Growth-stageFintech & Financial Services
Rebuilding lifecycle, scoring, attribution and routing on a live CRM
A lifecycle that timestamps itself
A lifecycle that timestamps itself
There was no reliable lead-lifecycle or attribution model. Leads moved between stages with nothing timestamping the transitions, marketing could not say which channels produced pipeline, and account tiering did not exist, so reps worked whatever landed in front of them. Routing ran on legacy form logic that was manual and error-prone, and existing customers were being routed as though they were net-new.
Rebuilt the marketing-engaged → MQL → SQL lifecycle with stage-timestamp workflows, and built a master scoring model that caps repeat email opens and weights the product-side engagement signals that actually predict intent rather than treating every click equally.
Built first-touch and last-touch attribution, a channel translator mapping raw traffic sources and UTMs to clean channels, lead-source stamping at MQL, and a lifecycle attribution dashboard for auditing what the model produced.
ClayEnriched thousands of accounts through Clay, built a tiering model and segment structure, and pushed the resulting tiers back into the CRM to drive routing and rep protected-account books.
SlackMigrated legacy form routing into RevenueHero with revenue-based distribution rules from third-party firmographics, round-robin and redistribution logic, customer-success-aware routing so existing customers reach their owner, and Slack notifications.
Ran weekly sprint calls with daily async support in a shared channel, executed a four-week hypercare period after go-lives, and covered deal-desk operations while the customer's own owner was on leave.
A lifecycle that timestamps itself
The full lifecycle now stamps its own stage transitions, so funnel conversion and velocity can be measured rather than estimated.
Channel-level attribution live. First- and last-touch attribution with a channel translator and MQL lead-source stamping went live, with an audit dashboard behind it so the model can be checked rather than trusted blindly.
7 steps
An automated ABM engine: batch enrollment, generated personalization, and the guardrails that stop a bad send
Workflow success rate materially improved and was measurable
Attribution
4 steps
Closing the gap between MQL and pipeline
~8,800 contacts re-attributed
Outbound
5 steps
Consolidating two sales-engagement systems into one: 22 cadences rebuilt one-to-one
22 cadences migrated one-to-one
