- When marketers need to narrow their focus, predictive applications help by defining segments and personas and prioritizing prospects for followup
- When marketing and sales don’t have enough good prospects, predictive applications can intelligently source net new high-propensity targets
- Predictive applications identify and prioritize the best prospects and reveal buyer preferences to improve conversion through the Demand Waterfall®
From the 1960s through the 1980s, Ron Popeil and his products, such as the Veg-O-Matic and Ronco Pocket Fisherman, achieved pop culture notoriety on the back of his iconic, rapid-fire enumeration of the seemingly endless uses for his gadgets.
“It slices, it dices!” was one phrase from his Veg-O-Matic advertisements that won improbable long-lasting fame after Dan Aykroyd’s parody on Saturday Night Live.
Today, predictive marketing providers claim a long list of possible use cases to which their predictive analytics engines can be applied. This list can be categorized into three major groups according to the problems predictive helps marketers solve.
Too many inquiries. B-to-b organizations with solutions that address a very common business problem or that answer a wide variety of use cases may have addressable markets that are too large and diverse to be addressed effectively. In these cases, organizations must often make difficult choices about where to invest marketing and sales resources. It behooves sellers to segment their addressable markets into groups that can be prioritized and handled efficiently. Today’s smart segmentation applications identify lookalike segments – groups of prospects that share relevant attributes and behaviors – and compute for each segment a predicted propensity-to-convert score. This allows effective prioritization of the resulting segments and leads to more predictable demand creation outcomes. In addition, the rise of inbound marketing has allowed many organizations to attract more prospective buyers than they can service. While this may seem like a good problem to have, organizations spending money to attract prospects that cannot be serviced effectively waste valuable marketing funds and may distract lead development and sales resources from focusing on higher-propensity targets. Prospect scoring prioritizes responders using sophisticated predictive analytics that use internal data and third-party profile and solution research (i.e. intent) data, allowing organizations to focus their efforts on the most valuable prospects.
Too few inquiries. Despite advances in inbound marketing, most b-to-b marketers still have too few prospects entering and converting through the Demand Waterfall®. As a result, most organizations must execute outbound marketing to engage prospects. Outbound tactics, such as small-net fishing, can be prohibitively expensive, however, if they aren’t targeted to prospects that are likely to have the motivation and means to purchase. In this scenario, predictive models use historical marketing and sales results to build ideal buyer models that are used as templates for sourcing new prospects. Late-stage buyers may be sourced and delivered directly to sales, while the majority of prospects are delivered to a teleprospecting team that must be willing and able to conduct effective cold calls. Organizations without an effective outbound teleprospecting function should first engage predictively sourced prospects through higher-volume outbound marketing tactics (e.g. email), and then use social and targeted display advertising to elicit responders.
Poor conversion rates. Whether attracting too few or too many inquiries, organizations that fail to optimize the conversion of legitimate buyers will struggle. Here, predictive analytics helps in a variety of ways. Predictive applications are used to score and prioritize warm inbound and cold outbound prospects, helping organizations focus their time on the highest-propensity targets. When smart segmentation is used, the resulting segments constitute precisely defined groupings of prospects that share attributes and needs, allowing marketers to tailor highly effective messages and tactics.
Focus on the why, not the how. While the how of predictive analytics (data science and statistics) can be enormously complex, the why needn’t be. Chances are that your organization has one or more of the problems described above. Identify which you should tackle first, then talk to your friendly local analyst – or even a predictive marketing provider – to dive into the details of how predictive might help. SiriusDecisions clients can also refer to our more detailed discussion of this topic in the Core Strategy Report “Applying Predictive Analytics.”