Case Study
Professional Services
How Apex Advisory Increased Proposal Win Rate by 31% with Pipeline Intelligence
A 250-person management consulting firm had a proposal win rate that had declined from 42% to 29% over two years without a clear explanation. The partners believed it was market pricing. The data told a different story.
+31%
Win rate improvement
+24%
Deal size increase
79%
Forecast accuracy
We're a tech company helping B2B teams extract CRM data, find revenue leaks, and unlock growth. Our approach is simple, combine AI with strategy so you can focus on closing what matters most.
The situation
Apex Advisory operated across strategy, operations, and technology consulting practices. Partners owned their own client relationships and were responsible for both delivery and business development. The CRM (Salesforce) was used inconsistently: some partners logged everything, most logged the minimum required for revenue reporting, and a few had their own spreadsheet systems that they reconciled with the CRM quarterly.
The win rate decline had been discussed in three consecutive partner meetings. The prevailing theory was pricing: the market had become more competitive and the firm's rates were above market for mid-market engagements. The proposed solution was a pricing review and potential discount authority for partners.
Before changing pricing, the managing partner wanted a data analysis to confirm the hypothesis.
What we found
The pricing hypothesis was partially correct but not the primary driver. When we segmented win rates by practice area, deal size, industry, and sales behavior, a more specific pattern emerged.
The win rate decline was concentrated in two areas: technology consulting engagements over $500K, and engagements where the proposal was sent without a discovery call logged in the CRM. The first category had declined from 38% to 21% over two years. The second category had always had a low win rate (19%) but had grown as a percentage of total proposals because partners were skipping the discovery step to move faster.
The discovery call data was revealing: proposals preceded by at least one logged discovery meeting had a 41% win rate. Proposals without a logged discovery had a 19% win rate. The difference wasn't about proposal quality. It was about whether the partner had the context to write a proposal that addressed the client's actual situation.
The technology engagements over $500K were losing to one competitor in 68% of cases. The lost deal notes (when they existed) consistently cited "more detailed implementation plan" as the reason. The proposals themselves were strong on diagnosis and light on execution specifics, which was the opposite of what large-deal buyers wanted.
What changed
Two process changes were implemented immediately. Discovery calls became required before any proposal could be advanced in the CRM (a stage gate rather than a recommendation). Proposal templates for technology engagements over $250K were rebuilt to lead with implementation specifics rather than diagnostic findings.
Partners were initially resistant to the discovery requirement. The data changed the conversation: when shown that proposals without discovery had a 19% win rate versus 41% with discovery, the argument shifted from process compliance to revenue math. Adoption reached 85% within 90 days.
Win rate moved from 29% to 38% in the first year. Average deal size increased 24% as the implementation-forward proposals attracted larger engagements that the previous format was underselling. The forecast model, now built on discovery-gated pipeline, improved accuracy to 79%.