B-to-B buyers are no longer nameless, faceless entities with nebulous purchasing timeframes. Instead, actual individuals are sending off signals all the time about who they are, what they want to buy and when — in the form of data that can be worked into razor-sharp predictive models, Keke Wu writes.
Wu — the director of analytics at Avention, a Concord, Mass.-based real-time B-to-B data provider — wrote a post for InsideBigData.com on Thursday titled "B2B Predictive Analytics in the Big Data Era." First, she provides an example of outcomes for marketers using predictive analytics, then tips on three ways they can find actionable information.
"Imagine a company trying to promote its next-generation IT security solutions," Wu hypothesizes. "The company identifies its ideal customers as mid-sized retail and health care companies that are technology adopters. If the company also knows which companies had recent IT executive changes, which companies just announced big data initiatives and which companies are surging on IT security intent on the Web, then it can combine this information and launch targeted marketing campaigns."
Here's what Wu suggests marketers find in order to create their predictive models:
- Demographic/Firmographics: Look for industry type, business size and company ownership type.
- Psychographic and Behavioral: Is the company a technology adopter? Does it take risks? How resilient is it? How diverse? Is it family-friendly, promoting a work/life balance?
- Real-time Activities: "These are the time-sensitive trigger events," Wu says. For instance, a retailer announces a new store plan. Another company hires a new CMO who likes marketing automation technology, based on the fact that she purchased it at her last company. Employees downloaded a particular whitepaper, and so on.
What are some other tips for B-to-B marketers wanting to create predictive models?
Please respond in the comments section below.