Whenever a classification problem comes at hand, the Logistic Regression model stands out among other classification models. With LiveRamp’s data onboarding solution, advertisers can deliver people-based audiences to platforms such as Facebook to then build lookalike audiences. The L1/L2 regularization (also called Elastic net). For example, if the table is ordered by most recent active date, then a "LIMIT" clause will return the most recent (or distant) active customers. It is a pragmatic solution that allows us to make the most of the multiple features available to us, leveraging the information contained in the original seed set. The lookalike modeling process typically involves joining a relatively small seed audience into a much larger universe of data, known as a reference set.

Uncover omnichannel insights and make actionable and informed decisions on your business or marketing data. Sklearn’s LogisticRegression uses penalty = L2 regularization by default and no weight regularization is done in Keras. The larger audience reflects the benchmark characteristics of the original audience, known as the seed audience. But it is also a curse because, given the high dimensionality of the data, finding the right set of features is challenging. age, gender, location, etc.).

The Lookalike Model assigned highest weights to the filters used to generate the source audience (gpo015 – GMV per order, dsl015 – days since last order, sex000 – sex), and included correlated features in the final ranking: dsl001 – Accessory - Bag, cto016 – Baby Core - Formula, gpq017 – Baby Core - Wipes. Logistic regression is a linear model, that maps probability scores to two or more classes. python script.py " test_src_srls.csv " " test_src_srls " 20 0.05 30000 If we knew this, we could calculate user similarity relying more on the most relevant features. As a consequence, it becomes harder to differentiate the activity of each user from that of everybody else, as everybody appears dissimilar from everybody else in some way. , advertisers can deliver people-based audiences to platforms such as Facebook to then build lookalike audiences. Maciej Zawadziński, Michal Wlosik, Learn about the platforms, processes, and players that make up the digital advertising industry. For more information, see our Privacy Statement. first-, second-, and third-party data) needs to be collected from a range of online and offline sources. If you’re interested in being part of this beta program, reach out to, Matching Your ABM Measurement Solution to Your Advanced ABM Strategy, Setting the Record Straight on the Personalization Paradox. python script.py " test_src_srls.csv " " test_src_srls " 20 0.05 30000 The stricter your look-alike model is (the more attributes you define), the better chance you have of finding your target — albeit smaller — audience, which will allow you to improve campaign performance. What could be better than finding users that behave like the potential customers they are already targeting? For example, let’s say you run an ecommerce store and you’ve identified that your best audience are people whose average purchase is over $100, buy cosmetics and perfumes, and make a purchase at least twice a month; look-alike modeling would allow you to find more people like that.

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