Pipeline forensics detects systematic overstatement in growth-stage pipelines —
giving investors empirical grounds to correct valuations, restructure negotiations or walk away
QoE validates history.
The forward revenue forecast – the number that actually determines your entry multiple – receives no empirical scrutiny at all.
If your multiple is built on forward revenue,
you may be paying deal multiples on fiction.
The empirical discipline that QoE (Quality of Earnings) applies to the past... applied to the future
Every finding anchored to specific deals, dates and outcomes in the company's own data. Not from benchmarks, interviews or opinion
Every adjustment reflects what actually happened — where forecasts diverged from reality and which patterns repeated consistently. The same empirical lens, applied to the live pipeline
Structured to mirror QoE
Integrates alongside existing QoE in your DD workflow
Methodology and data export for independent verification
PE & VC investors
Forward revenue determines your multiple – yet it's the one number in every deal that's never empirically validated. Pipeline Forensics detects systematic overstatement before you commit capital.
Zombie leads quantified. Conversion rates recalibrated. Pipeline value waterfall with confidence intervals
Validated pipeline vs growth claims. Deal velocity adequacy and time-to-close sensitivity
Negotiation opportunities ranked by value and statistical confidence – with evidence portfolio and counter-responses to management claims
Risk concentrations identified for targeted contractual protection
Delivered as a QoFR (Quality of Forward Revenue) report – designed to integrate with QoE documentation and DD timelines
Portfolio teams
Whether you're detecting early deterioration or unlocking unrealised revenue, Pipeline Forensics provides the empirical evidence to act – months before the revenue impact hits the financials.
Early warning:
Systematic bias trending reveals revenue deterioration while you can still intervene
Segment-level forensics distinguish systemic underperformance from random variance
Down-round risk assessed using forward revenue projections with confidence intervals
Value recovery:
Over-forecast segments and underperforming reps identified with statistical significance
Resource-reallocation scenarios quantified: revenue bridge from current to recommended allocation
EV bridge at exit multiple: potential EBITDA uplift potential over 3-year hold
Delivered as operational analysis with implementation roadmap, evidence trail and Excel export for verification
QoE validates historical revenue – it's table stakes. Pipeline Forensics adds the dimension your competitors don't offer: empirical validation of the forward revenue that determines multiples.
QoFR report formatted for seamless integration with QoE documentation
Complete statistical audit trail with Excel export for independent verification
Flexible delivery to suit your relationship: white-label, co-branded, or jointly presented
Referral and co-delivery arrangements available. Start with a conversation
CASE STUDY
Digital Healthcare Services
FORECAST VALUE EXPOSED TO KEY-PERSON RISK
PRODUCTIVITY VARIANCE ACROSS THE TEAM
FORECAST AT 30% – HISTORICAL CONVERSION 8%
Management was forecasting around £11.5m of pipeline at a blended ~29% probability, and was confident – the CRM metrics looked healthy. QoE validated the company's historical revenue, but the forward pipeline that drove the multiple remained unaudited.
We reconstructed 15 months of the company's own lead-evolution history – tracking how each lead's value, probability and timing estimates changed month by month – to surface performance patterns which no point-in-time report could show.
74% of forecast pipeline value sat with a single salesperson. Standard DD could see the concentration; without forensic analysis of that rep's historical conversion performance, had no basis to conclude that the remainder was largely undeliverable – or that one resignation would remove most of the forecast.
63% of sales capacity sat with the four lower-performing reps, generating 16% of revenue. The pattern called for pipeline discipline and redeployment of talent – not simply handing more to the top performer.
£3.1m of deals aged 15–18 months were still forecast at 30%. Historical conversion at that age: 8%.
OUTCOME
Immediate
Systematic pipeline overstatement detected across the whole team, invisible to standard DD and unquantified by management.
Hold period
A realistic pipeline model informed operational decisions and investor reporting through the hold.
Acted on
73 leads reallocated; £1.7m incremental revenue made accessible; professional sales hiring initiated.
Investor impact
£10–17m of valuation exposure quantified at financial-buyer multiples – exposure which the QoE that validated historical revenue could not have surfaced.
15-month longitudinal analysis. Company and individual names anonymised; analytical findings unchanged
Do any of these sound familiar?
Deals repeatedly slip quarters — but explanations always sound reasonable
Win rates look strong on paper, yet revenue consistently misses targets
High-probability deals stall for months — then quietly disappear
Deal values fall 30%+ between initial forecast and close
Management confident — but the board is increasingly sceptical
New leadership inheriting a pipeline they don't trust
If so, our pipeline forensics will reveal the root causes and quantify the hidden cost.
Pipeline Forensics was developed by Christopher Barker from 25 years in management consulting, predictive analytics and M&A execution — including PwC, two decades leading his own analytics firm, and operating partner roles. The methodology was validated on a live engagement, before being systematised into a repeatable forensic process.
Typically 5–10 working days from data receipt for a full QoFR report. Initial headline findings sooner if required for deal timing.
Ideally 12–36 months of CRM snapshots, but useful patterns emerge even from partial archives. We assess your data upfront so you can decide.
Any system that stores deal history — Salesforce, HubSpot, Dynamics 365, Pipedrive, and others. We work from data exports, so no integration or system access is needed.
We work with whatever data you have. Even partial snapshots reveal patterns. The question isn't 'Is this data perfect?' — it's 'Is there enough to detect bias?'
By design, the analysis runs on numbers alone — amounts, stages, dates, probabilities and reference numbers which only you can match back to real deals and people. Identities need never leave the system that holds them. Where a fuller export is shared, we anonymise on receipt and hold it in an encrypted environment.
We back-test every finding against the company's own historical outcomes — deals that have already closed, lost or expired. The calibration is derived from what actually happened in your pipeline, then applied to the live portfolio. Full statistical methodology and confidence intervals included.
FDD and QoE validate historical revenue — what was booked and whether it was real. But forward revenue — the forecast that actually determines your entry multiple — receives no empirical scrutiny at all. Our Quality of Forward Revenue (QoFR) report fills that gap: the same forensic discipline, applied to the pipeline. Designed to integrate with QoE documentation and DD timelines.
CDD assesses market positioning, competitive dynamics and revenue quality. We go deeper into the pipeline itself — reconstructing deal-level histories to detect systematic forecast bias. The two are complementary: CDD validates the market opportunity, Pipeline Forensics validates whether the company can actually capture it.
CRM dashboards show today's snapshot. They can't show you which leads match historical patterns of failure, which probability estimates are habitually overstated, where deal values consistently shrink between forecast and close, or how landing times slip against pipeline forecasts. We detect these patterns in the company's own historical data, identify where the same systematic bias is present in today's live pipeline, and show you the adjustments, with confidence intervals.
The forecast is built from individual human judgements. But lacking the evidence of how such judgements have turned out before, nobody can correct for the bias in them. That evidence already exists in the records left by those leads as they developed month by month: their perceived values, probabilities and timing, how they actually turned out, and when. We detect and quantify the systematic patterns of bias from that historical trail, and show empirically what it means for today's forecast — patterns that no snapshot can show and no observer can retain.
Contact: enquiries@funnel-iq.com | © 2026 Funnel IQ Limited