Through this two-part series, we’ll explore the Adobe Customer Journey Analytics solution. This post answers what is customer journey analytics, what differentiates Adobe CJA, what are common use cases and types of customer data that Adobe CJA works well with, and how it’s used within organizations.
It’s no secret that customers have high expectations these days for how brands engage with them. It’s also not lost on many reading this that martech platforms enable deeper insights about customer needs. But there’s a catch.
At the same time that martech platforms enable deeper insights, it’s becoming increasingly more complicated to for them to deliver truly personalized experiences that engage with customers on their preferred terms.
Why? Two reasons. First, there are new gaps within the traditional third-party data sources that we’ve come to rely on, shifting the focus to first-party sources. Second, the newer martech platforms are hungry for multichannel data sources to inform their insights, requiring greater skills to manage those sources and activate them using more advanced features.
Customer Data Platforms and Customer Journey Analytics solutions are purpose-built to close these gaps, and the number of these solutions is growing rapidly.
What is Customer Journey Analytics?
Customer Journey Analytics is, in the simplest terms, any solution that delivers cross-channel customer engagement insights. This may be through the lens of audience and segment identification, channel flow analysis, and/or cross channel data analysis. These insights are then used to identify new (or refine existing) audiences and segments, run experimentation and determine effectiveness of engagement tactics, and explore a more holistic 360 view of your customer’s interaction with your brand.
Changes to privacy and tracking protection—primarily cookie depreciation and decreasing reliability in third-party data sources—add new hurdles for how brands develop insights into the customer journey. These challenges mean that a brand’s own first-party data now needs to do the heavy lifting, and they accelerate the need for customer journey analytics and customer data platforms.
Adobe’s Approach to Customer Journey Analytics
Adobe’s flavor of Customer Journey Analytics (CJA) enables deep analysis on multichannel data sets, and it also provides the ability to deep dive quickly into segments and filtered views. It builds on top of Data Workspace (for those already familiar with the interactive tool in Adobe Analytics), but extends it to include some powerful services built into the Adobe Experience Platform (AEP).
Let’s talk about AEP for a minute. This is Adobe’s integrated platform that better enables real-time and personalized customer experiences through a combination of solutions and smart services. Three core foundations of AEP are:
- Its real-time data lake, purpose-built for customer data, enables the ability to act on new data as it updates,
- Its unified and extensible customer data profile enables rich, flexible segmentation of your customers, and
- Smart services, such as Customer AI and Attribution AI, assist with data stitching and analysis.
Adobe CJA is one of a number of integrated solutions that sit natively on top of AEP, the others being Adobe’s Real-Time Customer Data Platform (RT-CDP) and Customer Journey Optimizer. AEP, its services, and its applications also integrate with the rest of Adobe’s Experience Cloud (as well as non-Adobe solutions) through integration connectors and a robust API.
The power of Adobe CJA is its ability to effortlessly access and perform analysis on multichannel data sets and then push these insights (in terms of audiences, segments, etc.) quickly over to other tools like RT-CDP, Target, and Journey Optimizer to test or activate those new insights and apply them for business impact.
Use Cases and Data Types
Adobe CJA can explore a wide array of the many data sets you can throw at it once it’s available as a data view in AEP. Here are some of the more common use cases we see our clients exploring through CJA.
Cross-Channel Algorithmic Attribution – For when you need to measure the fractional impact of each marketing tactic across marketing channels to maximize advertising budgets, as well as to have flexibility with custom lookback. This typically involves data sets spanning marketing activation such as data from the paid search platforms, social channels, Account Based Marketing (ABM) and so forth.
Click-to-Brick – For when you need to understand if products purchased online are being picked up or returned at the store to understand the true ROI of digital campaigns and provide a better online-to-offline experience. This typically involved data sets spanning ecommerce and retail/point-of-sale.
Call Center Deflection – For when you need to stitch digital & call center data to understand which digital experiences can be improved to lessen the number of customers calling the call center. This typically involved data sets spanning web/ecommerce analytics and CRM.
Augmented Analysis – For when you need to leverage AI/ML capabilities within Analysis Workspace to improve segmentation (Cross-Segment Analysis and Comparison), provide objective attribution (Algorithmic Attribution), and understand contributing factors to anomalies (contribution analysis) on multichannel datasets. This utilizes the power of Adobe Sensei.
Beyond the use cases, the tactical benefits we typically see our clients taking advantage of through CJA also include:
- A reduced need to for SQL skills to drive insights
- Reducing time to insight and action
- Making more advanced analytics and data science techniques more accessible/approachable to a broader set of team members
- Rapid data exploration for multiple channels of data
- Consistent metrics – same calculations, same data views
- Ability to explore total customer journey questions beyond web analytics
- Native clickstream integrated with offsite data
- Validate customer journey orchestration hypotheses
- Ability to refactor or backfill data
- Ability to model for missing data (e.g., browser/mobile tracking preventions or consent opt-outs)
- Cross-device analysis
Who Uses Adobe CJA within the Organization?
Insights generation has traditionally varied within the enterprise in terms of roles and users. Some common roles involved include:
- Technical/Engineering – data source connectivity, integration, transformation
- Data Analyst/Data Scientist – model- and view-building, analysis facilitation
- Optimization and Advanced Analytics Teams – applying an experimentation approach to their channels
- End Business Owner – report consumption, hypothesis-building, follow-up requests for more analysis/additional data
Adobe’s CJA begins to blur the lines between these traditional roles and users by opening up more access to the end business owner, who can freely explore hypotheses without needing technical or model support in every case. CJA’s drag-and-drop visual interface across data views is what enables this ability and ultimately speeds time-to-value from such analysis.
To be clear, data engineering and data science are still critical roles in this process, but now they can focus more time on more complex tasks that truly flex their muscles and less time on follow-up requests that can be explored directly within CJA.
Wrap up and Next Time
Adobe’s approach to Customer Journey Analytics is notable for its fast access to relative data sets and the ability to more quickly push and pull audiences and segments throughout its other solutions like Target, Real-time CDP, and Journey Optimizer to activate and apply insights for impact to the business. It differs from some other Journey Tools, which tend to focus more on journey pathing analysis. Adobe Customer Journey Analytics, in contrast, looks to collate insights from a bigger picture of multichannel data sources.
Stay tuned for Part 2, when we’ll delve further into:
- Ways to Get Started with CJA
- Time to Value
- When CJA is a Good Fit
