We often get asked: what does AI have to with the Aftermarket? Isn’t the Aftermarket all about Field Service, and sending technicians out into the field when something in your Installed Base needs servicing or a customer needs parts? Well, yes, that’s the problem. The problem with the Aftermarket is that manufacturers are waiting for their customers to call them. They are being reactive, instead of being proactive and anticipating customers’ needs. That is where AI can help.
At a broad level, Entytle analyzes manufacturers’ historical ERP, CRM and Service data to predict when their customers need specific parts or service, and when.
How Does Entytle Help You Be More Proactive?
Entytle has developed proprietary algorithms that look at huge amounts of historical data. We look at everything from orders, to opportunities, to activities, to service tickets and more. Entytle extracts patterns from data such as parts consumption rates, true purchase intervals, mean times between failure and more.
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Unlike Business Intelligence tools that generate crude averages for similar metrics, Entytle metrics are specific to customer sites. This is critical because every customer site is unique and crude averages make for poor predictions. We apply sophisticated machine learning algorithms like supervised and unsupervised learning, nearest neighbors, cohort and trajectory analysis and anomaly detection. The installed base is segmented, divided into cohorts and tracked over time to make precise recommendations about customers’ needs. Another aspect of data science called reinforcement learning, provides continual feedback to the algorithms so that the algorithms learn and improve their predictions over time.
The output of the underlying data science is an easy-to-use SaaS application that lives in the user’s CRM system. This is critical, because data science insights are only as good as the action behind it. Therefore, making the insights consumable and eliminating any friction between insight and action is key.