One of the hardest things to do in business is to plan for the future. With so much to deal with on a day-to-day basis, it's easy to take sales forecasts at face value. Sales managers are under pressure to demonstrate progress in their accounts and in an effort to fulfil executive expectations, it’s not surprising that the probability of closing deals is overestimated. And so begins a chain of events, with resources invested in poorly-qualified opportunities and plans made on the basis that deals are ‘in the bag’.
When I look at the sales forecasts submitted to my CRM by the sales team, how can I have any confidence in those estimates without interrogating each opportunity individually? Salesforce, for example, attributes a ‘probability of close’ at 75% for opportunities that reach proposal stage, yet we know from both our own research and from industry reports that businesses struggle to close even 30% of the opportunities that reach proposal stage. And even though sales managers are free to assign any value they like to the ‘probability of close’, at best it’s just an educated guess.
When this type of guesswork is embedded within the sales process, it’s easy to find opportunities to pursue, proposals to write and sales projections to build plans on. And managers love to see activity! Unfortunately, because this activity is founded on best guesses, it comes at the expense of sales effectiveness; resources are wasted pursuing the wrong opportunities, while the potential of viable opportunities goes unrealised.
Sales forecasting, according to Sales Benchmark Index takes up 37% of sales management time. What is unclear is how much of this time is really adding value in terms of qualifying the opportunity, and how much is spent in justifying the decision to commit additional resources to the opportunity.
Data science offers the potential of a dramatically reduced sales forecasting effort and more accurate sales forecasts by automated opportunity assessments. By using historical procurement histories and competitive positioning derived from corporate repositories and CRMs, sales teams no longer need to guess probabilities of close. Instead, they can use an automatically generated probability of close that is based on data and provides a rationale for the values it surmises.
Helping sales reps by automatically qualifying their sales opportunities gives them more time to get on with the business of selling and gives sales managers confidence that sales forecasts are based on consistently applied rules and rationalisations.