Actionable Insights for CSMs and Scale

  • 27 May 2022
  • 2 replies
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With the vast amount of data at our fingertips, it’s tempting to measure everything. Because of this, many organizations suffer from a ‘more is better’ mentality. Organizations want to capture everything to make sure that they’re not missing anything. But, in doing so, they end up seeing noise and no signal!

When building an impactful customer data model, keep these 3 things top of mind:

  1. Not all data is important 

  2. Not all data drives insights

  3. Not all data that matters to you matters to your customers 

The most important consideration when selecting metrics for your data model are their actionable properties; can your team actually do something about this data? If not, it may just be a vanity metric. If you fall into the trap of including everything and anything, you’re inadvertently overwhelming your CSMs with data that they can’t do anything about and that may end up distracting them from directionally relevant data that will actually help them reduce churn and drive more value for customers. 

To make your data model as valuable as possible for your team, ask yourself the following questions before selecting key metrics to include:

  1. What does this data point tell me about my customers?

  2. Is it actionable? Can my team impact this metric?

  3. Do we need more than one data point working in concert to drive an actionable insight?

What are some challenges you’ve encountered building an actionable customer data model?


2 replies

In a recent exercise to review our event data gaps we took the approach of listing out a series of questions - what are we trying to use data to answer? How will that answer provide signals to support a prescriptive method that leads to improved outcomes for what the customer is trying to achieve?

Userlevel 3

Yes @jenniwashington and @EmilyRyan !  It’s so easy to forget the “What question are we actually trying to answer” question when tracking data.  Then you get lost in analysis paralysis and / or mindless tracking of metrics.  But sometimes what I’ve found with measuring community metrics (because that’s my sandbox) that the data I can access from various sources and the questions I am asking don’t always match up.  That’s what I think is so exciting about this new era of technology and AI.

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