Data decay: The WHY behind why your CRM data sucks.

Data decay: The WHY behind why your CRM data sucks.

  • “Wrong phone number”
  • “The company was acquired”
  • “Wrong title”
  • “I have duplicates”
  • “The data is old”
  • “The emails don’t work”
  • “My CRM data sucks”
  • “I just called someone who’s been dead for a year”

If you’ve heard any of the above comments or something similar; you have a CRM problem.  Are their solutions to these problems?  Yes, however; today I will give you a deeper understanding of WHY the problem occurs.  Many good vendors exist to solve the problems listed above.   I want to arm you with a deeper insight, the WHY.

If you understand the WHY, you will be able to:

  • Have a deeper understanding to the nature of the problem
  • Remove unrealistic expectations (solve the problem, don’t chase a rainbow)
  • Define best practices to minimize bad data
  • Be informed when choosing a vendor (flashy interface does not solve THE PROBLEM)
  • Understand how your CRM decisions effect CRM data
  • Help you be an advocate for change management within your organization
  • Make you a more informed client (some vendors will like this, others will not)

So what is the WHY?

Short answer:

Contact data decays

If you have a short attention span, if you are brilliant, or have limited reading time, we are done here.  That’s all you need and you know what I am going to say in the long answer.  Thanks for reading.

Long answer:

First, let’s establish a baseline from the US Department of Labor.

The national average tenure across all jobs in the US is 54 months.  That breaks down to 1.85% per month of job attrition.  For high-demand IT workers the tenure is shorter with 3% monthly job attrition rate.  The rate for Silicon Valley start-ups is almost ridiculous with the average tenure being just over a year.*

*Not from DOL. garnered from several Venture Capital blogs…take it as an extreme example.

A full year of data decay – base factors

A month at a glance does not show the full picture when factored across an entire year.  Look at the picture across a year’s time.


When reviewing an entire year, data will decay at about 12%.  However, that does not take into account many additional factors including: (more…)

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