Forward Revenue:
The last unaudited number in M&A
Pipeline forensics detects 20-30% systematic overstatement in growth-stage pipelines —
invisible to management, CRM dashboards and regular due diligence
Pipeline forensics detects 20-30% systematic overstatement in growth-stage pipelines —
invisible to management, CRM dashboards and regular due diligence
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 applies to the past... applied to the future
We reconstruct 12-36 months of deal evolution to reveal patterns invisible to quarterly reporting
Every diagnostic is back-tested against known outcomes before being applied to live situations
Excel data export for independent verification
QoE-compatible format for seamless integration
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 against empirical reality. 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 it shows in 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 90%+ confidence intervals.
Value recovery:
Over-forecast segments and underperforming reps identified with statistical significance.
Resource-reallocation scenarios quantified: revenue bridge from current to optimised state.
EV bridge at exit multiple: typical 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: white-label, co-branded, or direct to your client - whatever suits the relationship.
Referral and co-delivery arrangements considered. Start with a conversation.
CASE STUDY
Healthcare data company • Digital healthcare services • VC-backed
SYSTEMATIC OVERSTATEMENT
PIPELINE OVERSTATEMENT DETECTED
EXIT EBITDA MULTIPLE
Company forecasting £11.5m pipeline value at 28.6% weighted probability. Management confident. CRM looked healthy.
QoE validated historical revenue – but forward pipeline remained unaudited.
Best performer: 30% actual lead conversion vs 70% claimed. 2.3× overconfident – for the strongest rep on the team.
74% of expected value in a single rep. 24× productivity variance across the team. 63% of sales capacity allocated to underperformers.
£3.1m of deals aged 15–18 months, still forecast at 30%. Historical conversion at that age: 8%.
OUTCOME
Immediate
£7.0m overstatement detected – invisible to QoE and to management. Realistic planning enabled for hold period.
Exit validation
Forward revenue tracked through hold period. Company exited at premium 22.5× multiple.
Recommendations implemented
73 leads reallocated. £1.7m incremental revenue made accessible. Professional sales hiring initiated.
Investor impact
£10-17m total exposure quantified. Evidence trail supported negotiation and contractual protection.
15-month longitudinal analysis (n=245 leads, 5 reps). 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 PE/ VC operating partner roles. The methodology was validated on a live engagement, then 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?'
We anonymise on receipt; data never leaves our encrypted environment.
We back-test every finding against the company's own historical outcomes — deals that have already closed, lost or expired. You see the calibration before we apply it to the live pipeline. 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.
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