Report:

The Forrester Wave™: Customer Analytics Technologies, Q2 2024

Top Vendor Products for Customer Analytics Technologies in 2024

• SAS
• Salesforce

Customer Analytics Technologies Vendor Product Comparisons 2024

• SAS - Leaders
  

  Pros:

  • Best-in-class propensity modeling, churn analysis, and engagement analysis with transparent explanations and prescriptive advice
  • Excels at incorporating emerging data sources and types, automatically classifying video content in real time
  • Viya platform enables accelerated innovation focused on genAI and real-time decisioning capabilities
  • Reference customers uniformly expressed extreme satisfaction and praised ability to deliver value quickly

  Cons:

  • Does not offer robust industry-specific customer data models out of the box despite vast industry depth and breadth
  • User interface still feels quite technical despite efforts to make it more friendly for marketers and CX professionals
  • Limited approach to increasing user adoption, relying primarily on customer success team
  • Pricing model gets less transparent and more complicated for enterprises with manifold data sources and activation destinations


• Salesforce - Leaders
  

  Pros:

  • Data Cloud offers market-leading customer profile resolution capabilities
  • Best-in-class data visualization capabilities from Tableau including natural language explanations
  • Impressive range of prebuilt connectors to access data from third-party marketing and operational systems
  • Reference customers praised time to value, with one reporting 100% or more ROI based on tangible benefits
  • Actively nurtures robust user and partner communities through Trailhead and AppExchange

  Cons:

  • Does not offer customer lifetime value analysis out of the box
  • Model monitoring is manual, requiring regular user oversight and intervention to detect and replace underperforming models
  • Does not currently offer outcome-based pricing model despite transparent pricing structure available online


• Adobe - Strong Performers
  

  Pros:

  • Differentiated data enrichment capabilities through Segment Match for data collaboration and sharing in Adobe ecosystem
  • Strong next-best-experience analysis capabilities with closed feedback loop and real-time decision engine
  • Reference customers tend to be loyal - all had used Adobe at previous employers and were likely to recommend to peers
  • Well positioned to deliver on vision through integration of Creative Cloud genAI capabilities with Experience Cloud analytics

  Cons:

  • Limited customer propensity modeling - only churn and conversion predictions available through Customer AI
  • Limited model monitoring capabilities
  • Innovation and roadmap focused on incremental genAI features rather than transformational capabilities


• Medallia - Strong Performers
  

  Pros:

  • Thrives on unstructured data sources - can process audio call center data in real time
  • Best-in-class engagement analysis through proprietary Digital Experience Score from Decibel acquisition
  • Strong natural language processing capabilities honed over years of client engagements
  • Simple and transparent pricing with unlimited seat licenses standard - only variable is customer interactions
  • Reference customers uniformly likely to recommend vendor to peers

  Cons:

  • Lacks several out-of-the-box customer analytics capabilities including recommendation analysis, lifetime value analysis, and propensity modeling
  • Responsible AI efforts primarily focused on ensuring compliance in own NLP models rather than detecting issues in client-built models
  • Does not boast robust partner ecosystem for customer analytics compared to more established vendors


• Microsoft - Strong Performers
  

  Pros:

  • AI-powered identity resolution as part of customer profiles approach
  • Data Quality Copilot scores data quality and provides recommendations
  • First in market to offer 'natural language segmentation' - LLM-driven conversational interface for creating segments
  • Deep supporting services including Azure ML custom modeling and vast partner network

  Cons:

  • Offers few preconfigured additional analyses out of the box compared to other vendors
  • Lacks true next-best-experience analysis capabilities
  • Most innovation focused on upgrading interface with genAI rather than building predictive AI components
  • Does not offer community engagement models aside from a forum
  • Declined to participate in full Forrester Wave evaluation process


• Treasure Data - Strong Performers
  

  Pros:

  • Patent pending approach to segmentation leveraging clustering with ability to prioritize specific features
  • Leverages clustering for engagement analysis, then auto-labels segments and creates audiences for activation
  • All reference customers estimated ROI between 10% and 99%
  • Vision to extend CDP utility beyond marketing to inform all customer interactions

  Cons:

  • Does not offer unstructured data processing capabilities despite thriving on structured data
  • Does not offer industry-specific customer data models out of the box - clients must customize themselves
  • Pricing is more complex and less transparent compared to other vendors
  • Does little to foster user community aside from hosting virtual community and CDP World event
  • Offers comparatively limited supporting services


• Amplitude - Contenders
  

  Pros:

  • Strong journey analytics capabilities recently enhanced with session replay to identify root cause of experience issues
  • Top-notch optimization capabilities through Amplitude Experiment including multivariate testing, multiarmed bandits, and reinforcement learning
  • All reference customers likely to recommend vendor to peers
  • Roadmap features focused on turning vision of 'self-healing products' into reality

  Cons:

  • Vision likely to only resonate with product owners or developers, may alienate marketers and CX professionals
  • CDP built for structured data - does not process unstructured data sources like text, images, and video
  • Does not offer predictive customer lifetime value analysis out of the box
  • Limited responsible AI capabilities - only fairness evaluation and explainability techniques
  • Partner ecosystem less mature than other vendors


• FICO - Contenders
  

  Pros:

  • Best-in-class real-time decisioning and mathematical optimization with extensive experience and expertise
  • Best-in-class model monitoring honed by years working with banks on fraud detection and credit risk models
  • Strong innovation track record evidenced by multiple patents

  Cons:

  • Biggest gap is usability - business users likely to find it intimidating or inaccessible
  • Lacks out-of-box models such as churn analysis and lifetime value analysis - data scientists required to build from scratch
  • Vision assumes world awash in data scientists, impeding ability to make inroads where this talent is scarce
  • Roadmap prioritizes needs of technical users over business users
  • Pricing is not transparent
  • Declined to participate in full Forrester Wave evaluation process


• Oracle - Contenders
  

  Pros:

  • Unity's primary focus on harmonizing online, offline, and third-party data into single view of customer
  • Integrates well with marketing and other operational systems that consume insights
  • Has productized several analytical use cases including lifetime value analysis and churn analysis

  Cons:

  • Does not see itself as part of customer analytics market - considers Unity primarily a CDP, missing strategic importance of analytics
  • Lacks key analytical capabilities including algorithmic segmentation, recommendation analysis, and natural language processing (NLP)
  • Only large vendor in evaluation to lack any responsible AI functionality
  • De-prioritization of customer analytics manifests in approach to innovation and roadmap which treats analytics as afterthought
  • Lacks detailed documentation, tools, templates, videos, and training classes that encourage adoption
  • Declined to participate in full Forrester Wave evaluation process


• Qualtrics - Contenders
  

  Pros:

  • Best-in-class natural language processing capabilities from Clarabridge acquisition
  • Offers several differentiated CX-specific analyses out of the box including customer effort scoring and customer health scoring

  Cons:

  • Lacks many standard out-of-the-box models including churn analysis, engagement analysis, lifetime value analysis, next best experience analysis and propensity analysis
  • Limited decisioning and optimization capabilities
  • Basic privacy controls and little responsible AI capabilities
  • Focus on CX use cases and CX data rather than advanced customer analytics on larger behavioral datasets
  • Partner ecosystem short on relevant data and technology partners
  • Offers limited supporting services and offerings
  • Declined to participate in full Forrester Wave evaluation process


• Zeta Global - Contenders
  

  Pros:

  • Core strength is Zeta Data - proprietary data asset of over 235 million individuals with variety of useful signals
  • Robust set of prebuilt integrations and straightforward way to set up new connections to marketing and operational systems
  • Strong churn analysis capabilities including model explanations out of the box
  • Reference customers praised ability to deliver value rapidly and highly likely to recommend to peers
  • R&D investments yielded multiple innovations putting advanced insights directly into marketers' hands

  Cons:

  • Myopic focus on marketing limits relevance to other customer analytics stakeholders
  • Does not offer many core customer analytics capabilities out of the box including recommendation analysis and natural language processing
  • Lags others in responsible AI capabilities
  • Does not curate active community of peers through online communities, peer groups, or other means
  • Product roadmap focused on marketing rather than broader customer analytics needs

Reference

• Forrester, The Forrester Wave™: Customer Analytics Technologies, Q2 2024, 04-Jun-2024, ID

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