The work speaks for itself.
These are selected engagements — each produced measurable commercial impact: revenue growth, margin expansion, or structural improvement that contributed directly to enterprise value.
A construction supplier at $16M needed a commercial overhaul. I started with a Growth Diagnostic that identified the highest-value segments and exposed a data asset the company didn't know it had — proprietary transaction data that was directly monetizable.
The diagnostic converted into a full ICB Program. I built a graded ICP scoring model (A through D) that revealed underserved segments transacting millions annually with higher purchasing frequency and higher transaction values than the company's core customers. Grade A customers averaged $66K in annual purchases across 5.3 transactions per year — more than 4× the value of the company's traditional focus segments. Using the scoring model, high-value customers were matched with broader market prospects through a like-for-like algorithmic analysis, effectively acquiring new high-potential customers. Lower-scoring customers were transitioned to an inside sales strategy.
Revenue doubled to $32M in 12 months. The ICP model, combined with pricing redesign, comp restructuring, and campaign activation, sustained a 30% annual growth rate post-engagement. The data monetization capability I built became a standalone revenue line.
| ICP Grade | Score | Avg Order Value | Orders/Year | Annual Potential |
|---|---|---|---|---|
| A | 100 | $12,626 | 5.3 | $66,611 |
| A | 90 | $15,137 | 4.0 | $60,870 |
| B | 80 | $19,633 | 2.6 | $51,098 |
| C | 65 | $16,110 | 2.4 | $38,101 |
| D | 40 | $9,651 | 1.5 | $14,155 |
- Graded ICP scoring model (A–D) identified underserved high-value segments
- Grade A customers: $66K annual potential, 5.3× purchase frequency
- Like-for-like algorithmic matching acquired new high-potential prospects
- Data monetization capability became a standalone revenue line
- 2.0× revenue growth in 12 months, 30% sustained annual growth post-engagement
A PE-backed accounting firm at $20M in revenue was undergoing a complex transformation — integrating acquisitions, rationalizing service lines, and trying to cross-sell across a newly combined client base. The existing compensation structure rewarded partners for protecting their book of business, not for growing it. The result was territorial behavior, zero cross-sell activity, and revenue that tracked below the value creation plan.
I designed a behavioral incentive architecture that replaced trailing revenue metrics with leading indicators — pipeline generation, cross-sell referrals, and new client acquisition in target segments. The comp plans included accelerators that rewarded collaborative behavior and penalized sandbagging. The key insight was that the firm didn't have a revenue problem — it had a behavioral problem, and the comp structure was reinforcing exactly the wrong behaviors.
The new compensation plans drove $1M above plan in the first month of implementation — 5% of annual revenue in a single month. The behavioral shift was immediate because the incentive architecture was designed to make the right actions the easiest and most rewarding actions.
| Tier | Attainment | Revenue Range | Bonus Threshold |
|---|---|---|---|
| Tier 1 | 0–30% | $56K–$1.7M | $8.3K |
| Tier 3 | 51–80% | $2.8M–$4.4M | $44.4K |
| Tier 5 | 101–115% | $5.6M–$6.4M | $127.7K |
| Tier 6 | 115%+ | $6.4M+ | $159.6K+ |
- Behavioral incentive architecture replaced trailing revenue metrics with leading indicators
- Cross-sell activity activated across newly combined client base
- $1M above plan in first month — 5% of $20M annual revenue
- Territorial behavior replaced by collaborative incentive alignment
A PE-backed legal software company was charging based on a data storage model — and their customers had figured out how to game it. Law firms were purging historical data to avoid storage fees while continuing to process new matters through the platform. The pricing model was structurally broken: the more value customers extracted, the less they paid.
I conducted a Willingness to Pay Study to determine optimal pricing based on the metric that actually correlated with value: the number of matters processed per month. The study surveyed current and prospective buyers across 4,800 matters to establish price sensitivity boundaries. Competitive market intelligence revealed that a startup competitor was already charging $250–$300/mo per matter — validating the market's willingness to pay for this pricing metric.
The new value-based pricing model produced a revenue projection of ~$15.6M versus ~$4.1M under the legacy model — approximately a 4× increase. Two years after the pricing transformation was implemented, the client was acquired by one of their larger competitors. The acquirer cited the pricing architecture as a value driver in the transaction.
| Metric | Count | Historical | Value-Based Model |
|---|---|---|---|
| Number of Matters | 4,800 | N/A | $14.4M |
| Data Volume (GBs) | ~48K | $4.1M | $1.2M |
| Total | $4.1M | $15.6M |
- Willingness to Pay Study across 4,800 matters established value-based pricing boundaries
- Competitor validation confirmed market pricing at $250–$300/mo per matter
- Legacy data-storage model ($4.1M) replaced by per-matter model ($15.6M) — ~4× revenue
- Eliminated customer gaming of storage-based pricing
- Client acquired by a larger competitor — pricing model cited as value driver
A growth-stage technology company was undermonetizing its customer base with a flat pricing model and an unfocused acquisition funnel. Initial assessments revealed gaps in tracking and attribution, leaving stakeholders without the insights needed for data-driven decision-making. The company had explored PLG, allocated significant resources to events, digital campaigns, third-party partnerships, and outbound efforts — but low funnel conversion rates and below-average customer value highlighted misaligned targeting.
I redesigned the entire acquisition funnel — connecting fragmented data systems, mapping the full customer journey from awareness to acquisition, and optimizing every channel. An ICP model was created that drove a 3.17× increase in average customer revenue. I identified what was converting, cut what wasn't, and implemented price increases aligned to the segments delivering the most value. PLG was scaled back. Outbound and BDR channels were restructured for higher-value conversations.
The result was a 12.5× improvement in net new monthly recurring revenue, a 332% surge in funnel conversion rates, and a 3.86× increase in customer count. Not from one segment — from restructuring how the company acquired, priced, and retained customers across every channel.
| Channel | MRR Yr1 | MRR Yr2 | Growth |
|---|---|---|---|
| Events | $172 | $5,501 | 31× |
| 3rd Party | $130 | $7,713 | 59× |
| Outbound | $1,964 | $16,365 | 8.3× |
| BDRs | $94 | $1,383 | 14.8× |
| Total | $8,685 | $108,502 | 12.5× |
- ICP model drove 3.17× increase in average customer revenue
- 12.5× net new MRR growth year-over-year
- 332% improvement in lead-to-close funnel conversion rates
- Outbound MRR improved 8.3× with 3.1× average customer revenue increase
- Full funnel visibility established across 7 marketing channels
The organization had made a significant investment in expanding its sales team. While the incumbent team included high-performing individuals, the newly hired sales representatives were underperforming and inefficient. A performance assessment was conducted to establish a baseline for current performance and to revise the strategy.
I restructured the sales process between teams. High-performing incumbents continued to handle the entire sales cycle (stages 1–5), as this approach had proven successful. New hires were assigned responsibility for stages 1–3, with operational tasks for stages 4–5 shifted to managers and sales support roles. This ensured the right skills were applied at the appropriate stages. Territory design was revised, and a streamlined operating model was implemented. Off-sets were utilized during the turnaround to maintain budget.
The restructuring improved both conversion rates and sales volume, delivering $3.7M in accretive in-year growth across Q2–Q4 against a $2.0M Q1 baseline — nearly tripling the run-rate.
| Segment | Reps | Q1 Baseline | Avg Win Rate | Process | Projected Q2–Q4 |
|---|---|---|---|---|---|
| Incumbents (ramped) | 6 | $1.0M | 30% | Full cycle (1–5) | $1.5M |
| New hires (ramping) | 8 | $0.9M | 6% | Stages 1–3 only | $3.9M |
| Support / CSM | 2 | $0.1M | — | Stages 4–5 support | $0.3M |
| Total | 16 | $2.0M | $5.7M |
- 16-rep performance assessment established baseline and identified restructuring opportunities
- Sales process split: incumbents full-cycle, new hires stages 1–3 with manager support
- Territory redesign and streamlined operating model for new hires
- $3.7M accretive in-year growth (Q2–Q4) against $2.0M Q1 baseline
- Budget maintained through off-sets during turnaround
An experienced sales team of consistent performers had missed targets for two consecutive quarters despite no significant internal or external changes. The client required swift adjustments and had the resources in place to execute.
My assessment revealed that the funnel dynamics had shifted: instead of a strong focus on high-value opportunities, the pipeline had become skewed toward a larger proportion of lower-value deals with higher conversion rates. Grade A opportunities — worth more than 2× the value of high-volume transactions — were being deprioritized. Since the existing funnel and team were still productive, I didn't disqualify opportunities. Instead, I focused on creating accretive value: new targets for per-customer revenue, marketing campaigns to boost Grade A transaction volume, and a rebalancing of effort between high-value and high-volume opportunities.
Managers were trained on daily check-ins using real-time data. Executive sponsors were brought into Grade A opportunities earlier in the funnel. Pipeline coverage targets were restructured to include both value and deal volume. The result was a $2.7M revenue projection increase in FYH2, taking the in-year revenue from a $9.3M base case to an $11.5M better case — preventing a down-turn.
| Metric | Baseline | Grade A Target | Marketing Target |
|---|---|---|---|
| Opportunity Value | $28.3K | $59.4K | $26.3K |
| Conversion Rate | 20% | 14% | 23% |
| New Pipeline | — | $7.4M | $5.3M |
| Revenue Uplift | $9.3M base | +$1.0M | +$1.2M |
- Root cause identified: overemphasis on conversion rate without understanding revenue composition
- Grade A opportunities (2× value) re-prioritized with executive sponsor support
- Daily check-in operating rhythm with real-time performance tracking
- $2.7M revenue uplift projection in FYH2 ($9.3M → $11.5M)
- SPIFs and pipeline coverage targets restructured for value + volume
Stay In Touch