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New Balance Sports Research:
Uncovering a New Consumer Strategy After Scaling Marathon Runner Research
Research Data: Key Results
838
marathon participants
globally
18,000+
surveys automatically
delivered via AI triggers
89%
compliance for post-run
surveys
40+
unified contextual
variables
Identified a new high-potential runner segment shaping future growth strategy
Executive Summary
New Balance’s Sports Research Lab partnered with DashLX to take marathon runner research beyond survey-only methods and limited sample sizes into a global and scalable, real-world consumer insights model.
Across three marathon majors (New York City, London, and Berlin), New Balance ran a 10-12-week longitudinal research program per event, combining experience-based surveys (Qualtrics) with wearable data captured throughout real-world training. This enabled the team to observe how runners train, adapt, and perform every run across thousands of runners, geographies, terrain and weather conditions, grounded in what they did, not just what they said they did in a survey.
The most important outcome was strategic clarity. Research shifted from validating assumptions to defining who to build for, how to define performance, and where to focus for future growth.
The program revealed a large, high-potential consumer segment that the industry had not fully recognized as a primary growth driver, helping New Balance align research, product direction, and innovation strategy with greater confidence and discover an under-served market.
Research Study Snapshot
Use Case
Product Innovation & Sports Research
Research Owner
New Balance Sports Research Lab
Partner Platform
Qualtrics
Wearable Integrations
Garmin, Coros, Fitbit, Polar, Suunto
Study Design
3 global marathon majors (NYC, London, Berlin), 10 weeks for NYC, 12-weeks for London and Berlin
“We used to rely on surveys alone to understand how products performed outside of the lab. Now, with DashLX, we’re capturing real-time insights from wearable devices, pace, heart rate, duration, distance, step frequency, all tied back to what the athlete experienced.”
Jinger Gottschall,
Director of Sport Research, New Balance
The Challenge:
Scaling Real-World Runner Research Without Adding Operational Burden
New Balance’s Sports Research Lab has a strong legacy of research-led innovation. But as global marathon participation expanded and runner behavior became more dynamic across geographies, devices, and training environments, the team faced a growing gap between the questions leaders needed answered and what traditional research methods could support at scale.
Key challenges included:
1. Research scale was too limited to guide strategy confidently
Prior studies often included fewer than 20 runners, making it difficult to validate patterns and guide high-stakes product and growth decisions.
2. Wearable data existed, but wasn’t usable at research scale
Wearable signals were available, but New Balance didn’t have the infrastructure to capture, normalize, and analyze training data across multiple device ecosystems at the volume required for longitudinal research.
3. Survey-only research lacked objective performance context
Traditional surveys captured perception, but often weren’t timely or didn’t reliably capture the objective context shaping those experiences like training load, pace, elevation, or environmental conditions.
4. Insights were difficult to share and activate beyond the research team
Even when insights were strong, they were often hard to operationalize beyond research. Product, innovation, and leadership teams needed outputs they could use to make decisions, not raw data or disconnected reports.
New Balance needed a way to scale global, real-world marathon research without increasing operational overhead, while producing insights that teams could apply directly to product and growth strategy.
The Project: A Global, Longitudinal Marathon Research Program
New Balance designed a research initiative to understand who marathon runners are, how they train over time, and what factors shape the marathon experience leading up to and including race day.
Across three major marathons—New York City, London, and Berlin—New Balance ran a 10-12-week longitudinal study per event, combining experience-based surveys with objective wearable data captured during real-world training.
DashLX enabled the Sports Research Lab to run the program end-to-end through four core capabilities:
1
Scale the Research Study Size Without Scaling Complexity
Objective: Expand from small-sample testing to a globally representative runner dataset.
DashLX made it possible to scale marathon recruitment efforts without increasing internal resources.
Remote 4-week gait retraining intervention in runners with patellofemoral pain, designed to move injury research out of the lab and into real-world conditions.
2
Unify Wearable + Survey Data Into One Normalized Dataset
Objective: Combine multi-device wearable data and experience based surveys into a single, research-ready dataset.
DashLX automatically captured and unified wearable-derived Lived Experience Data (activity, pace, heart rate, cadence, weather) from multiple devices so training behavior could be analyzed consistently across runners, devices, and conditions, and linked directly to runner-reported experience.
3
Automate Activity-Triggered Surveys for Real-Time Insight
Objective: Move from recall-based feedback to run-based, in-the-moment feedback.
DashLX automatically triggered surveys after training sessions, ensuring runner input was captured in context immediately tied to the run, the conditions, and the training load.
4
Deliver Research Outputs That Teams Could Use
Objective: Package complex research data into decision-ready insight across the organization.
DashLX helped translate complex raw research data from wearables and surveys into outputs that were easy to share and act on, enabling product, innovation, and leadership teams to engage with findings without relying on researchers to interpret raw data.
Outcomes: A Scalable Runner Research Engine That Unlocked Strategic Clarity
With DashLX, New Balance transformed marathon runner research from small, survey-led studies into a global, high-volume research program, capturing real-world training behavior and lived experience at scale, without increasing operational burden on the Sports Research Lab.
1. 838 Research Participants From Over 50 Countries
New Balance scaled participation from dozens of runners to hundreds across geographies, climates, and training styles. Recruitment also extended beyond existing New Balance customers to reflect the broader marathon market.
2. 18,000+ Surveys Delivered Automatically With 89% Compliance
By automating activity-triggered surveys after training sessions, New Balance increased consistency of feedback collection and sustained engagement across the longitudinal study timeline.
3. A Research-Ready Dataset Built on 40+ Unified Contextual Variables and 40,000+ Training Runs
New Balance established a normalized dataset that combined wearable-derived training behavior with experience-based feedback into a single dataset, enabling consistent comparisons across runners, device types, and training conditions.
The dataset became a common reference point, aligning conversations across teams and shifting research from a team function to a company-wide collaboration for decision-making.
New Consumer Segment Identified to Drive Cross-Functional Growth
In a mature running footwear market, it’s rare for research to surface a truly new growth lever, but that’s exactly what happened. New Balance uncovered a high-potential consumer segment the brand hadn’t been prioritizing as a primary growth driver, creating a meaningful strategic advantage.
The insight will align Product, Innovation, and Marketing around who to prioritize, how to define performance for this audience, and where to focus to drive differentiated growth.
A Long-Term Research Foundation, Not a One-Off Study
The marathon dataset was designed to persist beyond a single season. This created long-term leverage from a single research investment, supporting ongoing learning, not just point-in-time insight.
