A new study by SciFin and research firm Censuswide has found that 78% of revenue leaders have been surprised by a deal falling through even when their systems indicated everything was on track.

The research, titled The Cost of the Revenue Context Gap, surveyed 500 US-based sales and revenue operations leaders. It found that 65% of respondents said their organisations had increased annual spending on revenue systems, yet many still struggle to identify deal risks and customer churn early enough to take corrective action.

The findings highlight a growing challenge for B2B organisations: investing in more sales technology does not automatically give leaders a complete or accurate view of their pipeline.

Why Are Revenue Teams Missing Deal Risks?

Sales teams often rely on customer relationship management (CRM) systems, forecasting tools, call records, emails, and customer support platforms to understand deal progress.

However, important information may be scattered across these systems. CRM records can also become outdated when sales representatives do not have time to update them or when customer circumstances change between reviews.

According to the study:

  • 81% said most CRM updates reflect what employees had time to enter rather than the full reality of a deal.

  • 74.6% had lost a customer they did not realise was at risk until it was too late to save the relationship.

  • 71% said that by the time their organisation identifies a risk, it is usually too late to change the outcome.

  • 77.2% regularly have to look outside their forecasting tools to understand what is actually happening.

  • 75% could not honestly tell their CEO or board that they were very confident in the current quarter's revenue forecast.

These findings suggest that the problem is not necessarily a lack of technology. It is the difficulty of connecting information across tools and turning it into timely decisions.

The Hidden Cost of Disconnected Sales Data

The study found that revenue leaders spend an average of 6.11 hours each week in meetings dedicated to finding deal status.

This time is spent reconciling records, checking progress with sales representatives, reviewing customer interactions, and trying to establish which opportunities are genuinely likely to close.

The result is less time for activities that could directly improve revenue performance, such as engaging customers, coaching sales teams, and resolving deal obstacles.

Disconnected data can also affect forecasting accuracy. The research reported that an average of 35% of forecasted deals slip into a later quarter, making it harder for organisations to plan resources and predict revenue.

Why Adding More Sales Tools Is Not Always the Answer

Businesses often invest in new sales engagement platforms, analytics dashboards, forecasting software, and AI assistants to improve visibility.

Yet each additional tool can create another information source that must be connected with the rest of the organisation's data.

The research identifies two major issues:

1. Reality divergence: Information recorded in business systems no longer accurately reflects the current status of a deal or customer relationship.

2. Context fragmentation: Relevant information exists across different platforms, conversations, and employees but is not brought together in time.

Together, these issues can leave revenue leaders making decisions using incomplete information, even when their organisations have invested heavily in technology.

How AI Could Help Revenue Teams Identify Risk Earlier

SciFin says it developed its Context Convergence platform to address this challenge by bringing structured and unstructured customer information together into a continuously updated view of business activity.

The platform is designed to combine information from sources such as CRM records, emails, calls, and other customer interactions. AI can then use this context to help teams identify emerging risks, understand changes, and determine where intervention may be needed.

Potential applications include:

  • Pipeline visibility: Identifying opportunities that may be less likely to close.

  • Customer retention: Detecting signals that could indicate churn risk.

  • Sales forecasting: Helping leaders understand changes behind projected revenue.

  • Sales coaching: Giving managers more context about deal progress and customer conversations.

  • Workflow automation: Reducing the time spent preparing reports and manually reconciling information.

AI-generated insights still need to be validated, and the quality of recommendations depends on the accuracy and completeness of the underlying data.

What B2B Businesses Should Do Next

The findings offer several practical lessons for sales and marketing teams.

Connect customer data. Integrate CRM information with relevant sales, marketing, and customer success systems to reduce information gaps.

Improve CRM hygiene. Establish clear expectations for updating opportunity stages, next steps, customer needs, and deal risks.

Monitor leading indicators. Track changes in stakeholder engagement, buying timelines, unresolved objections, and customer activity rather than relying only on a sales representative's confidence.

Align sales and marketing. Share information about campaign engagement, buyer intent, and account activity so teams can make better-informed decisions.

Measure technology by outcomes. Evaluate whether new tools improve forecast accuracy, deal conversion, retention, and revenue performance, rather than measuring adoption alone.