How to Leverage AI for Proactive Customer Service (Video)

SAP Customer Experience Vice President Volker Hildebrand explains how companies can deploy machine learning for predictive maintenance and more proactive customer service in this clip from CRM Evolution.

Learn more about CRM evolution 2019.

Read the complete transcript of this clip:

Volker Hildebrand: Predictive maintenance is when you have products that you sell to your customers that need to be maintained or serviced, repaired, parts need to be exchanged, maybe an oil change, or a filter exchange, other things repaired, or when something's broken. Predictive maintenance is basically leveraging data from IoT enabled products, because you need information about the usage, the conditions, kind of temperature. How many hours of operation for this printer or machine? What's the output?

You can have certain sensors that measure the temperature when it reaches certain level where there's going to be an alert as well. Then combine it with the machine learning to basically predict when the machine is going to fail, when the part's going to break. When the oil needs to be exchanged before the entire engine breaks down. Basically, do this ahead of time so you can schedule your fuel service technicians to go out and replace the part, perform the maintenance. Do whatever needs to be done before the machine or the equipment breaks down, or the product stops working which definitely improves customer satisfaction, because they don't have to deal with the repair and an engine breakdown. It also reduces overall downtime of equipment.

Think about if you're selling machinery and equipment. In a B2B context, for example, that's really important that these machines operate all the time, because more downtime means loss of revenue for your customers, and also, lower cost of service because typically if you fix something before it breaks, the cost of fixing it are typically lower, because there's not no additional damage. You also can plan much better and also reduce the cost of service operation.

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