fit, ranks the eligible base by projected response, and recommends primary and secondary target cohorts so marketing teams can spend campaign attention on customers genuinely ready for the offer rather than the full eligibility list.
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FAQ
Everything you need to know about Response Propensity Ranking.
Response Propensity Modeling is an AI-driven score of every eligible customer's probability of responding to the specific campaign at hand. The Playbook reads response history, behavioral signals, segment baselines, and offer fit to rank the eligible base by projected response, recommends primary and secondary target cohorts, and surfaces the low-propensity portion of the eligibility list that should be excluded to protect campaign effectiveness.
The Playbook reads from your marketing automation (campaign history, email and content engagement, response patterns), CRM (account context, segment metadata, lifecycle stage), product analytics (behavioral signals matched to the offer's use case), and billing system (price-point history, plan changes, prior offer acceptance). At least 18 months of paired campaign-to-response data anchors the propensity model in actual outcomes.
Lead scoring produces a single composite of overall fit and engagement. Response Propensity Modeling is campaign-specific: it scores each customer's probability of responding to this offer, on this channel, at this lifecycle moment. The two are complementary, but campaign-specific propensity is what produces measurable lift in response rate per campaign rather than a generic engagement signal.
Yes. The Playbook recommends primary, secondary, and exclusion cohorts grounded in the propensity ranking, with projected response volume and cost-per-conversion attached. The recommendation includes the follow-up wave structure for the middle cohort and the exclusion list to protect campaign-effectiveness reporting. Marketing leadership gets a concrete cohort plan rather than a generic propensity table.
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