INBOX INSIGHTS, February 22, 2023: Personality Tests, Purchase Frequency Analysis

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The Risks of a Personality Test

Do you know yourself well enough to say what your personality type is? Maybe a better question is asking whether or not you’ve ever taken a personality test, or used one to make decisions about your team.

If you answered yes to any of the above – congratulations! You’re normal!

Here’s the thing. A personality test can be fun, a light weight ice breaker, or a team bonding exercise.

However, there are a lot of risks with putting too much stock in a personality test.

Responder Bias

Whether we realize it or not, we don’t answer a personality test honestly. Well, not completely honestly. We all have an idea of who we think we are, or how we want others to see us. When we’re responding to a personality test we’re responding with the answers that will give us a desired outcome. If we see ourselves as leaders, we tend to respond in a way we think a leader would. If we see ourselves as introverts, we lean toward the answers that are more inward facing.


I can tell you that I’ve gotten INTJ on the Myers-Briggs test or that I identify as a Type A. But this won’t even scratch the surface of who I am. The risk with using these labels is that they are limiting. If I am truly an INTJ, then as an introvert I would struggle to lead, speak up, and get on stage. If I am truly Type A, then why am I also really good at laying on my couch and doing nothing for two days straight.


Very similar to assumptions, once you “know” someones personality type there is a tendency to put that person into a box and never let them out. The little bit of me that people know publicly is only a small piece of who I am as a whole. If I give a talk about Google Analytics twice, then I become the person that only gets to talk about Google Analytics. The same is true when we try to use personality test to assess people. If I’m labeled an introvert, I could lose out on opportunities where someone is looking for an extrovert.

Incomplete data

It should go without saying that if you can access a personality test without paying for it, you’re not getting something that would hold up in court. Ok, you probably don’t need to bring a personality test to court, but you will run the risk of judging and being judged based on the data produced. A personality test doesn’t take a lot of aspects of who you are as a person, or what you’ve experienced, into consideration. Most tests will ask opinions and preferences on made up scenarios. This means it’s only going to give you surface level feedback – not an accurate assessment.

But don’t just take my word for it. Here is what our community had to say about the topic:

“They’re fun to take, but hard to turn into action. And the danger is you make a snap judgment about what”type” a person is, then stop listening to them and just try to manipulate them based on their type. I’ve learned it’s much more important is just to listen to people, ask them what they want, and if they don’t know probe gently to find out more. Everyone is different, no one fits neatly into personality tests, and many of those tests aren’t even based in science.”

“They are based on junk science, putting on my psychologist hat my personal opinion: The Myers-Briggs and that ilk of personality test are meaningless bunk – circus tent astrology for the business suit set, and promote existing biases and prejudices. The ones that are based on a mix of pseudoscience and real psychology are more dangerous. Your employer is legally not allowed to ask about your physical or mental health – the better built tests are a back door to this knowledge, as they can reveal mental health diagnoses to your employer that they have no business knowing (and in some cases, that you may not even be aware of yet yourself) If you can avoid it, avoid it.”

“Personality is so fluid. There are long-term traits, for sure, but there’s SO much circumstantial variability that those personality types don’t reflect.”

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– Katie Robbert, CEO

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Data Diaries: Interesting Data We Found

In this week’s Data Diaries, let’s tackle a slightly different form of analysis, one uniquely suited to B2B marketing. In the past, we’ve talked about and stepped through RFM analysis – recency, frequency, and monetary value of B2C consumers. When B2C marketers want to understand their databases of customers, RFM analysis is one of the first, best analyses we recommend they perform. It’s fast, computationally efficient, but most of all, it’s directive.

That logically brings up the question, does RFM analysis work for B2B marketing? The answer is yes, but with a twist. In B2B marketing, the customer isn’t a person or even a household; the customer is a corporate entity. Performing RFM analysis on your CRM’s database of contacts would be a terrible idea because individual people, for the most part, are not decision-makers by themselves. The organization as a whole makes purchases.

Additionally, when we think about purchasing at an organization, particularly larger organizations, there may be multiple places within an organization to sell to. For example, suppose Trust Insights wanted to sell into Alcoa. How many different divisions of Alcoa could use our services? Certainly more than one, but if we performed RFM analysis at the person level, we’d miss those potential opportunities or insights.

So what’s the answer? Adopt the methodology of RFM but at the organizational level, a process known as purchase frequency analysis, or PFA. It’s also known as purchase frequency segmentation. We use the same general metrics – recency of purchase, frequency of purchases, and value of purchases – but at an organizational level instead.

Let’s take a look at a sample dataset. Below, we have recency and frequency on the axes, with dot size being volume:

PFA Recency Frequency

We see that Darden Restaurants, in this example, is one of the top performing customers – high frequency, high recency, and a large bubble indicating high purchase volume. If this were our customer base, we’d want to make sure that customer was super happy. In fact, the entire upper right quadrant is important here.

Note that part of PFA and RFM analysis is rescaling all your data so you can do apples to apples comparisons; these metrics have all been rescaled on a 0-100 basis.

In the lower right are high recency purchasers, organizations that have made a purchase recently. They haven’t made many, though. From a marketing strategy perspective, we’d want to try selling them more stuff.

In the upper left quadrant are high frequency purchases, organizations that have purchased multiple times, but not recently. These are organizations which need more contact, more outreach to see what else we can help with.

Next, let’s look at volume and recency.

PDF Volume Frequency

Again, we have quadrants; the horizontal axis is recency and the vertical is volume of purchases by revenue. Dot size is frequency, or number of purchases.

The upper right quadrant is again our ideal customer area – big revenue, bought recently. Those are customers we need to keep happy.

The lower right quadrant has bought recently, but not a lot. Those are customers we’d want to upsell, to find ways to increase revenue by increasing the size of purchase.

The upper left quadrant – empty here – are big spenders who haven’t bought recently. It’s good that this quadrant is empty! It means we’ve done a good job keeping our biggest customers happy.

The lower left, as always, is the area where we need to do something to motivate customers to do more, either by spending more or spending more often.

The value of purchase frequency analysis is how prescriptive it is. It’s a simple, easy to understand model for exploring your customer database. Chances are you already have the data in your CRM, so you don’t even need to go very far to find the information. Once you have it, developing an action plan is straightforward based on where customers fall in the charts.

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