Mark Hopkins is the Chief Information Officer at Park City, Utah based Skullcandy, leading the global IT, Digital, and Customer Service teams. During Mark’s tenure, he has helmed Skullcandy’s digital transformation and his team has successfully increased online revenue and presence in the digital ecosystems, evolved into a world class customer service organization, and enabled growth with innovative systems solutions. Mark’s team is constantly adapting to and meeting the challenges of a rapidly evolving business using cloud technologies, real-time analytics, data warehousing, and virtualization.


Skullcandy’s journey with advanced analytics started with our product development team daring to ask three big questions:

  1. What if we could predict return rates on new products before they were introduced?
  2. What if we could use insights around reviews and warranty claims to understand positive drivers, negative drivers, and sentiment to inform new product design decisions?
  3. What if we could use this data to focus our resources and deliver better products? 

I often compare those early days to a kid standing at the edge of a bowl at the skate park — getting ready to drop in for the first time. He knows it isn’t going to be pretty, but he has to start somewhere. We knew our journey with predictive analytics and sentiment analysis was going to be a gradual progression that would eventually help us understand and better serve our customers. We knew we might end up with some bumps and bruises, but to gain the advantage we had to take that first leap of faith.

SQL, Python, and R

As a long-time Sisense customer, we knew that we had an open analytics platform on our hands that would be the backbone of this next analytics frontier for Skullcandy. Here’s how we dropped in to answer the questions our Skullcandy product team dared to ask.

Predicting Return Rates On New Products 

The first piece of the puzzle was trying to see into the future — so we pulled BigSquid into the mix with Sisense to help with the heavy lifting. We were most interested in exploring if it was possible to predict the return rate on a new product based on historical return rates of products with similar features.

If you lack a data science team, integrating BigSquid with your open-platform BI tool is a powerful way to achieve the horsepower of data science while maintaining the ease of use that the average business user requires. We fed Kraken (BigSquid’s predictive analytics engine) information about historical warranty costs, claims, forecasts, historical product attributes, and attributes of the new products on the roadmap. Then we ran Kraken’s machine learning and predictive modeling engine to get the results.

Following a few false starts and some great iterative learning with the BigSquid team, we came away with a solid predictive data model of the warranty costs for future periods. That predictive output was then fed into Sisense so we could drill-down, explore, and use these predictions to make data-driven decisions, ask new questions, and understand our cost drivers. For our product development team, these kinds of insights are a goldmine for exploring opportunities for impacting warranty costs on new products before they’re even released.

Using Sentiment Analytics to Inform New Product Design Decisions

With our predictive data models telling us what might happen in the future with our products, our next step was to use sentiment analysis models to tell us what customers are saying and feeling right now. Again, with our BI housed within Sisense, we could integrate our text and sentiment data using a few different techniques. Our BigSquid partners suggested using Python and it’s natural language processing libraries to understand what customers are talking about, and Sisense helped us use AWS Comprehend to understand how our customers feel about our products.

With this integrated data tech stack, we could feed in text from customer reviews and warranty claims to be processed by a Python NLP Engine in order to pull out key themes. We could also feed the same data into Amazon Comprehend to measure the sentiment behind the themes, and which products were most associated with certain sentiments. Finally, we could load this robust sentiment analysis back into Sisense — turning what was previously a “needle in a haystack” exercise into a richly engaging data experience yielding very targeted analysis for our business users.

Where in the past we had to wade through disparate, siloed data, now we can correlate a stream of negative sentiment to reviews that mention a defect on the left side of a headset. And we could take it a step further to put a dollar value on that negative sentiment — by connecting those negative reviews on a product to the same product’s warranty claims. Incoming products with similar design and engineering could be given extra attention by our product designers and engineers before going to market. And we could easily visualize how a fix could impact our warranty claim forecast. Full circle data experience: achieved. 

Lessons Learned

If you’re considering a similar investment in your company’s data strategy, there are a few lessons we learned along the way:

  1. Be open to false starts with data modeling. Like any other data project, it won’t be instantly clear what your data model should look like. And bringing the predictive models into the mix means you’re connecting data points from the past, the present, and the “future” to build a model that provides actionable insights. It will be iterative. Be patient!
  2. Make the data easy to add to and modify. For our data team at Skullcandy, that meant using an easy to manipulate view in SQL as our main data source. This allowed our team to easily swap out code and change the view as we continued to build and iterate.
  3. You may not like what you see. It’s fun to explore the positive drivers and get lost in the positive reviews. One of the surprises that our NLP Engine identified in one product’s reviews was a predominance of the phrase “for my son.” It was fun to think about why that might be. 

    But we also came face-to-face with some harsh reviews. Not as fun to read — but when you invest in advanced analytics, sometimes that’s exactly what you came for. Your goal is to identify what isn’t working for your customers so that future products can deliver what they want and need.

Here at Skullcandy, we’re happy to report that “dropping in” to the predictive and sentiment analytics game was worth the initial uncertainty. We answered some of our most pressing questions, came up with some new insights we hadn’t originally considered, and this project has helped concretely demonstrate to the company what’s possible with advanced analytics.  As a result, we are further along on our journey as a data-driven company.

SQL, Python, and R
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