Líder strives to help its shoppers in Chile by offering quality grocery products at affordable prices. Owned by Walmart Chile, the retailer and grocer has more than 370 stores across the country. When physical shopping took a backseat to digital channels in 2020, the team focused its efforts on meeting online demand, which had soared to eight times its pre-pandemic levels.
Navigating a highly competitive online landscape, Líder decided to double down on its food delivery app. Throughout the last 12 months, other top grocers and retailers had launched new apps to improve grocery delivery and pick-up. In order to grow market share, Líder decided to extend its online delivery radius to reach 90% of the Chilean population, and planned to convert web shoppers over to the app. But by expanding to such a large area of the country so quickly, the team needed accurate tools with actionable insights.
Uncovering insights with the new Google Analytics
Líder first needed a single source of truth across its website and app to gather campaign insights, which would help it optimize its digital marketing campaigns based on user behavior. They chose Google Analytics 4, the next generation of Google Analytics that can be used across a website, an app, or both together.
One of the first insights Líder uncovered was that few potential customers were making a first purchase soon after they downloaded the app. With this insight, Líder discovered it didn’t just need to drive an increase in app downloads – it also needed to re-engage with customers who recently installed the app but hadn’t yet made a purchase.
Using the best of Google machine learning
To re-engage with these potential customers, the team at Líder decided to reach its Android app users with App Campaigns for Engagement in Google Ads. This fully automated campaign solution re-engages users who have installed an app, and across multiple Google properties, encourages them to take specific, in-app actions. Thanks to machine learning, App Campaigns for Engagement helped Líder test different combinations of text, images, and video, and then showed the best-performing ads to app users.
Líder also tested predictive audiences in Analytics within its App Campaign for Engagement. A predictive audience is built using conditions known as predictive metrics. For example, using the Purchase Probability metric, Analytics can automatically suggest a predictive audience made up of shoppers who show the highest probability of purchasing in the next 7 days. Líder decided to test out the “Likely 7-day Purchasers” audience.
Connecting with valuable customers
Within a few days of launching its App Campaign for Engagement, Líder began to see momentum. Predictive insights from Analytics kicked in, increasing the size of its predictive audiences. To measure the success of its campaign, Líder looked at conversions across its website, Android app, and total e-commerce sales, as well as cost per action (CPA).
Líder saw a decrease in the CPA of its app campaign by 85% compared to its previous efforts. The conversion rate for the “Likely 7-day Purchasers” audience also increased to 5.4%, a drastic improvement compared to a 0.3% conversion rate for the company’s other, standard audience lists used in App Campaigns.
“We’ve seen firsthand the value that the new Analytics has brought to our business and plan on using more new capabilities as they become available in Analytics 4 properties.”
, Manager of Marketing Technology, Walmart Chile
Líder’s in-app sales have continued to grow, solidifying the company’s position as one of Chile’s top retailers. The team now uses App Campaigns for Engagement with predictive audiences on an evergreen basis.