Behavioral Scores

There is a lot to learn looking at user-level behavioral data. Lytics evaluates behavioral data automatically in real-time and reports nine scores for each user.

These scores each represent a distinct behavioral quality and can be composed to build rich audiences. Please note, users must have behavioral data to make confident measurements before they have scores. If a user was added to Lytics via email upload, for instance, they would have no scores.

Note: If you are already a customer of Lytics, you can see the following explanation of each score alongside your own data in the app on the Scoring page.

Quantity measures a user's cumulative activity over their lifetime of brand engagement. The more activity the user registers, the higher the score. This score measures the user relative to all other users.

It is a common tactic to target a user based on the number of times the user has visited a website or has performed some other behavior. This becomes increasingly difficult as marketers add more data sources to their stack, and the user continues to engage over time.

Quantity takes into account a user's behavior on all data sources and measures that relative to how the most and least active users are engaging — so a user's score will always be between 0 and 100. You can think of this as a test score.

What is the point of scoring between 0 and 100? This is how we can ensure that any audience created with a score will always stay relevant. Perhaps 1,000 page visits seems like a lot for a user now, but the number will only grow larger as your site grows older and the amount of content you have increases. Another benefit of having a bounded score range is the ability to see the complete distribution of all users.

This is an example of how scores look like across an entire audience. The x-axis is the score (ranging from 0 to 100; 5 to 95 in the example for clarity) and the y-axis is the number of people who have that value as their score.

Frequency measures how consistent a user is interacting with your brand over time. More frequent interactions means a higher score. This score measures the user relative to all other users.

This serves as a measurement of user regularity. Do they visit once a week? Once a day? Once a lifetime?

Since this score is relative to all your users, you can easily target your most frequent users, rather than something like "users who visited in the last week", which will vary wildly in size.

Again, this score has a fixed range of 0 to 100. All the scores are like this. It is how we can continuously update user profiles without having to update audience definitions.

Recency measures how recently the user's general interaction has been. More recent activity means a higher score. This score measures the user's recent activity relative to the user's past activity.

Without scoring, this would be achieved by looking at the last time a user visited. Although better than nothing, that approach is kind of crude. Maybe an at-risk user opened your email by accident? It'd be an expensive oversight to assume that the user had recent activity and didn't need any nurturing.

Intensity measures the depth of a user's typical interaction with your brand. More sustained intense/deep usage means a higher score. This score measures the user relative to all other users.

The behavior a user exhibits during a single session is very telling of them as a consumer. If they have high interaction in a session (high intensity) they are more likely to be a deeper researcher or more curious. If they have low interaction in a session (low intensity) they are more likely to be casually browsing or engaged with a certain piece of content, but not your overall brand.

Momentum measures the rate at which users are interacting with your brand. Users who are interacting more than usual with your brand will have a higher score. This score measures the user's recent activity relative to the user's past activity.

It's easy to confuse how momentum and recency differ, but they are actually very different. Universally speaking, we've found them to have a 5% correlation. Recency measures absolute recency of activity, but just because a user has recent activity, doesn't mean they're not at risk of churning.

If a user maintains a constant rate of activity, their momentum score will be 50. If they are more active than they used to be, their momentum will be greater than 50 and might warrant a loyalty offer. If they are less active than they used to be, their momentum will be less than 50 and might warrant a win-back campaign.

Propensity predicts how likely a user is to return with subsequent activity. Users exhibiting positive interaction patterns are more likely to return and have higher scores. This score measures the user relative to all other users.

There are many reasons why users churn — changing interests, competition from competitors, bad experience, etc. — but from a data perspective, attrition of any kind starts to look similar.

Propensity employs an ensemble of statistical models to identify any patterns it can find for detecting how and when attrition starts to occur. With time, it's able to find more patterns in your data and become increasingly accurate in identifying when users start to exhibit those behaviors.

Consistency measures the regularity or stability of a user's engagement pattern. Users who engage with your brand at a regular cadence will have higher scores. For example, a user that registers behavior every 7 days will have high consistency, and would have the same consistency as a user who registers behavior every 30 days. As users' behavior starts to vary — sometimes every 7 days, sometimes every 30 — the users' consistency score will decrease.

Consistency scores can be paired with propensity scores to build more accurate Lookalike Models to predict customer churn and target users with win-back programs before they bounce. Learn more about how to reduce churn using Lytics Lookalike Models.

Maturity is a normalized measure of how long a user has registered behavior. This indicates how "old" a customer is relative to your other users. A user who has registered behavior over 5 years will likely have high maturity. A user who registered behavior over 3 years, but hasn't registered any in 2 years will have less maturity. A user who registered behavior over the most recent 3 years will have the same maturity as the user previously mentioned.

As an example, you could target users with high maturity scores inviting them into a loyalty rewards program via ads, emails, and in-app notifications. You may also want to consider suppressing engaged users who are likely to make their next purchase without additional advertising. Low maturity users, on the other hand, could be served more onboarding or educational content to nurture them into high-value, long-term users.

Volatility measures how stable vs. sporadic a user is interacting with your brand. It represents the stability of the volume of data that a user is generating, and serves as a slightly more nuanced version of the intensity score.

Consider a user where 100% of their daily sessions are considered "intense". Their intensity score would be 100, but the score doesn't yield any information regarding the volatility of a typical session.

Each Lytics score is accessible as a Custom Rule in the Audience Builder. They can be added to any audience definition as an intelligent filter when the size of the audience is larger than desired.

For example, when crafting an audience to be used to buy ads against, the size of the audience is critical. The size can be arbitrarily shrunk by taking 10% of the matching users, or it can be intelligently shrunk by creating a threshold with a Lytics score such as Propensity or Momentum. This way, the best fit users remain.

Interfacing with scores directly can be difficult. They are low-level building blocks that require expertise to use to their fullest. To make these scores more readily usable, Lytics offers out-of-the-box Behavioral Audiences that use scoring under-the-hood.

Lytics scores work best on behavioral data — that is, data that was generated by a user, like a web view or email open, rather than on list imports or other non-behavioral data.

Every supported 3rd party integration is already configured to include only behavioral data in user scores. By default, any custom integration is assumed to be non-behavioral. If you're using a custom integration that you want to contribute to behavioral scores, contact your account manager to update the setting.