TLDR: Have you ever heard something like “Verizon is happy” or “Caterpillar is an escalated account”?  These are someone’s attempts to personify a customer relationship. And it’s often meaningless bullshit.

Why rating “accounts” is broken

Think of a couple that you know.  How would you label them? “Fun” or “Sporty” or “Foodies”?

The reality is always more nuanced.  If you want to describe these people meaningfully, you start to describe each person in detail, and differently. “Ashley is really into hiking” and “Bob loves football”.  “Ashley and Bob love to travel together”. “Ashley hates hockey”.

Now imagine trying to label an account relationship when it’s comprised of an even bigger population of people.

What tends to happen? We take shortcuts:

• We personify accounts based on our relationships with key contacts
• We use our key contacts as a proxy for how the entire account feels about us

This tops-down approach is misleading; we become blind to churn risk and upsell opportunities.

A new account health model

Any model of account health has to recognize these facts:

• In any given account, not everyone dislikes your service/product
• In any given account, not everyone loves your service/product
• Every account is a mixed bag of supporters and critics

Because we often have a better handle on our key contacts’ feelings, we need better bottoms-up measurement before we try to draw conclusions about the account overall.

Using bottoms-up measurements, we can understand the landscape of people we’re responsible for much more accurately. And how they are likely to affect our key contacts’ perceptions in turn.

Bottoms-up usage measurement

Consider two customers’ differing adoption patterns.

Account A has widespread adoption across the user population. The “average usage” is decent, but there are still outliers. Two users are well above average and two are well below.

Is the account healthy?  If the average usage frequency fits what you would define as engaged, then the account is most likely healthy.  

The actions to take could be:

• Focus on the above-average users as internal champions and potential marketing advocates
• Focus on getting the average and below-average users more engaged with the product
• Improve their already healthy adoption with use of advanced features, and perhaps an up-sell or cross-sell

If the average login frequency is too low across the board, then you’re in trouble.

The actions to take could be:

• Drive awareness and training across the whole team

Account B has lower “average usage” overall. And, there’s one dominant user. The rest are less engaged.  

Is the account at risk of churn?  If the power user leaves, or if she changes her sentiment about you for some reason, churn is certainly an outcome you should be predicting.

The actions to take could be:

• Engage the power user as an internal champion asap
• Encourage the others to adopt through outreach, awareness, training, handholding, or whatever will mitigate the risk of having just one active user

Conclusion: you don’t really know the customer’s health and most effective action plan until you can peer into the user-level usage data across the user population.

Bottoms-up feedback measurement

Consider two customers’ differing NPS survey responses over a 6-month period:

Account A has multiple responses and several promoters.  These promoters can be engaged to help as internal champions. They can also be leveraged for marketing to others.

However, the love for the vendor in Account A is not uniform; there are detractors too. If promoters leave, there is weakened base of promoters.

The actions to take could be:

• Engage the lower-score respondents and understand what it would take for them to provide a higher score
• Act on their feedback

Account B has one promoter, several detractors, and less feedback altogether. This account is at risk of churn, but there is one promoter who can be engaged to help champion internal adoption. If that promoter leaves, the account is in deeper trouble; getting that person’s help is a matter of urgency.

The actions to take could be:

• Understand the reasons for the low Net Promoter Scores℠
• Act on their feedback
• Recruit the active user as an internal champion
• Engage the users who haven’t given feedback; can they be engaged to be made happier?

Conclusion: you don’t really know the right action plan until you can peer into the user-level feedback data.

“The chickens will come home to roost”

If you have bottoms-up measures that are concerning, like partial adoption or mixed user feedback, then your sponsor’s support can eventually wither and die.

If your product is widely adopted (and liked), then you’re insulated from churn.  After all, what new leader walks into a team and takes away a product their inherited employees are successfully using?

Supplement your tops-down measure of customer health with bottoms-up metrics to develop a more accurate and actionable picture of each customer’s health.

In a future blog, we’ll explore the engagement activities that “move the needle” on bottoms-up adoption and sentiment.