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Data is a scary word for most marketers. It brings forward a feeling of being overwhelmed — a constant stream of customer and visitor data points that is constantly growing into a rapidly flowing river seeping out of its banks.
Not only is data hard to keep track of, it’s even harder to unlock and utilize. Many experts have even coined a clever name for the phenomenon — infobesity — and claim that it’s causing marketers to suffer from information overload and not execute on stated goals.
At the same time, data also unlocks insights and opportunity that otherwise wouldn’t exist. You can identify cohorts of users that provide more revenue to your business, acquisition channels that you should be investing more spend in, and conversion rate optimization experiments (such as personalization and A/B testing) that can improve key KPIs such as trial and demo registrations. Data collection is all about gathering the knowledge necessary to deploy an experiment thoughtfully and strategically.
We’re believers that data shouldn’t be scary — and more importantly, it definitely shouldn’t be a hindrance in your decision to personalize your website. As B2B marketers, we can follow a simple framework to understand what data to collect from visitors, how to store it in a thoughtful way, and where to utilize it to deploy personalized experiences at scale.
To get started with data for personalization, you first need to understand the types of data points you can use to create an audience and personalize. In this chapter, we’ll cover the types of data points that you can collect and how to create audience for personalization.
Data helps answer the Who and What behind designing a personalized experience. In Part I, we’ll cover the data points you need to understand. And in Part II, we’ll cover how to use those data points to create audience buckets.
We’ve found that the best way to think about data for personalization is to understand the ins and outs of four basic categories of data:
Demographic data is data that is all around about a person. You’re likely familiar with this type of data from any government, focus group, or university survey (such as the US Census or a BLS labor report) — it’s all about data points around a person. For companies, demographic data is important because it answers the who behind your customers.
Here are a few common demographic data points and why they can play important in your personalization campaigns:
Firmographic data are data points all about the company. It’s similar in nature to demographic data, but all of the data is around the business rather than around an individual. Here are some common fields you’ll see in firmographic collection, and how you can start thinking about it for personalization:
While demographic and firmographic data are related to the who behind a visitor, behavioral data is all about what a visitor does while on-site.
Behavioral data shows everything about a visitor’s self-directed actions while on your site or inside your app.
And every person has unique behaviors when using digital products — just as they have unique behaviors and tendencies in the real-world. I might be inclined to take one route to the Austin airport while my coworker might choose a different group of roads all together. It just depends on how our brains are wired.
The same holds true for behavior online. I can take one set of actions on a page while a marketer at another company might use a site completely differently. Savvy marketers know that humans behave differently (it’s in our DNA after all), and when these nuances in behavior are identified, personalized experiences can be used to deliver & outperform competitors.
Behavioral data helps answer questions for your marketing team like:
Behavioral data is nearly limitless — the only thing that limits your collection of behavioral data is your creativity.
As you think of use cases for personalization on your site, you can start asking questions in the style cited above and start collecting interesting behavioral insights.
Contextual data is tangentially related to Behavioral data in that it is also related to a user’s unique properties. And as the name suggests, this category of data gives context to a visitor’s session on your site.
Here are some common questions that can give contextual information on a customer:
These data points might on first glance not to seem especially important, but as you dig into data, you’ll see that device type, browser type, location, and other contextual data points can have quite an impact on a customer’s conversion on-site.
Audiences are the groups of unique customers or visitors that will see a personalized experience in the same way. You create these groups and add rules to define who you wanted to be included and who you want to be excluded. It’s similar to defining an audience on Facebook’s ad manager — you create an audience based on criteria that you select from an audience builder.
For instance, if we want to target current customers with a Welcome Back message when they hit our homepage, we’d create an audience for that group of current customers using the following criteria in Proof Experiences.
We name this audience Users and we use Proof’s filtering to include all people that have hit our Segment event Logged in (count value is greater than 0). That event signals someone is a customer because our product is not freemium — so every Logged in visitor is a returning customer.
You can create an audience like above in a handful of ways, and you can get as refined or as broad as you want when creating one. An audience can be as small as 1 person or infinitely large.
Let’s look at another example. You can create custom audiences by targeting properties using AND and OR statements. For instance, if you wanted to target first-time visitors and the industry SaaS, you’d use the following logic in your personalization platform:
You can also use matching logic exclusions to select audiences that include everyone that match one criteria and exclude everyone that has another trait.
So if you want to target first time visitors in all industries other than SaaS (perhaps these are less valuable MQLs for you), you could create an audience using the following exclusion logic:
Here’s how we like to think about audiences when personalizing our own website. The first thing we use to segment our visitors into an audience bucket is industry (a firmographic data point).
From our analysis to date, we have identified five distinct industries where we most frequently see customers:
You can think of your most common industry by looking at your CRM data or introducing a survey somewhere on your site (more on that later).
Second, we want to create audiences to target people off of the lifecycle stage (behavioral data). We can ask a few questions around behavioral data to identify whether a customer is a:
With these audiences in mind, we can tailor our imagery, headlines, and on-page content for a visitor’s unique characteristics.
For instance, we wouldn’t want to position our software in the same way for a first-time visitor as we would to a churned visitor. While for the first-time visitor, we want to focus our messaging for our social proof product around our value prop of increasing conversions by 10-15% — and for the churned user, we want to cater more carefully and incentivize them to give our service another try.
By now, you have a great understanding of personalization and the data points you can use when deploying web personalization experiments. You’ve also learned about how you can create audiences, and how we think of audience creation at Proof.
In the next chapter, we’re going even deeper into the data side of personalization. We’ll cover how to collect data for personalization, the tools you need to do it, where to store it, how to create a tracking plan, and naming conventions.
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