INBOX INSIGHTS, February 23, 2022: People in Marketing, RFM Analysis, Data Analytics

INBOX INSIGHTS: People in Marketing, RFM Analysis, Data Analytics (2/23) :: View in browser

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Won’t Somebody Think of the People?!

This week, we’re talking about the unsung heroes of Marketing and Data Science. The people! We cover it from the perspective of data analytics on the podcast and talk about who needs to execute your marketing on the Livestream.

We spend so much time on the strategy, the tools, and the outcomes that we tend to forget that we need people to do all of these things. Chris asked me one time, why is that? I didn’t have a great answer (and still don’t) but it’s something I constantly think about now.

I started to think through the standard frameworks that I use, like the Software Development Lifecycle (SDLC), the AI Framework, or the Data Hierarchy. Even thinking about other frameworks like STEM, SWOT, Porter’s 5 Forces, or 4Ps of Marketing – none of these mentions the people who need to do the work, or the skillsets required.

While I can’t say why we forget to factor in people, I can talk through some tips on how to do better.

Don’t plan in a vacuum

This might be the best piece of advice I can give you for pretty much anything. Don’t plan in a vacuum. This means, involving more than just your out-of-touch executives in the planning process. Often, companies bring the highest-paid C-Suite people in to create a strategy without thinking about who actually needs to do the work. If you changed how you create a plan and involve the people you think will need to do the work you may be surprised at what you find out. You could learn that your team has deeper and more advanced skills than you were aware of. Conversely, you may learn that your team is stretched so thin that asking them to do one more thing could create a mass exodus. Involve your people. Engage them. Give them some ownership over what is being asked.

User stories

On one hand, thinking about the people who need to execute the plan is important. The other side of the is the people who need to make a decision with the information. They could be one in the same or perhaps they never interact at all. The best way to think about this situation is with user stories. Yes, I talk about these a lot. The reason is that they are incredibly useful when thinking through who cares about the plan, the project, the outcome. I would challenge you to dig deep and think through every single person internally and externally to your organization that might care about your plan. This exercise will give you really strong insight into the direction you need to take.

As a [persona], I want to [action], so that [outcome].

5P Framework

When in doubt of where to start, use the Trust Insights 5P Framework. The 5Ps are Purpose, People, Process, Platform, Performance. At a high level, you’re factoring in the people involved in the project. It’s an opportunity to determine if you have the right resources or if you need to start posting for new positions.

The bottom line is that all the tech, processes, platforms, algorithms, and gadgets won’t matter if you don’t have the people to operate them and make decisions. Put your people front and center when you’re making plans and creating strategies for your organization.

How do you factor in people?

Tell me about it in our free Slack group, Analytics for Marketers »

– Katie Robbert, CEO

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Data Diaries - Interesting Data We Found

In this week’s Data Diaries, let’s explore a powerful and often ignored analytics technique: RFM analysis. RFM analysis is traditionally used in retail B2C; it stands for recency, frequency, and monetary value of a customer. Retail marketers have used this technique for years to determine who their best customers are.

But RFM analysis doesn’t have to be limited to retail. What RFM analysis really is, is a dimension reduction technique, distilling down all the data about our customers into just three dimensions. With that in mind, how would we apply it to more of a B2B marketing context? Let’s take data from a system like Hubspot or Marketo.

First, let’s decompose the more than 200 variables available to us. When we think about what RFM analysis is all about, and we think about the three key features, do we have analogs in marketing automation? We certainly do.

Recency: when was the last time someone in our marketing automation software engaged with us? Today? Last week? Never? This is easily obtained information.

Frequency: how many times has someone in our marketing automation software engaged with us? Downloaded a single whitepaper and then left? Replied to us on Twitter every day? Again, easily obtained information.

Monetary Value: this is the field that’s most challenging. Certainly, if we have things like deal size or past purchases in our database, we could use that. However, for marketing purposes, that’s not always the best measure. In organizations that sell complex, high-risk purchases with complicated marketing organizations, a final sale may take months, perhaps years to close. If our goal as marketers is to hand over something like sales-qualified leads, then we may not want to use final value as our monetary value objective. We might want to use something like potential deal size or even lead score, if we’re trying to evaluate how to improve our audience segmentation.

RFM analysis has a key advantage in the privacy-first world we’re moving towards: other than some kind of unique identifier, it does not rely on any personal information. All it requires are recency, frequency, and some measure of value.

For us, we’ll take a blend of conversion events to use as monetary value for this example, because we otherwise won’t have a statistically significant analysis for events further down the marketing operations funnel. What does our marketing RFM analysis look like?

Marketing RFM Analysis

For charting simplicity, we’re rescaled all our data to be consistent; recency is normally measured in days since the last contact and is an inverse measure. A lead that visited a day ago is more valuable from a recency perspective than a lead who visited 90 days ago, so we’ve rescaled recency to be 0-100, where 100 is very recent and 0 is the distant past.

We see a few things of note right away. There’s a healthy distribution of recency in our leads, but they do skew more towards the right; this tells us that we’re doing a decent job of getting people to come back.

We see our frequency is very, very low. We don’t get people to engage as much as we should.

And monetary value – the size of the circle – is what we would call a powerlaw distribution. A few big whales and a lot of goldfish. So the question now is, what do we DO with this information?

We care, obviously, about monetary value as more of an outcome. We want to find valuable potential customers in our data. So if we plot out correlations among these three measures, what do we find?

Correlation of RFM

What we find is that monetary value very, very strongly correlates with frequency, at least in this analysis. The more someone engages, the more likely it is their value will increase.

So what? This tells us that high-touch, high-frequency marketing might be something to test. If we go back to our scatterplot, what can we do to encourage low frequency leads to engage more? Can we put additional conversion opportunities in front of them? Can we run advertising and email marketing just to that segment – and if we do so, will we see a commensurate increase in monetary value? That’s what we’d want to test next.

We encourage you to apply RFM-style thinking to your own customer database and marketing. See if the technique increases your ability to intelligently segment your marketing without relying on any kind of personal information – just when the last time you engaged with someone, how many times you’ve engaged with them, and the valuable actions they’ve taken.

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