A cloud managed services provider had pipeline that swung wildly month to month, a sales team that did not trust marketing's numbers, and no way to tell which activity produced revenue.
The company sold managed cloud infrastructure services on multi-year contracts, largely into IT directors and heads of infrastructure at mid-market organisations. Deals were substantial and slow, typically running four to seven months.
The presenting problem was volatility. Some months produced eight qualified opportunities, others produced one, and nobody could explain the difference. Forecasting was effectively guesswork, which made hiring and capacity planning impossible.
Underneath sat an attribution vacuum. Salesforce had been implemented years earlier and then modified continuously without governance. Opportunity stages had no exit criteria, so deals sat in 'Proposal' for months without anyone noticing. Campaign attribution had never been configured, so no one could say which activity had produced revenue.
The sales–marketing relationship had degraded accordingly. Marketing reported lead volume; sales reported that the leads were poor; neither position could be tested because the shared data did not exist.
Figures measured over the engagement period and reconciled against the client CRM.
| Metric | Before | After nine months |
|---|---|---|
| Monthly SQL volume | Highly variable, 1–8 | 4.8× baseline average, consistent |
| Average sales cycle | Baseline | 28% shorter |
| New pipeline | Not reliably measurable | $2.6M attributable |
| Attribution coverage | None | Multi-touch, reconciled to closed-won |
| Deals stalled over 60 days | Unknown — not tracked | Flagged automatically, reviewed weekly |
| Forecast accuracy | Guesswork | Within a workable variance band |
Measured against the twelve months preceding the engagement, reconciled in the client's Salesforce instance. SQL volume is stated as a multiple because absolute figures are withheld at the client's request.
Salesforce redesign, attribution model and forecast framework.
Publisher-network programme suited to long infrastructure buying cycles.
Restructured toward bottom-funnel intent and branded defence.
Stage velocity analysis and stall alerting that compressed the cycle.
Supplied the filtered IT-decision-maker audience for syndication.
Aligned MQL and SQL definitions to the actual buying process.
Because the platform was not the problem. The audit found sound architecture buried under years of ungoverned modification — no stage exit criteria, no attribution configuration, and accumulated custom fields nobody used. A redesign cost a fraction of migration and preserved several years of historical deal data that the forecast model depended on.
By making stalls visible. Deals were sitting in stages for weeks because no stage had defined exit criteria and nothing alerted anyone. Once stalled deals surfaced in a weekly review, reps either advanced them or closed them out. Most of the improvement came from removing invisible dead time rather than from selling faster.
Both, and the split surprised the client. A meaningful share of the month-to-month swing was inconsistent recording rather than genuinely absent demand. Cleaning stage definitions removed part of the variance before any new demand programme started.
The buying cycle was four to seven months for a multi-year infrastructure contract. Buyers in that mode research substantively on publisher networks they trust. Paid social was reaching the right people in the wrong moment, which produced clicks and very few opportunities.
Attribution and stage clarity produced visible change within eight weeks. Demand volume took until month four to stabilise. Cycle-time compression showed from month six, because it required a full cohort of deals to pass through the redesigned stages before the effect could be measured.
Figures on this page are reconciled against the client CRM at the end of the engagement window. Client identity withheld at their request.
The free CRM health check covers stage definitions, attribution coverage and data quality — the three things that determine whether your pipeline numbers mean anything.
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