Practical Ways Machine Learning in Sales Function is  Boosting Profits

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Salespeople are in one of the toughest professions with high workloads. They can spend up to 33% of their day writing emails or on calls, 12% of the day is occupied in internal meetings, 17% on entering data to create reports and that leaves them only 17% of the time to prospect new leads. One of the findings published by Salesforce in their State of Sales report says that high-performing sales teams have been found to use sales intelligence tools for smart sales and reducing repetitive manual tasks.

How machine learning is shaping sales and surplus

Sales is a business unit that is required to ensure that selling is carried out efficiently and intelligently so as to deliver the most effective return on the company’s products and services.  It is one of the foremost business areas that have and will continue to benefit the most from AI tools.

Companies that used AI and machine learning in their sales strategy saw their leads increase by more than 50%, call time decrease by 60-70%, and cost reduction by 40-60%.

The sales function relies significantly on predictions and insights. Not surprising then that companies that used AI and machine learning in their sales strategy saw their leads increase by more than 50%, call time decrease by 60-70%, and cost reduction by 40-60%. Read on to find out how this was made possible.

1. Sales forecasting

Sales intelligence tools leverage machine learning uses to accurately predict sales. ML models learn patterns from the data to generate predictions. The prediction algorithm can be run on a cloud machine learning environment or a virtual machine. Predictions are written directly to a database and then distributed to business users through interactive dashboards. One source of truth for the entire company makes sales and reporting more transparent.

Sales forecasting using ML is time-series regression dependant. Time-series regressions are tasks concerned with the estimation of a continuous quantity (sales) with an additional time dimension and are of two types. The choice of model is dictated by the business problem at hand, data availability and a rigorous model testing process.

  • Auto-regressive models likeauto-regressive integrated moving average (Arima), seasonal auto-regressive integrated moving average (Sarimax), and exponential smoothing predict future sales exclusively based on values of past sales and generate predictions by finding trends and seasonality patterns.
  • Multivariate models include linear regressions, neural networks, decision tree-