Case Studies 2019-07-15T12:37:30-05:00

Case Studies

How we help clients use data science to free their organization of pain points and rise above the competition

Using AI to improve the customer experience

When a fortune-500 company decided to double down on customer experience and collect consistent feedback more often, their customers gladly shared their opinions. Segmenting existing customers into promoters and detractors is powerful. Studies have shown that promoters are 6 times as likely to buy, while detractors are responsible for more than 80% of negative word of mouth. They went from a few hundred satisfaction survey responses a month to over 4000, a quarter of which were in a foreign language. While the satisfaction metrics were informative, the true value was in the qualitative feedback. Their customers were telling them exactly what they were enthusiastic about…and what they weren’t. However, without a quantitative way of looking at these responses, the root causes were difficult to spot.

Pandata used artificial intelligence to build a solution that identified critical customer experience issues in qualitative feedback, highlighting the most relevant positive and negative comments. Whereas divisions previously viewed data shared with them as anecdotal, the organization now has regular conversations around these qualitative responses. They are quicker to respond to customer needs, and as a result, their Net Promoter Score has increased by 20%.

Using AI & curated content to qualify purchasing decisions

When customers visit your website, they are telling a story about their intent. Marketers have long offered content that requires an email address to qualify prospects and customers. But what if you could qualify more complex behaviors like product or service considerations and readiness to buy? One fortune-500 B2B company decided to do exactly that by transforming the website analytics data into a powerful tool to identify intent.

With more than 500 million website interactions across 3.5 million unique website visitors a year, the data was too complex for traditional analysis. Pandata paired their website data with data from their CRM and used artificial intelligence to identify digital activity that resulted in specific customer actions like contacting customer service, making a purchase, and contacting technical support. The e-commerce team is now able to proactively identify anonymous website visitors demonstrating an intent to purchase within 30 days and with over 80% accuracy. This provides opportunities to target these customers with meaningful content, capturing their contact information and better qualifying them for
sales.

Using AI & transactional data to target and qualify customers

A benefits management company partners with insurance companies to enroll members in special government funded programs. But to do this, they must analyze billions of medical records to pre-qualify which members to reach out to. An analogy in the retail space would be analyzing historical transaction data to target customers for special offers.

With over 200,000 unique types of member data (think 200,000 columns in a database), this data is highly complex, and like most transactional data, imperfect and incomplete. Pandata used artificial intelligence to mine the transactional records of all members that had been either successfully enrolled or denied in the past. The resulting model is helping them uncover an additional 30%+ of qualifying members and significantly reducing the amount of time spent on members that are not likely to be approved.

Midwest Industrial Supply

Midwest Industrial Supply specializes in environmentally friendly dust control applications, operating out of a series of regional satellite offices. When pricing a new spray opportunity, the sales team is incentivized to maximize the variable margin, or price minus the cost of chemicals, without easily considering how the opportunity fits within the constraints of scheduling and resources of the satellite office, which can negatively affect profits. As is the case with many engagements, centralizing critical data and implementing processes to manage quality resulted in substantial gains. In this case, these fixes will initially help improve profits by $150-$250k and expectations are the full realization of $1.0 million in total profit improvement.

Cleveland Museum of Art

The Cleveland Museum of Art is a world-renowned art museum with a substantial collection of over 61,000 artworks. In January of 2019, they launched their Open Access Initiative in which they made over 30,000 public domain works and metadata for their entire collection public and downloadable (GitHub). Metadata includes title, description, artist, year, and department, among other details. As part of the Open Access launch, CMA asked Pandata to participate in demonstrating the power of such a data set. Using natural language processing and data visualization techniques, Pandata used the text descriptions from all art that had one (approximately 10,000 works) to visualize how we write about art across time and cultures.