Title	YEAR IN REVIEW: YANDEX.TAXI PERSONAL STATISTICS 2018
Brand	YANDEX
Product/Service	YANDEX. TAXI
Category	A05. Data Visualisation
Entrant	YANDEX. TAXI Moscow, RUSSIA
Idea Creation	YANDEX. TAXI Moscow, RUSSIA
PR	YANDEX. TAXI Moscow, RUSSIA
Production	YANDEX. TAXI Moscow, RUSSIA

Name	Company	Position
Maria Brish	Yandex.Taxi in-house creative studio	Creative Director
Vasily Podtynnikov	Yandex.Taxi in-house creative studio	Art director
Elena Yastrebova	Yandex.Taxi in-house creative studio	Producer
Kirill Kotov	Yandex.Taxi in-house creative studio	Producer
Grigory Shcheglov	Yandex.Taxi in-house creative studio	Copywriter
Andrey Coziy	Yandex.Taxi in-house creative studio	Copywriter
Ksenia Grazianova	Yandex.Taxi in-house creative studio	Designer
Daria Kolobova	Yandex.Taxi in-house creative studio	Analyst
Alexandra Komarova	Yandex.Taxi in-house creative studio	Marketer
Olga Veretinskaya	Yandex.Taxi in-house creative studio	Creative team leader

Why is this work relevant for Creative Data?

This is a creative visualisation of the app users’ types, which are created on the basis of differences and similarities in the users’ riding data.

Background

Yandex.Taxi has established itself as a leader in the Russian online taxi market, but 2018 market growth has led to market сommodization. To stand out from the competition, Yandex.Taxi used an emotional appeal to connect with its audience. We implemented a simple data-based project and that drove a positive emotional connection with the product, the personalized brand approach, and the brand’s credibility.

Describe the Creative idea / data solution (20% of vote)

Yandex.Taxi uses big data and machine learning to personalize communication with every user based on how they use the application. We analyzed users’ riding habits during the year and divided them into 17 different rider personas, based on how many rides they took, their duration, how they rated the rides,and so on. For example, an “early bird” usually rides in the morning, and a “jetsetter” frequently goes to the airport.

Describe the data driven strategy (30% of vote)

Data interpretation strategy helps to research into emotional drivers of certain types of user behaviour.

Describe the creative use of data, or how the data enhanced the creative output (30% of vote)

We analysed the data to discover the most frequent special aspects of riding, such as riding at the night time or certain tariff preferences. For the users who rode with us for a year or less we either compared their data to the midscores, or looked for a special correlation of their own riding features. For the users with 2 years+ riding history we compared their data for 2018 and 2017, so that they could see their progress.

List the data driven results (20% of vote)

In five days, we reached 70% of the app’s audience (KPI 55%) – 5.36 million users in raw numbers. 1.1 million users shared the statistics, which is 20% of the total reach. Posts in Telegram and VK launched a wave of memes reaching more than 1.5 million users. That’s 20% higher than the KPI, based on average coverage rates. The project made 36.8 times more money than it cost to produce.