Tips for improving quality
Choose the right segments for Lookalike Modeling
The best segments to enable Lookalike Modeling for are narrowly defined segments matching a small part of the audience. The segment filters should be based on directly observable event characteristics (for example specific URLs) or based on 1st party data you upload from your CRM systems. It is perfectly fine if the segment only matches a small part of the total audience. The behavior of a small, narrowly defined set of users has a higher likelihood of containing interesting and valuable patterns that the ML models can extract and apply to find similar users. It is intuitive that the bigger the original segment is, the more will the behavior of the users in that segment match the average behavior of the total audience. So try to keep the segment definition for the original segment as narrow as you can while still including the interesting properties that you are after
Set an appropriate fraction (1-20%), not too high
The fraction setting is a percentage setting that determines how many users are tagged as lookalikes. Setting the fraction is a quality/quantity tradeoff. The ML models produce ranked lists of users with the most similar users on the top of the list. The backend system includes similar users from the top of the lists until the fraction setting is satisfied. If you increase the fraction setting, the quantity of users increases at the expense of the overall quality of the results (as you then include users that are less similar).
A high fraction setting is anything above 20%, which should be reserved for very specific use cases only. Check with Piano Support if you are unsure about the appropriate fraction (percentage) to set. An appropriate fraction setting is anything between 1% and 20%. Set the fraction based on how many users you need to be in the lookalike segment and how many users you have in your total audience.
Specify a "Negative Segment" where possible
A "negative segment" is a segment of users that are as nonsimilar to the original segment as possible. An example is if you have 1st party data on gender from users that have logged into your site. With an original segment of women, the negative segment will be the segment of men. Machine learning models can utilize this information to give better quality results, so if you have two segments that are natural opposites, please use the /segment/lookalike/update API to set the "negative" segment. The ML models will then look for similar users to the original segment which at the same time are not similar to the users in the negative segment. In machine learning, this is called the positive and negative classes of training data. This way of training the ML models is proven to improve quality compared to the common case of having a positive class (the original segment) and an unknown class (the rest of the audience). But if you don't have a negative segment, don't despair. Our ML models still generate good quality results in either case
Bonus: Incorporating new patterns in the data
As long as Lookalike Modeling is enabled for a segment in Piano Audience (DMP), we periodically re-train the machine learning models on your data and events. The set of similar users is recomputed every 24 hours on average, to ensure that the models automatically take into consideration new patterns that emerge in your dataset. So there is nothing you need to do to ensure that new patterns are included. The model performance is continuously monitored and improved by Piano R&D to guarantee that the quality stays high over time.