Harnessing AI to get real time information and better customer insightBy Joseph Walsh,
Part three: Providing agents with real time information
The more informed we are, the better decisions we can make. It’s much easier to give people the right advice when we have all the right data to support their query. As the Internet of Things (IOT) reaches into more and more homes, it’s sending back a wealth of information in real time. This data is golden to any contact centre agent. But what could it mean for the future of buying and selling household items?
Have we bought our last washing machines?
Whirlpool* is experimenting with its business model by shifting to selling washes as opposed to machines. This approach mirrors the circular economy by using fewer materials in the manufacturing process and changing what customers buy. Rather than buying a washing machine which may need replacing in three years, manufacturers are looking to sell customers a set number of washes – because it’s the wash, not the machine, the customer’s interested in.
The machine is installed with an operating system and sensors connected to the internet. This means it can send real-time data back to the product database and an analytics engine in the manufacturer’s cloud. This real-time collection, assimilation and analysis of data lets Whirlpool use predictive analytics to determine when a washing machine might need maintenance, as well as making suggestions to the service agent about any other issues flagged up.
As a result, the advisor can proactively call the consumer with evidence-based information to make an appointment for a technician to visit. Equally, if the customer calls, the agent has a real-time machine-status dashboard available. Machine learning and AI run in the background, helping them make informed decisions, saving the customer money and time by fixing problems before they happen.
IOT is not just changing customer service, it’s changing commercial models
The same principles apply in the B2B market. General Electric uses predictive analytics for its industrial machinery - modelling past data to predict future outcomes for design and price offers accordingly. For example, GE measures, monitors, and models the performance of its aircraft engines to predict when they need to be serviced, to forecast future costs, and to structure its service system efficiently. As a result they’re able to waive standard fees or costs if certain metrics aren’t achieved, but receive gains when they are. Engines are priced and guaranteed by the flight hour – which means that if an engine goes offline unexpectedly, GE bears the cost and not the customer. By the same token, if costs are lower than expected, GE benefits from that margin.
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*Whirlpool Corporation, Whirlpool Corporation, IBM Collaborate on Cognitive Solutions for Connected Appliances, 2016