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As enterprises ramp up their efforts to unify customers’ offline and online journeys through solutions such as identity management, marketers and customer experience leaders should find the recommendations and messaging in this report critical in helping them invest in technology and solutions to help connect customers’ digital and physical behaviors.


  • As customers increase the number of channels and devices they use to connect to a brand, linking their offline to online journeys becomes increasingly difficult.
  • Enterprises can adopt various solutions to help link customers’ offline and online behaviors, including creating a customer graph; using Wi-Fi and location-based data to track customers; connecting first-, second-, and third-party data; building a customer data preference center; creating unique identifiers; applying machine learning; and aggregating data from Internet of Things-enabled devices.

Features and Benefits

  • Assesses the hurdles in linking offline and online customer behaviors and transactions.
  • Provides both technology and strategy solutions for linking customers' offline and online customer journeys.

Key questions answered

  • What are the technology solutions available to help solve this online-offline challenge?
  • How can I make sense of massive data sets?
  • What are the necessary steps prior to investing in technologies that unify customer data?

Table of contents


  • Catalyst
  • Ovum view
  • Key messages


  • Recommendations for enterprises or service/content providers
  • Recommendations for vendors

Overcoming hurdles

  • GDPR and privacy regulations
  • Complexities in sharing PII-based customer information
  • Tracking identities across devices is challenging
  • Getting data out of an in-app environment
  • Ineffective partnerships

Filling the gaps and connecting the dots between online and offline behaviors

  • Build a connected customer graph as an information framework
  • GPS and Wi-Fi will play an important role in bridging the divide
  • Connect first-party data from POS and contact centers with second- and third-party data
  • Build a customer data preference center
  • Unique identifiers provide a strong framework for cross-channel attribution
  • Machine learning assigns meaning to massive data sets
  • IoT offers in-depth end-to-end customer behavioral data


  • Methodology
  • Further reading
  • Author

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