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Automated Decision Engines

Automated decision engines use data and rules to optimize business decisions, adjusting as new information emerges. These tools work best for decisions that need to be made frequently and rapidly with information that is available electronically. Automated, real-time decision making can help a company test and learn from new customer experience efforts, with less human intervention. That frees managers to spend their time on more complex tasks.

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Our insights share how the right CX tools make customers’ lives richer and more fulfilling and strengthen a company’s economics by holding down costs and securing new revenue streams.

How companies use automated decision engines

Companies across most industries use automated decision engines to reduce labor costs, enforce policies and improve the quality of the customer experience. Here are some examples:

  • Airlines. Automated decision-making applications can set pricing based on seat availability and the hour or day of purchase.
  • Finance. Banks use these tools to make faster credit decisions, a repetitive process that relies on uniform criteria and available consumer credit data.
  • Insurance. Automated decision engines help insurers provide instant quotes to customers who enter their data via a website or app.
  • Credit cards. These tools can generate fraud alerts on credit card transactions that need immediate attention.
  • Government agencies. Automated decision engines can make fast decisions for government agencies in emergency situations, like when a power grid needs to be shut down in a natural disaster.
  • Healthcare. Automated care protocols for order-entry systems can recommend a particular drug or treatment for a patient.

Key considerations with automated decision engines

There are several keys for success when implementing automated decision engines:

  • Find the balance between automation and human intervention. Managers still have a role to play in reviewing and confirming decisions, and in many cases, they will continue to make them. Leading companies figure out which decisions to automate and which ones demand a human perspective.
  • Build or hire the right expertise. Designing and maintaining automated decision technologies requires specialized skills. Most systems rely on experts and managers to create and maintain rules and to monitor the results.
  • Differentiate your offerings. As automated decision engines become more common, companies will have to consider other ways to set their products and customer experience apart from those of competitors. They will need to figure out how they can use decision engines to target the right customer at the right time, and in the right context and channel.
  • Automated decision support
  • Automated decision technologies

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