Behavioral data describes interactions with customers, partners, applications and systems in granular detail. Richly contextual and predictive by nature, it is the best possible fuel for advanced analytics and AI applications.
Why use behavioral data?
There is simply no better predictor of future behavior than past behavior—our actions today are highly indicative of what we’ll do tomorrow.
Enterprises like Airbnb and Spotify have been creating their own data to power behavioral data products like churn propensity models, recommendation engines, and predictive lead scoring for years. These products give them a huge competitive advantage.
Smaller companies have typically relied on easier-to-use datasets, such as transactional and demographic data—because behavioral data feels too hard. Challenges include reconciling data from disparate sources; complying with GDPR, CCPA and other rules; ITP and ad blockers; and more, which we explore below.
This is beginning to change. Companies of all sizes are adopting behavioral data to power data products, which is ringing in a new era of hyper personalization and a marketplace in which companies compete on how deeply they understand user touchpoints.
How is behavioral data different to other data types?
Data products made better with behavioral data
Do you need behavioral data for all these data products?
While you can create many of these data products with other types of data, you won’t generate such predictive or insightful results.
For example, a Fraud Detection Engine could be created by analyzing patterns in transactional and demographic data, and looking for anomalies.
Behavioral data adds an extra dimension to our analysis. In the above example, we could look at the behavioral predictors of fraud, such as:
- Auto filling forms with different details
- Minimal scroll depth
- Low number of pages viewed
This would give us a better understanding of the indicators of fraudulent activity.
As you can see above, behavioral data is particularly powerful as it can be enhanced with other data types. Further, as a data application develops, you gain new, deeper insights into a given behavior—which in turn unlocks more value and spins off into new data products.
How Snowplow solves for the challenges with behavioral data
- Tracking – First party tracking means ITP does not apply—you can track users for up to 2 years, even on Safari and Firefox.
- Single source of truth – Data does not need to be reconciled across sources—it’s created from scratch to match your business logic and vocabulary, and then prevalidated so it arrives in an atomic data table ready for AI and BI use cases.
- Lineage – Created data has 100% transparent lineage. With Snowplow, the meaning of each metric is recorded in a human and machine readable format in JSON schemas.
- Alignment – A centralized UI helps teams navigate the complexity of managing behavioral data, controlling permissions, showing tracking in a visual way, and managing schemas.
- Consistency – By using Snowplow’s Universal Data Language, you can ensure meaning is tightly documented and versioned across teams.
- Compliance – You have full ownership of your data. The whole Snowplow infrastructure lives on your own cloud—meaning a choice of storage location, full GDPR compliance, the option to have multiple pipelines, and the ability to record the basis for capture with each event.