The work behind the results.
Three engagements, each shown as the client, my role, what I built, and the result. Modeled figures and targets are labeled as such.
Where the funnel leaked.
- Situation
- Only 1.4% of contacts became closed-won deals.
- My role
- Ran the AI diagnostic of calls, CRM data, and the sales process; built the funnel model and the AI plan.
- Approach
- Found four leaks, then ranked AI interventions by impact on each funnel stage.
- Result
- A plan that nearly doubles closed-won deals on the same lead flow. Modeled
Switch the funnel between today and the modeled AI plan. Each stage shows where deals were lost and how many the plan recovers.
Modeled from the client's funnel data. Client name withheld.
"We went from arguing about whose opinion was right to discussing how to implement what the data clearly shows works. It completely changed our coaching dynamic."
Deal data with no rep typing.
- Client
- Healthcare software company with 17 AEs, led by an SVP of Sales.
- My role
- Designed and built the system, and wrote the adoption plan.
- What I built
- Every external call recorded; MEDDPICC fields extracted into Salesforce automatically; a rep copilot for gap detection, multi-threading, and mutual action plans; a weekly deal-inspection brief for the SVP.
- Adoption
- A 13-week rollout, one or two tools at a time, with managers positioned as coaches.
Every qualification call, scored.
- Client
- A B2B technology company and its BDR team.
- My role
- Ran the diagnostic, then designed and built the call-scoring automation.
- What I built
- Each qualification call scored against a vertical-specific BANT playbook; scores and notes written to Salesforce; a scorecard and coaching tips emailed to the rep; a summary sent to managers.
- Result
- BDR qualification rate rose from 58.8% to 100%; win rate in one vertical rose from 10.5% to 18.9%; 13.5x ROI.
"Having the data to back up coaching conversations instead of just my gut feel changes everything. I can now coach 15 BDRs as effectively as I used to coach 5. The AI catches things I'd miss when reviewing manually."
Diagnose. Plan. Build. Make it stick.
AI shortens the diagnosis and the build from weeks to days. The plan and the behavior change still depend on judgment and on people.
Diagnose
Interviews from frontline reps to the CRO, multi-year CRM exports, and every recorded call, analyzed in hours.
Plan
The numbers needed to hit target, the process and messaging changes, and the team required, in a gated sequence.
Build
AI workflows, CRM fields, and copilots that put the new process into reps' and managers' daily work.
Make it stick
Manager coaching cadences, certification, and a 90-day re-score of recorded calls to prove behavior changed.
Skills trained on 15 years of practice.
Each system encodes what I have learned from dozens of sales organizations: what to look for, how to score it, and what good looks like. Every one keeps a person at the decision point.
AI sales operating system
Every external call recorded, MEDDPICC written to Salesforce with no rep typing, and copilots for reps and the SVP.
Every external call, recorded automatically
MEDDPICC fields extracted from each transcript
Salesforce updated; rep copilot plans multi-threading and action plans; SVP gets a weekly deal brief
The SVP uses the weekly brief to run deal inspection
Clients are described by industry, not by name.
My own firm runs on about sixty more.
I build and test every system on Dana Consulting first. Clients get the proven version of the one piece that solves their problem.
Reads my pipeline every morning, scores every sales call with evidence required for each score, and blocks a proposal until the deal clears qualification gates.
Names one bottleneck a week, proposes at most three fixes, and checks whether last month's fixes worked.
Every miss is logged and each fix is tested one change at a time. One skill went from a 56% to a 92% pass rate.
Every email, post, and client-facing action waits for my approval.