How to Clone Your Best Customers with Attribute Analysis

NPS filtering

Imagine you have a Net Promoter Score of 75.

That’s pretty amazing, right?

According to scoring guidelines, it’s world class!

With an NPS score of 75, you’d be in the neighborhood of the most successful companies in the world, including Apple, Southwest Airlines, and Netflix, just to name a few.

While that is a great score without a doubt, how much does it honestly tell you?

If you’re a regular reader, you’ve likely heard me say this before, but it’s worth repeating: Independent of additional data, your overall Net Promoter Score is largely meaningless.

Sure, it can be a useful indicator of your company’s organic growth potential, or as famed investor Jason Lemkin believes, a sign that you’re a good investment to outside investors.

Besides that, what information does your overall score actually provide you in terms of actionable data?

Not much to be honest.

But it can.

When you match your NPS data with the attributes of your individual customers, your score itself can quickly become a treasure trove of incredibly rich data.

Over the past few weeks, we had several conversations with customers who had yet to leverage the power of customer attribute filtering because they didn’t fully understand the benefits.

As a result of those discussions, we felt it was important to take a deeper dive into filter attributes: what they are, the benefits of using them and the type of information they can provide.

What are attributes and why are they important?

Put simply, attributes are the individual characteristics that help further define each customer persona.

customer attributes

Let me explain a bit further.

It goes without saying, but we all know that no two customers are alike. This cliched statement generally refers to the needs and preferences of each individual customer. But, customers can differ in other ways as well.

The differences can include their job title, physical location, how long they’ve been a customer, the plan they’re on, etc.

These are attributes. They are those additional columns of data that you keep on each customer.


When you’re able to align one or more of these attributes alongside your NPS score, your data becomes immensely more powerful.

Let’s say that you’re a company located on the East Coast with an overall NPS score of 15 and you’re curious to know what’s keeping your score from being even higher.

To get a clearer picture, you decide to filter your NPS results by customer service rep (CSR).

You notice that when looking at the customers associated to one of your reps, your NPS score goes from 15 to -10.

Bingo! You found your problem.

At this point, you may come to the conclusion that your customer service rep is to blame.

However, rather than stop there, you add in an additional attribute based on location.

Now you can see that of this CSR’s customers, the customer group with the lowest NPS score is coming from the West Coast, while the customers in the Midwest and East Coast groups have a score consistent with all other customers.

Now it looks more like a timezone issue.

Just adding this one additional point of data draws an entirely different conclusion, which is even more precise than the first.

This process can continue until you’re confident with the results.

Based on the scenario above, you can clearly see how much of a difference it makes in seeing the value of your NPS score when it’s filtered by attributes.

Here are a couple of other examples:

When some customer feedback is worth more than others

Let’s imagine that you’re the Head of Customer Success for a fast growing software company.

Once per quarter, it’s your job to present the company’s NPS results and findings to the executive team.

Unfortunately, it’s been a rough quarter and your NPS score has gone from a 45 down to a 30, so you’re not looking forward to the sharing the results.

In an effort to find a silver lining, you decide to create an attribute called ‘Customer Type’. This attribute identifies whether the customer is a ‘paying’ customer or a ‘trial’ customer. (A trial customer in this example is a non-paying customer.)

When you filter your results by ‘Customer Type’, you find that your NPS score for paying customers is 52, while the score for non-paying customers is 20.

Furthermore, you were able to go back to the previous quarter with this same filter in place and see that your score with paying customers actually increased.

By removing (i.e. filtering) non-paying customers you’re now able to get a better picture of your “true” Net Promoter results. And as a bonus, you now have a much better story to tell.

This is a fairly common scenario that we see with subscription-based or SaaS-based businesses. While feedback from trial customers is important, when it comes to measuring the true success of your business, it’s important that you can separate them from the results.

This is where attributes come in handy.

Cloning your best customers

Most companies today make up to 50% or more of their revenue from their best customers (aka advocates, aka promoters) via referrals and word-of-mouth marketing.  

[bctt tweet=”Most companies today make up to 50% or more of their revenue from their best customers.” username=”promoter_io”]

This is why I mentioned earlier that having a high NPS score can be a good indicator of growth.

Obviously, the more advocates you have, the more customers that are out there driving your growth.

NPS is, by far, the best way to lead this growth by identifying and activating those advocates.

But, what if I told you that NPS is also one of the best ways to clone your advocates?

I mean, it’s not the aquatic planet of Kamino with tall aliens armed with super-advanced cloning technology, but it’s about as close as you can get outside of the Star Wars galaxy.


The goal here is to identify your ideal customer persona using attribute filtering.  

You’ll start with everyone and begin to narrow down your data by layering attributes until you arrive at the highest filtered NPS score.

For example, let’s say you begin with an overall NPS score of 45. You start by filtering that score down by ‘Industry’. You find that your highest NPS scores are coming from customers in the Healthcare industry.

Next, you add in an additional ‘Role Type’ attribute and find that your score increases even more with customers both in the Healthcare industry and in an Operational role.

You continue this process until you either run out of attributes to filter by or you arrive at the highest possible filtered score.

In the end, you’ll be left with the characteristics that make up your most ideal customer profile.

This persona data can now be leveraged across all marketing and sales channels to improve targeting and key marketing copy. Your growth team will be able to utilize this data to build lookalike audiences which will ultimately reduce acquisition costs and drive more qualified customers.

Analyzing Your Key Trends with Customer Attributes

Up until now, we’ve only been talking about filtering your score by customer attributes, which I hope you can now see the value in.

But wait … it gets even better.

Armed with the characteristics of your ideal customer profile (based on attributes and score), imagine how powerful it would be if you could also find out what specifically drives their advocacy.

This is precisely what you can learn when you pair customer attributes with your trending data.


Note: If you’re not familiar with your trending data, this is compiled based on the verbatim feedback provided by your customers, presented in a way that surfaces the most important (and popular) positive, neutral and negative topics.

Now, in addition to having an ideal customer persona, you’ll also understand the motivations behind their behavior.

Let me give you an example.

Imagine you’re looking at your overall trending data and it’s telling you that your top positive trends are ‘customer service’ and ‘ease-of-use’.

Generally speaking, this is great information to have and can be extremely helpful in exploiting your strengths as benefits to prospects.  

What’s even more useful though, is when you filter your trends by the attributes you’ve determined to match your ideal customer.

For the sake of this example, let’s say that when you do, now, rather than ‘customer service’, you find that a specific feature of your product/service is a top positive trend.

This is a pretty critical piece of information.

Globally, focusing on your superior customer service may be a message that resonates with an audience at large. However, for your ideal customer, this product feature should be front and center.

Arming your sales and marketing team with persona data and this level of granular targeting is the holy grail of customer acquisition.

While I can’t speak for other Net Promoter solutions, I can tell you that with Promoter, getting customer attributes in place is an absolute breeze.

The really cool part is that even if your campaigns have been underway for any length of time, it’s never too late to add attribute data. With Promoter, once you add the data to your customer contact records, we retroactively apply it to all of our previous campaign data.

In other words, with ease of putting attributes in place along with the goldmine of value you’ll see in return, there is no reason you shouldn’t get started.

3 thoughts on “How to Clone Your Best Customers with Attribute Analysis”

  1. Hugely interesting. Completely overlooked but imperative. But my question is, you mentioned creating look alike audiences from your best customers. Does the Promoter platform create that or you mean exporting their emails and adding that to Facebook which is the popular way?
    Thanks for the insight.

    1. Thanks for question, @illohifeoma:disqus.

      Once your NPS campaign is complete (or really at any point within), you can easily export a complete report which will allow you to easily extract your promoters from the list. From there, you would take that list to Facebook, Adwords, etc. to create your custom lookalike audience.

      We’ve discussed making this a direct integration, so you may see that as a feature in the near future. 🙂

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