A framework for evaluating sales tech stack ROI in UK SaaS

A UK SaaS sales tech stack of 8-12 tools at 50-300 person scale is normal in 2026. Each tool was added because someone in sales operations or sales leadership made a buying decision with a hypothesis. Two years later, half the tools are paid-for but materially under-used, and the renewal conversations happen on autopilot because nobody has the time to audit usage.

This piece is a framework for that audit.

The four metrics

Every sales tool can be evaluated against four metrics. Tools that score on all four are clear keeps. Tools that score on none are clear cuts. The middle is where the conversation gets interesting.

Metric 1: Active usage. Of the seats paid for, how many had meaningful activity in the last 30 days? 'Meaningful' = the user did something the tool was bought for, not just opened the dashboard. For an outbound tool, this is sequences sent. For a conversation intelligence tool, this is calls reviewed. For a forecast tool, this is forecasts entered.

Active usage below 60 percent of paid seats is a renewal-conversation trigger. Below 30 percent is a cut conversation.

Metric 2: Workflow integration. Does the tool's output land in the place the team actually works? Tools that require the user to log into a separate dashboard to extract value have lower usage than tools that surface their output in the existing CRM, in Slack, or in email. A high-quality tool with poor workflow integration consistently under-performs a medium-quality tool that lives where the team already lives.

Metric 3: Outcome lift. Can you isolate a specific outcome this tool drove? Examples: 'sequence response rate up 12 percent after Outreach rollout'; 'cycle time down 18 days after Gong rollout'; 'forecast accuracy up 8 points after Clari rollout'. Vague outcome attribution ('AEs say it's helpful') is not outcome lift.

Outcome lift is the hardest of the four metrics to measure cleanly because confounders are everywhere. A useful test: if the tool was removed tomorrow, what specific number would degrade? If the answer is unclear, the outcome lift is unclear.

Metric 4: Switching cost. How hard is it to remove this tool? Tools with deep CRM integrations, historical data dependencies, or tight workflow embedding are expensive to remove regardless of underlying value. The switching cost is what makes sub-optimal tools persist year after year.

A high switching cost is not a reason to keep a low-value tool; it is a reason to factor in the migration cost when evaluating replacement.

The keep / replace / cut decision

Plot every tool on the four metrics. The decision logic:

• Keep: high usage + high outcome lift, regardless of workflow integration or switching cost. The tool is doing its job.