Three Things B2B Marketers Should Know About Data in 2018

Staying on top of data can give B2B marketers an edge in engaging prospects and customers

The most successful business-to-business (B2B) brands are buyer-centric.

They know who their buyers are, what their pain points are and how they make purchase decisions. These sorts of insights are possible, in part, due to data. In 2018, B2B marketers will continue to rely on data and deep background research to better understand and engage their prospects and customers.
Here are three things all B2B marketers should know about data in 2018:

No. 1 Data can come from many sources, in many forms

B2B firms must pull in prospect and customer data from multiple first-, second- and thirty-party sources for their marketing and sales efforts.
A September 2017 survey from Informa Engage found that 84% of US B2B marketers use data from their customer relationship management (CRM) tools and customer survey responses to inform their marketing. Other popular data sources cited by respondents included site registrations and transactions records (76%), web analytics/site traffic (71%), and qualified online leads (49%).

While data sources should be varied, B2Bs generally focus on two different types of data. The first is descriptive data, a category that includes demographic and firmographic information about an individual buyer and the company that buyer works for. This encompasses basics like names, titles and contact details.
But it can also add context like company organization charts, performance reports or even budgets.

The second type of data is behavioral data, which adds additional insight into buyer’s interactions with marketing and sales touchpoints across the web. This type of data tells things like what pieces of content have been downloaded, which web pages were clicked and which emails opened.

No. 2 Data needs to be analyzed

Of course, all of the data in a B2B marketer’s arsenal is pretty useless if it isn’t properly managed and then analyzed to glean actionable insight that both marketers and sellers can use.

July 2017 research from Bluewolf found that more than half of US sales professionals have invested in predictive analytics that apply statistical models and forecasting techniques to their data through machine learning or artificial intelligence. Other popular types of analytics include descriptive, which aggregates and mines data to provide a summary of historical data, and discovery, a method that searches through data for patterns to reveal previously unclear associations. Other less common implementations of data analytics are diagnostic, prescriptive and contextual.

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