We run impact assessments for every Endgame customer, measuring CRM outcomes against each team’s own pre-deployment baselines. We only bring forward what the data actually supports. Here’s what moves revenue per seller.
Endgame gives revenue teams AI-powered research on their accounts, contacts, and deals. Sales reps use it to prepare for calls, map buying committees, track competitive positioning, and build pipeline. We run impact assessments for every customer, measuring CRM outcomes against each team’s own pre-deployment baselines, and we only bring forward what the data supports.
Across those assessments, five patterns hold up:
The rest of this document unpacks each pattern, explains what to expect based on revenue model and deployment maturity, and describes what the highest-performing teams have in common.
Wherever we can measure deal outcomes by research depth, the same pattern appears: win rates climb in proportion to how much preparation a rep does on a deal. Not whether they used Endgame at all, but how deeply they used it. Every increment of depth helps.
At one company, deals where reps did heavy preparation with Endgame closed at 16× the rate of deals with no preparation, across more than 1,000 opportunities. At another, the multiplier was nearly 4× across 2,500+ deals. At a third, the lift appeared within specific deal-size bands, precisely where the complexity of the deal matched the depth of preparation invested.
The mechanism is straightforward: deeper preparation leads to better stakeholder mapping, which leads to broader engagement with the buying committee, which converts to higher close rates. Where we can measure contact coverage, deals with Endgame preparation engaged 50–100% more decision-makers than similar deals without it.
Before win rates shift, before revenue attribution is even measurable, pipeline creation per rep goes up. And at one customer, the evidence is unusually clean.
This is the strongest pipeline-generation signal we’ve measured, for a specific reason: the eight-quarter pre-Endgame baseline was flat. The two groups (reps who would later adopt Endgame and reps who wouldn’t) were creating pipeline at the same rate for two full years. The gap appeared only after Endgame became available and grew over three consecutive quarters.
This matters because the most common objection to any tool’s impact is “the reps who use it were better to begin with.” A flat pre-deployment baseline on the same population rules that out.
At other customers, we see a related signal: deal-qualification rates improve by 2–8 percentage points on accounts where reps used Endgame. Reps are not just creating more pipeline. They’re qualifying it better. The deals entering the forecast are more real.
Of the revenue metrics we measure, expansion win-rate lift is the one that appears most often and earliest. It shows up before new-business lift, and it holds across different types of expansion (upsell, cross-sell, platform expansion).
Expansion deals are a natural fit for Endgame because the rep already has a relationship with the customer. Endgame adds stakeholder intelligence: who else to bring in, what other departments care about, how the competitive landscape looks from the customer’s perspective. Where we can measure it, expansion win rates lifted by 5–12 percentage points on accounts where reps prepared with Endgame.
At one customer, the expansion lift was +12 percentage points across 448 deals, consistent across all three expansion paths. At another, the lift was +7.6 percentage points within comparable deal sizes. The pattern held at customers with very different revenue models and deal structures.
Expansion is also where the evidence matures fastest. New-business win rates take 10–12 months of deployment to produce defensible data. Expansion signals show up at 6–8 months because the deal cycles are shorter and the account relationships are already established.
The retention signal is real: +5–8 percentage points on renewal win rates, with millions in at-risk revenue retained. Like any retention metric, it requires a full renewal cycle to measure cleanly. Typically 10+ months.
At one customer with 12+ months of deployment, renewal rates were 7.6 percentage points higher on accounts where AMs had used Endgame to prepare. We confirmed the gap didn’t exist before Endgame. At another, $4 million in at-risk revenue was retained on accounts where account managers prepared with Endgame, while the overall renewal close rate stayed flat, ruling out a general market improvement.
Retention is inherently a longer-cycle metric. It requires a full renewal cycle before the data is meaningful, and that’s true of any tool or intervention, not specific to Endgame. The teams that see the strongest retention results are the ones where AMs have been preparing with Endgame long enough for a full cohort of renewals to close.
The single biggest mistake we see is measuring the wrong thing at the wrong time. Teams that try to prove revenue attribution at month 4 get disappointed. Teams that only look at usage metrics at month 12 are underselling real results. The impact shows up in stages.
| Stage | Timeframe | What you can measure | What needs more time |
|---|---|---|---|
| Early | 0–6 months | Pipeline created per rep, deal-qualification rates, how the team is using it in their workflow | Win-rate changes, retention impact, deal-size shifts |
| Mid | 6–10 months | Expansion win rates, how deeply reps research strategic deals, whether the tool is part of deal reviews and forecast calls | Full renewal-cycle attribution, new-business win rates at scale |
| Mature | 10–12+ months | Revenue per rep (before vs. after), new-business win rates by research depth, retention lift, same-team comparisons | New-hire ramp acceleration (needs 6+ months of post-hire data) |
This isn’t aspirational. It reflects what we’ve seen work and fail. The maturity curve is less about how many calendar days have passed and more about whether there’s enough data for a given metric to be meaningful. A company with short deal cycles might reach the “mid” stage at month 5. A company selling enterprise contracts with 9-month cycles won’t have enough closed deals to measure win rates until month 12 or later.
Not every sales team looks the same. The impact Endgame has depends on how a company sells. We see five recognizable patterns based on revenue model.
What these look like: Renewal revenue is the primary motion. Account managers or customer success carries quota. Churn has a quantifiable cost.
Where Endgame shows up: Account managers who research ahead of renewal conversations retain more revenue. The lift is +5–8 percentage points on renewal rates, with millions in at-risk revenue saved. This takes 10+ months to show up in the numbers.
Early signal to watch: Are your AMs researching at-risk accounts before renewal calls? If so, the retention signal is building. You just can’t measure it yet.
What these look like: Deals are large enough to justify deep preparation. The buying committee has multiple stakeholders. Differentiation matters.
Where Endgame shows up: Win rates multiply on deals where reps go deep: 2–4× when reps do thorough preparation. The reps who invest sustained effort on their biggest deals (mapping stakeholders, tracking competitive positioning, returning multiple times before close) close at dramatically higher rates.
Early signal to watch: Are your best reps doing deep, multi-step preparation on strategic accounts, or just a quick look?
What these look like: Large sales teams, meaningful attrition, ramp time matters. The cost of lost institutional knowledge is felt on every territory reassignment.
Where Endgame shows up: The same reps produce 12–35% more revenue after Endgame becomes available. New hires at mature deployments adopt within their first month at rates above 70%. The team runs more deals on the same headcount.
Early signal to watch: How fast are new hires picking it up? If they’re using it in week 2, the team has built a norm.
What these look like: Endgame has been available for less than 6 months. The team is using it, but the deal cycles haven’t completed a full turn yet.
Where Endgame shows up: Pipeline creation lifts (+50% on flat baselines). Deal-qualification rates improve. Revenue outcomes are still building, but the leading indicators are strong and the deal cycles are completing.
Early signal to watch: Pipeline creation per rep and qualification conversion. These predict the revenue outcomes that will show up at 10–12 months.
What these look like: A handful of large deals drive the bulk of revenue. Multi-person selling is the norm. The team is lean relative to the size and complexity of the pipeline.
Where Endgame shows up: Near-total coverage of the pipeline that matters. The tool is present on every strategic deal because the deals are important enough to justify deep preparation. The value is less about per-rep productivity and more about ensuring every deal gets the intelligence it needs.
Early signal to watch: What percentage of your enterprise pipeline has Endgame preparation behind it?
Not every team that uses Endgame gets the same results. Three patterns distinguish the teams that reach measurable revenue impact from the ones that don’t.
The teams with the strongest revenue results don’t treat account research as something reps do on their own time. It’s built into the sales process. Expected before key calls, visible in deal reviews, referenced in forecast conversations. The reps who map buying committees, track competitive positioning, and return to an account multiple times before close are the ones producing 2–4× win rates. And because every increment of research depth helps, the teams that make it a habit across the full pipeline, not just the biggest deals, compound the advantage.
The single strongest predictor of whether a team reaches revenue-level impact: whether the front-line sales manager uses the tool in their own workflow. Teams with an engaged manager reach significantly deeper adoption than teams without one. One customer grew from 6 to 300 active users in four months with no formal rollout, training program, or mandate. Purely because managers and peers made it visible.
The teams that get the most from Endgame track the right metrics at the right stage. They look at pipeline creation and qualification rates in the first six months, expansion win rates and stakeholder coverage at six to ten months, and revenue per rep and retention after ten months. Teams that try to prove win-rate attribution at month four get disappointed and lose momentum. Teams that track the maturity curve stay focused on what’s actually moving.