Comparing data warehouses to Adobe Customer Journey Analytics

With the current focus on customer experience, brands require advanced solutions to better understand the holistic customer journey. Understanding this full customer journey allows your brand to analyze and gain valuable insights into how online and offline channels engage customers and lead to increasing conversion, retention, and loyalty. A customer journey in this context can be the straightforward online order of a meal at a sushi food chain. Or the purchase of a new car, where the customer combines online research with visits to the dealer showroom, and a final in-person purchase.

Many organizations have consolidated their omnichannel data into a data lake or data warehouse. Business intelligence (BI) tools are used on top of these data stores to provide the reports, visualizations, and insights that the business requires to understand the customer journey. Often, this combination of solutions and tools is general purpose by nature and design and not explicitly focused on the customer. Adobe Customer Journey Analytics focuses on empowering those responsible for the customer experience, such as marketers, data analysts, data scientists. The tool allows them to visualize the customer journey in full context across all channels in real time without limitations that many other data warehouses and BI tools have.

This blog will focus on how Customer Journey Analytics and data warehousing solutions both play important roles in data management and analysis and how they compare:

What is a data warehouse?

Data warehouses specialize in storing and processing large amounts of structured and semi-structured data. While data warehouses offer integration with various sources and third-party tools, they are mainly focused on storage and computing power. Users pay for each query, and querying large datasets can be very expensive.

The need for SQL expertise restricts the solution’s scalability across an organization. While these solutions service lots of needs in an organization, they can end up being a disincentive to democratize insights and data analysis given the needs to scale data-driven decisions across many parts of the business. Marketing can struggle with this due to the nature, speed, and volume of many of the datasets they use, e.g., clickstream data.

What is Customer Journey Analytics?

Customer Journey Analytics empowers users to connect data from diverse omnichannel sources and construct a consolidated and comprehensive view of the customer. Customer Journey Analytics is optimized for analyzing customer experience data, such as customer events, engagement patterns, and interactions, and is designed to be extremely scalable. And it’s built for data democratization across organizations so any stakeholder can derive journey insights and improve customer experiences.

Customer Journey Analytics provides an environment to connect online and offline cross-channel data at the overarching customer level for the sole purpose of understanding the customer journey. It does require initial setup to connect and define views to the data you qualify as relevant. However, once completed, that data is readily available for ongoing analysis and exploration. You can progressively gain insights into and understand the customer journeys. By democratizing combined online and offline data, you can answer customer-journey-related questions in seconds.

Customer Journey Analytics is built natively on the Adobe Experience Platform, and all applications on the platform use the same underlying data. As a result, there is no need to create a separate unified profile, which eliminates inconsistencies when analyzing metrics across the organization.

The power of Customer Journey Analytics with a data warehouse

Customer Journey Analytics is not meant to replace data warehousing solutions but to complement them by analyzing customer experience data. Experience Platform offers integrations with various data warehouses and can connect to different types of databases to support data ingestion from third-party databases.

Having a data warehouse solution can accelerate the implementation process and reduce the time it takes to realize value from Customer Journey Analytics. This is especially evident where a myriad of data can be streamlined through a data warehouse and selected intentionally for use with analytics. Using the tools as complementary can maximize the value of each solution where Customer Journey Analytics will likely be the only dataset in the entire enterprise that is fully and completely organized around the customer.

Factors to consider

Factors
Customer Journey Analytics
Data warehouses
Querying cost
Unlimited queries across the entirety of your data
Pay per query (every time you run a SQL query in a data warehouse, you pay)
Querying speed
Most queries return a few seconds, even queries based on years of data or billions of records
Queries can take minutes or longer, especially for complex queries over long periods of data or billions of rows
Data sampling or cubing
Not required — analysis can be done on the full dataset with second or sub second query performance
Needed for larger datasets like customer event data — requires data pipelines maintenance and can result in inaccurate insights
SQL expertise required
None
SQL expertise required
Data democratization
Intuitive interface, user self-service
Scale to other parts of the organization through data ingest into BI tools for visualization
Customer journey analysis
Complete 360-degree view of the customer in contextualized
Must be custom built by a SQL expert; certain visuals like flow, fall-out, attribution and sessionization aren’t possible with SQL
Visualization
End-to-end visualization including cross- channel analysis journey flow and fallout
Third-party visualization tools required — SQL expertise also required
Identity stitching
Periodic historical restatement, done in a privacy-forward way
None, or build your own (major privacy risk to build your own)
Data types for analysis
Designed to work for customer experience event data — e.g., clickstream, call center, POS, etc.
More general support for many types of data
Insight activation
Integrates seamlessly with other Experience Platform applications such as Adobe Real-Time Customer Data Platform and Adobe Journey Optimizer for segment or journey-based marketing on the edge or to streaming or batch destinations
Third-party products or custom development required
Headless BI
Allows you to define metrics with complex query definitions without having to recreate with complex SQL for each analysis or visualization using Data Views
Third-party products can offer this in limited ways. Requires users to write all the SQL themselves and recreate complex data transformations manually

Discover how Adobe Customer Journey Analytics can meet the needs for an experience-led business, no matter what your data analysis maturity level is — no SQL required.

https://business.adobe.com/blog/basics/data-warehouse

https://business.adobe.com/blog/the-latest/adobe-customer-journey-analytics-powers-next-generation-data-flexibility

https://business.adobe.com/blog/the-latest/adobe-announces-reimagined-data-egress-and-delivery-management-services-for-customer-journey-analytics