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Yandex Eats: Putting takeaway on the map

Actions

Conducted fundamental research with my team, interviewing customers to uncover real-life scenarios for takeaway use and identify barriers. We mapped three main motivations (convenience on the way, independence from couriers, saving money on delivery) and key pain points (unclear which restaurants support pickup, no time-to-restaurant estimate, and low visibility of the option).

Situation

In 2020, the Yandex Eats restaurant food delivery service launched a pickup option for some restaurants. A year later, this functionality accounted for only 1% of all orders, while competitors saw up to 15% of orders placed for pickup. It was clear that the feature wasn’t resonating with users, and the company was missing an opportunity to grow this segment.

Task

Redesign the user flow for takeaway orders to increase adoption — with the specific goal of raising the share of takeaway orders from 1% to at least 5% of all Yandex Eats orders.

Studied multiple competitor apps and concluded that the most effective solution for these scenarios would be a map-based interface. This would let users easily find nearby restaurants and predict journey time.

Design exploration:

  1. Introduced a map view as an alternative way to browse restaurants.
  2. Designed a pin system with different sizes, active states, and cuisine icons, adapting to zoom level.
  3. Created a compact snippet for the map, highlighting distance, walking time, rating, and cuisine.

Results

The launch of the new flow tripled takeaway orders — growing from 1% to 3% of all Yandex Eats orders within the first months after release.

Other projects

Back to all cases

Yandex Eats: Putting takeaway on the map

Actions

Conducted fundamental research with my team, interviewing customers to uncover real-life scenarios for takeaway use and identify barriers. We mapped three main motivations (convenience on the way, independence from couriers, saving money on delivery) and key pain points (unclear which restaurants support pickup, no time-to-restaurant estimate, and low visibility of the option).

Situation

In 2020, the Yandex Eats restaurant food delivery service launched a pickup option for some restaurants. A year later, this functionality accounted for only 1% of all orders, while competitors saw up to 15% of orders placed for pickup. It was clear that the feature wasn’t resonating with users, and the company was missing an opportunity to grow this segment.

Task

Redesign the user flow for takeaway orders to increase adoption — with the specific goal of raising the share of takeaway orders from 1% to at least 5% of all Yandex Eats orders.

Studied multiple competitor apps and concluded that the most effective solution for these scenarios would be a map-based interface. This would let users easily find nearby restaurants and predict journey time.

Design exploration:

  1. Introduced a map view as an alternative way to browse restaurants.
  2. Designed a pin system with different sizes, active states, and cuisine icons, adapting to zoom level.
  3. Created a compact snippet for the map, highlighting distance, walking time, rating, and cuisine.

Results

The launch of the new flow tripled takeaway orders — growing from 1% to 3% of all Yandex Eats orders within the first months after release.

Other projects

Back to all cases

Yandex Eats: Putting takeaway on the map

Situation

In 2020, the Yandex Eats restaurant food delivery service launched a pickup option for some restaurants. A year later, this functionality accounted for only 1% of all orders, while competitors saw up to 15% of orders placed for pickup. It was clear that the feature wasn’t resonating with users, and the company was missing an opportunity to grow this segment.

Task

Redesign the user flow for takeaway orders to increase adoption — with the specific goal of raising the share of takeaway orders from 1% to at least 5% of all Yandex Eats orders.

Actions

Conducted fundamental research with my team, interviewing customers to uncover real-life scenarios for takeaway use and identify barriers. We mapped three main motivations (convenience on the way, independence from couriers, saving money on delivery) and key pain points (unclear which restaurants support pickup, no time-to-restaurant estimate, and low visibility of the option).

Studied multiple competitor apps and concluded that the most effective solution for these scenarios would be a map-based interface. This would let users easily find nearby restaurants and predict journey time.

Design exploration:

  1. Introduced a map view as an alternative way to browse restaurants.
  2. Designed a pin system with different sizes, active states, and cuisine icons, adapting to zoom level.
  3. Created a compact snippet for the map, highlighting distance, walking time, rating, and cuisine.

Results

The launch of the new flow tripled takeaway orders — growing from 1% to 3% of all Yandex Eats orders within the first months after release.

Other projects