Over the last several years, the evidence has become increasingly clear that personalization platforms that are able to offer high-quality recommendations will outperform ones that can’t, especially for returning customers and for large catalogs. For example, a study published in Information Systems Research found that the presence of personalized recommendations increased consumers’ propensity to buy by 12.4% for an online retailer.1 In another study, a large e-commerce marketplace randomly varied its recommendation quality by turning off personalization for a subset of users. As a result, the measured “Gross Merchandise Value” was 5.3x higher for those who were saw high-quality recommendations […]

One of the claims that we sometimes see in regard to modern personalization systems is that they offer a “Model Zoo” of ready-to-use models, and that they can rapidly build new models in a very short amount of time (for example, 30 minutes to 3 hours). How can a whole new model be created in such a short amount of time? And what on earth is a model zoo? In this context, a “model zoo” refers to a curated set of prebuilt, pre‑tuned models and pipelines that are used for common tasks like classification, regression or ranking, and that can […]

If you’ve been doing marketing for a while, you’re already familiar with the concept of A/B testing. Let’s say, we have two versions of a subject line, or of a landing page, or of an image, or whatever, and we want to know which one converts best. To determine that, we randomly assign one version to Group A and one version to Group B, probably on a 50/50 split. Next, we run a test, wait for statistical significance, and then roll out the winner. Or sometimes we test more than two versions (multivariate testing). The statistical test of significance is […]