{"id":208876,"date":"2023-03-31T10:00:00","date_gmt":"2023-03-31T17:00:00","guid":{"rendered":"https:\/\/sharethis.com\/?p=208876"},"modified":"2023-05-17T17:05:25","modified_gmt":"2023-05-18T00:05:25","slug":"the-power-of-dopplegangers-how-lookalike-modeling-works-to-boost-advertising","status":"publish","type":"post","link":"https:\/\/sharethis.com\/data-topics\/2023\/03\/the-power-of-dopplegangers-how-lookalike-modeling-works-to-boost-advertising\/","title":{"rendered":"Look under the Hood of Lookalike Modeling"},"content":{"rendered":"
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In today\u2019s fast-paced digital marketplace, staying ahead of the competition is a top priority. This means, as a media buyer, you\u2019re always on the lookout for ways to improve your targeting so you get the most out of your ad dollars.\u00a0<\/p>\n\n\n\n

Chances are, you\u2019ve used lookalike modeling to optimize your ad spend. With lookalike modeling, you can use your first-party data to create an expanded audience of people who are more likely to convert or engage. But without a full understanding of how lookalike modeling works, you may not be leveraging the full capabilities of this powerful machine-learning-based tool.\u00a0<\/p>\n\n\n\n

What Is Lookalike Modeling?<\/h2>\n\n\n\n

With the help of machine learning techniques, lookalike modeling<\/strong> takes a data set\u2014referred to as a \u201cseed set\u201d\u2014of your existing customers and uses this to create a new, larger audience of potential buyers who share similar attributes and behaviors.<\/p>\n\n\n\n

These shared attributes and behaviors mean you now have a highly relevant pool of people who are more likely to be interested in your products or services.\u00a0<\/p>\n\n\n\n

Behind the Scenes: How Lookalike Modeling Works<\/h2>\n\n\n\n

Now that we know the general definition of lookalike modeling, it\u2019s time to look at what\u2019s going on \u201cunder the hood\u201d. By understanding how lookalike modeling works, you\u2019ll be better able to optimize your advertising budget and reduce inefficient ad spend.\u00a0<\/p>\n\n\n\n

While each programmatic platform will have its own methods, there are a number of elements common to the lookalike modeling process overall:<\/p>\n\n\n\n

Data Collection<\/strong><\/h3>\n\n\n\n

Your \u201cideal customers\u201d seed set is built using first-party data. You most likely won\u2019t need to include all or even most of your first-party data to create your seed set\u2014in fact, each programmatic platform has different limitations on what data points they can match to your first-party data to find ideal customers in their network. But any first-party data you don\u2019t use can still be useful during the pre-seed stage, for filtering and honing in on who your most valuable customers are.\u00a0<\/p>\n\n\n\n

The seed set data you use will depend on your campaign objectives. For example, the data used to build a seed set for a campaign targeting people most likely to purchase Product A might consist of a list of customers filtered down by behavioral or purchase history information related to Product A, such as recent purchases, repeat purchases, and the action of adding the product to their carts.\u00a0<\/p>\n\n\n\n

Data Analysis<\/strong><\/h3>\n\n\n\n

Because the attributes (and behaviors) you choose to focus on will form the basis of your lookalike audience, you\u2019ll want to create audiences with attributes that are a good fit for your campaign objectives. Programmatic platforms will then use a number of techniques, including machine learning algorithms, to help analyze your seed set data and identify patterns and similarities in these attributes.<\/p>\n\n\n\n

For example, suppose your objective is to raise brand awareness. In that case, the platform might identify patterns in attributes and behaviors related to your broader seed set\u2019s interests, social media behavior, and brand affinity. For a conversion campaign, however, patterns in purchase history, online shopping behavior, product affinity, and in-platform conversions would most likely be better predictors. And with both objectives, certain demographic and geographic attributes could be relevant.<\/p>\n\n\n\n

You\u2019ll also need to balance quality versus scale when choosing the attributes you want to be analyzed and used for your lookalike model. More attributes will mean a more highly targeted audience, but you\u2019ll likely end up with a lookalike audience that\u2019s much smaller.\u00a0<\/p>\n\n\n\n

Lookalike <\/strong>Model\/Audience Creation<\/h3>\n\n\n\n

In this stage, the programmatic platform uses machine learning techniques<\/a> to build and train the lookalike model. This model includes the identified seed attributes and behaviors which can then be used to create a custom audience of people who share similar attributes and behaviors.<\/p>\n\n\n\n

Once created, your lookalike model isn\u2019t set in stone. Instead, the programmatic platform will continue to refine it, based on any new data or variables as they arise or are added. The platform may also adjust or modify its proprietary machine learning algorithms from time to time to reflect any changes in the target audience\u2014for example, inflation may mean customers become more price-conscious, causing a shift in their buying behavior.\u00a0<\/p>\n\n\n\n

Machine Learning & Lookalike Modeling<\/h2>\n\n\n\n

Machine learning drives the lookalike modeling process, and every programmatic platform has its own proprietary machine learning algorithms.\u00a0<\/p>\n\n\n\n

Several types of machine learning techniques are commonly used in lookalike modeling. The following are simplified explanations of some of the most popular techniques:<\/p>\n\n\n\n