uber eats marketing case study

How UberEats Used Personalized Recommendations to Increase Revenue

Increase in Revenue per Customer

When Uber launched their Eats service in Singapore, a key objective was to increase repeat orders on the application to maximize lifetime value. 

Analyses showed that high-value customers that ordered frequently did so by having consistent variety in restaurant and cuisine selection.

The key opportunity was to create more personalized ways to up-sell customers into ordering different varieties of restaurants to increase repeat orders and thus customer lifetime value.

A collaborative filtering recommendation algorithm was developed which recommended new restaurants for users to order from based on their order history and behavior. 

This was applied to Uber’s re-marketing campaigns via push, e-mail, and in-app notifications to incentivize users to order from different types of restaurants.

The strategy applied to Uber’s marketing campaigns successfully converted users into selecting different varieties of restaurants, increasing orders and revenue per customer by 7%.

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How Uber Eats Unifies Data from 50+ Social Media and Marketing Accounts

Main Challenge

Uber Eats, which operates in hundreds of cities across dozens of countries, was managing a massive influx of data from a wide range of sources. But the data management platform they were using wasn’t handling it very well. Consequently, two data analysts spent all their time collecting and manually cleaning this data, leaving little time for extracting valuable insights.

Key Outcomes

• 50+ man-hours saved weekly . The two data analysts now only spend ⅓ of their time on data management and ⅔ on more strategic tasks.
• 0 unwanted barriers to data accessibility. Any team member with access to Dataddo can use its no-code interface to move data as needed, without the intervention of engineers.
• Complete view of all marketing decisions. Out-of-the-box connectors enable Uber Eats to access data points previously inaccessible via manual data collection.
• Dataddo automatically collects and cleans data from 50+ accounts on global marketing platforms like Facebook, Instagram, Brandwatch, and Socialbakers.
• The data is funneled to Looker Studio, Google Sheets, and BigQuery for analysis and storage.
• All data connections are centrally manageable via Dataddo's transparent interface.

- Amalia Bornstein, Global Social & Content Marketing Data Analyst at Uber Eats

Full Case Study

The Challenge: 50+ Accounts, Inadequate Tools 

As a large company that's spread all over the world, Uber Eats requires cutting-edge data solutions to be able to collect and analyze their data. Their previous data management platform was simply not able to handle the data from all of Uber Eats' sources. 

Uber Eats requires many features and abilities from their data architecture , such as interoperability with online data sources , scalability, resiliency, flexibility, process automation, and the ability to share raw data.

Before Dataddo, two data analysts spent 100% of their time managing data from various global marketing platforms. This included logging into 50+ accounts every day and manually cleaning data in order to create the required reports. 

The Solution: One Central Management Platform

By using Dataddo, Uber Eats was able to centralize their data management and analytics operations, channeling all of their data analytics into a single dashboarding and reporting platform.

Dataddo synchronized data from multiple data sources, including social media accounts like Facebook and Instagram , and social listening platforms such as Brandwatch and Socialbakers . This resulted in hours of saved time by eliminating manual data collection and cleaning processes.

Outcomes: Two-Thirds Less Time Spent on Data Management

With the implementation of Dataddo, Uber Eats is uncovering valuable new insights. They are able to sync all their data sources with all their data destinations, as well as access new data points previously lost in manual processes.

This saves their analysts two-thirds of the time they used to spend on data management, which is over 50 man-hours per week ! Of course, this time is now spent on what's really important: gaining valuable insights from data in order to push company goals.

Dataddo's no-code interface is also a major advantage.

Now, anyone in their team with access to the no-code Dataddo platform can easily manipulate and send data to their desired destination. No more headaches or custom ETL engineering when implementing new, web-based marketing services. 

Category: Case Studies , facebook , socialbakers , google_data_studio , instagram , facebook_page , facebook_ads , instagram_ads , instagram_media , instagram_story , Data to Dashboards

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Topics: Case Studies , facebook , socialbakers , google_data_studio , instagram , facebook_page , facebook_ads , instagram_ads , instagram_media , instagram_story , Data to Dashboards

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Dara Khosrowshahi became the CEO of Uber in August 2017, following internal turbulence and serious headwinds related to the company’s governance and reputation. Five short years later, Uber was clearly back on course, building on the success of its technology platform to reach 150 million monthly active platform users—and a market cap of $125 billion by the end of 2023.

This case study traces the remarkable transformation of Uber from its early innovation as a ride-hailing pioneer in a handful of cities, to the global expansion of Uber mobility services that required close attention to local operational and regulatory practices, to solving the complex technical challenges to drive Uber’s food delivery services forward. Interviews with Uber leadership reveals the strategic approach to work on the engineering, data science, product management, and product design challenges involved in building and maintaining a customer-friendly app and create an optimized user experience—and scale this on a global basis while factoring in local conditions and practices.

Key to this success was a culture reboot within Uber, and a renewed focus on collaboration and value creation for all stakeholders. The company that had found its initial footing by disrupting and transforming the taxi industry, more than a decade earlier, now faced a future where artificial intelligence and autonomous vehicles would likely disrupt the mobility sector once again—but Uber was preparing intensively for what the future might bring.

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