Recommender system with SAS to improve cross selling etc for online business

Nowadays, the recommender system is a popular tool for online retailer business to predict the customers' next product to buy (NPTB) Based on the information collected by the retailer and statistical techniques, an efficient recommender system can suggest customers with meaningful NPTB. A useful suggestion can reduce customer's searching time for a wanted product and improve buying experience, which would increase the chance of cross selling for online retailers, helping them build customer loyalty. Within a recommender system, how to combine advanced statistical techniques with information available (customer profiles, product attributes, good selling products, etc. is the key part to bridge the retailer's database with the useful suggestion of NPTB for customers. This paper illustrates how to create a recommender system with SAS RECOMMEND procedure for online business. Using the recommender system, we can produce the prediction, compare the performance with different predictive models (i.e. decision tree, multinomial discrete choice models etc) and make business oriented recommendations from the analysis.  A recommender system refers to software tools and techniques that provide suggestions for an item to be adopted by a user. Item is the general term used to denote what the system recommends to users, which can include products, services and so on. Recommender system development initiated from a rather simple observation: individuals often rely on recommendations provided by others in making routine, daily decisions. For instance, individuals may rely on the comments of film critics when selecting for a movie to watch, or ask their friends for opinions with regard to which hotel to book. Recommender system mimics this behavior and produces useful and effective suggestions for the users who lack sufficient experience or capacity to evaluate the overwhelming number of alternatives. A recommender system can play various roles depending on whom is using it. Generally speaking, a service provider could benefit from it by increasing the number of items sold, selling more diverse items, increasing the users' satisfaction and fidelity, and better understanding what the users want. On the other hand, a user wants to use the recommender system in order to find some or all good items, recommend a sequence or bundle, just browse, find credible recommender, express himself, or even influence others. In this article, we will demonstrate how recommender system can be deployed to improve crossselling, a term describing the action or practice of selling an additional product (category) or service to the existing customers. Cross selling is a term which refers to the "sales of a number of related products which can occur concurrently with the sale of a major product"(Nash and Sterna Karwat, 1996) Deighton et al. (1994) and Nash (1993) describe cross selling as "encouraging a company's customers who have already bought product A to buy its product B" Typical examples of cross selling include a life Insurance company suggesting its customer sign up for car or health insurance, a wholesale mobile retailer suggesting a customer choose a network or carrier after one purchases a mobile, a laptop seller offering a customer a mouse, pen drive, and/or accessories. Companies use cross selling as a tool of customer relationship management (CRM) and It not only helps increase company revenue, but also reduce/prevent the churn. In order to succeed in crossselling, it is important to create customer intimacy through knowledge about the customer's preferences and needs. However, customers may react in a negative way when they are aware of they are observed. The perceived risk may explain why sometimes there is lack of information about sociodemographics in the customers' database. In comparison to using an intuitive model it is effective to use a Next Product To Buy (NPTB) model to optimize the cross selling. An NPTB model will use information about the customer to predict what product (category) they are most likely to purchase from next. There are some steps in developing an NPTB model: 1) compile data which consists of requiring customer specific data of a certain time period t, being the independent or predictor variables, and an observation of a purchase at time t+1, being the dependent variable. 2) select a statistical model (i.e. regression, decision tree, neural network etc predictive models. 3) estimate and evaluate the model: estimate the coefficients of the model needed to make the predictions. 4) score and target customers: customers are scored with probabilities generated by the best performing model and targeted based on the scores (Knott, Hayes and Neslin, 2002) Methodology In this section, we will first elaborate on the technique of the recommender system. Secondly we will discuss the discrete choice model (especially the use of the multinomial logit model) and the decision tree model. Then we compare the performance of the above predictive models built for an online business case and make business oriented recommendations. Recommender system Our cross selling model is built upon an individual customer's previous purchase behavior. It is more comparable to explore the user based collaborative filtering to build recommender systems. In order to implement its core function, identifying the useful items for the user, a recommender system must predict that an item is worth recommending. In order to do this, the system must be able to predict the utility of some of them, or at least compare the utility of some items, and then decide what items to recommend based on this comparison. One major feature used to classify the recommendation systems is especially in regard to the recommendation algorithm, i.e. how the prediction of the utility of a recommendation, is made. Burke, R (2007) distinguishes between six different types of recommendation approaches.  The system learns to recommend items that are similar to the ones that the user liked in the past. The similarity of items is calculated based on the features associated with the compared items.  The simplest and original implementation of this approach recommends to the active user the items that other users with similar tastes liked in the past. The similarity in taste of two users is calculated based on the similarity in the rating history of the users.  This type of system recommends items based on the demographic profile of the user. The assumption is that different recommendations should be generated for different demographic niches. Knowledge based: Knowledge based systems recommend items based on specific domain knowledge about how certain item features meet users' needs and preferences, and ultimately how the item is useful for the user.  This type of system recommends items based on the preferences of the users' friends. Evidence suggests that people tend to rely more on recommendations from their friends than on recommendations from similar but anonymous individuals. Hybrid recommender systems: These RSs are based on the combination of the above mentioned techniques. A hybrid system combining techniques A and B tries to use the advantages of A to fix the disadvantages of B. The collaborative filtering is the main technique used by PROC RECOMMEND. In general, user based recommender system recommends the top N products by computing the highest purchase probability for a particular group of customers. Firstly we identify the active customers who are the most similar to the group of customers in the database. Principal Components Analysis is used to reduce the predictors due to the high number of variables and their high multicollinearity. Then hierarchical clustering is applied to cluster the customers of the training sample. Afterwards k mean clusters are created to obtain the many similar users. Each product category is rated based on their frequency inside a group/cluster. Then the highest weight item is selected for each customer and finally the union of the products purchased by each cluster/group can be computed. In order for it to be comparable to our other predictive cross sell models we chose to recommend product categories instead of products and predicted only one category as opposed to multiple categories. As a final step, we select certain product categories from the union of each cluster. We recommend the item with the highest rating that has not been purchased yet to the active customers. Within this framework, the method used to determine the k most similar users and the scheme used to determine the importance of the different items plays the most critical role in the overall performance of the algorithm. Commonly, the similarity between the users is computed by treating them as vectors in the item space and by measuring similarity via the correlation coefficient functions, whereas the importance of each item is determined by how frequently it was purchased by the k most similar users (Han and Karypis, 2005) Discrete choice model As people have finite resources, they tend to have a priority structure of products. Products for more basic objectives are generally acquired before products for more advanced objectives. Acquisition pattern analysis provides insight into the order in which customers acquire such products. In fact, acquisition patterns deducted from cross sectional data can predict future product acquisitions by individuals, though not the timing of acquisition. Also, cross sectional data do not provide insight into divergent orders of acquisition. Divergence means that different segments of customers in a population follow different orders for acquiring products (Paas, Vermunt and Bijmolt, 2007) These drawbacks can be overcome by the use of certain types of models; this will be explained in the following paragraphs. The discrete choice process st