Realizing Data’s Potential with Lookalike Models

June 18, 2024

Authored by Dawn Valandra, Senior Director, Data Partnerships

Realizing Data’s Potential with Lookalike Models

The holy grail of targeting is reaching the right consumers at the right time and in the moments that matter most. And, if you’re reaching the “right” consumers it means connecting with the customers you know, as well as the ones you don’t. The best way to reach the consumers you don’t know is through lookalike modeling - identifying people who look and act just like your target audience. 

Lookalike models learn from a “seed audience” to determine key characteristics that are indicative of the target audience and then leverage that understanding to find people who are similar to the target audience. By identifying behaviors that match-up with your target audience, you have a greater chance of converting those new consumers into customers. Even better, some lookalike models (like the ones we build here at Semasio) offer the flexibility of having the seed composed of raw data or even an existing audience with reliable results produced from as few as three hundred records. Sounds great, right?

While lookalike models do offer the potential to increase a brand’s reach to likely consumers, there are some pitfalls to avoid.

  • Outdated demographics. By their nature, lookalike models rely on datasets used to train the model on key characteristics. If too much reliance is placed on demographic factors, rather than behavioral inputs, the model runs the risk of a homogeneity that reduces its effectiveness to increase reach. The over-reliance on demographics can easily result in biases, which is why demographic targeting is largely eschewed in highly regulated industries such as financial services, where equal opportunity must be protected.
  • Reliance on source quality. The effectiveness of lookalike audiences depends on the quality of the source. In the past, this may have meant only the quality of the data itself and that it was accurate. Today, the notion of “data quality” should also include the quality of data collection–that it was sourced compliantly and in accordance with regulatory guidance for collection and use.
  • Recency. Past behavior doesn't necessarily indicate future behavior. Models that rely on historical data alone fall short when compared to predictive audiences. Everyone has heard the story of a parent getting a “new baby” offer when their child is two. It’s important to ensure that models take into account sufficient signals to identify accurate interest and intent and is another reason probabilistic attributes are important to consider in the modeling mix.

When these pitfalls are successfully addressed, by recent, privacy compliant seed data that is transparently modeled based on a holistic view of the consumer and their behaviors–rather than just their demographics–the end result can be a lookalike model that delivers reach and value efficiently. For that to happen, data owners need to prioritize compliant data collection. Building and nurturing direct relationships with consumers and being transparent about its use. 

Most organizations can benefit from a strategic partnership as an economical choice for lookalike model creation. By leveraging a strategic partner well versed in model creation, the results are more likely to avoid lookalike pitfalls and ensure marketing efforts are focused in the right place, increasing positive outcomes through scale and improving ROI.

Lookalike modeling is a powerful tool in the marketer's arsenal, enabling more effective and efficient targeting of potential customers. By leveraging data to find and reach audiences similar to their best customers, businesses can enhance their marketing strategies, drive higher engagement, and achieve better overall performance.


For more information read Part 1 - Realizing Data Potential Series 

 Realizing Data’s Potential: A Guide for Data Owners.

Go back