December 21, 2020



The company manages the development of various restaurants. The goal was to promote personal mobile applications for two points: BRON and MAROON, and increase the number of active users in the delivery service. Critical metrics included DAU/MAU. The first metric indicates the number of unique users per day, while the second represents the monthly count. Both metrics are important as they reflect user behavior patterns. MAU primarily indicates demand growth, while DAU reflects visitors’ quick reactions.


The food delivery market is oversaturated. Personalizing service promotion is very challenging due to competition from aggregators offering coupons, discounts, bonus points, gifts, and more. Restaurants often cannot afford these incentives directly. Detailed strategy development was required with pinpoint selection of advertising sources. Creatives needed to be developed considering audience inertia. Limited smartphone memory added complexity, requiring motivation to install the application.


Initially, app analytics were reconfigured to obtain accurate and up-to-date user behavior data. Appsflyer, a mobile analytics platform, was additionally incorporated. VKontakte (VK) and Yandex.Direct were used as promotion platforms.

We experimented with formats and sought traffic optimization methods, focusing on call-to-action (CTA) models. Audience parsing and polygon work (geofencing, traffic share allocation) were employed. Manual bidding was used throughout the project due to potentially high automatic bids.

Specialists controlled click prices at each auction, helping to save the budget. Creatives, motion graphics, and food shoot scenarios were developed.


Promotion through search engines yielded zero results. Native and targeted advertising were utilized. Stimulating user actions was very difficult. Traffic had to be passed through analytics before selecting promotion sources. The client wanted immediate traffic, while a long-term strategy was necessary.

The app had few reviews, affecting App Store Optimization (ASO). Poor design also played a role. Improving user perception was necessary.


The non-trivial task fueled excitement and a desire to achieve the set goals. An adrenaline rush helped maintain interest in the project. Overall, the outcomes were satisfying.



We achieved an optimal outcome: 350-450 rubles per installation. It’s possible to further reduce the cost of conversion. Detailed work on optimization paths will be required for this. We are also considering adding new traffic sources. We have temporarily paused the project and shifted focus to other matters. We plan to develop a loyalty program for users aimed at increasing activity within the application.

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