Now that you know as much as you can about who is consuming your content when, it's time to connect the dots.
Welcome back! In the first part of our series, we learned all about the different types of data you need to collect to begin your foray into intent-based marketing.
Want to listen to this article instead? Click here!
But those data types are just a bunch of puzzle pieces, jumbled up in a box right now. It's time for you to start putting those pieces together.
Much like when you're sifting through nonstop middle pieces to find that treasured edge piece, businesses engaging in intent-based marketing don't always know how to find trends among their leads.
Even more challenging still? Knowing which trends carry actual weight. The solution sounds simple, but it requires a bit of elbow grease.
You have to combine deep behavioral data analysis with observations on individual activity to create a marketing strategy that is both personalized and predictive.
It's analysis time.
Find and use trends as your marketing cornerstone
As you sort through all of your data, you're going to notice a few patterns. Certain groups of buyers will act in very particular ways.
Those trends are what you're after. Keep digging until you've found every possible pattern that can be supported by sufficient data.
Once you've got all of that data, though, it can be difficult to know what is most relevant and how you should weigh trends and patterns.
Short answer: it depends. Long answer? Read on.
Strike a balance between individual activity and deep behavioral data
Intent data covers a range of topics, whether it's individual activity or deep behavioral activity.
Individual activity is tied to the contextual data you're collecting and answers questions such as:
Is this site visitor here for the first time, or the 13th?
Which pages or campaigns brought them here?
This data is helpful for building out the practicalities and methodology for responding in a particular moment that a lead interacts with your site.
However, the majority of your time and resources should be dedicated to finding trends in deep behavioral data. Here, you're looking at every possible metric for how users interact with your site and your content:
How many emails go unopened, and how many emails bounce back?
How many people view your social media posts, and how many click through to your site through email versus social posts?
You can get as granular as time on page, scrolls, hovering over links, etc.
By combining individual and deep behavioral data, you'll gain a better understanding of how your leads behave. If you notice that people who go to multiple articles on your blog have a higher frequency of clicking through to your product page or engaging with a chatbot, that becomes a direct link you can act on.
Much like understanding the efficacy of channels at different stages of the sales funnel, deep behavioral data is the key to understanding what messages to send to your leads and when.
Rebuild your buyer personas and customer journey maps
This is a big ask, I know, but it's time to clean house. You need to get rid of all your extant buyer personas and customer journey maps that existed before you collected this data.
Have you done it? Good. Now it's time to build newer, more accurate ones.
Buyer personas are essentially biographies of your ideal customers, while customer journey maps are about figuring out how they become customers rather than leads.
Take all this data about how people find your site and the steps they take when researching your product, and revisit your extant buyer personas to determine if the people you think are high quality leads are actually high quality leads.
If they aren't? You need to make new personas and maps.
The data you're collecting can/will help you do just that, while also ensuring that your understanding of the touch points your leads are using—and the order in which they use them—has a strong factual foundation.
There may be some channels you need to dedicate more time and resources to in order to fill in gaps in your customer journey map.
What to do next
You may find some pretty severe differences between the personas you thought were your ideal customers and the personas you develop after your combined deep behavioral data and individual data analysis.
That's okay, though. It's important to have found those differences. There are three things to do next:
Comment below, or send me a tweet (@Markidding) and we can work through the reason behind those differences together.
Keep collecting that behavioral and individual data in order to keep your personas and customer journey maps a living document as your business grows.
Start moving forward with these new models. How do you do that? Tune in for the last part of our series to find out!