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.
Three factors enhancing personalization
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
For personalization to succeed, the collection of accurate data is of utmost importance. Leveraging anonymous data for a personalized offer is always challenging and unreliable. Currently, organizations can collect only data that is relevant to their engagement with the customer. However, the customer has become increasingly dynamic, and any organization relying on data related only to their business is bound to face a challenge in personalization. With detailed personas, companies can outline better and more accurate use cases for personalization implementations. With enriched user personas and profiles, organizations will improve their customer engagement and experience.
- Real-time personalization
In today's world, most of the data used for personalization is not real-time: It does not occur within a few milliseconds of the data input. With advancements in computing technologies (like blockchain, app-based business models, and devices’ computing power), real-time personalization is a nearer possibility. Real-time personalization would result in a higher customer retention rate, better customer satisfaction, and increased revenue.
- Accurate contextualization
Text analysis through Natural Language Processing (NLP) is beneficial for accurate interpretation of the context of the word or phrases. However, since NLP is nascent, a few challenges remain. Some of the common challenges are:
- Contextual words and phrases: The exact words and phrases can have different meanings based on the context.
- Understanding human emotion: The intended meaning of the phrase can change based on human emotion.
- Error in text/speech: Due to different accents or pronunciations, NLP can misspell words.
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.
Using technology to drive personalization
Let's examine some of the technologies and enhancements in business models that can fuel some of our goals.
Shared ledger with user preference (blockchain)
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.
Shared ledger with user transactions (blockchain)
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:
- A food delivery/takeout app can list restaurants that align with the customer’s food preferences and are near the hotel.
- A travel app can show a list of tourist spots in New York, based on what is known about the customer’s previous trips.
On-device machine learning (ML)
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.
Move toward the future of personalization
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.