The use of data to inform key marketing decisions is by no means a new strategy. In fact, the popularization of data-driven marketing is commonly tied to the introduction of the CRM in the early 1990s. But, the humble beginnings of data-driven marketing look nothing like the data-driven marketing we know and love today.
Thanks to advancements in technology and the diversification and widespread availability of data, marketers now rely on more than just CRM data to achieve success. Today, we take a look at the different types of data you need to execute a successful data-driven marketing program.
Let’s get into it.
1. Demographic Data
Let’s start with the basics. Demographic data is one of the most commonly used types of marketing data and it includes data points related to specific socioeconomic attributes of a general population. These data points include characteristics like average household income, marital status, gender, religion, age, education level, and more.
This particular category of data has long been used to make targeted marketing decisions for the simple fact that it provides a broad look into the defining characteristics of any given audience. Although demographic data isn’t the most sophisticated or specific of datasets, it provides a great starting point for marketers just beginning to explore their audience.
2. Technographic Data
Technographic data is a relatively new addition to the marketing data landscape. For those who may not be familiar with the term, technographic data is made up of information regarding the essential tools and technologies a company uses to conduct business. This includes everything from payroll software to auto-dialers to marketing automation tools.
If you work in the world of B2B marketing, technographic data is especially valuable. The most obvious applications of technographic data include targeting users who work with competitors or who currently use products that integrate with the product you’re marketing. But, beyond these two relatively straightforward use cases, technographic data can actually provide marketers with significant insight into their prospects and buyers.
Every company’s technology stack reveals quite a bit about how they make purchases, the relative size of their budget, their technological know-how, and so much more. Let’s look at a quick example from VentureBeat:
A financial tech firm noticed that the use of Eloqua was a predictive signal for its top prospects. The company exists in a completely separate vertical, so it wouldn’t make sense to personalize messages around this platform. However, it did help them deduce a few things. They recognized that companies running Eloqua tend to have a certain level of technical sophistication, and are usually big enough to be able to afford premium enterprise systems.
Continuing with this example, a marketer in this scenario would then be able to use the technographic insights they uncovered to improve lead scoring, send out targeted campaigns, and more.
3. Behavioral Data
Sometimes called engagement data, behavioral data refers to the data points collected when a customer or prospect interacts with a business in some way. Common types of behavioral data include metrics related to website activity, advertisement interactions, content downloads, email clicks, and purchase history.
Behavioral data is often collected in several different systems or platforms – CRM, website analytics platforms, different campaign tracking interfaces, and so much more. The key to leveraging this type of data is to compile it into a singular platform or view so you can really understand the overarching behavioral patterns of your customers and prospects.
When combined with other data types, behavioral data can clue you into how, when, and where people interact with your brand in meaningful ways.
4. Firmographic Data
Firmographic data is similar to demographic data– but rather than describing a particular group of people or audience, firmographic data is used to describe businesses. Firmographic data points include company size, company location, revenue, industry, and more.
This data type is especially useful for B2B businesses looking to target a very specific type of account or organization.
5. Performance Data and Analytics
A major tenet of data-driven marketing is analyzing campaign results in an effort to optimize and tweak different initiatives. Because of this, it’s vital for modern marketers to have access to performance data and analytics surrounding each individual campaign.
Let’s look at a quick example. Say you work at a company selling compliance training solutions to several different industries. After running an email campaign and analyzing your results, you realize that, in general, the campaign performed worse than any other campaign you’ve run thus far. But, after digging into the numbers you realize the email performed exceptionally well with recipients from a single industry. Using this information you start segmenting your email lists into sends based on industry, which ultimately generates much better results across the board.
6. CRM Data
As previously mentioned, the introduction of CRM software allowed marketers to gain deeper insights into their prospects and customers. And although marketing technology has evolved beyond basic CRM data, customer and prospect data has been and will continue to be a staple in the world of data-driven marketing.
CRM data is most commonly used and analyzed by marketers to uncover patterns and commonalities among their buyers. This data is then used to develop buyer personas that inform almost every aspect of a marketing strategy. Without CRM data, customer profiling would be a much more difficult task.
7. Industry Benchmarking Data
Although most data-driven marketing strategies revolve around internal data, it’s also helpful to see how other companies in your space perform. This is where industry benchmarking data becomes helpful. This type of data allow businesses to compare their performance to other businesses operating in the same industry.
Although it’s important not to place too much stock in industry numbers, benchmarking data can be a good way to monitor progress – especially for new companies. Because this type of data relies on outside companies disclosing financial and strategic information, it can be slightly more difficult to gain access to. Start with a good old fashioned Google search for benchmarking data within your industry. This should lead you to companies, analysts, and other resources that provide this type of data to marketers – often for free.
8. Psychographic Data
Psychographic data provides a more qualitative look at an audience or persona than other types of marketing data. Personal values, character traits, interests, and attitudes are all considered psychographic data points. Although this may seem unnecessary to some businesses, psychographic data often provides context for a person or group of people who seem similar on paper.
Take the following for instance Two mothers, both 37 years old, live in the same neighborhood and earn the same amount of money. On paper, these two women may seem identical. But in reality, they have different spending habits, political views, and personality traits. In this case, the psychographic data tells us more about these two people than the demographic information.
9. Intent Data
Our final category of data is a combination of behavioral data and context data. Intent data explains a person’s or account’s behavior in relation to who that person is and how ready they may be to buy a certain product or service. Sales and marketing teams find intent data particularly valuable when it comes to prioritizing outreach.
And there you have it – a general overview of the different types of data marketers need in order to operate a truly data-driven marketing strategy. Of course, this is not an entirely comprehensive list, but it’s an excellent starting point for marketers looking to expand, scale, and improve their marketing efforts.
If you take one lesson away from this article, let it be this – the key to modern marketing excellence lies in the breadth, diversity, and quality of the data you have access to. As marketers, we can no longer rely on instincts and gut feelings. Instead, we must use the resources we have access to – intelligently and consistently.