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 and qualified 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 the business as a whole.
With an astronomical increase in available data about customers through various primary sources like LinkedIn, Yelp, 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.
CMOs and marketers of the future ought to make decisions on where and to whom to market and offload the practice of actually processing data to companies that specialize in data science. Marketing isn’t about building out an engineering organization, it’s about identifying qualified prospects and nurturing those qualified prospects to eventually buy. So if building an engineering organization is distracting, how can senior marketers take advantage of these new data points about potential customers? The solutions that we saw in the early 2000s for Inbound Marketing and lead nurturing are now popping up to help marketers make better decisions. Many of these companies describe this process as “Predictive Marketing”, the truth is that most Predictive Marketing solutions don’t quite encapsulate all of the processes CMOs need to implement to make their marketing efforts successful for the future.
“Decision Science” is a great way to describe the end-to-end process for the next generation of marketing and Columbia Business School has a new center dedicated to this practice called the Center for Decision Sciences. The Center describes Decision Science as “the intersection of several social and behavioral science disciplines, drawing on theory and methods from economics, psychology, political science and management, among other fields. Theory and research in the decision sciences has followed two paths: The first is normative or prescriptive, focused on specifying criteria for evaluating decisions and providing algorithms for achieving optimal outcomes; the second is descriptive, focusing on how people actually make decisions.”
Marketing is about understanding customer behavior and engaging with potential customers in the right way. Deciding who to engage with and where to engage with them is half the battle. As the cost of analyzing large data sets decreases, new tools and data analysis methods enable marketers to make better decisions. The process of Decision Science includes many different types of tools, but the myriad of startups and products labeling themselves as “Predictive Marketing Platforms” has, as the Financial Times writes, “confused” marketers rather than turned their processes into an “organised science”. For marketers to become decision scientists, they need to simplify their workflow and use end-to-end solutions rather than working with one provider that sells data, one that sells lead scoring services, one that enriches existing data, and one firm that ties all these solutions together. The best Decision Science platform for a senior marketer will be one complete solution that: (a) Prequalifies individual potential customers automatically using rich external data, (b) Identifies quality segments of potential customers, and (c) Unveils opportunities previously disregarded in the marketer’s CRM, and (d) Actually provides new qualified leads within the best segments.
Was the world better when we didn’t have Inbound Marketing? Was the world better when marketers bought an email marketing service, a landing page creation tool, and then hired an engineering team to build inbound lead workflows? No. Hubspot and Marketo brought the process of Marketing Automation to Inbound Marketing. It’s only a matter of time that one company brings the process of Decision Science to Predictive Marketing — and that will empower CMOs to make great decisions to find the best customers.
Darian is the CEO and Co-Founder of Radius, which is building a decision science platform for B2B marketers.