B2B marketing has evolved in leaps and bounds over the years. What started as a revenue channel reliant on branding efforts has now expanded into a balance of brand promotion and data collection. These days, a marketer is only as successful as the data they have acquired, with campaign performance relying on it. Data has refashioned how marketers, specifically demand generation professionals, think about reaching their audiences. One of the biggest drivers moving into the future of data-driven marketing is the application of predictive analytics. So, let’s look at how predictive analytics—in all its forms—can not only help you achieve greater success in your marketing campaigns, but also work as an enrichment tool to help better understand your ideal customers.
What Is Predictive Analytics?
A good place to start is to define what predictive analytics is. Mostly thought of as a way to measure decisional patterns within AI, this technology has recently been applied to marketing data as well. In theory, it enables you to predict the behavior of your target accounts and buyers as they move through the decision-making journey. According to Marketing Evolution, “predictive analytics uses data models, statistics, and machine learning to predict future events.” In marketing applications, predictive analytics works in tandem with an already established database—your CRM, for example. Therefore, it’s helpful to think of it not as a software you use, but more of a mindset you apply to what you’re already doing.
Types of Predictive Analytics
As we’ve already established, B2B marketing can be informed by various forms of customer data, and in the best use cases, thrives under each one. Here are the different types of information you can gather when creating a predictive marketing data model.
This is probably the most common, as it directly measures how qualified and engaged your buyers are, helping to identify how ‘ready-to-buy’ they are. Intent data technology uses keywords or phrases to track specific customer sets, all with the goal of prioritizing who to reach out to and when. It’s a great way to blend sales and marketing initiatives as both parties can use this information in their daily activities.
Defined as the measurement of your customers’ direct product and brand engagement, behavioral analytics is often used to expand on the information gathered from intent data. This strategy requires you to look at granular details of how your buyers take in information, generally from your owned media. Therefore, most of this data is gathered from your website.
Luckily, you’re more than likely already measuring the way customers engage with your content. The goal with content analytics is to gather information from your content campaigns and identify the common challenge or topic that your buyers care about the most. Your content, whether it’s on-demand, live, or on social media, all has an intrinsic value given to it. When measuring the predictive nature of your buyers, you’ll want to figure out what values stick with them the most.
What Do You Do with This Information?
Gathering predictive data is one thing. Putting it into action is a whole other story. When you enact a fully scaled predictive analytics strategy, you’ll find that the end result can be a little confusing. First off, it can be challenging to make sense of all the (seemingly) disparate data that’s been gathered. And even if it’s possible, many marketers struggle to work out how exactly they should apply the learnings from the data into their new campaigns.
There’s no single way to implement all your data into one stream, however there are solutions that exist to help combine and even source data points for your team. Software like Clearbit can help you find and augment data to fit your various CRM’s needs. On the other end, Domo is a great place to start when looking at data aggregation. It can organize, apply, and even collect your data, making reporting and analysis a lot easier.
Personalization of Campaigns
An overlooked value that predictive analytics provides marketing teams with is the ability to personalize your campaigns based on customer needs. With the information gathered, you can create targeted nurtures and outbound demand gen campaigns to capitalize on the buyers who are ready to make a buying decision. For example, if you found that your director level HR buyers are all interested in challenges centered around time and attendance, and a small set has engaged with your products, then it’s probably a good idea to create a targeted campaign centered around this information. This is a perfect example of predictive analytics’ biggest value point to marketers—its ability to expedite the marketing funnel.
In-Market Data FTW
DemandScience has made a conscious effort to create a stream of in-market data that applies to 100% of our campaigns. In fact, our intent engine, PurePredict, stacks multiple levels of predictive analytics to provide the most accurate view of your targeted audiences. Whether that’s through content syndication campaigns, ABM, or lead development, we take a data-first mindset to everything we do.
If you’re interested in learning more about DemandScience’s approach to analytics, feel free to reach out. We have a number of solutions to help you find your audience.