“We really can’t forecast all that well, and yet we pretend that we can, but we really can’t.” – Alan Greenspan

Three years ago, the former Fed Chairman confessed this to a dumbfounded Jon Stewart on the Daily Show while discussing the errors of the financial crisis. If the world’s leading forecasting gurus aren’t able to consistently and accurately predict the future, then it’s hardly astonishing that organizations find it tough to get a grip on their sales forecasts too.

According to CSO Insights, approximately 54% of all forecasted deals by sellers never make it to the finish line. As it turns out, sales forecast is still a field that relies heavily on a combination of gut feel and legions of spreadsheets. The challenge here is that people bring their own preconceptions that lead to wildly different definitions and expectations. Besides, the spreadsheet models that are used aren’t sophisticated enough to take into account the whole host of factors that might impact sales. As a result, the accuracy of numbers that are rolled up through the sales organization gets diluted and sales forecasts are way off the mark.

Inaccurate sales forecasts not only lead to improper planning and budgeting of resources, but also have   a dramatic impact on top line and bottom line performance, share price and investor confidence. Not to mention the future career prospects of the individual sales rep and his/her manager.

What’s the alternative?

Take the “Predictive Analytics Plunge!”

What business leaders need the most is forward-looking, predictive insight that will help them stay ahead of the curve. Businesses have a lot to gain by replacing manual forecasting processes with a new set of technologies driven by data science- an approach based on predictive analytics that relies on a combination of statistics, machine learning, data mining and modelling.

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These processes help “connect data to effective action by drawing reliable conclusions about current conditions and future events,” explains Gartner Research Director Gareth Herschel.

According to Entrepreneur Magazine those who use predictive analytics report levels of forecast accuracy to be as high as 82% on a deal-by-deal basis. The number is quite staggering when compared to the average win rate of 46% reported by laggards in CSO Insight’s 2016 Sales Performance Optimization Study. The key takeaway is: predictive sellers significantly outperform retrospective sellers across business metrics.

So, how does it work?

By using data, data and more data.

Predictive algorithms make use of thousands of data points from both internal and external sources. Internal sources essentially include the company’s internal CRM and marketing automation data, while external sources consist of data points as diverse as company revenue and income, executive management changes, number of employees, number and location of offices, credit history, social media activity, press releases, news articles etc.

Predictive algorithms then use data science to spot correlations between thousands of variables (historical data) and the final outcome (sales) to predict the likelihood of closing each prospect.

These algorithms can rapidly recalibrate themselves in response to emerging patterns of data. For instance, if an individual company makes an acquisition or undergoes geographical expansion, they can quickly pick up the nuances and adapt to changing circumstances, thereby ensuring the precision of predictions even in the most dynamic business environments.

Unearthing actionable insights

While gazing into the future is great, the more important issue is to arrive at practical findings that can help you correct various problems in order to improve the sales performance.

Sales deals, for instance, often get blocked at different stages of the sales funnel, which can lead to lost sales if the problem is left unattended.

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With the help of a sales funnel report for individual sales reps, with historical sales reports broken down by funnel stages, a sales manager can be alerted to potential problems in a rep’s selling process.  For instance, if a deal languishes in a stage for a month versus the average stay of 14 days, it may signal

neglect.  Another example could be the conversion rate for closed deals. After concluding the negotiation stage for 15 deals, if you get purchase orders for only 3, the loss rate stands at 80 percent. An 80 percent loss rate may signal potential problems if your company’s average loss rate is, say, 30 percent.

Armed with this information, the sales manager can now prescribe practical coaching advice and implement difference-making tactics to resuscitate sales.

Similarly, predictive analytics is also emerging as an important tool in sales enablement. It leverages algorithms that take into account every imaginable factor which impacts a customer’s decision to buy. This helps in unearthing even the most obscure buying signals that help sales organizations contact prospects at the right time and with the right product.

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For example, for a company selling office supply, a key signal to contact a prospect would be when they put out a press release about expanding to new cities. On the other hand, the number of design engineers a prospect is hiring coupled with the number of workstations in use could signal a sales opportunity for a company selling CAD-CAM software.

What’s the business value?

With objective and accurate predictions, it is easier to stay in total control of the pipeline and know exactly what will close and what won’t.  This ensures shorter sales cycles, higher rep quota attainment and an increase in average deal size, while reducing sales and marketing costs. Furthermore, it guides smarter decision-making by solving complex business questions in a fraction of time and also uncovers new business opportunities. It is for these reasons that predictive analytics is seen by many organizations as an ROI decision instead of a cost factor. Zendesk is one such example of an organization that has put the power of predictive analytics to work and transformed their sales forecasting into an exercise in precision.

The Bigger Picture

While it may be galling to discover that a computer thinking in 0s and 1s can get a better grip on the data than all of our human intuition, one can’t really argue if it works. Predictive analytics is revolutionizing sales forecasting by replacing the constraints of human inference and bias with objective models based on forecasting algorithms. To its early adopters will go the spoils, while the laggards will be left wondering what hit them.

Krittika Banerjee

Krittika Banerjee

Research Analyst at Aspire Systems
Fond of exploring contemporary technical and digital innovations, Krittika is always updated with what is new on this front. She writes about innovation and latest technology trends in various sectors.
Krittika Banerjee