How to recognize patterns in B2B marketing
New big data tools allow B2B marketing professionals to collect huge amounts of information. Many companies are storing this information without putting it to use.
They are aware of data importance but don't know what to look for in analyzing customer behavior and activities. Consilium, a global business advisor, suggested B2B professionals need to use data tools to look for patterns in how audiences interact with their marketing content (1).
The value of patterns
A marketing strategy should be adopted with certain goals in mind. If B2B content is created for publication on websites, social media or other platforms, it must be designed to influence a certain behavior in customers.
Tools that measure click-through rates or the number of times a post was shared are great first steps, but the numbers have to mean something to sales and customer conversion. If a B2B marketer starts publishing a blog, page views of that blog should be charted against new business acquisition or other metrics to determine performance.
As content is created, data is collected, performance is charted and patterns should emerge. The patterns demonstrate which strategies are most effective and what options need further development.
It takes time
Answers won't come overnight. Business2Community stated pattern recognition is a skill that takes time and practice (2).
When new content is created, there might be an immediate boost or drop in sales, but the two factors might not correlate the way marketers think. Multiple factors have to be analyzed by professionals who have a great deal of experience spotting trends.
The ability to spot patterns in content marketing strategies is a skill developed through trial and error. The more data acquired, the more informed the results can be.
People vs. technology
Big data software can find patterns people overlook or misinterpret. Search Engine Land argued humans are predisposed to recognize patterns where there are none (3).
Human bias causes marketers to add meaning to patterns that don't turn out to be true or ignore trends that go against previously held beliefs. If title tags are changed on content and the marketer expects an uptick in business acquisition, he or she is quick to attribute any success to the new strategy.
Technology does not hold bias. Big data tools utilize algorithms that crunch hard numbers and display direct results of actions. Search Engine Land shared the story of a learning machine that detected fake online reviewers with 90 percent accuracy. Software doesn't have the urge to believe positive reviews or dismiss negative ones, it just displays honest results.