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Customer Retail Evaluation

Business Problem: Evaluating the effectiveness of a premium membership program and the possible reasons for decline in revenue

Data: Customer and sales data from the past and current year

Analysis: A series of statistical hypothesis testing was conducted

Actionable Results: Premium program shown to be effective at helping to up sell customers, while high pricing was a driving factor in declining revenue in the loss of customers

Optometry Market Segmentation

Business Problem: Identifying best customer demographics to target for selling optometry and ophthalmology equipment

Data: Survey data gathered from 200+ medical eye doctor businesses with 40+ questions ranging from demographic/firmographic to attitudes and firm planning

Analysis: Principle Component Analysis with centering and scaling was used to pre-process the survey data followed by clustering analysis. Cluster averages were  used to identify key characteristics of each identified cluster

Actionable Results: 6 customer segments were identified and the top 2 were used for targeting best potential new customer demographics

Online Auctions

Business Problem: An auction site for rare collectibles needing estimated selling prices for their unique items 

Data: A sampling of characteristics and prices of past items sold on auction site 

Analysis: A combination of variable transformation, similarity analysis, and regression was used to create an algorithm for estimated selling prices 

Actionable Results: Algorithm that could be implemented in a variety of environments to estimate profit and sale price on each item

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Food Industry

Business Problem: Food manufacturer needs methodical solution to minimize waste of product based on individual product weights

Data: A sampling of weights of individual products coming off a particular assembly line

Analysis: Monte Carlo simulations were run on a variety of bagging/grouping algorithms. Parameters were used to determine the best solution after having received the typical weight distributions of products and numbers in each bag

Actionable Results: The best algorithm was selected with adjustable parameters based on weight distributions. This Algorithm could be programmed and implemented into manufacturing assembly line to minimize product waste

Employee Wages

Business Problem: Company had a wages dispute with former employees and needed more concrete information

Data: Geolocation of company trucks logged over previous time periods which is of interest to the employees

Analysis: Parsing geolocation data into working and non-working time periods based on stoppage time of vehicles. After that, the data was aggregated together displaying the working time for each driver

Actionable Results: Settlement for much lower cost to company while avoiding costly court and lawyer fees

Price Matching

Business Problem: Price matching algorithms for provider-client marketplace which has similar model to Uber. Client having trouble finding good matches in various circumstances

Data: Historical data on successfully completed jobs, and  on the bids of both successful and unsuccessful matched jobs

Analysis: A combination of data aggregation, key metric creation, regression analysis and hypothesis testing 

Actionable Results: Analysis returned top 5 contributing factors of unsuccessful matches to allow client to find places to introduce surge pricing


Customer Retention

Business Problem: A food subscription company is churning customers on regular basis and wants to find out the reasons why

Data: Demographic information, customer profiles, and  order history for past and present customers

Analysis: A combination of data aggregation, variable transformation, multivariate and logistic regression was used to find high risk customers and identify high risk factors

Actionable Results: Top 3 reasons for customer churn was identified to allow company to focus efforts on mitigating those reasons for all customers. A set of high-risk customers were also identified to allow targeted incentives to be put in place to help retain those customers

Online Customer Habits

Business Problem: An E-learning company needs better information on its users and their habits 

Data: A running log of user actions taken on the

E-learning site regarding the action taken, the user identification, and the time of the action

Analysis: A combination of data aggregation followed by cluster and segmentation analysis was conducted. Clusters were then analyzed and assigned profiles based on the cluster characteristics and habits

Actionable Results: 3 types of typical users were identified and allowed the E-learning company to customize the user experience based on projected user type

Health Data Monitoring

Business Problem: Health officials in Maryland and Baltimore area need to monitor lead levels in children

Data: Records of BLL (blood lead levels) in children by census tract number and year

Analysis: A heat map (with higher levels being brighter red) was creating using the data provided. An integration with census tract geography boundaries and Google Maps were used for easier visualization 

Actionable Results: Heat map can be used in areas of high concentrations of high BLLs and allow health officials to target resources appropriately.

Employee Selection

Business Problem: Need to identify lawyers who would be successful in a career transition

Data: A database of lawyers, their employment history, general demographics and schooling

Analysis: Created an algorithm that takes into account certain characteristics of a lawyer. It takes their employment history and then outputs a probability of moving law firms

Actionable Results: Algorithm implemented in a software version that allows law firms to target lawyers most likely to transition in their career. This aides in spending less time finding the right candidates

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