In a non-contractual
setting, cross-buying is measured as the total number of different
product categories that a customer has purchased from a firm from
the time of his/her first purchase. Current business trends and
past academic research clearly demonstrate the importance of cross-selling
– the specific marketing effort by the firm to increase
cross-buying -- in a retailing context. However, critical questions
that warrant answers based on empirical evidence include: (1)
why do customers cross-buy from the same firm? (2) what product
category needs to be promoted? (3) when is the best time to cross-promote
a product category? and (4) how much should a firm cross-sell?
Or, what is the optimal level of cross-promotion? We address these
questions and present answers based on empirical evidence in two
separate studies.
The purpose of the
first study titled “Why Cross-buy?” is to understand
the motivation of customers to cross-buy, and to identify the
key drivers of cross-buy -- exchange characteristics, customer
characteristics, product characteristics, and the firm’s
marketing efforts. Further, we empirically validate the positive
impact of cross-buy on customer-based outcome metrics such as
revenue/contribution margin per order, and the number of orders
in a given period.
In the second study
titled “What, when, and how much to cross-sell? Optimizing
Multi-category Catalog Mailing,” we answer the remaining
questions- what, when and how much to cross-sell. We address an
existing research gap -- lack of models to optimize multi-category
mailing -- by introducing a multivariate proportional hazard model
employed in a Hierarchical Bayesian framework, to jointly estimate
purchase timings and order amounts in multiple product categories.
In other words, the model integrates when and what components
of a customer’s purchase decision into how much component
of a firm’s cross-selling strategy using Genetic Algorithm
based optimization.
The results of the
studies have several implications for both practitioners and academics.
While a key managerial implication is to use cross selling as
a strategic tool to maximize Customer Lifetime Value (CLV), the
academic contributions relate to applying a multivariate proportional
hazard model to multi-category catalog retailing context.