Machine Learning: Innovations In The Sales Industry

Machine learning is a technology that combs through massive amounts of collected data to glean insights that are not picked up on by human programmers. Some examples of machine learning in practice include Spotify’s song recommendation and Google’s search engine optimization. While machine learning has and will continue to influence many sectors, we want to discuss the ways machine learning is – and will continue to – change the landscape of sales.

Machine learning can create AI apps that can handle transactions with customers

According to Harvard Business Review, consumers will complete 85% of their transactions with companies without human interaction. AI apps, like chatbots, voice assistants, and order processing engines, can meet this demand for 24/7 customer service while freeing up human sales force to build relationships with customers.

Machine learning can distribute sales best practices across companies

While machine learning will not replace human sales forces, it will help sales teams make better decisions. For example, as a salesperson enters data into the system, the machine will be able to make more and more accurate predictions by monitoring and learning from data culled from across the organization. Additionally, machine learning can help you more accurately automate your customer segmentation into like-minded groups with similar preferences. This information, in turn, helps the salesperson choose promising leads, effectively follow up with these leads, and, ultimately, sustain the relationship.

Machine learning uses collected data to predict consumer trends

Most sales teams have years and years of collected data about consumer habits, wants, and needs. Now, machine learning technology can help companies be more proactive in predicting consumer needs by utilizing big data in ways that help meet customer needs. This is likely the biggest benefit of machine learning: the machine will continue to learn based on the failures and successes of its predictions, always becoming more efficient and accurate.

One example of this idea is machine learning-powered recommendation engines. Take a Content Based Recommendation (CBR) in particular. CBRs use use logistic regression, SVM, and decision tree to match users' preferences with item descriptions and attributes.

A machine could also use data from previous deals like company size and solutions requested to predict factors about a current deal, including the likelihood the deal will close and the length of time required for closing.

Machine learning is helping B2B and B2C retailers close more deals more quickly, segment more accurately, and boost customer satisfaction with targeted offerings. If you’re ready to explore machine learning, ELEKS’ experts in machine learning configure predictive modeling solutions that will help you make data-driven and consumer-focused decisions. Let us know your big data goals, and we’ll customize a solution that works for you.