Lookalike Modeling For Advertisers: From Data to Strategy


Published: 15 August, 2023

Continuously acquiring new customers is a challenging task. It can be difficult to determine where to allocate your time and resources effectively, and speed is crucial to stay ahead of competitors. The solution lies in leveraging your existing customer data to identify potential audiences with similar characteristics swiftly.

What is lookalike modeling?

Lookalike modeling is a cutting-edge machine learning technique created for identifying potential customers that have behavioral and demographic characteristics that are strikingly similar to those of your current top-performing clients.
The process starts with choosing a "seed audience," which consists of your most profitable customers, and then figuring out the key characteristics they share. The next step is to find new customers who fit the defined criteria using this vital information. The seed audience can come from a variety of sources, including email lists, profile page data, website visits, converts, and ad engagers, allowing for comprehensive testing and analysis.
Lookalike audiences, in contrast with traditional approaches, which rely on predefined demographic and interest-based filters, build a target audience that closely matches your current customer base using advanced algorithms. As a result, the targeting strategy is significantly more precise and effective because the process is less reliant on assumptions and more solidly grounded in factual data.

How does lookalike targeting work?

To generate lookalike audiences effectively, the initial step involves collecting a diverse set of data from your existing customers, which encompasses a combination of demographic information and on-site browsing behavior. This valuable first-party data is obtained from your own channels. It is crucial to emphasize that the quality and quantity of data play a vital role in the accuracy of your modeling process.
Next, you must identify the key attributes and behaviors that distinguish your most engaged and profitable customers, often referred to as the seed audience. These attributes and behaviors include factors such as location, purchase history, purchase frequency and volume, browsing history, and specific product interests. By precisely defining these criteria, you enhance your ability to identify high-quality prospects.
Once the attributes and behaviors of your seed audience are determined, an algorithm is deployed to discover profiles that closely match those criteria. The lookalike model utilizes machine learning predictive modeling to individually assess users based on their similarity to the seed audience. Profiles with the highest similarity scores are then designated as your lookalike audiences. This process enables you to pinpoint high-performing audiences that may have otherwise remained unidentified.
Through the power of lookalike modeling, businesses can tap into previously untapped potential and target audiences with a higher probability of engagement and conversion, ultimately boosting the effectiveness of their marketing efforts.

Why consider lookalike audiences for your campaign?

Lookalike audiences make the most of the effort you've invested in refining a custom audience. They are a more promising option compared to reaching out to a completely unfamiliar audience, and they provide the opportunity to experiment with multiple target groups simultaneously. Lookalikes are especially valuable for connecting with a fresh set of potential customers, allowing you to test the waters and explore possibilities for expanding your customer base.
Improved Audience Targeting: You can reach out to new potential customers who are quite similar to your current client base by using lookalike audiences. Your advertising campaigns can be made much more precise and effective by choosing those who have comparable characteristics and behaviors to target.

Enhanced Conversion Rates: Prospects who are more likely to be interested in the products or services you offer might be identified via lookalike modeling. As a result, as you interact with audiences that are more likely to respond positively to your marketing efforts, you could expect greater conversion rates.

Effective Resource Allocation: Your advertising budget and efforts can be optimized by concentrating on lookalike audiences. You might concentrate on reaching out to people who have a larger possibility to become loyal customers rather than mindlessly seeking large and diverse groups.

Better return on investment: When compared to traditional targeting strategies, lookalike audiences provide a higher return on investment (ROI). You can expect a more positive response and increased revenue generation as you concentrate on audiences who have characteristics in common with your top clients.

Personalized Marketing Strategies: By knowing the characteristics of your most important clients, you may modify your advertising strategies to appeal to comparable audiences more effectively. Stronger ties and brand loyalty can be fostered by this personalized approach.

Embracing Contextual Advertising

Even while you can't exactly duplicate your best clients, you may find and get in touch with others who reflect the same characteristics and behaviors. Lookalike models operate under the assumption that especially when dealing with an extensive user base, the similarity between users greatly contributes to product sales and the optimization of ongoing advertising campaigns.
A pixel-based lookalike audience is an extension of an existing audience formed through pixel data. You select a custom audience to serve as the foundation, and your resulting lookalike audience will comprise users who closely resemble those gathered by the chosen pixel-based audience. For instance, with UBIDEX, pixel categories like 'visits,' 'logins,' 'registrations,' 'deposits,' and more can be utilized for this purpose.
Second-party data also can be used to create “lookalike” audiences. By identifying shared characteristics between a partner’s audience and their own, you can target new customers with share similar interests and behaviors.
CRM lists prove to be an effective tactic for implementing lookalike modeling strategies. You can analyze the attributes, behaviors, and purchasing patterns of your current customers. This comprehensive understanding empowers you to segment your customer base into distinct categories based on various factors. CRM lists allow you to identify the most valuable and engaged customers within your existing database.
Impressions/visits/clicks/views/conversions can help you to form a lookalike audience. You can target users who have engaged and interacted with the creatives of your previous or ongoing campaigns.

A user-friendly approach to advertising can be achieved by utilizing lookalike audiences, which can produce excellent results. The complex job, however, is handled by the algorithms of the advertising platform. However, it is still crucial for marketers to start by identifying relevant and well-defined demographic information to use as the basis for creating lookalikes. After all, an algorithm's effectiveness is inextricably related to the quality of the data that it is given to work with.

Discover the potential of UBIDEX's toolkit to unleash the capabilities of lookalike modeling.