Improving Your Sales Forecast

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The Importance of Organisation Wide Sales Process A well defined sales process that’s adopted across a whole organisation helps leaders make decisions and assess the likelihood of a deal closing (Rhett Power, 2019). An accurate sales forecast helps you track progress, identify potential issues and to find ways to avoid or mitigate them, before the end of the forecasting period (Fowler, 2017). This lesson will focus on the benefits of having a well defined company wide sales process.

Embracing Uncertainty There are a multitude of internal and external factors that can impact a sales forecast.

“Our tendency is to overestimate short term change because of our inflated hopes and expectations and when cold reality fails to conform to our inflated expectations we do the opposite, we underestimate the long term implications” – Paul Saffo, Embracing Uncertainty: The Secret to Effective Forecasting, n.d (Ted Talk)

Utilising Analytics It’s not uncommon for sales teams to provide significant discounts in order to speed up the customer buying process. Pricing transparency it encourages the team to revise their pricing strategy as they'll be able to pin point other deals with similar customers. (Shelby, 2018).

Utilising AI & Machine Learning with Tact

True or False? Forecasts derived from AI & Machine Learning should replace the forecasts provided by the sales team

Implementing data-driven forecasting technologies properly. There’s a fine balance when it comes to deploying predictive analytics to sales forecasts. It’s important to involve your sales team in the implementation of new technologies as it gives them ownership of the tool and will be far more likely to be adopted (Shelby, 2018). At the end of the day, technology should augment but not replace the forecasts provided by the sales team, as there are some things that predictive models won’t take into account like new competition entering the market. Therefore it’s important to continually assess the results produced by the predictive models against the forecasts generated by the sales team (Serven, 2020).