We launched Yandex.Audience two years ago, and a lot has changed since then:
Yandex.Audience is about two main features:
- Creating your audience by defining user segments for your advertising activities.
- Exploring your audience — getting insights or statistics about your audience.
Data can be:
· Online — usually linked with user activity on Internet — from analytical tools on your site or in your mobile app, e.g. goals or segments in Yandex.Metrica.
· Offline — from your CRM (email addresses, phone numbers, purchase history, RFM segments, etc.)
Classification by source:
- 1st party — it is your data, whether on- or offline
- 2nd party — 1st party data from your partner or data from your ad activities
- 3rd party — data from external providers and data management platforms (DMPs)
Classification by collection method:
- Personal data — based on different forms filled out by users, e.g. from social networks - age, gender, interests.
- Heuristic data — based on some logical condition e.g. a user visited an automotive site more than 3 times per month, then he is interested in cars.
- Probabilistic data — based on machine learning algorithms, e.g. Yandex Look-a-Like technology.
For example, you already have a search campaign for users searching for “buy a bicycle.” You can then increase or decrease your bid for users in this segment. Maybe you have a list of customers who have already bought a bicycle in your shop. You can exclude them from your future campaigns.
What about the second feature — getting insights?
For any segment, no matter its type, Yandex.Audience provides three types of statistics: the overall reach for your segment, gender and age distribution. Usually, it's quite interesting when you have an offline CRM base. If you don't know makes up this client base, you can upload it to Yandex.Audience and get insights on who these people are.
You can also connect a Yandex.Audience segment with Yandex.Metrica and learn how many users from your segment have already fulfilled your Yandex.Metrica goals or have just visited your site. Here are some real statistics on people who work in the Moscow Yandex headquarters:
The similarity scale, varying in color from red to green, is a useful tool when you want to use Similar users segments. If users' age and interests vary significantly (closer to red on the user similarity scale), we don't recommend creating Similar users segment. If you have a more homogenic group (green on the similarity scale), a Similar users segment will perform much better.
There are also insights for cities and devices:
The statistics for interests and categories are really incredible. The affinity index helps to see your segment in relation to the general population. We can see that average Yandex employee in Moscow is a travelling IT geek without pets and not interested in fashion.
5 best practices for Yandex.Audience (there are actually much more):
- Bring back your customers with data onboarding and targeting
- Upsell your customers — data uploads, targeting
- Find new customers — Similar users, hyper-local, DMP segments, targeting
- Optimize your current ad campaigns — Similar users, CRM data, bid adjustments
- Integrate brand and performance activities — pixel, targeting and bid adjustments
The hottest feature is new polygon-based segments — hyper-local targeting. Now you can create a geo segment of any form:
- No minimum square requirements
- Maximum of 10 polygons in a segment
- Minimum reach of segment = 1,000 users
Client case of hyper-local targeting usage:
If you have a huge CRM database and want to manage your segments and accounts more easily, you also have the Yandex.Audience API. Please contact us — we will be happy to help you with that and any other questions you may have.
