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Information Technology Case Studies

Title
Market segments for a select vehicle model
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. 


Probability of Escalation (POE)

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.


TitleOptimize Supply Chain of Parts Distribution
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.


New Truck Sales Potential

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

Market Segmentation for Vehicle XObjective 
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.

Used to New



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.

Product TransitionTitle
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

Improve Performance of Dealership NetworkObjective 
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

Sales of Training/Educational ServicesTitle
Sales of Training/Educational Services

Objective 
Develop a method to predictive which potential customers are most likely to purchase educational or training services

Industry 
Education/Training

Outline 
Developed logistic regression models based on a 50 question survey completed by potential customers. Identified the key questions and responses that indicated which potential customers were most likely to purchase services. The results were utilized in marketing programs to target new potential customers.


Title
New Customer Acquisition

New Customer AcquisitionObjective 
Develop predictive models that identify potential new sales

Industry 
Insurance

Outline 
Utilize both logistic regression and decision tree models to identify potential new customers based on purchase history and demographic and geographic factors.    The results of the models were the basis for targeting direct mail campaigns.  The implementation of these models resulted in a 20% higher response and sales rate versus random mailings.

Customer Lifetime ValueTitle 
Customer Lifetime Value

Objective 
Determine the lifetime value of current customers

Industry 
Insurance

Outline 
Develop logistic regression models to predict customer retention based on purchase history and demographic and geographic factors. Applied the retention likelihoods along with profitability per policy over time to determine a present value for each customer. The results were used for multiple purposes to include potential additional product sales and economics of campaign spending.

Fraud Detection

Title  
Fraud Detection

Objective 
Identify claims that are potentially fraudulent

Industry 
Insurance

Outline 
Develop logistic regression models of past claim characteristics to identify those that are potentially fraudulent. Results used to identify new claims that require closer investigation for possible fraud.

Call VolumesTitle
Call Volumes

Objective 
Develop a method to predict call volumes for a large security based call center
       
Industry 
Security

Outline 
Developed regression models to predict monthly call volumes based on a series of factors to include marketing specials, season, month, holidays, new leads, appointments, new sales, current customer level, etc.  The results were used to improve agent scheduling which reduced overall operating costs.

Title  
Increase Service Level and Minimize Shortages

Increase Service Level and Minimize ShortagesObjective 
Increase service level and minimize inventory shortages

Industry 
Energy (Middle East Region)

Outline 
Developed predictive model using information from customer demand, inventory levels and costs.  Predict feasibility of distribution center for the Middle East Area based on historical data of customer requirements, emergency shipments and inventory shortages. Developed predictive models and validated using simulation models to compare various alternatives and completed cost-benefit analysis.

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