In the past few months, Predictive Marketing has become more mainstream. CMOs and senior marketers are becoming scientific and more focused on understanding which potential customers are most likely to become new opportunities, but more importantly which potential customers are actually good andqualified potential customers. Marketing has historically been a numbers game coupled with relatively unmeasurable creative — marketers measure success by how many somewhat qualified leads they acquire. This numbers game has resulted in a constant debate between senior marketers and senior sales executives over the definition of an MQL, SQL, or SAL. Most marketing and sales leaders would agree that this ongoing struggle is unhealthy — more time is spent placing blame than working together to identify the best customers for thebusiness as a whole.
With an astronomical increase in available data about customers through various primary sources like LinkedIn LNKD -0.55%, Yelp YELP +1.72%, Government portals, and the broader web, marketing organizations now have a new problem: which data about prospects is relevant? Which data actually matters for marketers to identify more and better prospects? Answering these questions across billions of new data points is difficult and requires extensive data science experience. Should marketers spend time building out data science organizations and buying disparate tools to mine and understand these new data points? No.