You can’t truly understand your customers if your analytics are fragmented. This buyer’s guide will help you craft a better RFP when evaluating analytics solutions for customer journeys and show how Adobe Customer Journey Analytics brings that data together.
As customer expectations for personalization and privacy continue to grow, brands hoping to gain a competitive advantage must evolve their customer analytics strategy. Two of the most important components of a modern analytics strategy are building a unified view of the customer based on first-party data and developing a sequential view of the customer journey.
The convergence of multiple analytics categories — specifically marketing and product — underscores the need for brands to forge a comprehensive understanding of their customers. Of course, there are organizational and technical challenges to overcome along the way. Adobe Customer Journey Analytics, built on Adobe Experience Platform, is an ideal solution for addressing these challenges. It offers unparalleled cross-channel customer insights, efficient data management, advanced user-level reporting, guided analysis workflows, and secure handling of sensitive data, all while reducing IT overhead and democratizing data access.
To meet their customers’ expectations, brands need to understand customers’ intent, preferences, and behaviors across all channels. Brands also need the ability to rapidly transform cross-channel data into business actions that improve the customer experience — a complex undertaking.
Marketers, digital leaders, and product teams all want a unified view of each customer and their journey, but too often these teams operate in silos. Breaking down silos — with teams able to access, share, and act on omnichannel data from all analytics sources — is one of the first steps to operating at a competitive advantage. The primary obstacle to achieving this holistic view of the customer is technology.
Today’s customer analytics solutions are not built to accommodate the critical initiatives for customer-centric organizations, including:
In many cases, brands rely on general-purpose business intelligence (BI) tools to address these initiatives, but these tools are not designed to provide a comprehensive view of the customer’s journey. Traditional BI tools also require significant data engineering and code-intensive skills to meet most organizations’ modern analytical needs. The result is a gap in the analytics market, with brands searching for a solution to streamline their customer journey analysis workflows and improve time-to-insight — both of which lead to better business outcomes.
Most organizations have some form of analytics service in place but are still not delivering enough value to their customers or their business. Let’s look at a modernization roadmap to see how enhancing customer analytics capabilities and empowering teams with a deeper understanding of audiences and their cross-channel journeys can make a bigger impact.
Initially, most organizations depend on siloed digital analytics, leading to a fragmented view of the customer and their journey. To leverage customer data for actionable insights, organizations must first adopt a person-based, journey-oriented perspective.
The next step is to consider a robust customer analytics solution — like Customer Journey Analytics — that’s built to perform cross-channel customer analysis. It addresses the limitations of Adobe Analytics and other customer analytics solutions by providing a more comprehensive, AI-assisted set of tools to understand the customer journey.
1. Comprehensive customer insights
Brands often lack visibility into customer journeys across channels and devices, leading to unidentified friction points. Customer Journey Analytics allows you to ingest, unify, and visualize customer data sequentially from all touchpoints — digital and offline — without data science expertise. It helps you quickly discover insights to deliver seamless and personalized customer experiences.
2. Efficient data management
Current data collection and processing efforts are cumbersome, prone to errors, and inflexible. Customer Journey Analytics provides modern data management capabilities such as dynamically modifying and correcting data post-collection, making data handling more efficient and reducing the operational burden on IT and analytics teams.
3. Flexible data analysis
Organizations are currently restricted by the limitations of predefined data structures, hindering their ability to answer evolving business questions. Customer Journey Analytics allows you to modify data post-collection — adding new metrics or dimensions and modifying attribution or session windows.
4. Advanced user-level reporting
As customers spend time across more devices and channels, organizations face growing challenges in ensuring accurate tracking and reporting. Customer Journey Analytics unifies data from multiple datasets (which often lack a consistent ID) and uses an identity graph to create a single ID for each customer, enabling comprehensive customer journey analysis.
5. B2B-specific features and functionality
Customer Journey Analytics B2B Edition is purpose-built for B2B, addressing the complex B2B buying process, enabling cross-channel data visualization and actionable insights across the entire customer journey at the individual, buying group, account, and opportunity level.
6. Customer data protection
Organizations face a critical need to safely manage and analyze sensitive data, like Personally Identifiable Information (PII) and Protected Health Information (PHI). A secure customer analytics platform like Customer Journey Analytics complies with regulatory standards, so you can unlock deep insights into customer behaviors while safeguarding user privacy.
7. Reduced IT and analytical overhead
Business intelligence and analytics teams often spend significant time and resources on manual processes like data exporting and SQL querying, which is costly and inefficient. Customer Journey Analytics can automate these processes, freeing up resources for more strategic activities and reducing time-to-insight.
8. Decentralized data access
Organizations that depend on specialized analytics teams can experience bottlenecks in decision-making processes. With Customer Journey Analytics, you can democratize data access through guided analysis views, empowering product and marketing teams to make informed decisions quickly and independently.
9. Integrated generative AI
The use of generative AI to interpret vast amounts of customer data can significantly accelerate decision-making processes and uncover valuable insights. With embedded generative AI capabilities, Customer Journey Analytics transforms data analytics from a purely descriptive function to a predictive and prescriptive powerhouse, driving innovation, ROI, and other advantages.
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Adobe Customer Journey Analytics
