Sales Analytics: Data-Driven Forecasting for Better Quota Attainment

“A sales rep, an accountant, and a data scientist walk into a bar…” Is there really a joke in here somewhere? A recent Research Report will search for common ground in the world of business analytics, and explore how both B2B sales professionals and their operational counterparts can benefit from the adoption of best practices that support the use of sales analytics solutions.

Traditionally, B2B organizations have regularly pressured their front-line sellers to report, up the food chain, on all of their current and pending deals, particularly regarding the size and timing of each anticipated closed opportunity. Unfortunately, all too often, reps tell their superiors what they think the bosses want to hear. They also tend to overly rely on their gut instincts, “happy ears,” and “sandbagging” when communicating such information. Influenced by pending commissions, spiffs, and President’s Club vacations, reps and managers alike unfortunately let their emotions dictate the contents of the sales forecast.

Sales Forecasting is No Laughing Matter

The problem with this scenario is that, to paraphrase Star Trek, it involves too much Capt. Kirk, and too little Mr. Spock. A sales forecast based on gut feelings – “I’ve got a really good feeling about this deal, so I’m moving it to 90% likely to close” – is far more likely to yield an ineffective estimate of near-term sales results compared with a data-centric forecast that is devoid of emotion. Aberdeen’s Big Data for Sales: Are We Ready?, highlights the fact that more accurate sales forecasting is directly associated with better business results such as higher quota attainment by teams and individuals, annualized growth in revenue, and average deal size. Stronger forecasts can also contribute to success in reducing typical sales cycles. This is because delivering a more accurate sales forecast is not an exercise conducted for its own sake. Rather, many other corporate lines of business – supply chain, logistics, customer service, human resources, etc. – are better able to deliver just-in-time services when they know more precisely how much business they will be supporting immediately following the close of the selling period.

Enter the world of the corporate finance department and their brethren in IT discover that the business intelligence (BI) capabilities provided by the analytics tools at their disposal offer a significant amount of data-centric guidance to sales leaders able to recognize this bigger picture. To support this theory, Aberdeen’s Business Intelligence research practice recently conducted its “Business Analytics 2014″ survey, which included 83 respondents (among 664 total) specifically holding sales and sales leadership responsibilities. In Figure 1, we identify the business pressures that companies most frequently cite as barriers to producing a sales forecast efficiently and accurately.

Figure I: Key Sales Business Pressures Associated with Accurate Sales Forecasting

Most of these pressures revolve around problems with data: not enough of it, insufficient ability to analyze it, and a disconnect – think “garbage in, garbage out” – between the content provided by the field and the strategic decisions being made based on its limited realism. The issues here need, even before considering the adoption of technology applications to support the forecasting process, to be addressed by some basic business competencies that the more successful companies within Aberdeen’s research are more likely to already have in place.

Leading Companies Adopt Smarter Best Practices

The sales-oriented survey respondents were segmented into Leaders and Followers in order to help understand how the most successful organizations achieve their business results. In Figure 2, we see the variance in adoption rates of three business capabilities that support the kind of data-driven decision-making required for better sales forecasting.

Figure 2: Core Competencies Support Intuitive Selling and Forecasting

Leaders are 17% more likely than Followers to have an open exchange of operational data across business functions. Aberdeen’s report, Analytical Collaboration: The Whole is Greater than the Sum of Its Parts, details at length how all lines of business benefit when data is shared and folks in different roles put their heads together. A data point or insight from the sales team could have enormous value to analytical minds working in other functions, and vice versa. For example, customer service data on a particular client might tell a sales team why they are suddenly getting stonewalled and additional data could help mold a fresh approach. Open exchanges also help prevent the great analytical sin of data siloing.

Leaders are also 33% more likely than Followers (48% vs. 36%) to actively address the seamy underside of B2B selling – the cost of losing or exiting deals, and the harm caused by discounting. They also address the more positive activities associated with providing sellers and channel partners with both financial and non-cash incentives for both measurable achievements and desired behaviors. All of these common sales activities, ostensibly undertaken with the goal of maximizing quota attainment, are not immune from the kind of emotion-driven static referenced above. The use of analytical and business intelligence tools, however, can help isolate those individual contributors, sales regions, product lines, pricing strategies, and contest / spiff programs, which are more or less likely to support successful deal closures. For example, a well-oiled collaboration between sales and finance teams, supported by the kind of customer data integration described in The Path Best Taken: Leveraging CRM in Pursuit of the Elusive “Single View of the Customer” can more effectively identify which existing, high-value customers should be offered small discounts to motivate renewal deals. Additionally those unprofitable accounts that should only be re-sold at full cost, because of their slimmer existing profit margins, can more readily be called out.

Sales Analytics: Data-Driven Forecasting for Better Quota Attainment