Algorithmic-Guided Selling Will Have Significant Impact on Sales Productivity

Nearly 51% of sales organizations have already deployed or plan to deploy algorithmic-guided selling in the next five years, according to a  survey by Gartner. Intended to augment more traditional sales tools, such as sales playbooks, algorithmic-guided selling uses sales data to boost the seller’s ability to engage with prospects, manage the buying process and generate quotes.

In the survey of more than 250 sales leaders across 11 industries globally, respondents evaluated 47 established and emerging technologies to assess how they are currently being used, the return they provide organizations and what bets sales leaders would place on their future importance. “Although it is a newer, more complex sales technology, algorithmic-guided selling has tremendous potential,“ said Tad Travis, Research VP at Gartner. “Compared to other technologies in the survey, algorithmic-guided selling stands out as one of the most sophisticated and complex sales execution technologies to improve sales productivity.“

Algorithmic-guided selling leverages AI and existing sales data to guide sellers through deals, automating manual sales actions while reducing the need for individual seller judgment in the sales process. However, despite its potential, its effectiveness relies heavily on the data it draws on to guide sellers. As a result, incremental changes in the quality of the data source can lead to disproportionate changes in the accuracy of its predictions and ultimately ROI.

“A sales organization with inaccurate or missing sales data will probably not see the same value in this technology as one that ensures data quality prior to launch,“ said Travis. “Nevertheless, it is a technology most sales organizations are actively exploring.“ For sales leaders considering the adoption of algorithmic-guided selling, Gartner recommends they identify points in the sales process where automation would have the greatest impact to augment seller judgment or automate manual operations, implement strict data hygiene principles prior to launch to ensure accurate recommendations and prepare underlying content for integration so that it is available for algorithmic-guided recommendations.