Wednesday, August 22, 2012

JERATH Kinshuk, KUMAR Anuj, NETESSINE Serguei
An Information Stock Model of Customer Behavior in Multichannel Customer Support Services
INSEAD Working Paper 2012/73/TOM

Firms offer customer support via multiple channels, such as telephone, web portal, web chat and interactive voice-response units, but the efficacy of interactions at these channels is poorly understood. In this paper, we develop a novel information stock-based model to understand customers’ usage behavior for support services in a multichannel scenario. Our setting is that of a US-based health insurance firm. In case of a query regarding health insurance coverage or claims, customers can either call the firm’s call center to get the desired information from a call center representative, or visit the web portal and get the desired information themselves. We assume that each customer has a latent “information stock” which is a function of customers’ “information needs” (which arise when customers file health insurance claims) and “information gains” (which customers obtain when they contact the firm’s support channels to resolve their queries), and other factors such as seasonal effects (for instance, queries that arise at the time of annual contract renewal). We model a customer’s observed behavior, in terms of her query frequency and channel choice for queries, as a stochastic function of her latent information stock; this allows us to estimate the relative efficacy of different support channels. We estimate our model on individual-customer-level data from the firm, and we find that the average information gain for a customer from a telephone call is an order of magnitude greater than that from visiting the web portal. We further find that customers prefer the telephone channel for health eventrelated information needs but, interestingly, prefer the web portal for seasonal information needs which are typically more structured. Additionally, information needs also vary with the nature of the health event—for instance, claims associated with repeated health events (due to, say, a chronic disease) generate minimal information needs compared to regular claims. We also find that customers are very heterogeneous in terms of their propensity to use the web channel, and can be broadly classified into “web avoiders” and “web seekers.” Besides providing the above insights, our model can be used to aid in call center management and staffing decisions as it provides very good predictions for future query volumes on different channels, and it can even help to accurately identify customers who are expected to have high telephone call volumes in the near future.