Predictive Analytics Will Transform B2B Sales & MarketingExecution

Consumer marketers have become adept at driving revenue based on predictive analytics. Potentialcustomers are routinely scored on a wide variety of attributes from lifestyle to promotionreceptiveness. These scores allow consumers to be segmented into groups based on sharedinterests, purchase likelihood, and total buying power. By starting with highly differentiatedsegments, marketers can design programs that are highly relevant and effective.

This is not the way that B2B sales and marketing works in most organizations today.

Yet, B2B is a ripe environment for predictive analytics: selling costs are high, sales probability islow, and resources are very expensive. While the language of B2B marketing and sales is full ofreferences to probability -- customer funnels, response rates, conversion rates, close rates, call-t--close ratios -- it's rare to see B2B organizations leverage prospect and customer data to scorecustomer attributes, build discrete segments, and allocate resources to maximize the conversion andrevenue.

But all of this is about to change. Over the next five years, common consumer marketing techniqueswill 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 spendmore time interacting with companies online than they do with sales people in person or over thephone. For every successful sales call they attend, a typical prospect may spend hours interactingwith content, reading forums and blogs, and testing sample products. In today's world, every buyeraction 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 CRMand 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 andcomplex, B2B sales and marketing messages tend to be narrow and simplistic. The patterns thatwork most consistently are destined to be forever repeated. For prospects, this means that they areoften hit with messages and a pitch that ignore the nuance of their particular needs andsegmentation. 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 marketingcampaign, 60% of leads are accepted by sales, 50% of accepted leads become opportunities, and25% of opportunities close. When you look at the full marketing and sales funnel, a pathetic 1:667targets becomes a closed deal. Using predictive analytics to improve any stage of the funnel has thepotential to create incredible value.

Sales resources are expensive and easily tiered: It's not uncommon to see a three-tier sales modelwith 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 valueopportunities. Unfortunately, the allocation of accounts is typically very crude with simplisticmeasures like revenue or employee count determining which accounts go to a particular team. Byusing predictive analytics to allocate resources based on real-world potential, sales teams couldincrease revenues while reducing the cost of sales.

Marketing programs vary greatly in expense and effectiveness: If you have a stalled prospect thatyou want to move, a marketer has many choices. They could send an email, send a direct mail, invitethem to an educational seminar, or bring them to a hospitality event. The continuum of marketingcosts 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 probabilityand highest value segments. To do this, however, marketers need to use predictive analytics to scoreprospects based on their probability of purchase, their potential buying power, and the likely impactof a particular program or technique.

While smart organizations are beginning to put the foundation in place to better leverage data in themarketing and sales process, real obstacles still exist to efficient use of predictive science in mostB2B organizations. First of all, one legacy of sales-sourced CRM data is a mess of information that isinconsistent and difficult to leverage. Second, the new art of data-driven marketing and salesrequires 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 thesales and marketing process requires changes to the way that people sell, the way that leads andaccounts 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, theupside of change is immense.

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Tags: B2B Marketing, B2B Sales, Customer Segmentation, Predictive Analytics, Prospect Scoring

http://scienceofrevenue.com/2012/09/11/why-predictive-analytics-will-transform-b2b-sales-marketing-execution/