Contents
- What is a Look-alike Audience (LAL)
- Why Use LAL
- Key Characteristics for Collecting LAL
- What Audience is Used to Create Look-alike
- Why and How to Segment the Source Audience
- How Look-alike Audiences Work
- Strategies for Working with Look-alike Audiences
What is a Look-alike Audience (LAL)
A Look-alike Audience (LAL) is a group of users who share similar characteristics with a pre-defined audience selected by the advertiser. Advertising networks use complex algorithms to analyze the source data, such as interests, behaviors, and other criteria, to create a list of users that closely resembles the original one. Ads are then targeted at this audience, allowing for more effective targeting.
The collection of look-alike audiences can be configured on popular advertising platforms such as Yandex, VKontakte, MyTarget, and Google Ads. This feature allows advertisers to significantly expand their audience and improve the effectiveness of their advertising campaigns.
Why Use LAL
Utilizing LAL technology can bring numerous benefits to businesses. Here are the main advantages:
- Attracting New Customers: LAL helps to find users similar to existing customers, increasing the chances of attracting new buyers.
- Improving Targeting: Algorithms analyze the source list to identify common patterns, allowing for more precise ad targeting.
- Increasing Conversion: Targeted advertising finds a more relevant audience, increasing the likelihood of a desired action.
Key Characteristics for Collecting LAL
To form a look-alike audience, advertising systems rely on a number of key characteristics:
- Gender: The system considers the ratio of men to women in the source audience.
- Age: LAL users have a similar age to customers from the source list.
- Region: Algorithms search for users living in the same region as the source audience.
- Interests: Users' behaviors and interests are compared to patterns from the source list.
- Campaign Events: The system analyzes people who responded to previous advertising campaigns.
What Audience is Used to Create Look-alike
Various sources can be used to create a look-alike audience, such as:
- Customer database from customer relationship management (CRM) systems.
- Subscribers to business pages on social networks.
- People who participated in company projects.
- Website visitors collected using pixels.
- Retargeting lists.
- Subscribers to competitors' pages and communities.
Why and How to Segment the Source Audience
For a more accurate selection of look-alike audiences, it is advisable to segment the source data. Dividing by various characteristics, such as stages in the sales funnel, allows for the creation of more specific groups. The parameters for segmentation may vary, including the average purchase value. Using pixels helps track user actions on the website, which also contributes to more precise segmentation.
How Look-alike Audiences Work
The process of working with look-alike audiences involves several stages:
- The advertiser uploads a user base into the system, the quality of which is critically important.
- The system algorithms identify signs that unite users from the list.
- The system compares all visitors to the resource with patterns from the source base, creating a list of similar users.
It is important to note that to obtain a quality look-alike audience, there must be at least 1,000 users in the source list. Additionally, the system updates the audience every few days.
Strategies for Working with Look-alike Audiences
Depending on the goals of the advertising campaign, strategies for working with look-alike audiences may vary. The main approaches include:
- Expanding reach and attracting traffic — creating LAL based on all visitors to the website.
- Gathering interested individuals — forming a base from those who showed interest in the product but did not purchase it.
- Finding new customers — using data only about actual buyers.
- Balancing demand — creating an audience from those who interacted with specific products or sections of the website.