Technology

Boosting QSR app retention with machine learning

QSR brands struggle to retain app users after initial downloads, as traditional marketing focuses on installs rather than long-term engagement. Machine learning offers solutions by analyzing user behavior to personalize engagement, optimize loyalty programs and identify high-value customers for targeted marketing.

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March 21, 2025 by Dario Sheikh — Head of Agency, Moloco

QSR brands have increasingly turned to mobile apps to engage with their customers. From McDonald's notorious deals exclusive to app users to Starbucks rewards for in-app purchases, fast food companies excel at driving downloads — but are they good at retaining them? The reality is that QSR app downloads can be fleeting. As customers sometimes look to declutter their phone screens or reclaim some storage space, food apps can quickly get the boot.

QSR brands excel at attracting users, but many still face challenges in retaining them and identifying high-value customers. The core issue stems from the fact that these brands were not built to be mobile-first. Traditionally, digital marketing strategies prioritized app installs rather than optimizing for long-term customer engagement but that's no longer a sound plan for QSR brand apps to stand the test of time. QSR brands must refine their marketing efforts, improve user retention and identify high-value users to mitigate these challenges.

Using machine learning to transform retention strategies

While it's instinctive for many QSR brands to initially focus on maximizing app installs through broad advertising campaigns, there are limitations to this approach. App installs don't inherently translate into business value. In many instances, users download the app, take advantage of an introductory promotion and then churn without making repeat purchases. The real goal for these brands should be to acquire and retain high-value customers who demonstrate consistent app engagement and a high lifetime value (LTV).

Machine learning plays a crucial role in keeping users engaged with the app over time. By analyzing user behaviors, purchase patterns and engagement trends, machine learning models can predict which users are likely to churn and proactively trigger retention strategies, including:

• Personalized re-engagement campaigns. QSR brands can segment users based on their app activity and personalize engagement efforts accordingly. For instance, users who installed the app but never made a purchase can be targeted with a first-time discount. On the other side of the coin, former frequent customers who have dropped off can be re-engaged with tailored offers based on their previous orders. Since push notifications and emails have opt-out limitations, brands can leverage in-app ads and broader mobile placements to reach dormant users on external platforms like music apps or mobile games.
• Intelligent couponing and loyalty programs. Traditional couponing has long been a staple of QSR marketing, but machine learning can make these efforts significantly more effective. Instead of offering generic discounts, brands can use predictive analytics to determine the optimal incentives for different customer segments. For instance, users who frequently purchase breakfast items might receive tailored morning deals, while those who order family meals could be given bundle discounts. Loyalty programs also benefit from machine learning. By analyzing purchasing frequency and order preferences, brands can create dynamic reward structures that encourage repeat visits. A well-structured loyalty program, like McDonald's app-based rewards system, ensures that customers remain engaged for the long term.
• Gamification to boost engagement. Integrating gamification elements into a QSR app can also enhance user retention. For example, Panda Express incorporates interactive challenges into its app experience, encouraging customers to engage beyond simple transactions. Gamified experiences can also be extended into interactive ad formats that allow users to experience the app's entertainment value before even downloading it. Machine learning can further refine these experiences by identifying which game mechanics are most engaging for different user segments, ensuring that the gamification strategy remains relevant and effective.

Identifying high-value users

QSR brands can miss opportunities to leverage the right data for user acquisition and retention. Many campaigns focus on simple metrics like cost per install (CPI) rather than more meaningful indicators like cost per new customer (CPNC) or cost per retained customer (CPRC). Relying solely on demographic-based assumptions can lead to missed opportunities, as high-LTV users don't always fit traditional profiles.

Machine learning offers a powerful solution by analyzing vast amounts of behavioral data to predict and reach users most likely to become loyal customers. Instead of assuming ideal customer profiles based on age, gender or income, machine learning evaluates real behaviors — such as ordering frequency, basket size and engagement with promotions — to identify high-value users. This approach eliminates biases and uncovers "hidden gems" that may not fit predefined molds but exhibit strong revenue potential.

Additionally, despite research showing that only a third of mobile time is spent on "walled garden" platforms like Google and Facebook, many QSR brands still allocate most of their ad budgets there. Optimizing ad spend gives QSR brands another avenue to identify high-value app users. Machine learning allows QSR brands to expand their reach to non-traditional but high-performing ad placements, such as in-app advertisements in mobile games or even utility apps that folks are frequently visiting. By leveraging AI-driven ad targeting with a trusted and transparent partner, brands can successfully optimize their marketing spend to ensure they reach users who are more likely to convert into long-term customers.

Machine learning also enhances cross-channel marketing. With the rise of Connected TV (CTV) advertising, QSR brands can now track campaign effectiveness at a household level, linking ad impressions directly to mobile app engagement and purchases. This integration ensures not only better user acquisition but also improved retention through consistent and personalized engagement across multiple touchpoints.

Serving up success

QSR brands are operating in an increasingly competitive digital landscape where user retention and high-value customer identification are imperative for long-term success. Machine learning provides the tools necessary to optimize marketing campaigns, personalize customer engagement and maximize retention rates.

By focusing on data-driven decision-making and AI-powered targeting, QSR brands can move beyond simple app installs and build sustainable, high-value customer relationships. Those who fully embrace these technologies will not only enhance their app's performance but also unlock significant growth opportunities in the evolving QSR market.

About Dario Sheikh

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Dario has worked across aspects of business development, client strategy and marketing for nearly 15 years. As Head of Agency at Moloco, Dario empowers businesses of all sizes to grow through machine learning. Prior, he worked in business development under the App Tech Partners division for Google and handled client development and strategic partnerships for Bidalgo, a marketing intelligence platform. He also worked as a growth manager for French food services management company, Sodexo.

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