How One Retail App Uses Push Notifications, Predictive Churn Analytics & More to Grow Revenue
Published on 4 Dec 2017
Learn how Brazilian e-commerce brand Dinda grows revenue, increases app opens and curbs app user churn with smart app messaging and predictive churn analytics. Read on to see how they generate 60% more revenue coming from their app than desktop website purchases.
Beginning in October 2015, Dinda noticed a shift in where their traffic was coming from: more and more customers were arriving on their site via mobile devices and the mobile app. To address this change, retailer began focusing heavily on evolving its mobile app strategy.
Dinda’s two main app objectives are to increase its active user base — both by gaining new app users and retaining existing customers — and grow app revenue.
Their plan to achieve these goals? Create deeper connections with customers through the app by delivering real-time, personalized mobile messaging.
Check out this video sharing Dinda’s mobile strategy & app success. Or, download the case study to get the full story.
Sending Targeted, Relevant Push Notifications to Increase New & Active Users
To increase downloads and acquire new users, Dinda uses third party advertising, such as Facebook, to promote its app. Dinda shares daily deals by delivering segmented push notifications to relevant app users, via Urban Airship Engage.
For example, if there is a sale on shoes, Dinda can deliver that message to a specific list that includes only those who have indicated interest in shoes. An automated welcome series also onboards new app users by sharing special features of the app and the value of opting in to push notifications, which encourages user activity and repeated use. See how you can create your own here.
Optimizing Push Notification Copy
Daily A/B tests help optimize message copy — the Dinda team runs tests such as with/without titles, with/without emojis and even differences in wording like “Don’t get wet today” vs. “It’s raining,” or “Meow, Hello Kitty is here” vs. “50% OFF Hello Kitty Clothes.”
“We run A/B tests almost every day to test title, image, offers and segments,” said Rodrigo Santos, marketing coordinator at Dinda. “We get creative with our text - and emojis) - to get better open rates]”
Using segmentation in combination with A/B testing has resulted in a 25% direct open rate and an even higher influenced open rate for Dinda, meaning these notifications often prompt users to open the app. (This number outpaces typical direct open rates.)
“Another great feature of Urban Airship’s analytics tool Insight is the Lifecycle tab, where you can see what type of influence your push notifications are having on your users,” said Santos. “For example, if there’s a high number of uninstalls after sending a particular message, you can adjust your approach quickly.”
An example of Urban Airship's Predictive Churn Analytics report. Learn more here.
Predicting At-Risk Users & Re-Messaging to Retain These Users
Dinda quickly checks and monitor day-to-day metrics and success, including daily/weekly/ monthly active app users as well as authenticated users with Urban Airship Insight.
On top of that, Urban Airship’s Predictive Churn solution analytics allow Dinda to see users that are at low, medium and high risk of churning. They’re also able to see their app’s top 25 device tags broken down by risk distribution — for example, a high percentage of users who have items in their cart are at low-to medium-risk of churn, while other tags may have more high-risk users.
With this breakdown of information, Dinda can create retargeting campaigns re-engage at-risk users. By doing so, high and medium-risk users have decreased. Unsurprisingly, Dinda app users who have made a purchase are the least likely to churn, followed by those who have authenticated/logged in to the app.
Over the past year these tactics have led to an an uptick in revenue coming from Dinda’s mobile app over, with 60% more revenue coming from its app than desktop website purchases.
“Urban Airship Insight provides valuable information about our app, including an overview with general data, predictive models that help our marketing team take action on at-risk, inactive users as well as analytics to see if these actions are having a positive impact,” said Santos. He continued, “it provides a way for us to get a better view of our data and act on it more quickly to keep our users active, and positively influence our overall results.”
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