Personalization is the key to successful customer engagement and profitable growth in today's world. It reaps numerous benefits, including increased revenue through upselling and cross-selling, reduced customer churn through proactive customer retention, and more customer satisfaction and a better user experience. Rapid changes in business, as well as in technology, are going to make personalization more interesting in years to come.
Various factors — such as enriched user profiles, real-time personalization, and accurate contextualization — are critical for the successful implementation of personalization. To enable personalization, organizations can use technology to achieve success. Let’s explore these factors to understand the future of personalization.
Enriched user profiles and real-time personalization can help chatbots better understand the user's context. With NLP and search-query analysis, search engines can also provide more relevant search results to users.
Let's examine some of the technologies and enhancements in business models that can fuel some of our goals.
Blockchain has enabled customers to communicate their personal choices and preferences by offering a shared ledger with access to mobile apps. They’ll have control over the type of data that is shared, without disclosing their Personally Identifiable Information (PII). Blockchain enables customer ownership over the shared data and simultaneously creates enriched customer profiles.
Organizations can use the shared ledger to develop a roadmap for their products and services.
For example: If a consumer shares their movie genre preferences in the shared ledger, OTT platforms and eBook apps can use these preferences to offer the user relevant content without prompting them during each login.
Right now, transactional data functions within the confines of organizational silos. With the customer’s consent, organizations can collaborate and agree to share relevant transactional data attributes in the future. Additionally, aligning data privacy, retention, and sharing regulations will create a shared ledger for user transactions.
For example: Someone books a plane ticket to New York City on a specific date and books a hotel.
Assuming there is a collaboration between apps to share transaction data, the following are possibilities:
The advancement in device computing power offers the opportunity to run machine learning (ML) models on any device. On-device ML helps reduce latency and dependency on the network. Machine learning, when coupled with the shared ledger, provides the enormous opportunity to enrich user personas and give real-time recommendations to the user. On-device ML will guide users while they’re either active or searching for specific content on apps.
For example: A person is pursuing online courses that take four to six months to complete. Based on this data and the user’s existing profiles, a job-search app can share available jobs related to the coursework.
Technology can help organizations mitigate personalization challenges to achieve enriched user profiles, real-time personalization, and accurate contextualization. With shared ledgers on blockchain and on-device machine learning, organizations can use technology to enable successful personalization efforts. By improving personalization, your organization can reach the future of customer experience and benefit for years to come.
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