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With the surge in data from both consumers (via IoT and third-party data aggregation) and B2B enterprise customers (based on historical interaction and multifaceted business profiles), the capabilities of customer analytics are stronger than ever.


  • Customer analytics products drive informed, data-driven, testable, and quantifiable decision-making to support the entire customer journey.
  • B2B and B2C enterprises both stand to gain enormous benefits from customer analytics, but their unique needs will drive the selection process of individual tools.
  • Artificial intelligence will help in many CRM analytics areas, including assessment of customer satisfaction (e.g. through sentiment analysis), verifying sensitive data, optimizing pricing policies, and more.

Features and Benefits

  • Evaluates the current state of enterprise focus on customer/predictive analytics solutions.
  • Identifies differences between B2B and B2C approaches to customer and CRM analytics.
  • Identifies methods to nurture customer analytics buy-in from the top down in a company.
  • Assesses the capabilities of CRM analytics software and AI's role in service development.
  • Compares the use cases of B2B and B2C customer analytics solutions.

Key questions answered

  • What are the driving factors in customer analytics for the next five years?
  • How can vendors improve current customer analytics solutions?
  • What are the differences between B2B and B2C customer analytics solutions?
  • How do CRM prerequisites differ between B2B and B2C enterprises?

Table of contents


  • Catalyst
  • Ovum view
  • Key messages


  • Recommendations for enterprises
  • Recommendations for vendors

Predictive customer analytics have the power to drive better decision-making

  • Versatile customer analytics products account for informed, data-driven, testable, and quantifiable marketing and sales solutions
  • For widespread impact, customer analytics needs enterprise-wide buy-in

Variance exists between B2B and B2C enterprise approaches to customer analytics

  • Differences in audiences, purchase cycles, buyers' journeys, and more shape the requirements of a customer analytics tool
  • Differences in CRM methodology drive differences in analytics requirements


  • Methodology
  • Further reading
  • Author

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