Incorporating Experience Quality Data into CRM Models: The Impact of Gambler Outcomes on Casino Return Times
Enabled by modern interaction-logging technologies, managers increasingly have access to data on
quality levels in customer interactions. We consider the direct marketing targeting problem in situations
where 1) the customer's experience quality level varies randomly and independently from occasion to
occasion, 2) the rm has measures of the quality levels experienced by each customer on each occasion,
and 3) the rm can customize marketing according to these measures and the customer's behaviors. A
primary contribution of this paper is a framework and methodology to use data on customer experience
quality data to model a customer's evolving beliefs about the rm's quality and how these beliefs combine
with marketing to influence purchase behavior. Thereby, this paper allows the manager to assess the
marketing response of a customer with any specific experience and behavior history, which in turn can be
used to decide which customers to target for marketing. This research develops a novel, tractable way to
estimate and introduce flexible heterogeneity distributions into Bayesian learning models. The model is
estimated using data from the casino industry, an industry which generates more than $60 billion in U.S.
revenues but has surprisingly little academic, econometric research. The counterfactuals offer interesting
findings on gambler learning and direct marketing responsiveness and suggest that casino profitability
can increase substantially when marketing incorporates gamblers' beliefs and past outcome sequences
into the targeting decision.
Leveraging Loyalty Programs Using Competitor Based Targeting
Loyalty programs are widely used by firms but their effectiveness is subject to debate. These programs provide discounts and perks to loyal customers and are costly to administer, and with uncertain effectiveness at increasing spending or stealing business from rivals. We use a large new dataset on retail purchases before and after joining a loyalty program (LP) at the customer level to evaluate what determines LP effectiveness. We exploit detailed spatial data on customer and store locations, including locations of competing firms. A simple analysis shows that location relative to competitors is the strongest predictor of LP effectiveness, suggesting that LPs work primarily through business stealing and not through other demand expansion. We next estimate what variables best predict LP effectiveness using high-dimensional data on spatial relationships between customers, the focal firm's stores, and competing stores as well as customers' historical spending patterns. We use LASSO regularization to show that spatial relationships are more predictive of LP effects than are past sales data. Finally, we show how firms can use this type of predictive analytics model to leverage customer and competitor location data to substantially increase the performance of their LP through spatially driven targeting rules.