Predictions Become Predictive (It’s Prediction Season)

I generally avoid playing in the prognostications game.  After all, it is difficult enough to understand what happened over the past year without projecting it forward or relying on simple extrapolation.  Nevertheless, nothing prevents me from relaying a few of the more interesting sales and marketing related technology predictions during the Predictions Season (and you thought it was the holiday season!).

I will begin with James Cooke and others at Nucleus Research who wrote:

We’ve all heard “data science” creep into the CRM conversation over the past few quarters, but mostly as it relates to marketing. Although marketing was the first area to get predictive, the future for all three pillars (sales, marketing, and service) of CRM is predictive – taking advantage of the intelligence of the software to look forward, not just track progress. Some of the most interesting opportunities are in customer service, where better data about customer’s habits and the products and services they use can be used to proactively support them (think predictive maintenance of automobiles, for example). The concerns about customer data are less prevalent on the service front than in sales and marketing because they tend to opt in. The challenge for companies will be in addressing the human barriers to adopting process changes driven by more predictive and proactive CRM.

While I agree that marketing has taken the lead on predictive analytics with a focus on lead decisioning (e.g. scoring; nurturing vs. promoting to sales; customizing campaigns at the lead level based on behavioral, firmographic, and biographic forecasting) and best customer cloning (determining the “ideal customer” and providing similar leads), predictive has helped close the gap between sales and marketing.

For example, predictive scoring allows marketing to limit promotion to the top N percent of leads, resulting in much better set of Marketing Qualified Leads (MQL) being sent to sales.  While fewer leads are being distributed over the Sales/Marketing Wall, they are of a much higher quality.  Furthermore, because the leads are scored and the predictive companies are beginning to provide insights around those scores, sales can begin to develop confidence in the MQLs passed to them.  The funnel attrition rate from marketing qualified leads to sales qualified leads should decline sharply.  This is a point made by Lattice Engines and reinforced by today’s strategic partnership announcement between Infer and InsightSquared.

Infer-InsightSquared-Activity-Scorecard
The Infer Activity Scorecard within InsightSquared stratifies leads by Infer score. The tool answers the question, “How are different quality leads getting worked?”

You also will hear fewer arguments between the parties about lead quality.  Marketing has long complained that sales ignored their leads while sales complained that marketing sent too many junk leads resulting in sales quickly cherry picking the lists.  Predictive tools should help eliminate this infighting by emphasizing lead quality over volume while providing insight into which leads are dormant.

Infer-InsightSquared-Lead-Aging
The Infer Lead Aging report within InsightSquared helps answer the question “Are our best leads getting overlooked?” According to Infer, “this report lets leadership quickly zoom in on high quality leads and ensure that no good lead gets left behind.

Likewise, nothing prevents sales reps and sales ops from playing the cloning game and identifying similar companies of their own.  Historically, this was done via peer searching where reps took a single customer and found companies of a similar size in the same industry.  Ideal Profiles allow for a similar process, but one based upon the attributes of a group of customers.  These profiles are much more sophisticated in that they build their models from many customers instead of a single profile and utilize potentially thousands of business signals to determine the ideal profile.  Furthermore, many of the business signals may have been previously unknown because they were not available.

We are also seeing sales acceleration tools that

  • Make sure that customer questions and requests aren’t ignored.
  • Provide updated profiles and alerts to reps prior to meetings.
  • Warn service departments of pending customer sales calls so that the problem is given a higher priority.
  • Deliver messaging advice to reps around what to say and which products to lead with.

Thus, predictive analytics and machine learning are becoming more proactive and prescriptive.  The next few years are likely to transform marketing, sales, and customer support.

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