Quantitative and Qualitative Data: A Recipe for Knowing Customers

//Quantitative and Qualitative Data: A Recipe for Knowing Customers
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Quantitative and Qualitative Data: A Recipe for Knowing Customers

By | 2018-08-07T14:52:26+00:00 August 7th, 2018|Customer Loyalty|Comments Off on Quantitative and Qualitative Data: A Recipe for Knowing Customers

Knowledge is power.

In the B2B subscription economy, we expand on this and say something like:

Knowing our customer allows us to help them be successful, which keeps them engaged, and leads to higher customer lifetime values.

Whether it’s power or profit that we seek, the importance of knowing our customers isn’t up for debate.  Without knowing them, we can’t assess how successfully their adoption of our product meets their needs. The challenge we face is in HOW we go about accurately knowing them.

  • Does usage data reflect engagement?
  • How much do we learn from NPS?
  • What experiences are our customers really having with our product?
  • How do we get true intel from our customers?

If you are seeking answers to just one of these, you will still have an incomplete picture of your customer’s engagement, which could be misleading.

Usage Data: it’s not quantity or frequency, but quality that matters most.

Consider the users who continue to login and use the same portion of your software everyday, but are taking no steps to expand their adoption. Then, there are the teams who are doing substantial work with their CSMs to map out expanded usage, but, in the meantime, have relatively low or stagnant usage trends. The usage metrics on these two customers would be deceptive. The first might appear loyal, but may actually be frustrated that they aren’t meeting their desired outcomes. The latter may appear like a churn risk, but may be preparing for a broader adoption.  The quantity and frequency of usage for these two companies are simply not legitimate indicators of engagement or churn risk.

Instead of looking at how much or how frequently customers are using your software, you may want to consider other usage trends:

  • The breadth of their usage
  • The changes in their usage after updates
  • The changes in usage after a completed service agreement

These usage patterns are more informative of engagement than basic time logged in. However, alone, they still do not provide a full picture of a customer.

The Good, Bad and Ugly of NPS (in a paragraph)

Net Promoter Score is used widely in the B2C world as an indicator of customer experience (and loyalty). It can also provide a pulse check on B2B customers if you make a few adaptations:  

  • aggregate user responses within an account to get a single NPS for a customer, and
  • note where your customer is in the lifecycle (and correlate your responses to lifestage)

The problem with NPS is that it’s a snapshot. So, even if you follow these suggestions for B2B, you still capture a moment in time with your customer. It also only reflects an overall willingness to refer you to others. While that is a strong corollary to loyalty, it is not enough to truly know what your customer likes most about your product, how they are using it, or why they would (or would not) be willing to refer you. In a nutshell, NPS is a good data point, but it’s definitely NOT a stand-alone way of knowing your customers.

The Importance of Data Aggregation

Mikael Blaisdale of the Customer Success Association writes about the power of data in predicting churn, citing disconnection and disengagement as the “two biggest churn generators for SaaS B2B vendors”.

Do you know how disconnected or disengaged your customers are?  Usage and NPS metrics are not enough to know if your customer will churn, is ripe for expansion, or is willing to advocate on your behalf.

Our friends at Gainsight hit the nail on the head when they say that usage metrics can be “red herrings” and that you need to consider things like feedback, engagement with marketing content, and the “sophistication” of their usage. These are much more informative data points in knowing customers, and anticipating their behaviors.

We need a way to aggregate the numbers (usage, surveys, etc.) with the stories (interviews, feedback, accessibility, etc.) to be able to say that we know our customer and can fully anticipate their behaviors (and, therefore, proactively address their concerns).

Gathering Meaningful Qualitative Data

So, you need more than usage data and NPS to know if your customer is engaged. How do you go about gathering this kind of meaningful qualitative data?

Ask.

It really is as simple as having actual conversations with customers. Interview them. Conduct regular retrospectives. Read their reviews. Document their feedback. And find a way to simplify the data you get from those conversations so that you can aggregate them with your quantitative data to get a more comprehensive picture of your customer’s overall health.

Quantitative + Qualitative = Comprehensive

At Bolstra, we use two predictive, aggregated indices to understand how loyal our customers are: a Churn Predictive Index and a Growth Predictive Index. These indices are aggregates of a variety of data (both quantitative and qualitative)  that our customers have determined are indicative of engagement. They are then weighted and aggregated to form a score that anticipates the churn risk or growth potential of each customer.

While measuring customer success is not the same as managing customer success, we cannot know our customers without some method of “measuring” them. But, measuring (or assessing the level of engagement) is more complex than simple metrics. If you want to be comprehensive – and accurate – in knowing your customers’ engagement and loyalty levels, you’ll need to aggregate both quantitative and qualitative data points.

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