Use Data!

Use Data!

Leaders in organizations that use Net Promoter Score® (NPS) know the benefits of this key indicator.  Often, we see NPS replaced with a client attrition value – all obviously aimed at measuring the client base satisfaction and retention rates.

If you haven’t done an NPS survey recently, or want to compare your results to others, here are what some of the Promoters (those that have rated the company a 10 or 9) have said about the companies they are doing business with:

  1. Understand client’s needs and wants
  • They know our requirements and align their priorities with them. 
  • We both are in the customer service business, they understand and have always tried to help make my customers happy.
  • From start to finish they were extremely professional in paying close attention to every detail and our
  1. Provide a frictionless experience; Outstanding service!
  • Extremely responsive
  • The team is always up for the challenge. No matter what we are asking for and the timeframe.
  • Communication is easy
  1. Deliver Innovation and ROI
  • Great value for great service. Continually offers great ideas and feedback.
  • Always feeling like I have a partner to navigate waters I don’t necessarily understand.
  • The onboarding process was easy and their response times to issues have been excellent. A great value for great service.
  • They adapt their programs tailored to the business specific needs and become a true business partner.

Feedback gathered through the survey from loyal customers serve to create a whole new library of customer testimonials and reinforce the things that make clients happy.  The benefit to negative feedback is also of supreme importance and it may be useful to go to a second level of analysis.  What if… we could actually use your data to find the origin of negative client feedback?  Well we can! 

An example of one view could look like this:

Negative feedback example item/theme #1:  Customer service/issue resolution is not satisfactory.

First, we want to identify the potential origins of this issue.   At this point this could be a multitude of originating factors, so let’s identify client attrition as it relates to a wide array of cohorts:

  1. Year of client start
  2. Year of client exit
  3. Geography sectors (perhaps State is the best method here)
  4. Client size (create reasonable strata)
  5. Client profitability level (create reasonable strata)
  6. Associated Sales Representative
  7. Associated Client Service Representative

I know what you are thinking.  Why not just run the client attrition against the Client Service Representative and voila?!  Well, here is the issue:  What if…the client feedback is related to another factor?  Once you examine the data associated with each cohort related to the specific client attrition factor another answer may emerge.

Perhaps client expectations relating to client service are/were not being properly set during  implementation, within a specific year, in Georgia, who are 15-25 WSE size, with very high prices and associated with a specific sales representative.  Wouldn’t that mean that the client relationship was destined for failure from the start?  In addition, wouldn’t the additional cohort analysis help you pinpoint the origin/location of that expectation?  It would.  So, correlation does not necessarily mean causation.  We must dig a bit deeper if we really want that causation.

The good news is that this insight is within your reach.  You have the data to find these answers and many more!

Please let us know how we can help you find your answers.  The Profit Center Practice area at McHenry Consulting is ready to help you use your data to generate more profit!

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