How can you use decision analysis methods to evaluate customer service strategies in operations research?

1 What is decision analysis?

Decision analysis is a branch of operations research that focuses on helping decision makers identify, evaluate, and compare alternative courses of action in situations that involve uncertainty, multiple objectives, and trade-offs. Decision analysis methods can help you structure your decision problem, quantify your preferences and uncertainties, and analyze the outcomes and consequences of your choices. Some of the common decision analysis methods are decision trees, influence diagrams, multi-criteria decision analysis, and value of information analysis.

2 How to use decision trees for customer service strategies?

A decision tree is a graphical representation of a decision problem that shows the possible actions, events, and outcomes along with their probabilities and values. A decision tree can help you visualize the structure and logic of your customer service problem, as well as the expected value and risk of each strategy. For example, you can use a decision tree to compare different customer service options, such as self-service, chatbots, phone support, or email support, based on their costs, benefits, and customer satisfaction levels. You can also use a decision tree to incorporate uncertainty and sensitivity analysis, such as how the demand, preferences, or feedback of customers may change over time or under different scenarios.

4 How to use multi-criteria decision analysis for customer service strategies?

Multi-criteria decision analysis (MCDA) is a method that helps you evaluate and rank alternative courses of action based on multiple and conflicting criteria. MCDA can help you balance your customer service objectives, such as customer satisfaction, operational efficiency, and profitability, as well as incorporate your subjective preferences and judgments. For example, you can use MCDA to assess and compare different customer service strategies based on their performance on various criteria, such as response time, resolution rate, error rate, customer feedback, and cost. You can also use MCDA to elicit and weigh your preferences and trade-offs among the criteria, using techniques such as scoring, weighting, pairwise comparison, or outranking.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?