Inside Gilt’s mobile marketing machine

Mobile attribution is becoming increasingly important as customers flock towards apps. Learn how Gilt uses mParticle to power its mobile marketing machine.

How Gilt uses mParticle to unify the mobile customer experience and boost advertising efficiency +20%

Gilt began as a flash sale site providing exclusive access to designer brands not usually available at a discount. A decade later, their business model has changed—Gilt now differentiates itself through its curated selection, as well as exclusive services and experience. With this shift in retail focus, Gilt’s customer experience has also shifted.

Between their shopping app and mobile web experience, 60% of Gilt’s sales can be linked directly back to mobile shopping. After being acquired by Hudson’s Bay in 2016, the owner of Saks Fifth Avenue and other department store chains, the Gilt team focused more on omnichannel marketing.

To optimize user experience while minimizing engineering cost, complexity, and risk, Gilt needed to create an agile marketing stack. Read on to learn how Gilt leveraged mParticle’s customer data infrastructure to power and optimize their mobile marketing machine.

mobile attribution

The data platform

From their first foray into mobile, Gilt’s team considered it an important conversion point. As a result, they built a data backend capable of keeping user data organized around a proprietary mobile id to take advantage of mobile id persistence. However, as the app ecosystem matured, and Gilt’s iOS and Android apps grew with it, the business needed a more flexible, extensible way to deploy collect and connect data to all the platforms and tools that would need to consume them—without constantly throwing in-house engineers at the problem. mParticle’s customer data platform was selected as this central data layer, integrating and orchestrating all of the other components of the mobile data strategy while enabling IT to focus on core product development.

User acquisition

Gilt’s mobile growth team invested a considerable amount in paid advertising. Their goal was to deliver quality new users — who will actually use the app, and make purchases— not just “installs”. However, trying to determine what content, channels, and platforms will resonate with these high-value users can become an expensive endeavor quickly without the right optimization strategy.

mobile attribution

Ad platforms need large-scale analysis of real-time conversion data to optimize ads so Gilt’s acquisition team used mParticle to quickly set up and test campaigns. By centralizing data collection, organization, transformation, and reporting, the acquisition team was able to increase their insight into customers’ journey toward conversion. Before, it was difficult to optimize toward any events beyond app install. With mParticle, Gilt is able to set specific events as optimization objects to power predictive bidding and resource allocation. And so, the Gilt team was able to start making data-driven decisions to increase paid advertising ROI.

Re-engagement

Acquiring new users and driving installations is only half of the equation for mobile retail success. Gilt consistently continues to test the waters of the re-engagement advertising space to foster relationships with customers and guide them towards repurchase. Users that have become dormant or risk becoming dormant are targeted with ads to drive them back to the app directly.  

mobile attribution

 

For example, Gilt automatically identifies users based on their past purchase and product viewing history and syncs these ids with Facebook Custom Audiences, Google Similar Audiences, and others at scale. Then, ad content is aligned as closely as possible for a relevant user path to conversion.  For example, a user who had recently viewed shoes and/or put a pair in their cart would be served shoe ads:

mobile attribution

By leveraging mParticle and in-house data infrastructure, Gilt’s data teams harvest first-party data and delivers it to people-based advertising platforms in a fraction of the time it would take to do manually.

Pricing is another important factor in establishing reengagement ad campaigns since determining the ideal cost per click (CPC) for driving someone back into the app can depend on many variables, including seasonality. Through mParticle, the Gilt team tracks and places weight on customer interactions within the app and site to determine the optimal bidding strategy. From there, Gilt can deploy a number of new tools, content forms, and attribution systems available through the mParticle integration ecosystem. By tailoring re-engagement content and channels according to real customer data, Gilt creates ads that are exciting enough to re-engage lapsed customers while keeping ad spend in check.

Full-lifecycle analytics

Luxury retail e-commerce businesses like Gilt look at the projected lifetime revenue metrics, or LTV, of their customers as KPIs for their acquisition marketing spend. In this revenue projection model, repeat shoppers who continue to shop are considered more valuable than a one-off customer, and KPIs focus on the duration of time needed for an ad campaign to pay back. High-LTV customers are the holy grail for retail businesses, but identifying them can prove to be almost as tricky as earning their loyalty.

As a practical matter, conversions are not always tied to long-term customer metrics in many instances.  This happens when the tracking and attribution methodologies of media systems are varied, producing measurement systems and thus produce measurement systems that live in isolated data silos.  Modern data orchestration allows retailers to bridge data silos to see the full customer journey and get a clear sense of performance between organic and paid attribution sources, as well as post-acquisition behaviors.

Gilt was able to centralize much of their data into ABY using the mParticle data platform to collect and orchestrate their customer data to create a single view of each customer across devices and advertising campaign partners.  The team then tested attribution methods to find the right models and partners to fit their business needs.

Results

Gilt’s advertising programs improved efficiency over 20 percent with an increased testing and optimization strategy.  

mobile attribution

With LTV metrics as priority, the duration of time needed to reach a positive ROI was measured for each cohort delivered by ad campaigns.  Where Gilt was previously limited to testing partners one at a time, mParticle allowed Gilt to test many at once to lessen time required for setup. With more time to focus on the next test, Gilt’s ad program moves quickly to market and keeps new and existing digital users coming back to shop with Gilt on web and in-app.

Conclusion

Online retailers know ecommerce is fickle; brands not only have to figure out how to acquire users, they also have to figure out how to retain customers with increasingly high expectations for their experience across web and mobile. Were every team given an unlimited budget and all the man-hours they could ask for, UA and retention wouldn’t be such a challenge. But, the truth is that growth and ad teams have to work within their resource constraints to strategize and execute ad campaigns efficiently. That means marketers need to continually test new content, platforms, and channels then allocate spend according to performance, and do so as quickly as possible.

Using mParticle, Gilt built a mobile marketing machine that was flexible and agile enough to deploy collect and connect data across their stack while minimizing the growth team’s dependency on in-house engineering. With this deeper understanding of their customers, Gilt is able to continue optimizing their user acquisition to focus on quality and maintain their existing customers engaged. If you’d like to learn more about how mParticle can help you collect, transform, and forward your customer data across your marketing stack for increased ROI, feel free to reach out to us today!

AuthorAlexandra Maguire
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