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Lookalikes settings

This feature allows you to leverage your customer data to identify new prospects who resemble your best customers. By selecting specific attributes that describe your customer profiles, our algorithm creates lookalike audiences, enabling you to target potential customers more effectively.

Enabling Lookalike Segments

The Administration/Lookalikes tab allows setting attributes to enable the possibility of creating lookalike segments under the Segments tab. Configurations are usually done by the Meiro technical consultant. Ensure your role grants you permission to view and edit Lookalike settings.

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Select under Lookalike settings up to 10 string attributes without any specific order. Note that compound attributes are not supported. These selections will help create lookalike segments by identifying profiles with similar attribute values. At least one attribute must be selected to activate the creation of a lookalike segment within the Segments tab.

Learn more: discover the creation of Lookalike segments and their diverse use cases in this article.

Examples of lookalike settings

When you compare a black box solution for creating lookalike segments to a customizable solution where you can choose the parameters you want, you get an effective strategy tailored to your needs. Several key differentiators stand out:

Tailored Attributes: Users have the flexibility to select and design the attributes that are most relevant to their specific business needs and customer profiles. This allows for a more targeted approach to creating lookalike segments.

Greater Control and Transparency: Offers more control over the segmentation process and greater transparency in how the lookalike segments are derived. Users can understand and adjust the criteria based on which the segments are created.

Industry-Specific Customization: Enables the creation of segments that are tuned to the nuances of a particular industry or market segment, leading to potentially higher accuracy and relevance in targeting.


Scenario: The client operates in e-commerce and wants to optimize lookalike segments for conversions.

Attributes selection

    • Transactional attributes examples: Average order value, frequency of purchases, recent purchases, time between purchases

    • Behavioral examples: Purchase history, website engagement score, product affinity, preferred communication channel, items browsed or added to wishlist

    • Demographic examples: Age, gender, region

Industry-specific Tips

    • Fashion Retail

      • Focus on style preferences, size ranges, and seasonality of purchases.

      • Consider loyalty to certain brands or designers and frequency of purchase during fashion seasons.

    • Electronics

      • Pay attention to technology affinity, brand loyalty, and purchase frequency of accessories.

      • Look at the upgrade cycle of customers for tech products.

Banking Industry

Scenario: The client operates in the banking sector and aims to optimize lookalike segments to increase new account openings and credit card applications.

Attributes Selection:

    • Product in use Examples: Average account balance, types of owned accounts (savings, checking, investments), frequency of transactions, loan history.
    • Behavioral Examples: Online banking usage, mobile app engagement, response to past financial offers, types of transactions (e.g., domestic, international).
    • Demographic Examples: Age, income bracket, occupation, geographic location.

Industry-Specific Tips

    • Retail Banking

      • Focus on customers' account management patterns, such as savings habits or regularity of deposits.

    • Wealth Management:

      • Look at the lifecycle stage of the customer, such as new professionals, families, or individuals nearing retirement.

Media Industry

Scenario: The client is in the media industry, looking to enhance lookalike segments to increase subscriptions and viewer engagement.

Attributes Selection:
In media, attributes that reflect content consumption preferences and interaction with media platforms are key:

    • Behavioral Attributes Examples: Favorite genres or shows, average watch time, peak viewing times, frequency of app or website usage.
    • Monetization Model Examples: levels of subscription - anonymous, registered, premium
    • Consumption Preference Examples: device usage patterns, like the predominant use of mobile devices, smart TVs, or desktops for streaming.

Industry-Specific Tips

    • Streaming Services:
      • Focus on viewing habits, such as binge-watching tendencies or preference for certain content types (movies, series, documentaries).
    • News Media:
      • Pay attention to the types of news content consumed (political, entertainment, sports)
        Look at engagement with different formats, such as video news, podcasts, or written articles.

Converting numerical attributes to a string form

For the creation of lookalike segments, we use string attributes as parameters. Choosing the attributes allows you to define the selection process and directly manage it, providing a more qualitative approach to audience targeting and segmentation.

Numerical parameters typically express the value that a customer brings to a business from a transactional perspective. In e-commerce, the standard division for that is RFM (Recency, Frequency, Monetary) analysis. We translate them into strings via the definition of clusters that are based on a numerical range.

Divide these numerical values into meaningful segments. For instance, you could create categories like "High Spend," "Medium Spend," and "Low Spend" based on monetary values, or "Frequent Buyers," "Occasional Buyers," and "Rare Buyers" based on purchase frequency.

After that, it’s just an easy process of adding a new string attribute describing in what category the profile belongs to.

Example of RFM segmentation segments division

Customer profile Segment






Bought recently and often, spent the most





Loyal to the brand, consistent purchase




Potential Loyalist

Bought recently, spent a good amount, and bought more than once





Recent first purchase and did not spend a lot





Have not purchased recently, spent a good amount, and bought more than once




At Risk

Have not purchased for quite some time, spends a good amount, and bought often





Have not purchased for quite some time, did not purchase often, and low purchase amount




Example for the retail bank industry 

Customer Segment


Account Balances

Transaction Frequency

Product Usage

High Net Worth Individuals (HNWIs)

High-value clients with significant assets

Typically > $250,000

Regular, high-value transactions

Diverse investments, jumbo mortgages, large personal loans

Mass Market Customers

Average retail banking clients

Between $1,000 to $50,000

Regular, smaller transactions

Standard credit cards, auto loans, small personal loans

Young Adults/Students

Clients in the early stages of financial life

Low to moderate, aligned with student life

Frequent, small transactions

Debit cards, small credit lines, beginner investment products

Retirement Age Customers

Older clients, often focused on savings and investments

Varied; may include retirement accounts and fixed deposits

Fewer, larger withdrawal transactions

Retirement accounts, fixed deposits, less focus on new loans

Small Business Owners

Clients owning and operating small businesses

Varied, reflects business cash flow

Varied based on business activities

Business accounts, business loans, lines of credit

Publishers industry example 

Customer Segment


Reading Frequency

Subscription Level

Content Engagement

Engaged Readers

Regular and dedicated readers

Daily/Weekly reads

Premium or full-access

High interaction (comments, shares, reading time)

Casual Readers

Occasional content consumers

Few times a month

Basic or trial subscription

Moderate (some article reads, limited time spent)

Inactive Subscribers

Subscribers with minimal usage

Rarely, less than once a month

Maintained subscription

Low (little to no article completion)

Academic Readers

Readers primarily for research or academic purposes

Varies, often in bursts

Access to specialized or academic content

High during research periods, focused on specific topics

Genre-specific Enthusiasts

Readers with an interest in specific genres

Regular within genre

May subscribe to genre-specific content

High engagement with preferred genre content