How Adobe Analytics compares to other analytics solutions.
Why analytics capabilities matter.
Adobe recognises that a marketing analytics platform must be a central system of intelligence, designed to amplify the value of an entire marketing stack. We also understand that every organisation is at a different stage of analytics maturity and needs unique capabilities and levels of sophistication.
Many enterprises begin with basic analytics tools, but as business complexity grows, their limitations become a roadblock. The inability to see a unified customer journey or seamlessly activate insights can hinder growth and reduce competitive agility. Ultimately, ambitious, data-forward organisations require a solution that can evolve with them.
Selecting a new analytics platform demands diligent research. Each feature should be carefully considered to ensure the solution chosen can truly transform data into a strategic asset.
Adobe Analytics features comparison.
- Often collect data from a limited number of sources — primarily web or app interactions — which can create data silos.
- Frequently resort to data sampling, especially in free or lower-tier versions, which means only a portion of the data is visible, potentially masking crucial insights or misrepresenting user behaviour.
- May struggle to unify disparate data into cohesive customer profiles, resulting in a fragmented understanding of the customer journey.
- Collects data from virtually any channel — including web, mobile apps, email, social media, call centres, in-store interactions, voice assistants, audio, IoT devices and even off-line sources — for a truly holistic view.
- Provides access to 100% of your data, unsampled and complete. This ensures analysis is based on the whole picture, leading to more accurate insights and confident decisions.
- Centralises and normalises data at the customer level, creating rich, unified profiles that power deeper understanding and personalisation across every touchpoint.
- Leverages graph-based stitching to enhance customer journeys views by joining datasets with different identifiers without complex extract, transform and load (ETL), improving identity resolution.
- Rely on static dashboards and predefined reports that are difficult to customise, making deep data exploration cumbersome and
time-consuming. - Often requires specialised skills (like SQL or data science expertise) even for fundamental ad hoc analysis, creating bottlenecks and delaying insights.
- Often requires specialised skills (like SQL or data science expertise) even for fundamental ad hoc analysis, creating bottlenecks and delaying insights. May offer visualisation tools, but these tools often lack true free-form analysis capabilities, limiting the ability to answer follow-up questions in real-time.
- Empowers users with Analysis Workspace, a fully interactive and flexible canvas designed for power analysts yet accessible to marketers. It lets teams drag, drop and segment data on the fly to uncover insights at the speed of thought.
- Enables accurate self-service analytics. Marketers and business users can easily explore data, build customised reports and find answers to their questions without relying on dedicated data teams, freeing up analysts for more strategic work.
- Offers robust, intuitive visualisation
- capabilities integrated with deep analytical tools, enabling users to create compelling visualisations, apply complex segments and perform dimensional analysis to understand the ‘why’ behind the ‘what.’
- In addition to features like derived fields for math, next/previous, summarise and deduplication functions within data views, it also provides a table of contents for Workspace projects to ensure straightforward navigation and analysis.
- Offer limited data science capabilities, making it difficult for most users to understand, trust or act on AI-driven recommendations.
- May require dedicated data scientists to operate and interpret AI and machine learning features, which limits their practical application in everyday marketing decisions.
- Often cannot apply AI to analyse data from all marketing channels, resulting in an incomplete picture for predictive modelling.
- Embeds AI directly into the analytics workflow to go beyond basic reporting to uncover hidden opportunities and anticipate customer needs.
- Provides explainable AI. Understand the ‘why’ behind AI insights and recommendations, building trust and enabling the customisation of models as the business evolves.
- Offers AI Assistant for Adobe Customer Journey Analytics, allowing natural-language questions to get answers based on documentation, further simplifying access to insights.
- Suffer from clunky, limited or nonexistent integrations with other marketing and business systems, making it difficult or impossible to turn insights into timely action.
- Often require manual data exports and imports to share insights or activate audiences, introducing delays and potential errors.
- May lock users into their limited ad networks or platforms if any actionability is offered, restricting marketing reach and flexibility.
- Seamlessly integrates with Adobe Experience Cloud products like Adobe Experience Manager, Adobe Target, Adobe Campaign and Adobe Journey Optimizer, enabling instant activation of insights and personalisation of experiences.
- Offers robust APIs and prebuilt connectors for two-way integration with a wide range of third-party solutions, ensuring insights flow effortlessly to where action happens.
- Automates actions based on discovered insights, trigger personalised messages, optimise campaigns and deliver the right experience at the optimal time, across all touchpoints, without manual intervention.
Why organisations choose Adobe Analytics.
Enterprise businesses choose Adobe Analytics to provide:
- Customer journey insights to improve user experience.
- AI that powers automated insights and advanced modelling.
- Structure for businesses that need to track large data volumes.
Customer journey insights to improve user experience.
Adobe Analytics lets organisations move far beyond the surface-level metrics and facilitates a profound understanding of intricate, multichannel customer journeys. This is achieved by consistently delivering unsampled, granular data from every conceivable touchpoint. Such a comprehensive and unified view is indispensable for accurately attributing conversions by identifying the most effective pathways to customer acquisition and retention. While some platforms are noted for their multichannel attribution capabilities, Adobe Analytics typically offers greater depth and customisation for complex enterprise needs.
Adobe Experience Cloud integrations with Adobe Analytics can help organisations gain a deeper understanding of their customers. Insights gleaned from customer data in Adobe Analytics can directly and often automatically inform and trigger actions in other Adobe solutions. The Adobe Experience Platform integration Query Service allows teams to connect Adobe Analytics data sources to business intelligence tools. For example, a newly identified high-value customer segment can be immediately targeted with personalised offers via Adobe Target or a campaign can be dynamically adjusted in Adobe Campaign based on real-time performance data from Analytics. This creates a closed-loop system where data informs action and the results of that action feed back into the analytical models, driving a continuous cycle of optimisation and enhancing customer experiences in real time. This ability to seamlessly connect analytics to experience delivery facilitates a fundamental shift from passive reporting to active, data-driven customer experience management.
AI-powered automated insights and advanced modelling.
Existing and new customers can use Adobe Customer Journey Analytics to identify algorithmic attribution, uncover unexpected anomalies in conversion rates or understand how customer interactions drive business outcomes through the Adobe Journey Optimizer integration.
The Adobe Data Insights Agent offers AI capabilities that enable current Adobe Analytics and Customer Journey Analytics customers to gain a deeper understanding of their target audience. The Data Insights Agent utilises a conversational AI interface that allows users to easily identify strategic recommendations that help drive expected business outcomes.
Structure for businesses that need to track large data volumes.
Enterprise businesses often manage large volumes of data, diverse brand portfolios, global operational footprints and complex business rules. Adobe Analytics provides comprehensive data governance capabilities, ensuring data quality, security and compliance with relevant privacy regulations. The Adobe Analytics architecture is designed for high scalability and unwavering reliability, often backed by comprehensive service-level agreements that enterprise clients expect. This capacity to handle complexity and scale is a hallmark of an enterprise-grade solution, setting it apart from tools that may perform adequately for smaller businesses.
Make every decision count with Adobe Analytics.
Adobe Analytics stands apart by providing a comprehensive data foundation, powerful and accessible AI capabilities, flexible and deep analysis tools and seamless activation pathways that help enterprises understand their customers, anticipate their needs and deliver superior experiences.
Choosing the right analytics partner is an important strategic decision that directly influences an organisation’s ability to convert raw data into a high-value asset. Adobe Analytics is engineered to empower businesses to make data-driven decisions for continuous optimisation that deliver measurable results.
Find out how Adobe Analytics can help your organisation gain deeper customer journey insights and achieve unique business goals.
See Adobe Analytics in action by requesting a demo or watch a video now.