INBOX INSIGHTS: Predictive Analytics, Practice Data Sources
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Predictive Analytics for Your Customer Journey
In 2018, I stepped onto the stage for the very first time at INBOUND. I spoke to 1200 people about predictive analytics.
Six years later, it is still my favorite presentation. Not just because it was my first experience public speaking, but because the topic is still relevant.
In this talk, I covered:
The Data Analytics Hierarchy
The Predictive Analytics Process
Using Predictive Analytics through each stage of the Customer Journey
And caveats about Predictive Analytics
All in all, it was a solid presentation. Many times when we look back at earlier work it makes us cringe. The only thing cringy about the presentation is my lack of design skills. In 2024, that hasn’t changed either. Despite that, I still feel really proud of the presentation. I wouldn’t change the topic or the message. Because, even six years later, it’s all still accurate. There are finer details that would need updating. Especially with generative AI being part of the conversation. Overall though, the message is still the same. You can use predictive analytics in each stage of your customer journey.
So, why isn’t predictive analytics used more often in marketing tactics as of 2024?
This week, let’s do a review of how to use predictive analytics with your customer journey.
First, what is your customer journey? At a high level, these are the stages that your customer goes through when making a decision. Your customer journey is as simple or as complex as you want it to be. A simple customer journey consists of four basic stages: Awareness, Consideration, Evaluation, and Purchase.
You may have noticed that a customer journey maps to your sales and marketing funnel, with awareness at the top and purchase at the bottom.
Where does predictive analytics fit in? Let’s see, shall we?
Awareness:
This is the first stage of the customer journey. This is when people find out about you, visit your website, and check out your public content. Since this is the stage when people learn about you, you know the least about them too. Using publicly available data sets like Google Trends and keyword data from SEO tools is your best bet. This data, run through a time-series predictive analysis, can tell you what topics people care about and when. This will allow you to create your content calendar to align with the topics that are the highest searched tied to specific dates.
Consideration:
In this second stage, people are engaging a bit more with you and the content. They might be signing up for your newsletter or following you on social media. There are a couple of data sources you could use to reach people at the right time. The first being Google Trends data and when people are searching for setting their “out of office”. This indicates that they won’t be checking their email. Trying to send them a campaign or offer will get lost. You can also use your newsletter subscriber data to understand the seasonality. Weeks when you predict a lower number of subscribers you can ramp up your marketing efforts.
Evaluation:
Your relationship with your potential customer is becoming more serious. You like them, they like you, and they want to get to the next stage, evaluation. They are ready to become a lead. You can look at your CRM data and the number of leads generated over time. Using predictive analytics you can project when your audience will move to evaluation.
Purchase:
This is the stage that tells you you’ve done your job well. This is when someone buys something, or takes the intended action. A good starting place for using predictive analytics is your revenue data. You can run an analysis to understand when you are more and less likely to make sales, and plan your campaigns around the highs and lows.
If you run these analyses, and then look at all the phases together, you’ll start to see the purchase patterns of your customer. You’ll also start to understand the typical life cycle and how long it takes to close a sale from awareness to purchase. And you did this all with marketing unsung hero, predictive analytics.
Now, I’m talking in broad strokes and at a high level. You’ll want to go through the 5P Framework, create your user stories, and then establish your data governance. However, once you’re set up, you can rerun a predictive analysis and adjust regularly as you gather outcomes.
I’m proud of 2018 Katie. She nailed it. I think I’m going to have to update her presentation and give it new life.
Are you using predictive analytics? Reply to this email to tell me or come join the conversation in our Free Slack Group, Analytics for Marketers.
- Katie Robbert, CEO
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In this week’s In-Ear Insights, Katie and Chris examine the use of generative AI and lead generation. How do you use generative AI to improve lead generation? How do you build an ideal customer profile with generative AI? Tune in for step by step details!
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Data Diaries: Interesting Data We Found
In this week’s Data Diaries, let’s talk about data and data analysis. In our Slack group, Analytics for Marketers, Aly recently asked about where to practice analytics skills, particularly with marketing data sets. This is a very common question, especially for folks early in their analytics career journey - many companies aren’t just going to hand over their data.
The good news is, we don’t have to wait to be given data. Let’s look at some of the ways we can obtain data to practice on.
First, governments often publish datasets we can download and use. For example, in the EU, data of all kinds is published freely with the EU’s data agency, Eurostat. Citizens from around the world can botain data from the EU as a whole or about member state economies, analyzing them for trends.
In the USA, the US Government’s portal, Data.gov, offers hundreds of thousands of high quality datasets, available to the public. Virtually every federal and most state datasets are available for download.
For marketing data, few tools are as widely used as Google Analytics, and users of the software can access Google’s free Demo Account. The demo account has nearly every feature turned on and uses data from Google’s Merchandise Store as its data source, providing you with rich, compliant data to access. Once added to your Google account, you can even practice writing code against the Google Analytics API with its data.
For things like CRM data, Trust Insights has published some R code that will create basic synthetic data for a CRM. You’re welcome to download the code, use it, and generate as little or as much fake CRM data as you like.
Finally, depending on how much data you need, you can use a tool like ChatGPT or Google Gemini to generate the data for you. Give it a specific prompt to create data in a downloadable format, then tune your prompt until you’ve got data in exactly the format you want and download it.
For more advanced, technical users, you can have generative AI create code for you that will programmatically generate synthetic data, which is useful if you need to introduce specific patterns in the data or you need to generate datasets that are significantly larger in scope than what generative AI can make.
Finally, one of the best sources of data to work with is your own. If you have your own personal website, install the requisite software like Google Analytics, a CRM, and maybe some marketing automation software so that you can analyze your own data.
With all these options, there’s no shortage of data available for you to work with and practice your analytics skills. Choose the avenue that best suits the kinds of visualizations and analyses you want to practice.
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