Consequently, an improvement in CRM can be obtained by enhancing the databases on which these information technology tools are based. This study shows that a salesperson’s personal attitudinal and behavioral characteristics can have an important impact on his sales performance. This salesperson effect can be easily included by means of a generalized linear mixed model using PROC GLIMMIX. This can significantly improve the predictive performance of a purchasing behavior model of a home vending company
In an increasingly competitive business environment, a successful company must provide customized services in 1 order to gain a competitive advantage. As a result, many firms have implemented information technology tools to 2 customize marketing strategies in order to build up a long-term relationship with their clients. This study will try to improve such customer relationship management (CRM) models by taking the salesperson effect into account. Traditional CRM models are typically based on variables related to the individual such as socio-demographics, lifestyle variables and the individual past purchasing behavior of the customer.
The Review on Logic Model Development Guide
Logic Model Development Guide Introduction If you don’t know where you’re going, how are you gonna’ know when you get there? –Yogi Berra In line with its core mission – To help people help themselves through the practical application of knowledge and resources to improve their quality of life and that of future generations – the W.K. Kellogg Foundation has made program evaluation a priority. As ...
This study suggests that the purchasing behavior of a particular customer can also depend on social surroundings that have an influence during the purchase occasion. In a home vending environment the most important social surrounding is the interaction between the customer and the salesperson. A salesperson’s personal attitudinal and behavioral characteristics have 3 an important impact on his sales performance. because a home vending company decides in advance which salesperson will visit which customer at what time. This makes it possible to already include this knowledge in a highly dynamic model that scores the customers on a daily basis.
Hence, PROC GLIMMIX in the SAS® 9. 2 program is introduced to capture this effect. This procedure makes it possible to estimate a generalized linear mixed model (i. e. a multilevel model) with a binomial outcome variable. This study will investigate whether data augmentation with the salesperson effect will result in better purchasing behavior prediction. These predictions generated daily can be used for several applications. For example, when the demand is too high to visit every client, these predictions can help to select the most profitable ones.
On the other hand, in a situation of overcapacity the salesperson has extra time left, in this situation the predicted probabilities can be used to generate revisit suggestions of the most profitable clients that were not home during the first visit. For this study, data is collected from a large home vending company, specialized in frozen foods and ice cream. This company uses about 180 salespeople to distribute their products to approximately 160,000 clients, visited on a st th regular basis in a biweekly schedule.
Transactional data is used from February 1 , 2007 to November 30 , 2007 to build and validate the model. The same period in 2008 is used for out-of-period testing. Because a lot of promotional activities take place during the holiday period of Christmas and New Year, the months December and January are excluded and should be scored with a different model. 1 SAS Global Forum 2012 Including the Salesperson Effect in Purchasing Behavior Models Using PROC GLIMMIX, continued Statistics and Data Analysis The data from the home vending company has been captured in explanatory variables.
The Term Paper on What is the ‘covering law’ model of explanation?
Carl Hempel’s “covering law” model of explanation states essentially that an explanation for an event can be drawn from a set of general laws or, in the case of the social sciences, universal hypotheses. Hempel claims the study of history is not generally associated with the search for general laws governing historical events. However, history is a discipline within which the theory of “covering ...
In Table 1, an overview of all variables used in this study can be found. The purpose of the proposed model is predicting whether a customer will buy at least one product conditional on him/her being at home. Therefore, only observations where the customer is at home are retained in the model. In a next step, this model can be combined with a second model predicting the probability a client will be at home, but this is beyond the scope of this research. In order to avoid correlation between purchase occasions of the same customer, only one visit per customer is randomly selected.
If the customer was at home during the visit, (s)he bought at least one product in 46% of the purchase occasions. This signifies that the analysis table for this study is rather equally balanced between events and non-events. Variable name Description Dependent variable: Sales A binary variable indicating whether the customer purchased at least one product Independent variables: Transactional variables: Recency visit Recency bought Frequency visit Frequency bought Monetary value Sales ratio Avg. monetary value Last time visit Last time bought Last time amount Sales person variables: Salesperson
