Predictive analytics is something of a white whale for business-to-business (B2B) marketers. They look at their counterparts on the business-to-consumer (B2C) side of things with envy: Every life event, every feeling, every thought is a trigger. Customer-facing marketers have it all at their fingertips. Even before the Web made data more accessible and richer, predictive analytics gave B2C marketers the insights they needed because they simply had access to more information.
Thankfully, things are beginning to change. Big data has shifted the B2B marketing paradigm by enabling marketers selling to other businesses to learn more about their prospects and improving their ability to analyze the information. All of that data about target markets has given B2B marketers a chance to develop strategies based on deeper corporate information, real-time triggers, and behavioral information.
Good campaigns rely on data and use it effectively. Great campaigns transform that data into predictive analytics that offer even deeper insights for strategy.
The most basic firmographic – the demographic-style information for businesses – data is perhaps the most significant development for B2B marketers to come from access to massive stores of information. Predictive analytics are based heavily on the detailed foundational information of certain companies. Now, B2B marketers have those details. They know companies’ sizes, locations, recent purchases, and the ways they use different products and services.
With this foundational information, marketers can perform broad segmentation, create basic buyer profiles, and run marketing campaigns against target segments. For example, an information service provider might segment its market by vertical and size to target enterprise companies in the financial services and high-tech verticals. The company is able to estimate the addressable market size, analyze its penetration rate in each segment, and develop marketing strategies against the target segments. But using the kind of predictive models that really give B2B companies a competitive edge requires even more.
A Step Further
Predictive models become even stronger when companies take the data they use for prospective customers further. Beyond firmographics, there’s more behavioral information to integrate and psychographics that suggest companies which may be ideal for targeting at a specific point. Big data enables companies to create richer buyer profiles, perform hypersegmentation, and run highly targeted marketing campaigns with relevant content.
Imagine the information service provider from the example above is launching an innovative cloud-based solution. With the help of big data, it can hypersegment its market and target early technology adopters. Along with the basic information that made B2B marketing good, integrating additional data sources enables B2B marketers to target precisely the audience they are after – for example, analytics-driven organizations or companies with strong diversity policies. These insights help marketers adjust different aspects of their campaigns, such as content and timing, in line with prospects’ behavior. The data inform predictive models by pointing to specific actions prospects are likely to take based on how they run their companies. Frequently, purchase decisions at these companies involve a few different people. This, also, is the kind of data that B2B marketers now have at their disposal, so they can develop and launch strategies aimed at convincing the right people.
It all comes down to understanding the fine nuances of prospects to deliver marketing content when it’s most likely to compel a conversion.
Going from Good to Great
Here, it becomes about timing. This is where all of that data leads B2B marketers who are trying to develop predictive models to make their campaigns as actionable as possible. The firmographic and psychographic data that B2B marketers have are even more useful when paired with behavioral triggers.
For example, imagine that a company recently hired a new chief information officer (CIO). If the information service provider from the previous example has this information, it can distribute targeted content to this company after the new CIO is on the job for a couple months and get the right information in front of a decision maker when she’s ready to start making adjustments. It’s all about understanding each aspect of a company and its current position to maximize the value of different strategies. Predictive analytics helps B2B marketers do what B2C has done for years: Pair high-level data with more specific, actionable information so that strategies are more likely to drive conversions.
Effective marketing to businesses calls for a thorough understanding of who the prospects are, where they are, and what they need. Predictive analytics helps B2B marketers take their marketing efforts to another level, making good campaigns great by using big data to lead the way. There are some challenges to face, of course. Data management and analysis are integral to launching any successful campaign. Executive buy-in is important, as well. But B2B marketers simply cannot let barriers like these interfere with big data’s ability to deliver predictive analytics that result in sales. For years, marketers believed this information was the key to improving on good campaigns. Now the opportunity exists, and a great campaign is the reward for doing it right.
Keke Wu is the director of analytics at Avention. Keke was previously director of analytics at Monster Worldwide, where she provided data-driven insights to a wide audience from C-level decision makers to marketing, sales, and pricing teams. She has a deep background in marketing analytics, data mining, predictive modeling, and business intelligence. Keke holds an MBA from the Tuck School of Business at Dartmouth College.
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