Data is everywhere nowadays, and thanks to the combination of cloud computing, data science and enterprising companies, data analysis drives an increasing part of our lives. Yet human judgment continues to dominate B2B sales forecasting.

For example, the two most common forecasting methods (the weighted pipeline and “forecast categories”) are 100% judgmental. And most sales managers and sales operations managers would argue that forecasting is “more art than science.”

It is true that, unlike marketing, sales doesn’t naturally generate the thousands of data points required by statistical forecasting methods, for most SMEs at least. Does that imply that sales reps and managers are condemned to guesswork when discussing potential revenue?

Drawbacks of demand forecasting

Forecasters have traditionally responded to the shortage of company-generated data by turning the problem on its head. If your company’s unit sales are too low, derive your sales forecast from market data:

1. Estimate your market size (“MZ”)
2. Apply your market share (“MS”)
3. And there is your sales forecast (“SF”): SF = MZ x MS

This is called demand forecasting. It usually comes in more sophisticated flavors than the basic model above. Here are some of the most common refinements:

• Future market size is derived from the statistical analysis of past market data.
• Future market size is derived from an econometric representation of past market dynamics.
• Future market share flows from a model taking pricing, product mix and competitive pressure into account.

Describing these refinements makes the two main drawbacks of demand forecasting for SMEs obvious:

• How are you supposed to find that information - does it even exist?
• Where will you find the competencies to design and update these models?

Yet until 10-15 years ago, that was all professional forecasters had to offer, which explains why so many companies are stuck to judgmental methods. Thankfully for B2B sales managers and sales operations managers, the state of the forecasting art has changed.

3 change factors of sales forecasting:

1. Behavioral economics

Behavioral economists have shown that errors in human judgments are not random, but follow patterns that can be studied and corrected. Simple cognitive tricks will often improve forecast accuracy significantly.

Behavioral economists have also demonstrated that, judgmental forecasts have intrinsic value and, combined with quantitative forecasts, improve overall forecast accuracy. In a financial portfolio, each asset comes with its own combination of risk and return, but at portfolio level individual risks cancel each other out (to some extent) and the risk/return sum is greater than its parts. The same diversification mechanism is at work with combinations of judgmental and quantitative forecasting methods.

2. Big data

 Big data is non-conventional internal and external data sources. Thanks to our connected world, the data sets that are available for analysis and forecasting are now much larger than traditional company or market sales data. On the Salesforce AppExchange, you will find applications offering to refine your forecasts using:

• The activity of your prospects on social networks
• Your email traffic and the unstructured data in your mailbox
• The activity of your prospects on your website

3. Algorithmic modeling

Contrary to traditional statistics, this approach to data analysis doesn’t assume that data is following an underlying law. It takes data patterns as they are and explores their implications through intensive data crunching.

Algorithmic modeling is on the rise for three main reasons:

• The underlying mathematics are new and exciting
• As a computer intensive method, it benefits from advances in parallel processing and cloud computing
• It works!

It is well adapted to environments where data is relatively scarce and unstructured, just like B2B sales, which is why we at SalesClic have chosen to leverage its potential.

Takeaways 

• For years, B2B sales pipeline management has been a kind of sales accounting, based on static reports and ad-hoc methods.
• It is becoming much more dynamic, placing sales managers and sales operations managers in the position of modern financial asset managers: they can assess likely outcomes precisely and fine tune the risk/return profile of their portfolio (the sales pipeline).
• With Salesforce being the CRM platform of choice, most of the applications enabling that revolution are on the AppExchange. So my advice would be: why not try them out? The results may surprise you.

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