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Health Score and Support Tickets

4 min read

There was a Forrester report covering customer success solutions was written by Kate Leggett a few months back

In the report, Kate discusses the fact that Customer Success organizations: “...use an array of company and customer data to create a health score." And one of the five data types that is said to affect the health score is support cases.

Clearly, support interactions is a very important piece of information for the Customer Success Team, but the arguments in support of this we hear often tend to be over simplified.

The most common point of view seems to be:

Support tickets signal poor customer experience or a critical barrier to achieving value from your product. Thus a lot of tickets should affect the Customer Health Score negatively. Particularly if they don’t get ‘resolved’ quickly. At the same time, many support tickets show engaged customers – your customer cares about their investment and wants to get things right and that’s a positive!

While the above is a simple and perfectly valid line of argument, it is, at the same time, an argument that is very difficult to take any action on.

So we took the argument one step further (added some complexity), and found tangible value created.

Our P.O.V

Support tickets can be sliced and categorized in an infinite number of ways, but for this post, we’ll group them in five distinct types (or buckets):

  1. Missing feature (need to have stuff)

  2. Feature Requests & Suggestions (nice to have stuff)

  3. Bugs (irritating stuff)

  4. Help (client asking for actual support stuff)

  5. Other (business related stuff)

In addition, let’s make five assumptions:

  • Problems that require posting tickets are bad for the health score

  • Long response (and resolution) times are bad for health score

  • People only ask for help if they actually need help (and if you can’t be helped, you want to find out quickly)

  • People get happy when their problems are solved – good for health score

  • The more you speak to someone, the closer you get to that person. The closer you are, the better understanding and the client will have more patience – client interaction is good for health score.

The Point System

Case: Acme (a.k.a the Customer) submits a bug report, the bug report is adequately handled and resolved within 5 days.

Customer Success Analysis: Assuming the bug wasn’t critical it’s likely to have an overall neutral or slightly positive effect on customer health.

The initial negative experience (finding a bug while you working), fueled by a few days waiting is likely compensated by the fact that you solved their problem and that you managed the process well!

What would happen if you implemented a structured approach where you gave “Success Points” to different support tickets? Here are some example scenarios:

A) Different type of tickets give different (negative) effects on the Health Score;

  • Missing Feature = -50

  • Suggestions = -2

  • Bug = -10

  • Help = -10

*For each day it takes to close the ticket you lose another point (-1)

B) Solving people’s problems is positive, more so if it isn’t expected;

  • Missing Feature = +100

  • Suggestions = +30

  • Bug = +10

  • Help = +10

C) Each interaction, assuming you manage the support process well, brings you closer to the customer. Let’s assume any interaction would give you +5 Success Points.

The above points system gives you all you need to understand if a customer support ticket is having a positive or negative effect on the overall health score. The example case at the top, for example, would translate to:

  • Bug reported: -10

  • Waiting: -5

  • Problem solved: +10

  • Engagement: +5

Total: 0

i.e a neutral net effect.

There are structured and tangible ways to approach support tickets from a customer health score perspective. By taking the standard reasoning one step further we’ve accomplished a simple yet general and extensible model. While there is a lot of room for improvement this approach should be way better than looking for a general and oversimplified correlation between support tickets and customer health score.

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