You Ask, I Answer: Missing Analytics Content?

In today's episode, Katie asks in the Analytics for Marketers Slack group, which you can go see at TrustInsights.ai/slash analytics for marketers, what content is missing about analytics? And this is a really interesting question because there's a ton of content out there about analytics, right? There's how do you set up Google Analytics? How do you do attribution analysis? How do you calculate ROI?

There's so much content. Go on to YouTube or Google and you know let's start Googling for content about analytics. This is a kind of a neat question because it's a white space question. It's a question of what's not there. And the reason it's so difficult for people to answer a question like this is because it's predicated on you having enough understanding about the subject matter, having some real subject matter expertise so that you can look at the landscape and say, huh, there's a big gap in stuff about X, right?

We've got all this other stuff. There's no there's no content here about this. There's a there's a missing place, there's a space here that we need to fill. And if you don't have subject matter expertise about that uh subject, you don't know what's missing. Right.

Let's say uh let's say you're you're you manufacture coffee machines, right? Uh, what content's missing about coffee machines? Well, you go out on the web and there's no shortage of places to buy them and reviews and ratings and stuff like that. But that maybe you know that there's comparatively little content about how to properly clean a coffee machine, right? There's a lot of content that's out there about it, but it's not particularly good.

And you know there's some secrets and things that you can do to help clean a coffee machine faster. By the way, uh, one of those secrets for at least for the caraf is to use salt instead of you know other harsh scrubbers. Um what content is missing about analytics? Well, if we think about content, uh sort of the the six W's, right? Who, what, where, when, why, and how?

There's no shortage of how content about analytics. How do you set up Google Analytics? How do you set up a goal in Google Analytics? How do you set up tag manager? Right?

We're all good with that. There's a lot of content about why. Why is analytics important, right? Measurement, approving ROI, etc. We're pretty solid there.

There's a fairly good amount of content about what is analytics, right? What kinds of uh kind of analytics are there? Data analytics, business analytics, marketing analytics, web analytics, email analytics, you name it. There's there's a good amount of coverage on that. So that leaves us with when, where, and who.

Now, when should you be, for example, uh using analytics? That's somewhat implicit in the what, right? Not always explicitly stated, but it's it's like the the spaghetti sauce commercial says it's in there. Um if you don't remember those commercials from the 80s, I apologize, it's a lost reference. Where do you use analytics?

I think is an interesting topic. One though there isn't a lot of coverage because it's kind of assumed from a marketing perspective that it's along the customer journey. Well, where in the customer journey do you see the greatest weaknesses, the least amount of insight about analytics? It's not on the buyer's journey, right? We got no shortage of web analytics, full funnel metrics, uh marketing automation, CRM.

We've got the data, we've got analytics, we've got products and services that spit out reports, you know, left, right, and center. But it's on the owner's journey where we really fall short, right? We have things like NPS scores, which are good, customer service evaluations, uh, ratings and reviews, uh, either privately or publicly, the amount of uh angry email that you get in your customer service inbox, the number of angry calls you get to your call center, and those are somewhat good, but very few companies spend nearly as much money on market research in the customer journey side and the owner's journey as they do on the buyer's journey. Companies spend a lot of money on market research on the buyer's side to see if they can get more buyers, but they don't spend the same money on can we keep our customers? Can we keep the customers we already got?

What would make them not just satisfied or happy, but in the words of Malcolm Gladwell, what would make them deliriously happy with us? We don't know, right? So there's a there's a lot to be said about the where of analytics. Where should we be measuring? If we accept the axiom that a new customer is uh is 10 times more expensive than keeping an existing customer, well, then shouldn't we be investing 10 times more on the keeping customer side or five times more?

We'd if we'd invested five times more on the keeping custom the customer side, we'd have an ROI of 2x, right? Because instead of spending 10x to get a new customer to replace one we've lost, we only spent 5x on keeping the customers we have. So we've cut our spend essentially in half to keep the customers. And we don't do that. So one area I think that would be very interesting to see more content about is in retention analytics.

Looking at things like RFM analysis. Do you know how to retain your customers? Something that I've got on my to do list is to do some RM RFM analysis on things like newsletter subscribers. How do we keep the newsletter subscribers that we've uh we've earned it's not just send them better emails. I mean that's a big part of it.

But there's other aspects to to that type of analysis. And finally the one area where I think there is a substantial gap is in content around the who of analytics. Who should be doing analytics? Is it something that should be left to people with analysts in their titles? How does it relate to data science and machine learning and artificial intelligence?

Analytics to me is like cooking and you've heard me use so many cooking analogies over the years. Everybody should be able to cook the basics right to boil water to make toast straightforward simple processes the can you make a breakfast sandwich? Can you make a bowl of pasta for dinner? Can you cook a piece of chicken or tofu or the the protein of your choice and make it taste okay like you know edible. Those are basic cooking skills analytics are the same.

Who should do analytics? Everyone should be able to to use analytical tools and to do analytics. You don't necessarily need to be to have a PhD in statistics and probability, right? Just like not everybody's gonna be Gordon Ramsey or Kat Cora or uh you know any of the celebrity chefs uh out there. You know, no very few of us are gonna be a Rachel Ray.

That's okay. We don't have to be to be able to cook dinner for our families or to make food that's you know palatable for uh a party or a gathering. We do need to have enough basic skills to do those things. So I think there's a lot of missing stuff, empty space in analytics around the where and especially around the who, helping people understand the skills that they need, the outcomes that they should be after, and reassuring people that you don't need a PhD, you don't need to be able to do linear algebra or fourth order differential equations to get value out of analytics. Those skills help for sure, just like having the right tools helps, right?

If you have only a frying pan and you're trying to make soup, yes, you can do it. It's not gonna go as well as having a soup pot, right? That's what's missing in analytics. And I hope that uh we s we spend some more time on these topics because they're important topics. The who part, especially because you can own great appliances and have wonderful ingredients and things, but if you don't know how to cook, right?

If you as a chef have no skills, all that's a waste. If you have great data and expensive analytics tools, maybe you bought you know IBM Watson Studio or Palantir Foundry or any of these really cool tools. If you can't do analysis, it's like owning a Porsche that you don't know how to drive, right? Why? Why bother?

So that's what's missing in analytics. Really fun question.