Flash Forward: Remember When We Called it "Predictive" Analytics?

Isn't it funny how rapidly popular business terms come into vogue, and then serve as a big forehead "L" when their 15 minutes of fame have expired? Much as "big data" has been virtually retired, the moment is soon arriving when prescriptive analytics for B2B sales professionals will have wiped out the current rage around “predictive data.”

Once upon a time, Aberdeen's research looked at analytics from a perspective limited to how Best-in-Class companies generated more accurate sales forecasts. Thisresearchwas actually quite valuable at the time. As other line of business leaders such as customer service, human resources, and supply chain VPs were being tasked with running leaner, just-in-time departments, their tolerance for wildly inaccurate sales forecasts fell to an all-time low. As a result, they began pressuring Sales Operations for a more realistic anticipation of the raw materials, hiring, logistics, and revenue that would soon be flowing through their individual domains.

In response, sales leaders began investing in (mostly cloud-based) solutions that amped up the rudimentary forecasting functionalities of CRM platforms, and learned to rely less on emotions, and more on facts in creating and weighting their forecasts. Predictably (pun intended) top-performing firms were early adopters, and their results evident.

图1：销售预测准确性，按一流和销售周期窗口

Moreover, the research revealed that companies deploying these solutions, and havingpredictable sales pipelines, achieved better business results around a variety of sales KPIs examined for year-to-year metrics changes.

图2：销售分析的早期采用支持年度绩效改进

Most of these sales analytics engines relied on the significant processing power of what were once leading-edge "big data" and business intelligence products. These products analyzed enormous quantities of data to help sales leaders identify trends such as:

• Which products are more likely to be purchased by specific types of buyers?
• 我可以使用地理，人口统计，公司和客户购买历史记录来更好地隔离哪些最有可能结束的交易和交易交易？
• How do we determine which deals are bad ones, i.e. more likely to end in no-decision or, worse, no-profit?
• Are there individual contributors, channel partners, regions, or sales teams who are more reliable in their forecasting?

寻求协助销售领导人——主要是以前的年代uccessful reps who traditionally had not been the sort of people to spend the day buried in spreadsheets -- with new ways to crunch data, technology solution providers began revving up their algorithms to help produce systems of forecasting weighting. Designing easy-to-use interfaces and dashboards, these applications gave sales operations practitioners enhanced abilities to accept raw forecast data from the field (complete with whatever sandbagging or happy-ears emotion that suited the rep or channel partner), and more objectively spin the dials by relying increasingly on predictive data than on gut feelings.

认为Moneyball用于销售，隐喻应该是有道理的。

A Little Pavlov Never Hurt a Sales Team

Thus, predictive analytics grew quite competent in illuminating the challenges of B2B sales managers with data. And yet, it still only tells us things such as: who is better at forecasting deal probabilities, what opportunity will more likely close, when will the contract be returned, and perhaps, even at which specific margin the deal will be finalized.

But, if we seek closure around the common vocabulary of journalism -‒ the five Ws -‒ these platforms are not as strong as they should be around "why." As in, why did a sure-thing opportunity not come through? Was it something the rep said? Something they didn't do? Do we really need to wait for win/loss deal post-mortems to learn how to better influence sales outcomes?

These questions bring us to prescriptive analytics, the newest wave of sales effectiveness technology enablers, in which the platform not only calculates deal probabilities, but prescribes legitimate coaching opportunities directly linked to the same data. Behavioral modification for quota-carriers, of sorts.

The possibilities here are awesome. Imagine this scenario: An enterprise B2B sales rep, Wendy, told by her manager that her Q3 pipeline isn't full enough, decides to elevate one of her current prospecting conversations, with Acme Widgets, to "opportunity" status in the CRM. This gives the deal a sense of formality and internal publicity that Wendy may not like, but as the boss needs to be satisfied, she enters a deal for $55,000 with an estimated closing date of August 1.

If the CRM is integrated with a true prescriptive analytics platform, the sales force automation (SFA) system does not simply accept Wendy's deal in toto. Rather, it alerts her that while her Acme deal has now been accepted by the official sales data system, the value and timing are actually $35,000 and October 15. "What the … ?! " Wendy exclaims. "I need $55k in early August to fill out my pipe!"

What is the rationale and logic behind this robotic, cold-hearted deal reduction? The holistic SFA "knows" Wendy's deal isn't as strong as she believes, because of any number of single or combined variables. Some of these data findings might be out of her control, such as the historical weaknesses of selling the specific product she's offering to Acme, or Acme's own history in dragging on negotiations or low-balling on price.

Others, though, provide an instant opportunity for hard-earned, professional sales coaching. For example, maybe her own forecasting history reveals over-confidence, or perhaps she hasn't absorbed the relevant product training that her firm knows leads to better sales wins. It could be that she hasn't registered enough BANT criteria about Acme, or she hasn't checked inventory or production capacity before making promises to a buyer. These or a hundred other potential weaknesses mean that Wendy wouldn't last very long on Bill Belichick's New England Patriots, because she simply isn’t doing her job. Or, at least, doing it well enough to earn the right to forecast independently.

在温迪（Wendy）镇定下来之后，她结束了这个不太虚构的故事，实际上是解决了自己的问题 - “现在我必须去找更多的管道！”- 通过解决方案，通过更具实质性的销售准备来改善ACME交易的解决方案，以类似的方式提升其他机会。

每个人都知道，销售是一款零和游戏的游戏，更好的管道导致了更多的配额，而错过了足球比赛。这个故事背后的规范性分析能力表明，如何利用企业内的销售最佳实践，并有效地缩放，以提供提供的需求coaching at an individual contributor level.

Let's Go to the Tape

The body of Aberdeen's research supports this assertion. Data investigated for September 2014'sSheldon Cooper, Sales Whisperer: Applying the Science of Data to the Art of Selling显示,49%的Best-in-Class companies provide "real-time coaching for specific deals, opportunities, or accounts," compared with 44% of Industry Average and only 32% of Laggard firms. These top performers back up this commitment by deploying sales technologiesmore frequently than under-performing survey respondents,such as portal-based libraries of marketing, sales, and training content, customizable sales playbooks, virtual or on-demand learning tools, and internal social collaboration platforms.

查看完整的研究报告这里.