Consumer marketers have become adept at driving revenue based on predictive analytics. Potential customers are routinely scored on a wide variety of attributes from lifestyle to promotion receptiveness. These scores allow consumers to be segmented into groups based on shared interests, purchase likelihood, and total buying power. By starting with highly differentiated segments, marketers can design programs that are highly relevant and effective.
Yet, B2B is a ripe environment for predictive analytics: selling costs are high, sales probability is low, and resources are very expensive. While the language of B2B marketing and sales is full of references to probability — customer funnels, response rates, conversion rates, close rates, call-to-close ratios — it’s rare to see B2B organizations leverage prospect and customer data to score customer attributes, build discrete segments, and allocate resources to maximize the conversion and revenue.
But all of this is about to change. Over the next five years, common consumer marketing techniques will find a happy home in many B2B marketing and sales organizations.
Here are 6 reasons why:
- Electronic sales processes are creating massive amounts of useful data: Today, B2B buyers spend more time interacting with companies online than they do with sales people in person or over the phone. For every successful sales call they attend, a typical prospect may spend hours interacting with content, reading forums and blogs, and testing sample products. In today’s world, every buyer action leaves a trail of digital clues that signal their context, needs, purpose, and intent.
- Prospect attributes can be easily deduced from observable data:Most B2B organizations with CRM and content marketing capabilities have enough data to score prospects on purchase probability, likely problems or interests, and potential solution needs.
- Relevancy matters: Even as the typical portfolio of products and solutions becomes more varied and complex, B2B sales and marketing messages tend to be narrow and simplistic. The patterns that work most consistently are destined to be forever repeated. For prospects, this means that they are often hit with messages and a pitch that ignore the nuance of their particular needs and segmentation. For many prospects, this is a turn-off that is difficult to reverse.
- Sales & marketing funnels are based on probability: Typically, 2% of targets respond to a marketing campaign, 60% of leads are accepted by sales, 50% of accepted leads become opportunities, and 25% of opportunities close. When you look at the full marketing and sales funnel, a pathetic 1:667 targets becomes a closed deal. Using predictive analytics to improve any stage of the funnel has the potential to create incredible value.
- Sales resources are expensive and easily tiered: It’s not uncommon to see a three-tier sales model with tele-prospecting/demand generation representatives, inside sales, and field sales. Typically, these teams are divided with the goal of aligning the highest cost resources to the highest value opportunities. Unfortunately, the allocation of accounts is typically very crude with simplistic measures like revenue or employee count determining which accounts go to a particular team. By using predictive analytics to allocate resources based on real-world potential, sales teams could increase revenues while reducing the cost of sales.
- Marketing programs vary greatly in expense and effectiveness:If you have a stalled prospect that you want to move, a marketer has many choices. They could send an email, send a direct mail, invite them to an educational seminar, or bring them to a hospitality event. The continuum of marketing costs ranges from pennies to hundreds of dollars with corresponding variations in conversion rates. To maximize impact, marketers should save the big dollar investments for the highest probability and highest value segments. To do this, however, marketers need to use predictive analytics to score prospects based on their probability of purchase, their potential buying power, and the likely impact of a particular program or technique.
While smart organizations are beginning to put the foundation in place to better leverage data in the marketing and sales process, real obstacles still exist to efficient use of predictive science in most B2B organizations. First of all, one legacy of sales-sourced CRM data is a mess of information that is inconsistent and difficult to leverage. Second, the new art of data-driven marketing and sales requires a new set of skills that are hard to find in most B2B organizations.
But most critically, it’s hard to change both structure and behavior. The better use of data in the sales and marketing process requires changes to the way that people sell, the way that leads and accounts are allocated to sales people and territories, and the way that performance is measured. These type of changes can take a long time.
But with the current B2B shotgun marketing and sales techniques working just 1 out 667 times, the upside of change is immense.
Thanks to Paul J. D’Arcy is a CMO, entrepreneur, and writer based in Austin, Texas