As a marketer, data in all forms can be overwhelming. There’s just so much available that it's difficult to know where to start.
However, data and the ability to leverage it in marketing is truly a gift. And what makes data most impactful is when you layer different types on top of each other to gain deeper insight into your audience.
Leveraging the combination of different types of data leads to a better understanding of your target audience, more targeted messaging, stronger communications, greater response and conversion, and overall, greater ROI.
Additionally, data-driven insights can be helpful in identifying which behavioral science principles to leverage in your marketing, as these principles can increase the relevancy of the message and drive action.
But knowing where to start is critical, so you can effectively and efficiently use data to build targeting, then craft messaging against each of your identified target segments. To effectively use data in targeting, it’s important to understand the following four types of data and how to leverage each to your benefit:
- Demographic Data
- Psychographic Data
- Attitudinal Data
- Behavioral Data
The first and most well-known type of data that I’ll touch on is demographic data. It refers to how we look at and break populations down by age groups, gender, ethnicity, geographic regions, income brackets, etc.
For example, a newspaper publishing business wanted to grow their footprint in undersaturated markets by way of new customer acquisition.
To best understand who to go after, take a look at the — age, income, zip codes, and educational status — in those markets and recommended identifying look-a-likes to determine the “best fit” prospects to go after.
Once the prospect audience was defined, leverage the behavioral science principle known as “social proof” in our communications to let them know that people “like them in their area” were enjoying this particular paper and were regular subscribers.
As you can see, demographic data can tell you a lot about your audience at the surface level. Often, it’s the first set of data that marketers look at, because it’s quantifiable and statistically sound. It’s also what most of us have on hand without doing additional data-driven research or purchasing additional data.
Just understanding the demographics of your audience can help you get started in identifying who they are.
To dive in a little bit deeper, the second and third most commonly used types of data are psychographic and attitudinal data. This data can be more subjective, because it gets at feelings, beliefs, opinions, lifestyle traits, and interests.
Layering psychographic and attitudinal data onto demographic data gives marketers a more holistic view into their customers and prospects.
- Psychographic data allows us to look at people’s personalities, their values and opinions, their interests, and what their lifestyles are like.
- Attitudinal data on the other hand allows us to better understand what consumers attitudes are, for example, toward specific company and product attributes. It also allows us to understand what motivates them to purchase and what they like or do not like.
Leveraging psychographic and attitudinal data about current customers allows you the ability to build models based on known attributes of your customers.
For example, a predictive model can be built by identifying key attributes, which are quantifiable and statistically proven, among current customers and then run against a prospect list to identify prospects with matching attributes to those customers.
Building models based on data is often a proven way to ensure you’re going after the right audience and not wasting marketing dollars against people who are unlikely to engage with you. However, it’s important to validate the model by testing, especially if you haven’t used models before.
To make your marketing even tighter, you can also apply behavioral data, which looks at consumers’ behaviors. This is the most valuable, because it’s the most actionable. Behavioral data is primarily gathered based on digital behavior, such as site visits, online purchases, cart abandonment, online searches, etc.
With all the data that is now collected digitally, marketers and third party data sources are now able to determine interests, motivations, and spending behavior, simply by looking at how consumers are engaging with online content. These insights can help marketers predict who might be the right fit for their product or service based on past online behavior.
Layering with demographic, psychographic, attitudinal, and behavioral data can greatly strengthen the knowledge of your audience and help you more closely define who would be the best “fit” targets to go after.
To further bring the application of multiple data types to life, we recently completed a targeting exercise for a client, whereby we used data analytics and data-driven research to identify our core target audience so that we could ultimately deliver them the right message.
Unlike the past example, this exercise was to inform targeting to drive repeat business.
To identify a core audience gather demographic data to determine the most prominent age cohort, gender, ethnicity, educational background, residency type, income, marital status, and household type, to name a few. What was found was that our audience was primarily female, of diverse ethnicity, with low income, and skewing younger.
Then layering psychographic and attitudinal insights that was gained from data-driven research. Some of these insights included that our audience was skeptical, thought someone was always out to get them, and valued transparency.
Adding behavioral data to the analysis gained insight that this audience wasn’t brand loyal, rather they searched around for the best deal, were early purchasers, and didn’t have disposable income — based on the fact that they had a very small digital footprint from an online purchase perspective.
It also could be speculated that because their digital footprint was small, they likely didn’t have credit cards and dealt primarily with cash transactions.
Insights from these data exercises then allowed us to craft a targeted key message and build communications relevant to our audience. To do this we:
- Used a messaging style targeted more toward women and a younger audience mindset
- Used behavioral science principles such as the Consistency Principle to reinforce that we knew they had done business with us before
- Leveraged the principle of labeling to label our audience as “early purchasers”, based on behavioral insights.
So, in summary why should these four types of data matter to marketers? Well, leveraging them in combination with each other can help to:
- More accurately define customer profiles and target audiences, and potentially even create personas or segment groups
- Develop and provide tighter direction on messaging and creative that aligns to the profiles, especially if multiple segments or personas have been identified
- Identify which behavioral science principles will be most relevant to the audience in connecting with them and driving action
- Effectively use budget dollars to target against the right audiences vs. taking a “spray and pray” approach
- Improve response, conversion, and overall ROI