B2B marketers are often envious of their B2C counterparts getting all the cool stuff. But when the B2B crowd tries to learn a few tricks from their consumer-facing colleagues, they find that many B2C tactics are better suited to selling candy bars than copying machines. Plus, a candy bar isn’t usually a considered purchase with a long sales cycle and multiple influencers. Unless you have kids.
The B2C advantage has held true in the realm of data as well. There’s a huge amount of consumer data available, gathered from millions of sources. Cookie-based ad tech enables retargeting, for instance, so B2C marketers can serve up targeted ads based on the websites consumers visit (as you know when you see an ad for the product you just viewed on Amazon in the next ten places you click online).
Personalization Is Critical For B2B, And Doubly So For ABM
B2B marketers can use IP targeting to get company-level information and determine where site visitors work (assuming they’re at their desks), but unless every employee in your prospect company has an equal say in the buying process, that info is nowhere near specific enough to identify decision makers.
Retargeting and cross-channel personalization may be old hat for B2C, but it can sound awfully good to B2B marketers looking to boost engagement and conversion. For marketers attempting an account-based marketing (ABM) approach, personalization is especially important. ABM makes it crucial to identify not just the right accounts, but the people inside those accounts. That allows you to engage them with a personalized message that addresses their specific needs.
The Oracle B2B Audience Marketplace Defines A New Direction For Ad Tech And Data
This breadth and depth of data will let marketers target their buyer visits online very specifically across a wide range of places, including news websites, publisher sites, premium publishers and long-tail niche content sites. B2B marketers will be able to place digital ads in front of people and companies with a B2B focus — the kind of targeting previously reserved for B2C.
Predictive Analytics Makes Third-Party Data Work For B2B Marketing
More isn’t always better (except maybe when it comes to pizza). Marketers can certainly benefit from a bigger pool of contacts when building campaigns, and adding third-party data — from digital activities across the web and social media, for instance — to the first-party data in their own CRM databases. However, sorting and segmenting that data to make it actionable is no simple task.
Predictive analytics is the practice of applying sophisticated algorithms to large datasets to derive more accurate and detailed insight about prospects. For B2B, it lets marketers identify, find and score prospects not only with traditional firmographic data such as company size, revenue and industry classification, but also with individual behavioral characteristics and attributes of the people themselves (you know, like in B2C).
A comprehensive predictive analytics solution also “enriches” the data already in a company’s CRM, adding missing fields and updating out-of-date information by aggregating and comparing records from multiple sources, including the open web and social media.
With predictive analytics built around an Ideal Customer Profile (ICP), you can target the right people even more effectively. For example, you can score contacts based on the skill sets and job functions of the individual decision-maker, even if they may not be reflected in their job title.
When you combine large amounts of data about prospects gathered from multiple sources, enrich and score it with predictive analytics and then organize it by relevant audience segments, you get volume, specificity and accuracy that even a B2C marketer would envy.
And if all of that data and all of those capabilities are available automatically inside the marketing stack, integrated with CRM and marketing automation, it can succeed at scale.