A Global Fashion Retailer Onboards First-Party Data to Increase Second Orders and Customer Lifetime Value Through the Use of Data Science

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1 CASE STUDY

2 CASE STUDY A Global Fashion Retailer Onboards First-Party Data to Increase Second Orders and Customer Lifetime Value Through the Use of Data Science ASOS.com is a global online fashion and beauty retailer primarily aimed at 20-somethings who shop online and through mobile devices. They sell over 850 brands as well as their own brand range of clothing and accessories and market with a customer-first approach, starting with people and not advertising, in everything they do. Background With the ever-increasing proliferation of devices, marketers need to understand the 360-degree customer journey across screens. After recognizing this shift in consumer behavior, ASOS realized they needed a cross-device identity solution to fully leverage their first-party audience data in order to effectively find their customers and offer a personalized experience. Without a programmatic team or strategy in place, and measuring their efforts through a last-click attribution model, ASOS chose MediaMath as its partner in 2014 because of its technology capability, managed service and consultative offering to successfully drive positive behavior and results through a resonant customer experience. Objective ASOS wanted to prove to the business that it could leverage programmatic to add tangible business value throughout the entire purchase funnel and not solely at the bottom. More specifically, they wanted to reduce customer Hit & Run, a term given to customers who purchase once and not again within 12 months, by targeting them with tailored ads to stimulate and nurture a second purchase. The company forecasted that reducing Hit & Run by a single percentage point would generate profit in the millions. As a long-term strategy, ASOS built inhouse capability to include its data science team s customer lifetime value, propensity to buy and churn risk models, as well as its own product recommendations for known customers to act as a blueprint for all future in-house programmatic initiatives.

3 Solution The in-house data science team at ASOS uses proprietary first-party data to build predictive customer models, by using hand-crafted techniques that look specifically for signals indicative of a customer s propensity to buy or churn, as well as predicting their lifetime value over 12 months. The ASOS programmatic team worked with MediaMath to onboard this first-party data along with their CRM data to target customers qualifying for the Hit & Run initiative. ASOS used the combined data set of data science and CRM to determine the right creative message and products to deliver within the advert, personalizing the experience at a customer level and stimulating an increase in second-purchase journeys. ASOS worked with MediaMath to bring on their ConnectedID identity solution to solve for crossdevice, since 70% of their customers are on mobile and own three or more devices. This allowed ASOS programmatic team to focus its overall spend and individual bidding decisions at customers with a particular behavioral makeup, due to the activation of its data science models, to chase incrementality over volume. MediaMath leveraged its DMP to cluster customers into audiences through its Adaptive Segments product, which was then activated through the company s media buying platform. Foreseeing that a last-click attribution model would not be fit for purpose for this initiative, ASOS programmatic team used a hold-out control group at source, removing 10% of its matched audience within MediaMath so they could measure their behavior against those who were exposed to Hit & Run advertising, revealing lift. ASOS compared the behavior of the two groups to measure the Hit & Run rate in both. They found that those exposed to ads showed a much higher propensity to place a second order. The lift derived was then analyzed further to calculate incremental orders which gave ASOS their ROI for the campaign, running across UK and France during the testing phase, scaling out thereafter. ASOS used multiple variables within their CRM database to target their customers with more precision. Some of these variables included gender, recency since first purchase and number of returned items, alongside browsing data and favorite brands to determine their creative message and products delivered. ASOS created brand relevancy through better imagery and personalization by providing meaningful product recommendations and proposition messaging to individuals, at the right time and when it mattered. Each month, ASOS looks at which customers are going into both groups and then denotes credit where an impression has been served prior to the second purchase, before comparing the lift seen against that of the control group.

4 Creative Examples Female Male

5 Results The launch of this campaign drove incredible results, with ASOS achieving their objective to reduce Hit and Run and more. Results include: Hit & Run was reduced by an average of 4% month-on-month, driving incremental revenues in the millions of pounds. ROI between was achieved, looking only at those second orders deemed incremental after lift was determined. ASOS also noted a 1.8x increase in purchase frequency of those who placed their second order when exposed to Hit & Run advertising vs. the control, which suggests that intentionally nurturing the second purchase creates stronger advocacy long term. User experience was optimized with better dynamic creative, by leveraging first-party data to tailor personalized imagery and messaging to the individual customer and improving the brand experience. Using a seven-day attribution window, ASOS found that customers who placed their second order within seven days of seeing their last ad accounted for the majority of second orders. 3.9x ROI 1.8x Increase in Purchase Frequency ASOS found the partnership with MediaMath on this campaign to be consultative, strategic and innovative. It also really helped challenge the company on the way they ve looked at targeting their customers, as well as measure the success of their marketing efforts, and elevated their customer-centric focus. ASOS has rolled out this approach in other global markets, applying it to their always-on retargeting strategy. This campaign was very sophisticated, complex, hugely rewarding and innovative. The return has been significant. Also, we ve not had to spend much to see huge bottom-line profit, which is very encouraging because it forms the blueprint for how we can target DEAN MURR Senior Manager, Programmatic Marketing, ASOS other customer segments in the same way. This case study was based on the work of: Dean Murr, Sally Ritchie and Evie Samouris, with special thanks to Braden Lang and Carlos Peralta.