Athos Changes the Game

Athos makes unique performance apparel to help elite athletes get the most out of their workouts.

Key Insights

• Shave hours off customer support inquiries

• Leverage real-time user data to fix defects in production

• Improve productivity of research and business users

What if your shorts could tell you if you’re using your left glute more than your right? Or your shirt could tell when you're phoning it in?

Athos’ athletic clothing comes loaded with built-in sEMG sensors that monitor biosignals like heart rate and muscle activity. Data from the sensors is sent to the Athos app, where it is analyzed to help athletes tune their workouts in real time.

The company makes truly innovative products with a huge wow factor. But pioneering a new market category takes more than an incredible product.

“Rather than use opinions to drive decisions about how to grow the business, we knew we had to make better use of our data,” explains Brett Jaffe, business development manager at Athos. “Speed is really important at our stage, and that’s why we chose Mode. Mode gives us a low-touch, agile solution for business intelligence.”

In addition to typical business data, Athos has a massive repository of biometric data generated by each of its garments. That data holds important insights into how Athos’ products are performing and how customers are using them. Athos’ research team is responsible for getting that data out of Amazon S3 and analyzing it to drive product development.

As the company grows, business users are increasingly turning to that data to make critical decisions for the business. They’re thinking of new questions for ad hoc analysis and building analytics into their day-to-day operations. They depend on the research team for help extracting that data and making it actionable.

When Athos’ research team needs to explore data, they turn to Python, which allows them to show patterns and investigate data through more complicated plots. Before Mode, this analysis was done in Jupyter notebooks. But because the output of a Jupyter notebook can only be viewed from another Jupyter notebook, sharing results with non-programmers was a manual and time-consuming process.

Now, they turn to Mode. Mode’s integrated Python notebooks makes it easy for Athos’ researchers to create and share beautiful Python reports quickly.

“Every week I’d spend far too much time trying to get my analysis out to the company through PDFs or images copy-pasted into email,” says Nahiyan Malik, engineer at Athos. “That all changed with Mode. Mode freed up a half hour of my time every week, which adds up quickly. When Mode came in, I set up a weekly email send and that was the end of my PDF creation days.”

One of the company’s most popular reports is a cohort heat map created using the Seaborn library in Mode’s Python notebooks. The heat map takes the huge volume of data from the garments and distills it down to highlight trends and patterns in usage, helping business teams make better decisions about how they market, sell, support, and distribute products. It has also become a catalyst for collaboration between business users and the research team.

“Someone might look at the cohort analysis and ask, ‘Can I get a list of people in this group? When did we ship product to them? What app version were they running on their first try?’ That’s when we work with them to do a deeper dive into the data,” explains Malik.

Mode’s interactive reports empower non-technical employees to quickly access and explore data on their own. Athos was quick to adopt Mode’s pivot tables to enable deeper data exploration without having to use SQL or export data to Excel. Self-serve access to SQL results makes business users more productive and less dependent on the research team. That also frees the research team to spend less time on routine analysis for business users and more time digging deeper into the data to improve products and grow the business.

“For me, the most beneficial aspect of Mode is that it gives me my time back and gives me the freedom to get the information in the way that I want it,” adds Jaffe. “I can look at the data 100 different ways with a dozen different parameters and do it quickly. That impacts day-to-day decisions at every level of the company.”

Here’s an example: The customer advocacy team uses a report generated in Mode to explore customers’ usage over time. Using report parameters, the team can quickly drill down into a customer’s history to understand their challenges and offer a resolution. This helps representatives resolve issues faster, freeing up their time while improving customer satisfaction.

“In customer service, timeliness is of the utmost importance,” says Jaffe. “Data that used to take our customer advocacy team a couple of hours to dig through is now at their fingertips. That’s had a dramatic improvement on the customer’s experience.”

Rapid access to data helps every aspect of the business move faster. Athos already boasts an impressive roster of pro athlete organizations, including the Los Angeles Clippers, Philadelphia Phillies, Philadelphia Eagles, and Ohio State. These elite athletes are both powerful brand champions and rigorous product testers. Strengthening those relationships is critical to Athos’ growth strategy.

To make certain that the athletes’ garments are performing well at all times, Athos continuously monitors their workout data to check for problems. EMG data is pulled from the data repository in Amazon S3 and converted to tables that can be queried by Mode. Mode then sends reports every hour to the business development team, enabling them to move quickly if anything looks wrong.

“It helps us be proactive,” adds Malik. “If there’s an issue, often we will know about it before the athlete even realizes. If that happens, we can say, ‘We noticed a problem with your garment, a new one is on its way to you now.’”

According to Jaffe, Athos is just scratching the surface of what’s possible with Mode.

“As we grow, there will be a lot more moving parts, and each of us will have less and less time,” says Jaffe. “When that happens, Mode will be even more important to our ability to extract actionable insights quickly.”