Teams managing the post-sales side of the business often struggle to understand where they’re losing upgrade opportunities. The data exists in the CRM, but it’s split between deal and company properties, making it hard to spot patterns or explain why certain upgrades end up closed-lost.
In this example, Emil explains how you can create a merged dataset to analyze deals alongside company properties like customer lifecycle stage, channel, and others.
With this context, post-sales teams can quickly see which types of customers are most associated with lost upgrade opportunities, and can prioritize follow-ups and focus effort where it’s most likely to improve expansion.
Upgrade outcomes are rarely explained by deal data alone. Company attributes like lifecycle stage or acquisition channel often reveal why certain opportunities are more likely to stall or be closed lost.
Instead of building separate views for each segment, filters let you explore performance across customer stages, channels, and account types as questions come up. This makes it easier to see which segments matter most and where your team should focus next.
High-level trends show volume, but decisions come from detail. Drilling into row-level data makes it possible to inspect the specific deals behind a number, including deal size, stage, company attributes, and other factors tied to lost upgrade opportunities.
How can customer success teams identify where upgrade opportunities are lost?
By analyzing upgrade deals alongside account attributes like lifecycle stage or channel, teams can see which segments consistently underperform. This context helps isolate whether losses are tied to customer type, timing, or engagement patterns.
Why is it important to combine deal and company data when analyzing upgrades?
Deal data shows what happened, but company data explains who it happened to. Viewing both together reveals patterns that would be invisible if each data set were analyzed in isolation.
How can teams tell which customer segments need attention in post-sales?
By comparing deals created and deals lost across the same dimensions, teams can quickly see where outcomes diverge. Segments with high loss rates signal where deeper investigation or intervention is needed.
How does detailed deal analysis improve post-sales decision-making?
Drilling into individual deals provides the context behind aggregated trends. This makes it easier to define specific actions, such as targeted outreach or process changes, rather than relying on generalized assumptions.