collaborations
case studies
SPRING 2019 PROJECT AREAS
Students utilize the following resources for data science projects
1. R and Python
2. Virtual computing environment on client's server or on a 3rd party provider to provide security
3. Machine learning packages
Marketing Strategy
HP: How do we position our core enterprise cloud computing product as a market leader?
1. What is the right messaging and channel mix for each of our customer segments?
2. How do we position ourselves to a non-technical audience?
Integrated Marketing Communications Class developed an omni-channel marketing plan based on original qualitative primary and secondary research
IHG Hotels: Develop a thorough understanding of the preferences of millennial customers
1. What will increase satisfaction among millennials
2. What will build lifetime loyalty?
3. What are millennials preferences in our space?
4. How can this be incorporated into our CRM strategy?
CRM Strategy class formulated a methodology to capture and use both explicit and derived preferences for millennial travelers to be incorporated into company’s overall CRM strategy.
Analysis included using preferences to drive behavioral change and to create new forms of interaction, service and experiences.
New Balance: Develop a holistic strategy to grow in a new category
1. What are the current purchasing behaviors of the specific stakeholders related to this market?
2. What brand message would resonate with and stand out for each stakeholder?
3. Develop a multi-channel marketing campaign plan to reach each stakeholder in to drive the final purchase
Marketing Strategy class formulated a holistic marketing strategy, branding, and an execution plan to launch the new line of business, based on analysis of the multiple stakeholders involved.
Data Science and Analytics
Advertising Agency: Analyze a customer behavior data set to determine a dollar value per customer action
Analyze a client data set that contains customer behavior meta data across multiple campaigns and channels to determine a specific dollar value for specific actions across specific products.
Business Analytics students utilized Python to develop a formula that can be utilized to input campaign and product information and output a predictive dollar value
Online Retail Brand: Understand drivers of customer churn
Better understand the drivers of customer churn as well as the predictors for the LTV of each customer (in dollar terms). It has a historical dataset with different characteristics of each of its customers and how long they have been subscribers of the company.
Students built a regression model to better understand the drivers of customer churn for the company. Using a hold-out sample, train the model to be predictive. Test the model against the hold-out sample to estimate the accuracy of the prediction algorithm
Build a regression model to better understand the drivers of high LTV. Use this predictive engine as a foundation for what type of customers the company should target in its advertising process
Students utilize the following resources to develop their marketing strategies
1. Quantitative surveys and analysis
2. Qualtrics-provided national survey panel
3. Qualitative in-depth interviews
4. Internal company documents
5. In-store shopper interviews
6. Interviews with B2B stakeholders
7. University business subscriptions such as Mintel and IRI
Focus on the emerging whitespace between traditional veterinary office care and retail pet store offerings.
Market Research class created a “white space portfolio” of products with market size and growth estimates based on a detailed analysis of current products and unmet and underserved needs.
Analysis included a definition of the “Healthy Pet” market from the pet owner perspective along with projections of future opportunities as pet health migrates from the vet’s office to the retail pet store.
3M Petcare: differentiate pet care division in the Health Pet Market
1. Develop in-depth research about the next generation of cooks and their cooking and recipe habits
2. Recommend a strategy for how to engage the online experience and brand profile with these segments
Consumer Behavior class conducted extensive qualitative and quantitative research to develop a profile on various millennial and GenZ segments and their cooking habits. Students then developed specific improvements that can be made to the online recipe experience and the overall brand strategy to better resonate with this target audience.
The Weather Channel: Market Research and Product Improvements as part of a Brand Strategy Shift
1. Delve into weather news consumption preferences of current and prospective viewers
2. Develop a content recommendations that better suit the needs of the brand direction and viewer preferences
Students conducted research and presented recommendations for how to transform the brand perception from a utility/information source for occasional viewing to a more relevant, emotional destination for infotainment. Based on research, students recommended new educational programing and promotional avenues which would change consumer brand perceptions and increase the frequency and length of viewership. Findings specifically recommended against adding reality programming, seen as off-putting to enthusiasts, saving channel from poor investment. Methodology combined original research among channel content enthusiasts including a quantitative survey and qualitative interviews among with secondary sources on TV viewing overall.
Students utilize the following resources for market research projects
1. Quantitative surveys and analysis
2. Qualtrics-provided national survey panel
3. Qualitative in-depth interviews
4. Internal company documents
5. In-store shopper interviews
6. Interviews with B2B stakeholders
7. University business subscriptions such as Mintel and IRI
