The New Definition of Data-Driven Sales


Business-to-business sales is, was, and always will be a numbers game. From the paper-based forecast forms and quarterly business planners of the analogue days of selling, to today’s world of smart systems, mobile sales force automation (SFA), and real-time dashboards, B2B sales is ruled by data.

And with every upgrade of an SFA system, and every deployment of a new marketing automation platform, sales enablement solution, or sales tool, more data is generated — digging deeper, providing greater transparency, and creating new insights based on the data.

As a result, forecast accuracy with strong funnel values and supporting activity reports is no longer sufficient to be considered data-driven. Integrated demand generation/sales qualified lead data, opportunity volatility indices, or customer connectedness ratings provide increasingly more precise visibility to the best ways for identifying new business, closing business, and retaining or getting new customers.

Connecting and interpreting all this data has driven the growth of analytics for sales. And it’s this active use of analytics that now raises the bar for what it means to be data-driven.

The Importance of Active Analytics

Monthly forecast, total pipeline revenue value, or pipeline amount by sales stage present a one-dimensional view of the data. Sales analytics combines data points potentially from multiple systems to create a multidimensional perspective and deeper insight into sales performance.

Taken at face value, one-dimensional data is essential to understanding and reporting the current state of the business, but provides little insight to the drivers of sales. SFA’s ability to capture, combine, and compare multiple data points as analytics enables salespeople, managers, and leaders to analyze the dynamics of selling.

Average sales cycle length measures the time it takes opportunities to go from qualified to closed, an important data point. Analyzing sales cycle length by sales phase highlights where deals get stalled or slow. Identifying where this occurs most commonly in the sales process allows sales leaders to surgically target training and prioritize resources.

As an example of how active analytics can drive action, let’s consider stalled deals. A stalled deal can clog and distort pipeline data and quarterly business reports. Using analytics to determine average time-in-phase for winning deals creates a threshold for determining a stalled deal.

Analysis of winning opportunities will establish time-in-phase guidelines. Opportunities that exceed the time-in-phase range can be classified as stalled. In some cases there are perfectly good reasons for stalling, but statistically these are deals that have a much lower potential to close.

Pulling stalled deals from the pipeline allows the sales rep to focus on active deals and develop a nurturing strategy to continue to develop the stalled deals. Analysis of closed deals on a time-in-phase to conversion creates a digital footprint of success. Opportunities that fall outside those guidelines should be removed or quarantined until something happens to change their status.

If an organization can identify where these opportunity fail points occur, steps can be taken to improve phase conversion rates. Sales enablement teams can look at the messages and tools available to sales at the fail point to improve conversions, or specialist resources can be deployed to push through more deals.

The Higher Bar for Data-Driven Sales

The active use of analytics is just one requirement to be considered data-driven in 2016. SFA systems have done a great job of providing salespeople with access to internal account and customer data. The inclusion of complementary, external account and contact data sources is now easily achieved, cost-effective, and a requirement to be considered data-driven.

Now the salesperson must reciprocate by consistently and objectively maintaining the currency and accuracy of each opportunity in the system. This means sales teams must move past the adoption issues. Analytics teams only have power when they connect meaningful data points. The most significant data in sales is active opportunity data. It powers not only the forecast and pipeline reports, but it also connects sales activities (what we do) with customer outcomes (what they do).

The significance of accurate opportunity assessment data becomes critical when behavioral data gets added. Salespeople and customers spend all day using various forms of technology: email, phone/voicemail, chat, webinars, and smartphones — the list goes on.

Each of those applications or devices is generating data about what we say, hear, and do. Connecting behavioral data to the activities and outcomes of the sales pipeline can create a model of sales precision that will be a competitive advantage for the early adopters and an eventual requirement for survival.

It’s easy for leaders to say they embrace data-driven sales management, given the sheer volume of data and reports available. It’s an entirely different proposition to actively embrace the power of data, leverage the insights from analytics, and prepare for the digital transformation that all salespeople, managers, and leaders will face this year and beyond.


Forecast accuracy with strong funnel values and supporting activity reports is no longer sufficient to be considered data-drive.”

Joe Galvin | Chief Research Officer, Vistage
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