Sales Analytics Diagnostic · Sample Engagement
Cadence
Pipeline & conversion diagnostic — B2B workflow automation, ~$31M ARR
A sample of the diagnostic we deliver from a company's existing CRM data. Want one for your sales data? ↓
The headline finding
Deals that complete a technical evaluation close at 3.4× the rate of deals that skip one — but only 38% of qualified opportunities ever start one.
This single pattern accounts for most of the gap between Cadence's 27% win rate and comparable teams at 33–38%. The sections below trace where it shows up in the pipeline, what moves with it, and which open deals it puts at risk right now.
61%
Win rate — eval done
18%
Win rate — no eval
Trailing 4 Quarters
·Change shown vs. the prior four quarters
Win rate (qualified → won)
27%
+2 ptsvs. prior 4 quarters
Median sales cycle
74 days
+9 daysvs. prior 4 quarters
Pipeline coverage (next Q)
2.8×
−0.4×vs. prior 4 quarters
Average new ACV
$42.1K
+$3.8Kvs. prior 4 quarters
Section 1 — Pipeline
Where deals stall
Stage-to-stage conversion and median time in stage across the qualified pipeline. One stage accounts for both the steepest drop-off and the longest wait.
Sources: CRM opportunity + stage-history export, 1,860 closed opps
Recommended Next Steps
- 1Instrument the technical validation stage first: pull exit reasons for every deal that entered it and lost, and separate “failed the eval” from “eval never happened.”
- 2Compare time-in-stage for won vs. lost deals inside technical validation — the 28-day median likely hides a bimodal split worth acting on.
Section 2 — Conversion drivers
What moves win probability
Modeled effect of each factor on win probability, in percentage points versus a baseline deal, holding segment and deal size constant. Blue lifts the odds; red drags them down.
Sources: Logistic regression on 1,860 closed opps · pseudo-R² 0.38
Associations, not proof of cause on their own — but they line up with the stage-level leak in Section 1.
Recommended Next Steps
- 1Treat “technical evaluation completed” as the primary lever: it is both the largest modeled effect (+34 pts) and the stage that leaks most.
- 2The two largest drags — single-threading (−16) and repeated reforecasting (−22) — are process signals a manager can catch early, not deal qualities. Build alerts for both.
Section 3 — Open pipeline
Open deals, ranked by modeled close probability
Current-quarter pipeline scored on the same model. The point is not the score — it is the specific next action each score implies.
Sources: Model applied to 214 open opportunities; top and bottom of the range shown
| Opportunity | ACV | Close probability | Strongest signals | Recommended action |
|---|---|---|---|---|
| Meridian FreightEnterprise | $88K | 0.79 | Eval complete · 5 stakeholders · exec sponsor | Confirm procurement timeline; hold the close date. |
| Vantage HealthMid-market | $44K | 0.71 | Eval complete · exec sponsor · fast response | Push for signature this quarter — no blockers modeled. |
| Brightline RetailMid-market | $37K | 0.58 | 3 stakeholders · fast response · no eval yet | Schedule technical validation now — the missing +34-pt lever. |
| Kestrel LogisticsEnterprise | $120K | 0.41 | Exec sponsor · single-threaded (1 contact) | Multi-thread before proposal; map 2+ additional buyers. |
| Orchard MediaSMB | $19K | 0.33 | Reforecast 3× · slipped two quarters | Set a hard close date or move to nurture — stop carrying it. |
| Solstice BankEnterprise | $150K | 0.28 | No eval · 1 contact · open 94 days | Re-qualify against exit criteria or disqualify this week. |
Recommended Next Steps
- 1Work the middle of the list, not the top: Meridian and Vantage will likely close regardless. Brightline (0.58) and Kestrel (0.41) are where a single action changes the outcome.
- 2Run this scoring weekly and track how many deals move category after the recommended action is taken — that is the fastest read on whether the model is useful.
Section 4 — Rep performance
What moves rep performance
Each point is one rep: the average buying-group size they work per deal against their personal win rate. The relationship is steep and holds after controlling for tenure and territory.
Sources: 47 reps · CRM contact-role data · min. 20 closed opps per rep
Reps who engage 3+ stakeholders per deal win 24%; those who don't win 14%. Tenure and territory do not explain the gap — multi-threading behavior does.
Recommended Next Steps
- 1Pair the four lowest reps on the curve with the four highest for deal reviews — the behavior gap is coachable and the data points to exactly who.
- 2Add “stakeholders engaged” to the pipeline review cadence as a leading indicator, alongside stage and close date.
Section 5 — Method
What this analysis held constant
So the patterns above reflect how deals are run, not who they happened to be with.
Segment & deal size
Enterprise deals win more and take longer; every comparison is made within-segment.
Rep tenure
Ramped and still-ramping reps modeled separately — the evaluation effect holds in both.
Lead source
Inbound vs. outbound carried as its own term (+5 pts), not left to confound the others.
Seasonality
Q4 push and Q1 reset removed with quarter fixed effects.
Data pulled 3 Oct 2025. Deals still open at pull date are excluded from the win-rate model and scored separately in Section 3.
Recommendations
Recommended actions, in priority order
Sequenced by expected impact per unit of effort. The first two are process changes; the third is a hygiene rule.
01
Make technical validation a gated stage with exit criteria
Require a named technical owner and a documented validation outcome before any deal moves to proposal. No exceptions for “trusted” deals.
Est. +6–9 pts win rate on qualified pipeline within two quarters
02
Trigger a multi-threading play at discovery
Auto-flag any deal with fewer than 3 contacts for manager review before it can advance. Give reps a stakeholder-mapping template in the CRM.
Est. recovery of ~40 stalled single-threaded deals per quarter
03
Add a 60-day re-qualification checkpoint
Any deal reforecast twice, or open past 75 days, gets a commit-or-disqualify decision with the manager. Kills the long tail that distorts the forecast.
Est. 8–12 days off median cycle; a cleaner coverage number
Want a diagnostic like this for your sales data?
We work from the CRM, pipeline, and customer data you already have. The Sales Analytics Diagnostic is a focused, fixed-scope engagement: we find the patterns your existing reporting can't surface, explain why they're happening, and hand your leadership team a short list of concrete changes.
Start a conversation“Cadence,” all figures, rep counts, and deal names on this page are illustrative and built for a representative engagement — not a real client's data.