A short introduction to lookalike modeling
The search for new potential customers can be long and exhausting. Where do you start looking and how do you reach new prospects? And how can you target them in a cost-effective way while maximizing your conversion rate? Imagine just collecting data on your most valuable customers and being able to reach a whole new audience that is very similar to those customers. Sounds impossible? With lookalike modeling it isn't!
What is it?
Lookalike modeling is the process of identifying groups of people who look and behave like your current audiences (your most profitable customers for example). Marketers use this methodology to target audiences who are most likely to engage with their marketing messages. Based on your seed audience (a set of data on your current, most valuable customers) the model seeks for users who share similar characteristics and attributes with the help of machine learning techniques.
For example, let's say you run an e-commerce site for a big retailer and want to improve conversion and optimize the reach of your marketing messages. After analyzing your data you were able to identify people who buy video games at least once a month as your most profitable customers. You could then create a behavioral profile of these customers and expand this segment with profiles of users with similar attributes through lookalike modeling. Thereafter you will be able to use this expanded segment for future campaigns and consequently reach a larger high-value audience.
How does it work?
Lookalike modeling works by identifying the composition and characteristics of a seed audience and identifying other users who show similar attributes or behavior.
First of all you should understand your goal. Who are you trying to reach and what do you want from your audience? Usually you'd want to find new potential customers who share similarities with your most valuable and profitable customers. Start with identifying the attributes and characteristics of these customers. The dataset consisting of high-value customers with these attributes will then serve as your seed audience.
In order for lookalike models to produce proper results, you should collect lots of data from online and offline sources. The more data you collect, the more likely you are to produce accurate audiences. Begin with a seed audience consisting of your own first-party data and enrich this with second- and/or third-party data. Combine this data in one centralized location, preferably a Data Management Platform (or DMP).
The data will then be processed and analyzed for behavioral patterns of similarity with the help of algorithms and machine learning. The result of this process will be a new lookalike audience that shares similarities with your original seed audience and is ready to be targeted.
Where does a DMP come into play?
A Data Management Platform (or DMP) collects non-personally identifiable information (non-PII) from different sources (mostly 2nd and 3rd party data, but also 1st party data) and enables marketers and advertisers to categorize this information into different segments and eventually target their campaigns to the right audiences.
Some DMPs, like Adobe Audience Manager for example, have built-in lookalike modeling features. The technology can be used to ingest all available data into one central location and let machine learning and AI tools run complex algorithms in order to generate valuable new audiences for your campaigns.
Why use it?
Lookalike modeling is mainly used to find new prospects, identify a larger audience of potential customers and even broaden the reach of online campaigns. Instead of relying on demographic data like age and gender, marketers can now leverage lookalike modeling to target people based on comparable browsing patterns or other attributes. By expanding their pool of most valuable customers with similar users, they are able to allocate their online advertising budget in a more effective and efficient way and thus save money. Campaigns that were successful for certain customers in the past can hereby be extended to target similar audiences.
In addition lookalike modeling can also improve the understanding of the way customers behave and what drives them to convert. This way campaigns and content can be optimized in order to maximize conversion.