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Snowplow or Adobe Analytics?

Understand the difference between Snowplow and Adobe Analytics, so you can make the right decision for your business.

A side-by-side look at the key similarities and differences between Adobe Analytics and Snowplow BDP

The Adobe Analytics column covers Adobe products across their experience cloud. The (XDM) annotation refers to features which are only available by using their Experience Data Model.

Full flexibilityCustom data structures (XDM)
Customer success manager
Data collection across unlimited devices (e.g. IoT)
Decoupled data collection and modelling
Incremental and configurable SQL data model
Ingestion of data from 3rd parties and webhooks
Load your data into BigQuery
Load your data into Snowflake DB and Redshift
Load your data into S3
Load your data into real-time streams (Kinesis, Pub/Sub)
Real-time data delivery SLAs
Real-time data enrichment (1st and 3rd party, and fully configurable)
Unlimited custom properties
Unlimited use cases
Quality assuredCloud outage and data loss protection
Common data format across all platforms (XDM)
Data quality console for debugging
Fully observable data pipeline
Local queuing for offline event capture
Proactive data quality alerting
Provides a single source of truth for data mature companies
Real-time validation of data against schemas (XDM)
Reduces time spent cleaning or wrangling data (XDM)
Reprocessing of bad data
Unopinionated data
Uptime and latency SLAs
Total control1st party server-set cookies for reliable user tracking on Safari
Built on an open standard
Complete data ownership
Control over where your data is processed
Data is trusted by data, product and marketing teams
Framework for writing unit tests against your tracking
Modern ad blocker circumvention
Server-side tracking
Support for data structure and data schema evolution (XDM)
Support for fully anonymous tracking (XDM)
Support for pseudonymizing data
Zero vendor lock-in

Where Snowplow outperforms Adobe Analytics

Even though Adobe Experience Cloud boasts many powerful features, here are a handful of the issues you may want to look out for:

  • Adobe Analytics is notoriously hard to implement. While this can be expected from complex packaged tools, it is less forgivable when the outcome includes vendor lock-in
  • Given the steep learning curve, businesses may feel the need to hire someone with hands-on Adobe Analytics implementation experience to navigate the complexity
  • Deep knowledge about how Adobe processes data is required in order to successfully implement it, especially for more advanced implementations or more unusual and interactive websites and mobile apps.
  • Adobe is not well suited to collecting data from non-web and non-app environments e.g. IoT and wearables. Once you go beyond web and mobile use cases, you may have outgrown the implementations options available
  • Once implemented, implementations are brittle and therefore hard to change without compromising data that has already been collected, or making your analytics workflows much harder. (You can only really add to an implementation, not change the way the way that particular variables have been used.)
  • Data quality is an issue: once bad data is “in Adobe”, there is no way to get it out or clean the data. One Snowplow customer migrated over from Adobe because of the following common scenario. They had accidentally collected some personally identifiable information which contravened GDPR and needed to get rid of it. Within Adobe, the only way to delete that small piece of data was to delete the entire data set. Such difficult situations would not occur within a Snowplow pipeline, protecting you from data loss due to human error
  • End users of Adobe tend to build custom segments to exclude problematic data, and other ‘workarounds’, rather than improving collection and validation further upstream
  • While Adobe has got much better at making the underlying data available outside of Adobe (e.g. exporting to AWS S3), the data is reportedly hard to work with
  • Adobe’s app analytics is reportedly not popular with mobile first businesses, suggesting a weakness in Adobe’s mobile tracking and data collection capabilities
  • Adobe’s data structures are not particularly well suited to businesses, websites or apps where the business model isn’t simply media or ecommerce. (E.g. two sided marketplaces, aggregator sites and subscription businesses)
  • Adobe customers report that the tool can be very expensive depending on your budget, use cases and your event volumes.

Using Snowplow alongside  Adobe Analytics

While the comparison table goes into more detail, here are a few reasons why it can be worthwhile to setup Snowplow alongside Adobe.

  • Loading the data into a wide variety of storage targets (BigQuery, Redshift, Snowflake, Elasticsearch, S3, PubSub, Kinesis, Kafka) in real-time
  • Data quality. Snowplow data is validated as part of the processing, with the ability to spot bad data, quarantine it, fix it and reprocess it
  • Data structure. Snowplow data is significantly easier to work with than exports from Adobe. (Data is schema’d with proper field names etc.)

Get Snowplow behavioral data management built in to your products today