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.
No. 3 Applying successful data practices isn't easy
It probably won’t come as a surprise that nearly nine in 10 B2B marketing and sales professionals worldwide said that implementing a data-driven marketing strategy is complicated, according to July 2017 research from Synthio.
The reasons for those complexities depend on each B2B company’s unique circumstances. A poll of North American B2B marketing and sales professionals from Demand Gen Report (DGR) found that 83% said having old or outdated data was a big challenge to maintaining data quality in their contact database. In addition, 71% said they just don’t have the time or resources to implement an effective process. Finally, two in three replied that they don’t have enough data on current customers.
Expertise is another major obstacle to data-driven marketing success, according to an October 2017 survey from Adweek Brandshare and Dun & Bradstreet. Four in ten US B2B marketers said they have a lack of data expertise. Also noted as hurdles were reliable third-party data sources, accuracy of data, and integration of technical platforms.
In 2018, B2B marketers will need to hone in their data expertise to continue understanding their audience. That means getting quality data from multiple sources, building the right infrastructure to house and manage all the data and, finally, making sense of it using analysis and data science.
One Liberty Plaza9th FloorNew York, NY 100061-800-405-0844