In-Ear Insights Predictive Analytics If You're Data-Poor

In-Ear Insights: Predictive Analytics If You’re Data-Poor

In this week’s In-Ear Insights, Katie and Chris discuss how to do practices like predictive analytics and classical AI/machine learning when you’re data-poor. What data is available to forecast and work with? How do you create data when you don’t have it, and what strategic advantage might this confer? Tune in to find out!

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In-Ear Insights: Predictive Analytics If You're Data-Poor

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Machine-Generated Transcript

What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode.

Christopher Penn 0:00

In this In-Ear Insights, what do you do with your analytics when you are a data poor? So this is a question that came up when I was on the road recently, I was in Canada at a at a speaking gig doing a talk on predictive analytics, which was kind of fun, because I’ve done that one in a while.

And one of the folks said, I love this, this is this is great.

This makes sense.

Canada as a whole is a data poor nation.

We don’t have the same federal agencies like the US does that provides all this great data.

And it’d be nice to be able to forecast, you know, this industry stuff.

What do we do when we’re data poor, not the individual company, but like the consortiums, the public agencies, they just don’t have data.

So Katie, what do you do when your data poor?

Katie Robbert 0:46

That’s such a good question.

Because I feel like, you know, we take for granted the fact that in the United States, we are not data poor.

If anything, we are overwhelmed with the amount of data out there, our challenge is finding the good quality data that we can really trust.

But there’s no shortage of data that we can use.

So I would say to someone who feels like they are data poor, the first place to start is to try to find something similar enough.

So you know, Can Can these Canadian resources use a proxy? So a proxy being like, can they look at European data or United States data or Australian data? Is it close enough? And so there’s, there’s obviously a lot to unpack with that.

But that would sort of be my first instinct.

You know, Chris, what would you say?

Christopher Penn 1:32

So, a couple of answers I gave was, one, there are data sources that are geographically agnostic that you have access to.

So Google search trends, for example, is planet wide, you can see how many people are searching for flights to Edmonton, or flights to Cairo or flights to Melbourne and and get that information.

And it’s, it’s good, it’s clean.

It’s from one or more search engines, Google Trends has it, Microsoft Bing has some of that data that’s available as well.

And you can extract that data out, you can get it out of good SEO tools, you can get like a three year retrospective out of SEMrush are some for some terms, which is pretty terrific.

You have competitors.

And you can get competitor data.

Again, a tool like SEM rush will let you type in a domain name, and get three years of back data on it, which is pretty incredible.

So if you say if you know, for example, maybe you make, I don’t know lefthanded Smoke Shifters, and you go to, you know, the lefty, lefty, making this up, you can get that domains, month over month traffic for three years.

And to your point, it may not be in your locale, geography, but if you know, that’s your, your, where your competition is, you’ve got that data, you can do some forecasting with that.

And you have, of course, social media data, social media is geographically agnostic as well.

There are great tools like Talkwalker, or brand 24, that can do listening on broad topics, bring all that data in.

And it’s in many cases, it’s not just the Trends data, it’s actually the raw verbatims themselves.

So you can bring in the things people are saying on Reddit or YouTube or whatever they whatever the network formerly called Twitter’s these days and have that data and be able to do stuff with it, too.

So there are data sources that even if your your country of origin, doesn’t produce, you can still have access to

Katie Robbert 3:20

I want to go back to Google Trends.

I feel like that is the unsung hero of you know, marketing data.

It’s been around for a long time, you can get what up to 14 years worth of data, 15 years at this point, 2020 years of historical data.

Now, with the caveat that the term itself has to have existed, so you can’t go back 20 years and get data on generative AI because it didn’t exist at the time.

But Google Trends is free.

They don’t ask you to log in, you just go there.

And you can break it down by geographic location.

But you could also break it down even more, you know, discrete than that.

So you have worldwide you have country, you have state, you know, it’s really interesting how specific you can get, and then you can go down by date.

So right now we’re looking at 2004 to present which is crazy that they have 20 years worth of data.

They also have categories.

So their categories are the different verticals.

So you have you know, science and shopping and sports and travel.

So if you’re in the tourism, you might look specifically for travel.

And then they have another drop down that you can change which is web search, image search, New Search, Google Shopping YouTube search, because YouTube is part of the Google ecosystem.

And so you can really do some powerful things with Google translator I, it breaks my heart a little bit that more people don’t use this as a data source because once you figure out what you want, and you can compare terms, you can X worth the data and do analysis with it specifically, predictive forecasting.

And to your point, Chris, it’s complete, it’s clean, it’s good quality edits, just it’s just not used enough.


Christopher Penn 5:11

one that very people, few people remember exist is the Google Books Ngram Viewer, this goes back to 1800.

Well, you can look for specific terms, words and phrases, the last corpus update is five years ago.

So it’s the English corpus goes to 2019.

Let’s look at artificial intelligence.

Because I’m, I’m genuinely curious.

That term, really, the first time you see it in books is as early as 1899.


And then, of course, you see its problem come up in the 50s, when the field of AI really began as a discrete research industry, you saw a big spike, and then early, the mid 1980s, in a lot of books, and now, of course, you’re seeing the resurgence of it in the modern era.

But this lets you see data, how often a term appears in language in books for centuries.

Katie Robbert 6:05

And that, again, it’s a rich data source that is not being used by marketers, you know? And so to the question of what do we do if we’re data poor? The answer is, there is no possible way you can be data poor with these resources.

Now, of course, it depends on what it is you’re trying to do.

So the first thing you should probably do is run through the five P’s, which is purpose, people process platform performance.

So in the example of Canadian tourism, what is the purpose of doing a predictive forecast? Who are the people who need to be involved? What is the process? What platforms? And then what is the performance? And I would even say, I got it wrong with the purpose because I’m already choosing a process, I’m saying what is the purpose of a predictive forecast, you need to not choose the thing in the purpose, you need to say I want to understand the trends of tourism so that I can build on it.

And that might tell you, Okay, predictive forecast is the right methodology, or it’s something else.

But basically, the point is, go through the five P’s process, do some user stories first, and then you can start to figure out, do I have this data in house? Or do I need to go to these external sources? And as we’re seeing, there’s no shortage of external data.

But it depends on what it is you want to do.

So Chris, you know, one of the questions is probably like, Okay, that’s great.

If I want to create content, what if I’m trying to do email marketing? Can I use those same data sources for email marketing?

Christopher Penn 7:41

Of course, you can use the same data sources for anything if you are predicting behavior, behavior is at the person level, not the marketing channel level, the format of your your marketing, co creative will vary like your what you put in your email should not be what you put on Tiktok.

But absolutely, because time series forecasting is about predicting when something will happen, when is something likely to happen.

We know for example, we work with a number of people in the travel and tourism industry.

We know there are specific times a year when people are looking for very specific things like people are looking for vacation packages in the early part of the year, like they’re trying to figure out what are we going to do this year? We’re gonna go this year, what’s what kind of vacation what can we afford this year, you will always see very popular pictures like cheap vacation packages, and things.

And one of the things to keep in mind as you’re doing predictive analytics is to see okay, when the five P process is super important, but you have to do two versions of it.

You have to do the version for you operationally, how are you going to take this data and make it make something out? But you also have to do the five P’s from the customer’s perspective.

So if you are in travel, what is the purpose? What is somebody traveling for? Why they vacation? They just want to go someplace? Or they do want to go simply Sunny? Do they want to have a unique cultural experience? Do they want to have an adventure, they just want to lay on a beach and eat bonbons? And there’s a lot of different purposes to

Katie Robbert 9:07

Yes, please.

I don’t know how long bonds are but I would like to just lie on the beach.

Christopher Penn 9:11

There are pieces of ice cream covered in chocolate.

Okay, I’m out.

The people who are the people involved.

Some people are solo travelers, some people are group travelers, some people are family travelers, you what you need to know who those people are.

What is the process for which they make a travel decision? Are they constrained by budget? Right? Are they constrained by destination? Something like only I want to say 40% of Americans even have a passport, right? So if you are an international travel destination, you automatically have disqualified 60% of the American audience because they can’t leave the country.

Well, they can but they can never come back.

What are the platforms people use to search for travel? And that’s not just Expedia or kayak or Google but also word of mouth, social media Tiktok trends, how do people get information and then how Do people make decisions for the performance in from the audience’s perspective is, here’s the decision I’m going to make.

I’m going to go on a cruise, I’m going to go on a rocky mountain adventure.

How did people make those decisions? How’s that performance happen? And how does it brought to life.

And so, I would strongly encourage people, as you do the five P’s, you have to do your version for you, and the version for your audience.

Otherwise, if you, if you make decisions solely based on you, and not the audience, as well, you will miss the boat, possibly witness.

Katie Robbert 10:30

It’s a best practice in general, as you’re going through user stories, always make sure you’re creating at least one from the perspective of your audience, because what do they need, because it isn’t just about you.

And so that’s, you know, the travel is a really good example, because, you know, we start to introduce bias, as we’re creating these requirements of like, well, I don’t like to fly, so nobody else must like to fly.

So I’m just only going to show, you know, destinations, you can get to by train or by car.

Well, that excludes a lot of destinations, especially if you’re somewhere that’s landlocked.

And so you really have to think beyond just what you want, even if you don’t realize that’s what you’re doing.

And this is why it’s also really good to get more more than just yourself involved in creating these user stories and running through the five Ps.

Because, you know, Chris, you and I travel very differently, we like to have very different experiences like so I would be thinking, okay, great, where are places that I can drive to that I can, you know, be in the mountains, I can be disconnected from the internet, I can bring dogs to it can be quiet, and you’re probably looking for, let me find a different cultural experience, you know, let me be as, you know, internet connected as possible, so that I can like really immerse myself and get into it.

You don’t mind crowds, I don’t like crowds.

So like, it’s a very different approach.

And so we collectively would need to be creating these requirements.

Because we have different perspectives, especially when it comes to travel, it’s a very personal, unique experience, you can’t just try to create it for everybody just from your own singular experience.


Christopher Penn 12:06

So if you have it, this is one of the other things people forget, you have your own data, if you are marketing on behalf of company, you have data through the funnel, right? So you have who shows up in your website, you have who shows up fills out a form you have who plays a video, you have who books something or buy something on your website, or through an agent or whatever.

And this is not just traveling tourism, this is every company, you have that data, you probably have that data over time unless you are unless you’re a startup that just opened up last week, you have that data of some form over time, and therefore you can forecast and to your point, Katie, you can also segment it and if you have enough data, you can set forecast the segments as well.

So who are the buyer, for example, with Trust Insights? If you are someone who has bought a course from us, who are our course buyers? Can we forecast the course buyers? And are they different than people who hire us to do say analytics governance, you have the data.

To your point, no one is really data poor, in the sense of yes, it may not be made for you, you may have to generate it.

But the data does exist.

And that’s a really important part is that you can if the data does if the data doesn’t exist, that is an opportunity for your business or your organization to establish a leadership perspective position within your industry because you can be the source of that data.

So for example, if you are in travel, right, and no one else has this information about what the buyers interested in.

If you’ve got the money, run some surveys, use a certain platform like Survey Monkey or MailChimp, or one of the many platforms offer surveying, start running surveys, start running focus groups, start running interviews, you can even start collecting information from public data sources.

So for example, if you could take, if you have the time, this is a time thing that a money thing.

You can type in vacation packages, ideas on YouTube, download all the videos, transcribe them all, you can do this for free with AI and then search for it sale, how often what destinations are most commonly mentioned.

Now you’ve got your 2024 Travel forecast for the top videos about travel on YouTube.

Now you’ve got your 2024 Travel forecast.

And you can figure out well, here’s the data we have that we’ve generated, what can we do with it?

Katie Robbert 14:23

And I think the what can we do with it is a really important question, because I think there’s a misunderstanding that you have to create a new analysis for every for every use case.

One of the first talks I ever gave on stage was at inbound in 2018.

And it was five use cases of predictive analytics.

And so what I basically did was I took one instance one analysis of a predictive forecast, and I walked through the customer journey and I said, if you’re driving awareness, use your one analysis like this.

If you’re driving consideration, use your one analysis like this and the analysis never changed, the context changed.

And that’s one of the reasons why a predictive forecast is one of my favorite analyses, because it’s so flexible.

It’s just a matter of you the human deciding, what else can I do with this one thing.

So it’s like finding a really good, you know, piece of craft supply, and taking that one thing and turning it into like multiple different crafts.

And you’re really stretching it and figuring out like, what else can I do with this thing, take that same approach with your analysis.

So don’t do okay, I have to do one analysis for awareness.

And I have to do another analysis for consideration.

And after do another analysis for purchase, see how much see how far you can get with that one predictive forecast, and all of those different contexts.

So the caveat there is that you need to understand who the people are in each of those stages of your data driven customer journey.

But once you know that, you can then apply one single predictive forecast.


Christopher Penn 15:59

So again, a lot of that can come from qualitative research, right? If you can get 10 people on the phone and talk to them for 15 minutes about their buying process.

From you.

One of the one of the best things to do as a business, regardless of your industry, is to talk with your existing customers, assuming that you’re still working with the same decision maker and say, How did you choose us? Why did you choose us what and now that you’ve worked with us, for however, short or long? What are the things that you would add as items for consideration? And what are the things that, you know, maybe were not true or not as clear, when you were when you were shopping around? Those insights can Power Search, right, because it will change the content, your pages, that will change your social media conversations, it will change what you promote, on podcasts and blogs, it will change all your content.

And you can generate that data.

So if you’re if you feel like your data poor, you are, it’s because you’re not creating the data.

And to your point, Katie, with just that focus group exercise and those one on one interviews, you can generate some data that can be used extensively.

Katie Robbert 17:15

Well, and you know, so to the original question is, what do we do if we’re data poor? I think that we’ve been able to demonstrate that you’re just not really looking in the right places.

So you know, definitely look at Google Trends free.

What was the other one the,

Christopher Penn 17:32

the engrams, Google Books Ngram Viewer,

Katie Robbert 17:35

I think that that’s a really interesting resource.

But then, you know, also take a look at the SEO tools like SEMrush, or Ahrefs, take a look at the social scheduling tools like Sprout Social, or Talkwalker, or any of those other tools that have the analysis built in and get some information about your nearest competitors, to use as a proxy.

So when Chris and I started Trust Insights, that’s what we did, we didn’t have all the historical data on our company.

Now that we’ve been around for almost six years, we have our own historical data.

But in the first, you know, 18 to 24 months, it didn’t exist, we were busy collecting it.

So we had to use those other external data sources as a proxy until we had our own but we never felt like we were data poor.

Christopher Penn 18:23


Even something as simple as your email newsletter can be a rich source of data, right? How many people opened each issue? How many people clicked on it? What ratings and reviews did have you gotten on? And one of the things that I do in my personal newsletter all the time, is there’s a there’s a a weekly survey for each issue, like bring that up here so that we can we can just take a real quick look at scroll past like the the the lengthy stuff, and you’ll see a little survey here, where’s this? How was this issue? Good, neutral, bad, you know, smiley face, frowny face flat face, that data goes into Google Analytics, and then that gets turned into charts.

And so I can visualize the likelihood by topic, although it’s been mostly generative AI of of what people like and don’t like, if you have any kind of customer interaction data, right? You want to look at it, test it for seasonality cyclicality.

And then if you can, if it’s their forecast and way, way back in 2017, you and I were working for a local casino that was way way back seven years ago, that is way back.

They gave us just their their Florida earnings, so a slot for tables and interactive games.

And they said what can you do this day we forecasted that data.

We did a predictive forecast.

We took the five years worth of data that they had forecasted forward a year, and we gave them a calendar and we said these are the weeks of the coming year that you must have additional marketing promotions in the air to cover up those weak spots when you’re going to have revenues.

workforce, they did it, they followed our advice.

And they saw a 29% year over year increase in revenue, right? Because they took the data that they had, because they weren’t data poor, and the forecast, and it worked really, really well.

Your company, or your competitors, or your industry has some kind of data like that somewhere, and you can make use of it.

So to your point, Katie, no one is data poor.

It is that you don’t know what ingredients you have.

It’s like saying you don’t you don’t have any food in the house, we actually do.

It’s just not cooked.

You just have to cook it yourself.

Katie Robbert 20:38

Well, and I think that that’s a really good opportunity just to sort of give a gentle plug that if you think your data poor, give us a call, Trust Insights at AI slash contact, and we’ll take a look at your data will audit, your infrastructure will audit your data sources and say, Actually, you have all of this data right here.

And we will say, actually, to make you feel talked down to No, I’m just kidding, we won’t do that.

But what we will do is we will help you we will help you understand all the rich data you do have and show you that you’re not data poor.

And so we’ll help you figure out here’s what I can really do with this information.

Christopher Penn 21:15

Maybe we should do it in a live stream sometime just do like a live live action.

Let’s let’s pick something like pick a like a local restaurant client or something like okay, it’s a five restaurants nearby what their websites are, take some of the trend data and things and just show you how, how the stuff all comes together.

Katie Robbert 21:34

I think that that’s a really good idea because I think that we tend to not just be resourceful enough and take a look around at what we’re already you know, have access to.

Christopher Penn 21:43

Yep, exactly.

How do you feel? Are you data rich? Are you data poor? Come tell us in our free slack, go to AI slash analytics for marketers where you have over 3000 other marketers are repeatedly told that you are data rich on a daily basis.

Wherever does he watch or listen to the show if there’s a challenge you rather have it on instead, go to trust podcast where you can find us on most major podcast channels.

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Thanks for tuning in.

I will talk to you next time.

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Trust Insights ( is one of the world's leading management consulting firms in artificial intelligence/AI, especially in the use of generative AI and AI in marketing. Trust Insights provides custom AI consultation, training, education, implementation, and deployment of classical regression AI, classification AI, and generative AI, especially large language models such as ChatGPT's GPT-4-omni, Google Gemini, and Anthropic Claude. Trust Insights provides analytics consulting, data science consulting, and AI consulting.

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