{PODCAST} In-Ear Insights: 2019 Planning and Predictive Analytics

In this episode of In-Ear Insights, cofounders Katie Robbert and Christopher Penn discuss predictive analytics as a planning tool for your 2019 marketing. What data should you use? How should you approach a predictive analytics project? How much planning should go into predictive analytics? What pitfalls should you look to avoid?

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Christopher Penn
This is In-Ear Insights, the trust insights podcast.

In this episode of In-Ear Insights today, we are talking about the future, your future. Specifically, predictive analytics is predictive analytics is one of the top terms besides prescriptive analytics as forecasted for more than 50% growth in 2019. So let’s start today with what should people be doing? Because we’ve heard a lot about predictive analytics this year. Katie, you gave a one of the most popular talks at inbound on predictive analytics, what should companies be doing now getting ready for 2019, or in 2019, to use predictive analytics intelligently,

Katie Robbert
first and foremost, it’s their infrastructure, make sure that you are measuring things correctly, so that you can use your own data to be running a predictive forecast, I think that’s one of the biggest challenges that companies are seeing right now is that want to be using predictive analytics to help them understand what they should be doing moving forward, but their own proprietary data set is just not in good enough shape. And in order to do that analysis, so they have to rely on third party data, such as Google Trends or data from data.gov in order to run a predictive forecast to inform their strategies and plans. So the goal is for a company to be able to use their own data, their own CRM data, their own

Google Analytics or armature data in order to understand what’s going to happen next. So that first and foremost, is get your house in order.

Christopher Penn
What should you predict? What are the things that like, that’s not that doesn’t make good sense to try and forecast?

Katie Robbert
That is a really good question. I think, you know, one of the things that we need to remember about predictive analytics is that predictive analytics based on human behavior. So it’s based on the actions that your audience takes that any one person takes. And I one of the cautions that I always give with predictive analytics is that it’s a guide, but it’s never going to tell you 100%, what exactly is going to happen, because humans are still unpredictable creatures. You might know somebody for 20 years for or for your entire life. And 98% of the time, you can predict the way that they’re going to react to something they were team that they’re going to have the thing that they’re going to do, but how often do you find yourself saying, I didn’t see that coming, or I didn’t expect that from you, or I didn’t know you were capable of that. And it’s that 2% that is that caveat of predictive analytics is a really good guideline for what you should do. But there’s always going to be something unknown. So guess your question about what Shouldn’t you be predicting

time series analysis is a really good methodology to use. So it basically takes a large set of data from a period of time, we usually recommend about five years, that’s timestamps. So it’s that more quantitative data where you have five people did this this day, two people did this this day. And you can project that forward. So you can look at your Google Analytics traffic data, you can look at your CRM lead gen data, you can look at your sales revenue data, you can look at your email, open rate data, those are really good things to try to measure.

Trying to measure something that you’ve never done before, such as a new product launch is going to be a really difficult thing to do for your company specifically, because you’ve never done it, you don’t have that sense from your audience how they’re going to react. So trying to use historical data for something that you’ve never done before. That’s a mismatch

Christopher Penn
makes sense. The other thing I would say is on that front is don’t try to predict things have too many hidden inputs. So one of the classic questions that folks and data science get is, well, why don’t you Why can’t AI predict the stock market? Well, because there’s a lot of things that go into the stock market. And not just numbers, there are there’s politics, there’s economics, there’s climate change everything. And there’s so much going into it that you can’t really today predict that you may be able to in a few years, but we will see how that goes. So in terms of predictive analytics, the data sources that marketers should be paying attention to most one of the ones that you’ve seen do really well, in terms of predictions that that come true in the end,

Katie Robbert
Google Trends is a underutilized tool for marketers, it’s a really clean data source. And it goes back far enough historically, that marketers can use it knowing that they’re going to have enough data. So if you’re not familiar, Google Trends, basically, is exactly what it sounds like. So it tells you the search volume for any given term or a couple of different terms, you can break it down by region, by state by country, you can even look at like YouTube search data, you can look at any sort of the Google properties, Google marketplace data, I think that that’s a really great place for marketers to start if they’re unsure about how predictive analytics is going to fit in, because a forecast from Google Trends can help inform their SEO strategy, it can inform their content marketing strategy, it can inform their ads, then strategy, there’s a lot different ways to look at that particular data set using that one data source. So I think that that’s a really great place for marketers to start, especially if they’re not feeling super confident about their own data set.

Christopher Penn
Okay, in terms of those sort of those predictions and things,

how does someone take the prediction and put it into action, because it’s great to have the prediction, but if it just sits in a binder, then it really wasn’t all that helpful.

Katie Robbert
So one of the ways that we use predictive analytics is we use, we use the forecast to help inform our content, calendar, and even our calendar for what we should be talking about on our podcast. And so we basically did our own SEO research, we know the terms that we want to be known for our company. And then we pulled the data for each of those terms from Google Trends. So for example, if we want to be known for a data analytics, we pulled five years worth of search volume search data from Google Trends for that particular term. If we want to be known for predictive analytics, we did the same process. So we pulled the five years worth of search data for all of the terms that we care about the most. And then we ran the algorithm our our predictive algorithm on those search terms to project forward 365 days. So we know that starting this week and moving forward to the next few weeks, topics such as predictive analytics and Instagram influencers and influencer marketing are the things that people are going to be caring about the most. And so we use that data to then write content to record audio, to create video to double down on advertising budgets around those specific things, and then pull back budgets around topics that people aren’t searching for,

Christopher Penn
how much lead time should you be taking into account for these things, because a lot of cases if someone’s going be searching the most for I don’t know robotic process automation in in two weeks, a lot of marketers are not the world’s best project managers and planners and things. So how, how does how do you take into account the your production times with these predictions so that you’re ready for them.

Katie Robbert
I think it depends on what you as a company do for your content. So if you produce YouTube videos get started, get started right away. Same thing with the content, it depends on how fast you write content, or how deep into the content you want to go. So if you’re just writing about what is robotic process automation, that should theoretically come together pretty quickly, if you’re writing about the different applications that might take a little bit longer, we know that Google indexes about it takes about, what 24 to 48 hours. So if you’re looking to have something ready on the first of the month, you should probably have it published no later than the 28th of the previous month, so that Google can properly index it so that when people are searching on the first they’re coming up with your piece of content, your thing, you can also use this for longer lead. So if you know, three months from now or next quarter, that there’s going to be a topic that you care about, that’s going to be start to spiking start to spike three months from now, you can start the process of creating perhaps a bigger campaign, or you can use it for your longer lead pitches in PR to start to get that information out there knowing that it’s going to take that long. So it’s really sort of taking that moment in time that you know, it’s going to be the most popular and working creating your work plan backwards from what you need to have prepared for that period of time.

Christopher Penn
That brings some something that is a concern of, I see a lot of organizations where something like a predictive forecast is handed to the most junior person say, hey, go blog about these things. So whatever. But we know that you need some of that domain expertise and some of that experience some of those gray hairs in order to be able to look at a predictive forecasts and say, okay, it’s not just this keyword phrase, it’s popular this week, it’s this entire topic. And, you know, here are five or six additional terms that are that are related that you should be creating more thorough content about not just slavish Lee going off of what’s written, you know, line by line on the spreadsheet, how does a company How does a company deal with that, and, and develop a process that allows you to take full advantage of predictive analytics, rather than just, you know, copy pasting off of the spreadsheet and hoping that your content doesn’t suck,

Katie Robbert
it comes down to planning, and it comes down to priorities. And so for example, we have a spreadsheet of a, let’s call it 1000 terms that we care about, because they’re all variations of each other. When, when we take a look at that week, over week, we might see 10 terms, okay, all of these terms are going to about spike. But we need to then make the executive decision of Okay, we don’t care about eight of these right now, we only care about two of these. So we need to prioritize and say we’re going to take these two terms. And we’re going to write three pieces of content about these two terms, specifically. So then we need to go back to our seo keyword planner and figure out what are some variations that makes sense for us that aren’t just trying to jam this awkward phrase into a piece of content, hoping that it gets some SEO juice? So you’re right, you can’t just hand off a spreadsheet and say, write content about these things, because it might not make sense. Or to your point, you might not have domain expertise, what what would you do in that situation?

Christopher Penn
It depends on the data source. So one of the things that we built in our keyword planning tool is the ability to bring in data from professional SEO tools to help with that forecast. And one of the nice things that several of the tools do is say, hey, for this given keyword, this is the parent keyword. So predictive analytics, the parent keyword for that is actually analytics. And so if you forecast the big parents first, you can then say, Okay, oh, can we know that in this big parent of these 28 terms. So all of these are up for grabs in these topics. And you can you can then drill down and say, I want to look at the the parent analytics term. Okay. Now within analytics when is predictive when it’s prescriptive? When is when is descriptive? When is Google Analytics within this topic, and be able to develop the content that Google is looking for, because Google’s looking for no longer just just the keyword itself. Google’s now looking for the searchers intent, Google’s looking for authority, which means that if you’re using the sort of that parent child relationship, and keywords, you can say, Okay, I’ve got the analytics topic covered. And within analytics, I’m going to cover predictive, prescriptive, descriptive, Google Apps, politics, data, studio, etc. And if you put all that into your content, you’re developing that rich authoritative intent base content that Google is going to say, You clearly know what you’re talking about. So we’re going to rank you higher. The trouble is, most people don’t use that data. It just kind of, you know, they extract the keyword list from their favorite SEO tool and hand it to the internet and say, Go Go blog and there isn’t that thoughtfulness about using the data. That’s one of the reasons why I was asking in terms of that process. How do you make that the default process for a company like, hey, before you go diving into the data, step back and actually think about what you’re doing?

Katie Robbert
So now you’re getting into the realm of change management and organizational behavior? And those are two separate topics from predictive analytics. But that goes back to the original question of, you know, how do people get prepared to use predictive analytics in their planning, and it comes down to infrastructure. Now, I was talking about your technical infrastructure, but you also need to look at your team infrastructure, your company infrastructure, and make sure that you know, what that strategy is, what that plan is, what those goals are, before you start to introduce a tool such as predictive analytics, because then you’re just spinning your wheels and wasting time. And so

we’ll be sharing within the show notes that data hierarchy of sort of those rungs of data. So you start at the very bottom wrong with your quantitative data, what happened, and that’s where a lot of companies are stuck. They don’t know what happened. They don’t know what their goals were in order to track against it after that, you get that qualitative of why did it happen? Well, that’s when you start to ask people, okay, you took this action, why did you do that specific thing? That’s that your market research those years surveys, those two bottom rungs are often skipped in order to get to that predictive analysis. Because it’s the shiny, fun thing. It’s going to tell you what’s going to happen. Well, if you don’t know what happened historically, how the heck are you going to know what’s going to happen moving forward? I mean, that to me, is that’s crazy town. That’s banana Cuckoo. And that, I guess, sort of in a roundabout way, is getting back to your question of what do teams need to do? And they need to do those two things first, before even thinking about introducing predictive analytics, they need to get their quantitative data squared away, they need to get their quality of their what in their why, before you can start to move on to even how, and then what’s going to happen next, and then what do we do about it? So we’ll share a link to that image, or we’ll put it in the show notes for you. But it does, it comes down to change management of what are we even doing as a team? What’s our goal? And so understanding what your goals are first, within your team within your organization for your clients, for yourselves, you have to do that first before you can introduce tools like predictive analytics. And I’ll get I’ll step back off my soapbox now.

Christopher Penn
No, I mean, this is my concern with this, because one of the things that I we’ve heard this year, this past year when we’re working with companies with predictive analytics is people saying, I’m not ready for this, I can’t make use of this. Well, it’s not a technology problem, because, you know, trust insights is doing it for you. So the problem then, is a people and process problem, not a platform problem. And I think, how else can we help people prepare for making use of this, and it sounds like if you can, if you can develop the process, then eventually, you’ll get the people to do the thing rather than trying to course, the people to do something without a process.

Katie Robbert
So you know, the word process, I’m putting that in air quotes, people can’t see me. But I’ve been talking a lot with my hands, because this is a topic that I’m very passionate about

processes a dirty word, because people tend to think that process is going to slow things down, it’s going to, you know, gum up productivity, or we’re creating process for the sake of process. And that’s not true. If you have a repeatable process, it could be two steps, it could be one step, first step show up sometimes that’s all you need for a process is just show up and everything else sort of falls into place. But you know, in this particular instance, yes, you’re going to have a process that has a few more steps in it.

But having that repeatable process, maybe perhaps something you could even automate, use that robotic process automation for allows you to free up that headspace that mental real estate or even some of that overhead of other tools in order to be able to use a tool such as predictive analytics. So step one, have your team collect your analytics once a week about the various things that you care about, related to your business goals. That might be something that you can automate step to look at the data,

make sure that people understand what the data is step three, take action with the data, collect data that you can do something with not just data to look at for the sake of collecting data, and then you can start to move on to why did that happen and what we want to do about it moving forward.

Christopher Penn
Make sense? So wrapping up, if you’re not currently using predictive analytics, make sure that your houses in order first get your data, make sure you have to know what your data says why it says those things and what you’re going to do about it and then start looking at predictive analytics. And of course, if you would like help doing that, including getting your house in order we here at trust insights would be more than happy to do so you can visit a trust insights.ai. Please subscribe to the YouTube channel and to the podcast on trust insights.ai and we will talk to you soon.

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Trust Insights (trustinsights.ai) 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.