Domain · Sales
Sales Analytics: Our Core Competence Since 2010
Since 2010 we have developed sales dashboards and analytics systems. In this time, we have consistently observed the same fundamental problem: data quality in CRM systems is poor, generated reports are not used by stakeholders, and forecast models lack credibility with business leadership. This challenge is not primarily a technological problem. It is a process and organizational problem. This insight forms the foundation of our more than 15 years of experience.
Fields of analysis
Where sales analytics comes in.
Three fields of analysis, each starting from the questions sales leadership and the CFO ask.
Pipeline Analytics & Hygiene
The sales leader reports a pipeline volume of 50 million euros. The CFO asks the follow-up question: what close rate is realistic, 30 percent or closer to 80 percent? Is the deal from January still active or already closed? How does the acquisition-to-coverage ratio compare, is it 3:1 or 1:1? These are fundamental questions about pipeline quality. The answers typically cannot be derived from the CRM. We construct velocity analyses that reveal how long a typical deal remains in a particular pipeline phase. This allows us to identify when a deal qualifies as ready for closure. In parallel, we implement automated hygiene processes that archive stagnant deals without apparent progress.
Sales Forecasting & Predictive
Sales team members typically assure that planned sales closures will be realized, until a few days later it becomes clear that they will not occur. The CFO regards this unreliability with justified skepticism. Pure data-driven forecasting methods are more trustworthy, but also cannot be perfect. We therefore implement scenario-based models that cover multiple dimensions: a conservative scenario with 80 percent of pipeline at 20 percent close rate, a middle scenario with 50 percent of pipeline at 35 percent close rate, and an optimistic scenario with 30 percent of pipeline at 50 percent close rate. The CFO receives a probability interval instead of a single point estimate.
Conservative scenario
Pipeline share80% Close rate20%Middle scenario
Pipeline share50% Close rate35%Optimistic scenario
Pipeline share30% Close rate50%Win/Loss, Territory & Incentive Analytics
Your sales organization loses against SAP five times as often as against Salesforce. This could mean your positioning against SAP is weak. Alternatively, the pattern might indicate that you generally lose more frequently in larger deals. Win-loss analysis therefore requires contextualized assessment. Simultaneously, geographic balance is relevant: does the Berlin region generate the same revenue per sales head as the Munich region, or not? Another question concerns your incentive structure: do you pay commissions primarily for new customer acquisition or for upsell among existing customers? This incentive structure often explains why one team ignores new business while another neglects existing accounts.
Why Barling for Sales Analytics
DifferentiationSales is Our Core Focus
Our first project in 2010 involved developing a sales dashboard for a mid-market organization. Since then, we have continuously conducted comparable implementations and observed recurring errors and established solution patterns. Our understanding of which methods and systems work and which fail in sales contexts is based on this long-term focus. This specialist knowledge surpasses the generalized understanding of generalist consultants.
We Speak Sales Language
When your Chief Sales Officer states that average ramp-up time should be six months, we immediately understand the underlying conventions: newly hired sales team members do not receive a sales quota at the beginning of their role. This quota is gradually increased until the new employee reaches full quota after six months. This metric is calculated as: average calculation across new sales reps from the past two years, segmented by hire date. This requires no lengthy clarification discussions. The requirement is immediately understandable.
Master Your CRM Data Quality
Many consulting firms immediately construct appealing dashboards. Our approach sets a different emphasis: we first build fundamental data hygiene processes. This includes automated rules such as the following: a pipeline phase cannot be saved without a timestamp. The deal amount must reach a defined minimum of, for example, 1000 EUR. Deals that stagnate for longer than six months without apparent progress are systematically flagged. This task requires no dashboard interfaces but business logic implemented in the CRM itself. Only when data is governed in this way does reporting function reliably.
From Reporting to Steering
Dashboards and pipeline reports are the foundation. The next step is the steering logic behind them: customer value management, potential analysis for markets and accounts, AI-supported forecasting, and a sales cockpit with binding KPIs.
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