INBOX INSIGHTS, October 12, 2022: The Importance of People, NPS Scores in GA 4 - Trust Insights Marketing Analytics Consulting

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People Are the Most Important

I focus a lot on the 5Ps (Purpose, People, Process, Platform, Performance). Maybe to an annoying point. Today, I want to deep dive into the most important P – People.

Why People? People are the common thread through each of the Ps, through everything we do. Sure, each of the Ps relies on the others, but they are all dependent on People. Without People, the rest of the framework falls apart.

People set the Purpose. People establish the Process. People choose the Platform. People determine the Performance.

When put that way, it sounds really straightforward. People are at the center of all of it. So why am I so focused on People? Because that’s where most companies get it wrong. They forget about the People.

Why? Well, I don’t believe that companies go out of their way to not factor in People, it’s more of an unconscious omission. The flip side of that is that perhaps they do factor in People, but not at a deep enough level.

Let’s say you want to do even better. You want to make sure you’re factoring in the most important part of your planning. You want to make sure People are completely represented. Let’s walk through what that looks like.

People determine the Purpose.

At the end of the day, you’re doing something to solve a problem. And those problems are People’s problems, or rather, problems created by and experienced by People. You can use your Purpose statement as a hypothesis, or you can do the research to better define your Purpose. The point is, that your purpose is a problem that people are having and that you can solve. If your Purpose is to increase your marketing leads, you’re talking about understanding how to reach people with your solutions to their problems. If your Purpose is to automate your reporting, you’re talking about removing redundant tasks so that People have more time for meaningful tactics. You’re also trying to get access to information faster, giving People what they need to make decisions.

People determine the People.

This sounds redundant, but it’s not. If one person is deciding the plan then they haven’t factored in all the right people. Who are you solving the problem for? Who needs to solve the problem? This is where having a variety of user stories is helpful. You can understand the Purpose from multiple perspectives. Limiting who you think the People are will limit your ability to be successful. When thinking about the problem you’re trying to solve, ask for lots of feedback on potential solutions.

People determine the Process.

You may have automation, but People program the scripts that run the automation. So, you’re back to needing People. People need to create, audit, and maintain the Process. People will determine if something changes and the Process needs updating.

People determine the Platform.

Even if machines ingest the data and analysis, there is a component that People need to understand. The machines are not making the decisions, the People are. You want to choose a Platform to collect, analyze, and report your data. This could be one or this could be three Platforms. But People make the decisions about what to use to solve the problem, the Purpose. Because at the end of the day, People need to use the Platform. It only makes sense that People would choose it.

People determine the Performance.

A piece of tech won’t tell you if something was successful. You can program it to give you the output, but People determine whether it was acceptable. This should tie back to the Purpose. Did you solve the problem for People? Just like People program the Process and choose the Platform, People determine the Performance.

This was a bit of a tongue twister, but the point I’m making is that even when you think you’re factoring in People, take another look. And then look again. Selecting a new piece of technology won’t solve a problem if you haven’t talked to your team or if you haven’t talked to your audience. It just becomes a bandage. It might stop the bleeding but it won’t heal the wound. You need to dig deep into the real problem you’re trying to solve before deciding on a solution.

Do you factor in your people enough?

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

– Katie Robbert, CEO

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In this episode of In-Ear Insights, Katie and Chris discuss the sudden re-emergence of people promoting go to market strategy (GTM) and what it means. Why is this suddenly a thing again, when GTM is as old as product marketing itself?

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In this week’s Data Diaries, let’s talk about (Net Promoter Score™) NPS scoring and how we implement our own variation of it at Trust Insights. You might have seen our poll in last week’s newsletter that looked like this:

How likely are you to recommend Trust Insights as a consulting firm to a colleague in the next 90 days?

What’s happening behind the scenes is fairly straightforward; each of these URLs has a custom query parameter:

nps-poll-answer=likely
nps-poll-answer=neither
nps-poll-answer=unlikely

When you click/tap on it, you’re taken to the same landing page, but those query parameters are tracked as separate, distinct events in Google Analytics 4:

Why do we collect our survey data like this? A few reasons:

Survey data like this is inherently anonymous, which is good for privacy
Survey data collected with Google Analytics is collected in real-time
Survey data collected with Google Analytics can be accessed easily through APIs and other data retrieval mechanisms – anything that works with GA 4’s Data API can work with this data

What happens next? The official Net Promoter Score™ is calculated based on survey responses on a scale of 1-10, where 1-6 are called detractors, 7-8 are neutral, and 9-10 are promoters. The percentage of promoters minus the percentage of detractors is the official Net Promoter Score™. This system is… a little complicated. Not for us to compute, but for survey respondents to answer.

We want our surveys to be as fast and as easy as possible to fill out, so that basically means three reactions: thumbs up, thumbs down, and don’t care/no opinion. How do you mentally differentiate between a 7 and an 8 in a survey response? I’m not going to invest in those mental gymnastics, and as a result I usually ignore such surveys. Thumbs up, thumbs down, and no opinion require substantially less effort for most folks, which is why we use that version.

What we do is take that Google Analytics data, flatten it down to just the responses, aggregate them to a monthly level, and perform the same general mathematics as the official NPS score – percentage of likely answers minus the percentage of unlikely answers:

Generally speaking, the way to interpret these scores is that anything above +0.5 is good – it means you have more promoters than detractors. Anything below 0 is bad. Anything below -0.5 (which is possible, since these scores range from +1 to -1) is REALLY bad and means you’ve screwed up spectacularly.

Once you have enough of this data – like a year’s worth or more – you can start using it as an outcome, an objective for things like customer service. Measure your satisfaction scores and then use math like regression analysis to see if there are known contributing factors which lead to these scores – and do it all just with your web analytics software.

Here’s a roundup of who’s hiring, based on positions shared in the Analytics for Marketers Slack group and other communities.

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