HBR: Digital Exhaust for Sales

Mark Kovac of Bain and Company wrote an interesting piece on the topic of digital exhaust in Harvard Business Review.  The short piece, titled “Using Digital Exhaust to Improve Sales,” provides three examples of how software vendors are combining big data and analytics to provide new tools to sales management.

Kovac defines digital exhaust as “the data generated from the regular activities of a sales force or their customers, to change the behavior of frontline sales representatives in ways that dramatically improve sales productivity and effectiveness” and provides examples from three firms including Lattice Engines.

The first firm, Volometrix, was acquired by Microsoft last year.  Volometrix performs resource analyses to determine where sales reps are spending their time and which behaviors are positively correlated with sales performance.  Unfortunately, the use case Kovac provides is about a firm that realigned company priorities providing more time for selling and therefore “too much sales capacity.”  While efficiency is desirable, if software solutions result in efficiency improvements without much improvement in efficacy (ability to sell), then they will be resisted.  This isn’t too say that Volometrix doesn’t provide efficacy gains (I only know them from the story), but case studies which focus on cost savings (efficiency) over revenue gains (effectiveness) may create situations where sales reps refuse to cooperate.

The second vendor discussed is GoToMeeting which is performing voice-to-text semantic analysis and discerning which phrases and approaches are more effective.  The data gathered is anonymous (though I still see issues with recording calls, particularly in certain jurisdictions) and provides insights in how to more effectively sell.  The software sounds a bit like SalesforceIQ but instead of focusing on email analysis, GoToMeeting is using conversations.

Lattice Engines stratifies leads by probability of closing with the highest probability leads immediately sent to sales and the remainder held for nurturing.
Lattice Engines stratifies leads by probability of closing with the highest probability leads immediately sent to sales and the remainder held for nurturing.

The final case study was predictive analytics company Lattice Engines which helps firms improve “call response rates, close rates and average order value.”  Predictive analytics for sales and marketing is a growing class of recommendation software with a broad set of competitors including Leadspace, Infer, 6Sense, and Mintigo.  Lattice Engines combines first-party and third-party data sets to score leads and recommend sales approaches.  Unfortunately, Kovac focuses on the matched third-party data instead of the digital exhaust captured by the firm (he does mention loyalty scores and product purchase history).

There is a growing set of SalesTech vendors that are applying digital analysis to previously inaccessible datasets.  While sales remains an art, these solutions are shifting the sales profession from a craft to a science.  If vendors are to be successful, they need to focus more on efficacy versus efficiency.  While sales could certainly become more efficient by reducing non-productive administrative activities (and reps are always happy to reduce dead weight time spent tracking problems, navigating contract signatures, and entering data), the focus should be on top-line growth and exceeding quota, not cost reduction.  Otherwise, sales reps will resist their adoption.

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