Marketers have always been a curious bunch. Since Nielsen started conducting formalized surveys in the 1920s, there has been a long history of consumer-based market research that helped explain buyer personas, identify propensities to purchase, and explore the psychology behind the overall journey. Much later, and driven by the advent of the web, companies like Amazon, Target, Netflix, and Google took advantage of the explosion in new data-points to create robust recommendation engines using predictive analytics. The idea was to use statistical models examining historical behaviors to anticipate possible future actions. So if Pandora can suggest which musical artists a user is likely to enjoy based on listening patterns, why is the equivalent in B2B sales and marketing only coming into recent fruition?
Marketing leaders are constantly trying to gain a competitive advantage. IDC predicts that CMOs will drive $32.3 billion in marketing technology expenditures by 2018. Clearly, the answer is not rooted in frugality. The answer is rather multi-faceted, and has to do more with advancements in technology and the subsequent adoption rates. In the past, only the biggest companies had the resources to acquire the computing power, data warehouses, business intelligence tools, and data scientists necessary to forecast certain buying patterns. Similar to the “consumerization” of enterprise software during the previous several years, there has been a “democratization” of data science. The technology that was used to crunch vast amounts of data became faster and less expensive to install and maintain. New analytic tools also came into the market that didn’t require data scientists to derive actionable insights. The access to these observations was realigned from a select few to those business-line managers who needed it the most.
Another coinciding factor that has been a driving force behind predictive analytics in the B2B arena has been the Big Data movement. Anyone with a pulse on the Big Data craze and earlobes towards advocates started realizing the potentially game-changing information their organizations were churning out. Sales and marketing groups were no exception. As savvy CMOs fueled the early-adoption of marketing automation software, disparate marketing processes (and gobs of data) came under one roof. This was the leap of the Data-Driven B2B Marketer, who approached the free-flow of information like an engineer. Alongside web analytics and sales automation tools, marketers could pool data from multiple systems to paint a more complete picture. Reporting was frequently conducted on proactive level, and dashboards and KPIs were established to measure effectiveness of marketing campaigns.
However, the process of successfully converting marketing-qualified leads (MQL) to sales qualified leads (SQL) has still been fraught with uncertainty and waste. Even the marketing teams of high-tech companies are sending out mass email campaigns that have poor click-through or response rates. A recent State of Salesforce study was presented at the Dreamforce conference, which indicated that only 7% of polled marketing executives found good ROI out of their marketing automation investments. Part of the problem with the available tools lies with marketers’ limited horizon. It is common practice to look at analytics in backward terms, rather than forward. Data-driven B2B marketers can explain what is happening in precise detail, but have a harder time explaining what could likely happen in the future based on those findings. Identifying leads that are coming into the funnel is easy; identifying which of those leads will likely convert into customers is not.
Luckily, there are predictive analytics vendors that are making this process easier, and accelerating the leap for predictive B2B marketers. For instance, Infer offers predictive scoring applications for sales and marketing teams that prioritize the most promising leads based on historical behavioral models. Customers can integrate the solution with data from Salesforce, Google Analytics, Pardot, Eloqua, Marketo, and other unstructured web sources. The technology behind the platform is driven by machine learning and data science, and leads to increased conversion rates, larger deal sizes, and highly visible sales efficiency. Marketing resources spend less time picking apart good leads from bad, and more time focusing on passing quality leads to the sales team. It is this type of automated analytics that is delivering true ROI, supercharging the current state of marketing automation software, and arming future marketers with powerful revenue-generating tools.
Going into 2015, experts agree that predictive analytics will provide competitive differentiation to companies in increasingly competitive markets, and those firms standing by idly to the movement will be putting themselves at a disadvantage. The predictive analytics software market is on a trajectory to reach $6.5 billion by 2019, with much of the growth coming from cloud-hosted vendors. As more highly innovative and accessible tools emerge, more B2B marketers will make the leap from being simply data-driven to being fully prediction-driven. The crystal ball will no longer be driven by gut instincts, but rather by algorithms.