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Automotive Case Studies
Title
Market segments for a select vehicle model

Objective
Develop market segments based on purchase behaviors

Industry
Automotive

Outline
Starting with purchase history data provided by a major auto company, a series of additional variables was created to use in the modeling process.  These new classes of variables included measures for share of garage, recency, longevity, migration, loyalty, finance, warranty and service, and dealer information. Principle component analysis, clustering algorithms, and descriptive measures were utilized to develop a series of market segments based on purchase patterns over the past five years. The purpose of this behavioral approach is to assist customer relationship management and marketing in targeting the appropriate customers with the appropriate messages during their campaigns.  

 
 
Title
Probability of Escalation (POE)

Objective
Determine those hand-raising customers who are most likely to go through the Mediation/Arbitration process

Industry
Automotive

Outline
The population of interest was first defined, followed by a data collection and cleansing process.  New variables were created and models developed using logistic regression and decision tree models that predict the probability of a hand-raiser (complaint) ending up in the mediation process.  The resulting models were highly accurate and identified a handful of key drivers such as severity, longevity, vehicle purchase history, geographic region and number of complaints amongst others. These results will allow the determination of the most appropriate response/settlement to return the customer to a satisfied state and maintain brand loyalty.
Title
Optimize Supply Chain of Parts Distribution

Objective
Determine the optimal configuration of the supply chain for the auto parts distribution in North America including the ports of entry for the parts, multimodal transportation mediums to be employed (rail, truck) and the location and capacity of Distribution Centers to support the dealerships network.

Industry
Automotive

Outline
Optimization and simulation models were developed to minimize the overall cost of the supply chain including transportation, distribution center, and inventory costs to support the parts needed for all the models that were sold in North America.
  
 
 
 

Title
New Truck Sales Potential

Objective
Develop a methodology to predict which businesses have select truck classes along with their distribution in states where data is not available 

Industry
Automotive

Outline
There were three parts to this study the first being the development of logistic regression models to determine which businesses were most likely to have trucks of any type.  The second part was the application of clustering algorithms to determine common groupings of truck classes and finally the development of discriminant models to predict which businesses had what combination of truck classes. The study utilized vehicle registration data from states where available and the Dunn & Bradstreet business database.  The results were used to determine which business to target for new sales as part of campaign management and messaging.


Title
Market Segmentation for Vehicle X  

Objective
Develop behavioral segments for Customer Relationship Management applications
 
Industry
Automotive

Outline
Principle component analysis, clustering algorithms, and descriptive measures were used to develop a series of primary and secondary market segments based on purchase patterns and demographic information for both specific brands and division line product families. These studies became a key element in the corporate marketing strategy by targeting smaller niche segments to correspond to customer purchase behaviors. The resulting behavioral segmentation for vehicle X increased profitability by an estimated $30 million in one year.
 
   

Title
Used to New
  
Objective
Develop a methodology to predict the likelihood of a current used vehicle owner to purchase a new ABC vehicle over the next year.
   
Industry
Automotive

Outline
Utilized logistic regression, decision trees, and clustering algorithms to develop a series of predictive models based on vehicle buying history, demographics, and behavioral segments. The modeling process was able to identified groups of used car buyers who were two to three times more likely to consider a new vehicle as their next purchase. These results were incorporated into campaign management programs that selectively targeted used car buyers.


Title
Customer Satisfaction
  
Objective
Develop models that identify the significant factors of customer satisfaction with dealer service
     
Industry
Automotive

Outline
Decision tree methods were used to determine the factors that most affected a customer’s perception of the quality received for automotive dealer service.   This not only provided the ability to estimate the satisfaction level for customers who did not return surveys but also to understand the drivers of dissatisfaction.  The results were utilized in the development of focused dealer programs to improve customer satisfaction levels.
 
 

Title
Product Transition  

Objective
Develop predictive models to be used in customer communication strategies for determining the likelihood and timing of customers transitioning across product categories
     
Industry
Automotive

Outline
Applied decision tree methods and logistic regression to determining the likelihood of a current customer purchasing a vehicle in another segment over the next year.  Of particular importance was the likelihood of moving upward to more profitable segments. The results were utilized in campaign management to target customers for cross-selling and up-selling


Title
Improve Performance of Dealership Network

Objective
Improve Customer Satisfaction for Sales and Service Areas across Central Dealership Network

Industry
Automotive (Mexico)

Outline
Customer Segmentation based on Sales and Service Information gathered via surveys and customer information.  Data Mining project to improve Customer Satisfaction Index Across Dealership Network. Performed customer segmentation to root-cause poor performance in Service and Sales areas across top 10 dealers in the company’s Mexico Network, made recommendations to improve data mining and surveys.