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Lookalike Modeling: An Effective Way to Find High-Value Prospects

To grow your brand, you need to grow your customer base, but how can you do this beyond your existing customers while remaining relevant? One solution is to use lookalike modeling to identify similar audiences to your current customers. So, what is lookalike modeling? How can marketers benefit from it? And how does it work?  

What Is Lookalike Modeling?

Lookalike modeling is a machine learning-based analytical technique that identifies groups of people who display similar behavioral and demographic characteristics to your best, existing customers’.

For example, you are an e-commerce brand and your best customers make two purchases per month, spend an average of US$150 per order, and buy technology and gaming items. Lookalike modeling would help you identify people who show similar traits.

The people identified as your best customers are known as your seed audience. The groups of people who are similar to your best customers are your lookalike audiences.  

How Can Marketers Benefit From Lookalike Modeling?

Marketers can benefit from lookalike modeling in a number of ways, including:

  • Improving lead generation

    Lookalike modeling enables you to identify high-quality potential customers beyond your existing customer base, thanks to the accuracy of machine learning in finding prospects who act like your best customers.
  • Reducing marketing spend

    Using lookalike modeling, you can focus your efforts on high-value lookalike audiences only, instead of everyone in your target audience pool. This helps you reduce acquisition costs, and boost your return on investment.
  • Boosting brand awareness

    Because lookalike modeling provides you with new audiences, it is effective in boosting brand awareness. Simply by putting your brand and product in front of more people who fit your target profile, you can drive interests and engagement – the first step to purchase.
  • Enabling more accurate targeting

    Lookalike modeling uses machine learning to analyze and compare data on your existing customers and audiences from the wider web, in order to find the significant and most accurate traits of your best customers.

    These traits will not only help you enhance the accuracy of your targeting, but also enable you to personalize your retargeting campaigns.
  • Better understanding your customers

    The process of lookalike modeling can give you a better understanding of your audience segments and the actions driving them to convert. These insights can be used to optimize your marketing strategy and future campaigns across all channels.
  • Increasing conversions

    Because lookalike modeling identifies high-quality leads, it can significantly increase your click-through rate (CTR) and the number of conversions you are likely to achieve. This can boost your profits, improve your bottom line, and, in turn, grow your business.  

How Does Lookalike Modeling Work?

Like any type of computer modeling, lookalike modeling works using a combination of data, mathematical algorithms and machine learning. The process is typically carried out through a data management platform (DMP) and involves three key steps:

Step 1: Collect data

In order to generate lookalike audiences, you first need to collect a wide range of audience data, including first-party data collected from your own channels, as well as second- and third-party data, such as other websites and surveys. Importantly, the more data you have available, the more accurate the modeling will be.

Step 2: Define attributes and behaviors

With all your data collected, the next step is identifying the attributes and behaviors that best define your most engaged, profitable customers or seed audience. Examples of attributes and behaviors to consider include location, purchase history, frequency and quantity of purchase, browsing history, and interest shown in a specific product.

The stricter you are in defining these attributes, the better your chances of identifying quality prospects. However, while keeping your seed audience parameters narrow can help you find the people most likely to convert, it will limit the size of your lookalike audiences. This is because the more attributes you select, the more potential customers you will filter out.

If your goals are less focused on conversions and more focused on reach and awareness, selecting broader, less specific attributes and behaviors can be a good strategy as it will help you generate a larger audience.

Step 3: Generate your lookalike audience

Once you have decided on the attributes and behaviors of your seed audience, an algorithm is applied to find profiles that match. Users are then scored on an individual level based on their similarity to the seed audience using machine learning predictive modeling. The highest scoring profiles become your lookalike audiences.

Through the lookalike modeling process, you can pinpoint higher-performing audiences than you may have otherwise been unable to identify.  

Lookalike modeling offers big wins for brands looking to grow their business. The key to success is using quality data combined with smart modeling software. To ensure your marketing efforts don’t look like your competitors, consider what untapped datasets you could mine to build better lookalike audiences.  

* Looking to expand your customer base with lookalike modeling? Appier’s data science platform, AIXON , can help you turn data into actionable insights, and find the most profitable potential customers! Get in touch with our team today for an exclusive consultation.