I saw this interesting report in the McKinsey Quarterly - Anticipating customer queries in call centers. The article is free if your register and the summary says:
A telecommunications company trying to optimize the economics of its call centers hesitated to automate its customer interactions because it didn't want to lose revenues from additional sales made by its agents.
Analyzing the potential revenues from each type of inquiry helped the company identify which kinds of calls should be automated and which handled by live agents.
A focus on the decisions in this scenario identifies several distinct areas of applicability for EDM:
- There is the decision as to what options to present to a customer when they call in and related decisions about menu options/referral to a representative
- There is the decision as to which representative or group of representatives to rout specific calls and customers to
- When should up-sell and cross-sell offers be made
- What offer should be made when an offer is appropriate
- What script should a representative use for a given call/customer combination
- Plus all the decisions that a customer might want made like a refund for an error, a change in plan, eligibility for a promotion etc.
EDM involves business rules, analytics and adaptive control (at the most basic level). Each element matters for different reasons in these decisions. Consider:
- Descriptive analytics to segment calls and customers into different, statistically significant groups to assist in routing and treatment
- Predictive analytics to turn data about customers' past behavior into propensity to buy and retention risk predictions (for example) to assist in routing calls to specific groups (sellers v transactors v retainers)
- Rules-based automation of decisions to allow automation of processes (so that customers and call center representatives can use them)
- Rules- and analytics-driven personalization of scripts based on what is known (and predicted) about customers
- Rules-driven menus based on incoming phone identification and matching to customer data (so that customers are only presented with menu options that are relevant)
- Rules and analytics used to identify best next action for a customer (an offer, a suggestion, a request, a freebie)
- Adaptive control used to continually test new approaches against existing ones to ensure that the best approach is being used
Clearly there are more but I hope I have given a flavor. I have blogged before about using EDM to boost call center performance and about some McKinsey statistics about call centers that likewise show the value of EDM in call centers. I also wrote "Smart-Enough Customer Decisions" for Montgomery Research, based on the material in Smart (Enough) Systems.
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