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Lululemon has long been a front-runner in activewear, known for its innovative, performance-driven apparel and community-focused go-to-market strategies. Yet, even the strongest brands need to adapt to macro shifts like changing consumer behaviors, rising customer acquisition costs, and fierce competition.
Last year, Lululemon’s performance marketing team faced high-revenue growth goals amid macroeconomic uncertainty. They needed transformative impact and used AI-powered solutions to deliver it.
Business goals
From the outset, the team had three clear goals: drive sales, acquire new customers efficiently, and maximize return on ad spend. They set up three priority workstreams, each mapped to a goal: restructuring and optimizing their shopping campaigns; building a new customer acquisition engine; and strengthening their measurement foundations. Here, we dive deep into how the brand implemented each of these priorities — and the impressive results.
1. Restructure and optimize shopping campaigns
The team began by completely overhauling the structure of their shopping campaigns. This critical step allowed Lululemon to fully utilize Performance Max, which uses AI to optimize and distribute ads across Google channels — like Search and YouTube — from one campaign. It also involved a mindset shift toward gaining brand efficiency while accepting a lower return on ad spend (ROAS) for new customers unfamiliar with Lululemon.
Taking this approach allowed the team to implement more strategic bidding and personalized ads as well. Shopping ads were tailored to different customer segments, with distinct approaches for new, undecided, and returning customers. This precision ensured the right message reached the right people at the right time, maximizing engagement and conversions.
2. Build a new customer acquisition engine
Next, using Performance Max, Lululemon created an engine designed to acquire new customers. It operated as a real-time data pipeline to new customer information. This data provided the insights needed to optimize its campaigns more effectively.
From there, the team launched three dedicated Performance Max campaigns designed to attract new customers, using customer lifecycle goals to improve efficiency. The success of this data-backed approach justified allocating more budget to acquiring new customers, without negatively impacting campaigns geared to existing customers.
Buoyed by success in Canada, the team exported their strategy internationally to reach even more customers.
3. Strengthen measurement foundations
Finally, to further strengthen its AI-driven approach, Lululemon implemented a “measurement trifecta” by blending marketing mix modeling (MMM), experiments, and attribution to gain a more holistic view of performance. MMMs revealed how different marketing activities influenced sales over time; experiments allowed the team to test and refine their strategies; and attribution helped them pinpoint the most effective touchpoints in the customer journey.
Next-level gains in customer acquisition, revenue, and ROAS
Lululemon’s results were game changing for the business and earned top honors at the Google Search Honours Awards in Canada last fall. The approach drove a substantial reduction in customer acquisition costs on brand excluded Performance Max campaigns in Canada, increased the percentage of new customer revenue (from 6% to 15%), and boosted ROAS by 8%. Buoyed by success in Canada, the team exported their strategy internationally to reach even more customers.
By implementing AI across its performance marketing, Lululemon shows the transformative power of AI tools and a forward-thinking mindset.
