In today’s fast-paced business environment, customers want a service that is immediate, accurate and efficient.
This can be a challenge for businesses that are dealing with high volumes of customer interactions. A decision engine can help businesses meet these customer expectations by providing a way to automate decision-making.
Decision engines use data and analytics to identify the best course of action in a situation. This can help businesses take the guesswork out of customer service and ensure that every interaction is handled in the most efficient way possible.
How Does a Decision Engine Work?
A decision engine is a piece of software that makes decisions based on data. The data can come from many sources, including customer interactions, business processes, and sensors.
This data is then analysed by the decision engine to identify patterns and trends. Based on this analysis, the decision engine will decide the best course of action to take.
The decisions made by a decision engine are typically based on pre-defined rules. For example, a business might have a rule that all customers who call the customer service line should be offered a discount.
A decision engine can make these decisions automatically, with no human intervention. This can help businesses to speed up their decision-making process and make it more consistent.
Automating customer service with decision trees
Consider a business that receives a high volume of customer service calls. In many cases, the questions asked by customers will be similar. For example, a customer might want to know how to return an item, or how to track an order.
To deal with this volume of calls, the business could use decision trees. The tree would start with a question based on the customer’s input and then branch out to provide different answers depending on the customer’s response.
For example, the tree might start with the question, “What can we help you with today?” If the customer responds with “I need to return an item,” the tree would then provide instructions on how to do that. If the customer says “I’m trying to track an order,” the tree would provide different instructions.
This system would allow the business to automate customer service, as calls could be routed to the decision tree and customers could get answers without speaking to a human agent.
This would free up agents to deal with more complex issues, and it would also reduce wait times for customers.
Other customer service areas that could be automated similarly include account management, product support, and billing inquiries.
Dealing with complex customer service inquiries
There will always be some customer service inquiries that cannot be handled by a decision tree. In these cases, businesses can use decision trees and rule sets together to provide a more personalised service.
For example, consider a customer who is trying to cancel an order. The customer might have ordered the wrong item, or they might have changed their mind about the purchase.
In either case, the business needs to gather information from the customer before they can proceed.
A decision tree can ask the customer questions about their order, such as when it was placed and what item they ordered. Based on the customer’s responses, the business can then decide about how to proceed.
For example, if the order was placed recently and the customer wants to cancel it, the business might offer to refund the purchase.
Another rule set might state that orders can only be cancelled within a certain time frame. In this case, the decision tree would need to take this into account and appropriately respond to the customer.
Based on the answers, the decision tree would then provide the customer with instructions on how to proceed. This could include a refund, a replacement item, or a call to an agent.
if it’s determined that the customer spoke with a representative, then the call could be transferred to the customer service department.
Once the call is transferred, the customer service representative can then handle the inquiry. This would allow businesses to automate customer service and still provide a high level of personalised service.
Conclusion
Decision engines are a powerful tool that can help organisations enhance customer service levels.
Automating means never having to worry about human error, and the ability to rapidly process data means that decision engines can provide near-instantaneous results and lead to better customer experiences and increased satisfaction levels.
