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AI-powered Personalization
Across the Customer Journey

WhitePaper
Tuesday, May 31, 2022

AI-powered Personalization Across the Customer Journey

Introduction: AI-powered Personalization Across the Customer Journey

Leveraging Artificial Intelligence (AI) will improve the customer journey across the enterprise, from personalized shopping experiences and customer acquisition use cases to contextualized customer engagement and customer service.  Highly personalized insights impact key performance indicators (KPIs) like sales revenue and retention rates, costs, and satisfaction, and can help organizations realize the promise of AI in areas like digital transformation and the development of client-centric solutions.

But most organizations are challenged to understand customers and prospects at a personal level - a granular or contextual level of detail that can enable personalized insights and drive business value.   Various customer data platforms (CDPs) and Customer 360 (“C360”) initiatives cover some prerequisites like data aggregation, preparation and availability, but true personalized insights that change the customer experience for the better are elusive.

Organizations looking to drive hyper-personalization need to collect data and information about individuals from across the enterprise (not just Marketing and Sales data) and externally, leverage this data for personalized insight ‘engines’ and AI-powered applications, and drive actions from these insights that deliver value.

Challenges with Personalization Initiatives

Organizations looking to leverage personalization solutions in sales, service and customer engagement are challenged in a number of ways:

  • Data Collection in Support of Personalized Insights: Collect data and information about individuals from disparate systems across distributed networks both internally (across the enterprise) and externally.  Personalized insights are driven off of customer profiles (or prospects, leads, consumers - even other entities like products, transactions or businesses) and can include a variety of information:
    • Declared and observed data
    • Internal data from across the enterprise - not just Marketing or Sales (as is the case with most CDPs
  • Turning customer data into personalized insights: Leveraging customer data for AI-powered personalization applications and decision support solutions requires data engineers and data scientists to work together to produce model, algorithmic, and decisioning system outputs - the “personalized insights” - that can change customer journeys.
  • Personalized insights that deliver the “next best actions” that can change customer journeys: this requires additional capabilities attached to customer profiles like rules engines and the ability to combine multiple models, algorithms, rules engines and skills.
  • Productionalize and operationalize personalized insights as AI-powered applications that deliver value.
  • Multi-dimensional profiles are required to deliver insights that can alter customer experience, for example, longitudinal patient records in Healthcare consist of internal and external data sources, all of which are required to improve care.


       

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