Harley-Davidson Grew Revenue with Personalized Customer Experiences

Cole worked at Harley Davidson full-time from 2023 to 2024. Harley-Davidson was not a consulting client, but the results outlined in this case study represent Cole’s individual contribution and expected results from working with Cole as a Scalable Marketing client.

Customer Analytics: We connected data from across H-D’s ecosystem to get a full view into customer behaviors, interests, and lifetime values.

‍Predictive Modeling: We developed custom recommendation algorithms to predict the products and the content consumers were most likely interested in based on prior purchases, bike ownership, site & app behavior, and more.

‍Personalized Content & Recommendations: We built personalized email campaigns and site experiences that showed customers personalized product recommendations, substantially increasing revenue from email campaigns motorcycle leads from site visits.

Cole was recruited by Boston Consulting Group in 2023 to work at Harley-Davidson on their Personalization initiative. Harley’s goal was to move beyond static experiences on their website, mobile app, and marketing campaigns in favor of individualized product and content recommendations on a per-consumer basis. Harley believed that by tailoring what consumers received to what they have expressed interest in, Harley would see growth in both motorcycle and retail/e-commerce sales.

Cole was tasked with leveraging a newly-built internal Customer Data Platform to drive personalized product and content recommendations based on dealership, website, mobile app, bike ownership, and other customer data sets.

Cole began by auditing Harley-Davidson’s internal Customer Data Platform and existing data science models. While work had been done to ingest and standardize customer data from various sources like dealership visits, riding academy classes, website & mobile app sessions, and retail & e-commerce purchases, this data was not connected into one customer record or ready for use in predictive modeling.

Cole also worked with the data science and marketing teams to understand existing models and campaigns and how the teams could further integrate. He found that the marketing team worked closely with analysts and data scientists to understand which products to promote based on seasonality and style trends, but that these product selections were manual and not personalized based on individual customer data or history.

Customer Analytics

Cole began by connecting all of Harley’s first-party data available within their Customer Data Platform. He created a “customer golden record” that brought the most important customer data points into one view that could be used to create recommendation engines for products, content, and more.

He then integrated third-party data provider Liveramp to supplement demographic and interest data for known consumers as well as identify anonymous web visitors that Harley had previously identified on-site or in-store. This allowed Harley to create more accurate predictive models and increase the number of users that would receive personalized site and marketing experiences.

Predictive Modeling & Conversion Rate Optimization

Cole worked with the data science and marketing teams to create several predictive models that generated individual product recommendations based on past purchase history, interests, behaviors on H-D.com, bike ownership, and more. These models were built to integrate directly into H-D.com’s content infrastructure via Adobe Experience manager and H-D’s email campaigns via SalesForce Marketing Cloud.

Cole also led an A/B testing team that facilitated collaboration across growth, marketing, and product on unified customer experiences from advertisement to email to site & app content.

Personalized Customer Journeys Across Web and Email

Cole brought each of these pieces together and launched personalized website and email recommendations via the A/B testing team.

Email campaigns including personalized product recommendations generated over 30% more revenue per campaign on average when compared against standard product recommendations.

Similarly, website experiences that displayed personalized bike recommendations and/or calls-to-action (i.e. “Get a Quote” vs “Get Financing”) generated over 10% more leads on average when compared against standard experiences.

By connecting all of Harley’s first-party customer data and leveraging custom predictive models based on customer interests and behaviors, Harley was able to deliver personalized content and product recommendations to customers that drove real business impact. These recommendations resonated with customers and generated significantly more motorcycle and retail/e-commerce revenue for Harley-Davidson using their existing infrastructure.

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