Finding the perfect pairing
Girish Punj, Professor of Marketing at the University of Connecticut, takes up the story: “As part of our MBA program, we offer an innovative course that brings our business and data science students together and gives them a real-world challenge to solve, using both their business acumen and their analytical skills.
“I was looking for a commercial organization that would be willing to let us partner with them, and when I saw Amy present at an IBM conference, I immediately put VineSleuth at the top of my wish-list. I spoke to my contacts in the IBM Academic Initiative, they provided an introduction, and Amy was enthusiastic about the partnership and quickly identified the perfect problem for our students to work on.”
John A. Elliott, Dean of the School of Business at the University of Connecticut, concurs.
“Our students were able to use their marketing and business analytics skills to help devise a sophisticated tool that will enable consumers to select a wine that satisfies their unique tastes,” Elliott says. “This is the type of hands-on learning that our students most enjoy—challenging, purposeful and directly relevant to the business world.”
The project itself involved the analysis of two highly confidential datasets—one containing data generated by VineSleuth’s own proprietary taste profiling methodology, and the other containing unstructured tasting notes provided by one of VineSleuth’s corporate partners. The students’ task was to analyze and attempt to correlate the two datasets, to see if it was possible to find consistent similarities between the results of VineSleuth’s assessments and those of the other organization’s wine experts.
Professor Punj comments: “We gave the students a free choice of using whatever analytics tools they liked—including SAS and R as well as IBM products such as SPSS®. It was notable that although we had a wide mix of skill levels in each student team, almost everyone picked up IBM Watson Analytics and used it to explore and get to grips with the datasets quickly.
“From the data scientists who could already code in Python or Java to the MBA scholars who only knew Excel, they were all able to learn how to use it within a week.”
Following this initial exploration phase, several of the student teams decided to use IBM SPSS Modeler Premium as a powerful statistical modeling engine for deeper analysis of the data.
“Several of our students also took the opportunity to close the loop by importing the results from SPSS back into Watson Analytics and taking advantage of its visualization capabilities,” says Professor Punj.
Vintage results for retailers and consumers
The findings of the seven student projects differed in their details, but one outcome was clear: there was no consistent way to correlate the results of VineSleuth’s objective taste profiling methodology with the more subjective data found in the tasting notes.
Amy Gross comments: “I think some of the students saw that as a failure on their part, and were disappointed that they couldn’t connect the two datasets—but that absolutely wasn’t a disappointment from VineSleuth’s perspective. It provided robust independent validation that our model is unique, and that our data provides something that the rest of the industry currently cannot match. That’s a very healthy indicator for our business.
“It means, too, that we have a genuinely new tool to help retailers market their wines—and consumers expand their palette. For example, a retailer could embed our service in interactive kiosks in the wine sections of its stores. When a customer scans their loyalty card, the kiosk could ask them whether they had enjoyed the bottles of wine they bought last week. It could then use the customer’s preferences to recommend a wine they have never tried—or thought of trying—before.
“Or, to take it one stage further: they could integrate our service into the recommendation engine of their online store. When a customer has, say, salmon and chili peppers in their basket, it could recommend a white wine that pairs well with that flavor combination. Or vice versa—if they have a full-bodied red wine in their basket, it could recommend picking up steak and other ingredients that would complement it. Ultimately, that’s going to translate to bigger basket sizes and a higher average spend per customer.”
She adds: “It has been a great experience working with Girish and the University of Connecticut team, and it’s something we would definitely recommend. If you’re a small company and you don’t have an army of data scientists that you can throw at every problem, it’s great to have access to a team of intelligent, motivated students who can augment your in-house resources.”
Professor Punj concludes: “We would like to thank VineSleuth for giving us the opportunity to work on this fascinating project. The students really enjoyed the challenge of taking a deep dive into a subject area that is so innovative and unusual from both a business and a big data perspective.
“By bringing data science and MBA students together, we created a peer-learning environment that helped each student gain new skills—and using IBM tools like Watson Analytics really helped to bridge the gap between the technical and business scholars.
“The lessons we’ve learned from this initial partnership are now being put into practice: we’re already working with IBM to line up another corporate partner to work with over the next few semesters.”