Enterprises are striving to find greater meaning in the massive amounts of data they generate and save every day. Machine learning is providing the needed algorithms, applications, and frameworks to bring greater predictive accuracy and value to enterprises’ data sets and contributing to diverse strategies succeeding.
Apple’s Siri automated assistant, pre-approved credit card offers, saving and investment offers from your bank, suggestions on Amazon, Expedia or Netflix are all examples of machine learning in action. What all these uses have in common is that each looks to create the highest quality prediction possible of future behavior based on history. Machine learning excels at solving complex problems that are predicated on creating accurate predictions.
Unlike advanced analytics techniques that seek out causality first, machine learning techniques are designed to seek out opportunities to optimize decisions based on the predictive value of large-scale data sets.
Machine learning is proving to be effective at handling predictive tasks including defining which behaviors have the highest propensity to drive desired outcomes, which companies like Apttus use to drive business decisions like discounting or automated approvals. Enterprises eager to compete and win more customers are the applying machine learning to sales and marketing challenges first.
The Accenture Institute for High Performance recently completed a study that found the following key takeaways:
• At least 40% of companies surveyed are already using machine learning to improve sales and marketing performance. Two out of five companies have already implemented machine learning in sales and marketing.
• 38% credited machine learning for improvements in sales performance metrics. Metrics the study tracked include new leads, upsells, and sales cycle times by a factor of 2 or more while another 41% created improvements by a factor of 5 or more.
• 76% say they are targeting higher sales growth with machine learning. Gaining greater predictive accuracy by creating and optimizing propensity models to guide up-sell and cross-sell is where machine learning is making contributions to omnichannel selling strategies today.
• Several European banks are increasing new product sales by 10% while reducing churn 20%. A recent McKinsey study found that a dozen European banks are replacing statistical modeling techniques with machine learning. The banks are also increasing customer satisfaction scores and customer lifetime value as well.
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