Most businesses have no shortage of metrics. Analytics platforms produce hundreds of data points by default, and adding custom tracking extends that further. The challenge is rarely having too little data — it is having too few metrics that are genuinely useful for making decisions. A well-designed KPI framework is smaller, more opinionated, and more action-oriented than most organisations currently operate with.
The Problem with How KPIs Are Usually Chosen
KPIs tend to accumulate rather than being deliberately chosen. A new channel is added to the marketing mix, so new channel metrics enter the reporting stack. A stakeholder asks about a particular number, so it gets added to the dashboard. Over time, the set of tracked metrics reflects the history of conversations and requests, not a considered view of what actually matters for the business.
The result is reporting that is wide but shallow — many metrics tracked, few that are consistently reviewed, interpreted, and acted upon. Dashboards become data libraries rather than decision tools.
What Makes a KPI Actually Useful
It Connects to a Decision
A useful KPI should have a clear answer to the question: if this number moves significantly, what decision changes? A metric that would prompt the same action regardless of its value is not a KPI — it is a data point, and while data points have their place, they should not occupy premium real estate in a management dashboard.
It Is Within Influence, If Not Full Control
KPIs should track things that the team reviewing them can meaningfully influence. A metric that is entirely driven by external factors — seasonal demand patterns, platform algorithm changes, macroeconomic conditions — is informative as context but poor as a performance indicator, because performance against it cannot be fairly attributed to internal decisions.
It Has a Target That Came from Somewhere
A metric without a target is context without direction. Equally, a target that was set arbitrarily or simply carried over from a prior year without review has limited value — it tells you whether you are above or below an arbitrary number rather than whether you are performing well. KPI targets should be grounded in something: historical performance, competitive benchmarking, capacity modelling, or strategic intent.
It Is Legible to the People Who Use It
A metric that requires specialist knowledge to interpret is a poor candidate for a management-level KPI. This doesn’t mean dumbing things down — it means choosing the level of abstraction that is meaningful to the audience. Different audiences need different metrics: a channel manager needs granular channel performance; a CMO needs aggregated impact on pipeline and revenue.
A Practical Design Process
- Start with decisions, not data — identify the three to five most important decisions a team makes regularly, and work backwards to what data would improve those decisions.
- Distinguish between KPIs (three to seven per team, tied to decisions), health metrics (larger set, reviewed only when something looks wrong), and diagnostic metrics (deep-dive tools for investigation, not routine reporting).
- Set targets explicitly, record the rationale for each target, and build in a review schedule so that targets remain meaningful as context changes.
- Review the full KPI set annually — retire metrics that are no longer decision-relevant and replace them with ones that are.
How Digital Analytics Lab Helps
Digital Analytics Lab works with clients to design KPI frameworks before building any dashboard. By starting with the decisions that matter to each team and working backwards through the data, we avoid the common outcome of dashboards that are full of metrics but light on insight. We also help translate KPI frameworks into practical dashboard structures, with appropriate visual hierarchy that reflects the relative importance of each metric and makes it easy to spot what needs attention.
