January 20, 2021

Personalization Gets ‘Hyper’ with Machine Learning

In the not-so-distant past, sending an email by just introducing the recipient with the first name (“Dear John”) was considered as a “personalized email”. We have come a long way from there. The technology advancements have empowered brands to leverage customer data, derive valuable insights and deliver highly personalized communication at the right time via right channel.

Today, consumers are swamped with endless marketing messages and offers, making it more and more difficult for brands to create experiences that will make them stand out of the competition. Hyper personalization is the key to this problem. According to Forrester, 77% of consumers have chosen, recommended, or paid more for a brand that provides a super personalized service or experience.

Hyper-personalization takes personalized marketing one step further by leveraging machine learning. Decisions and recommendations are made in real-time and for each individual.
A research with Forbes Insights revealed that most brands are already investing heavily in data initiatives. Machine learning helps brands in making the most of the data by diving deeper into consumer behavior, creating dynamic content, and enabling personalization at scale.

Personalization in the age of Machine Learning

Even though personalization is becoming more of a norm, consumers are often left frustrated by the not-so-personalized marketing communications from many brands. Like, for example, when a bank encourages its customer to apply for a credit card they already have. Or a retailer sends emails about a flash sale in a category which is not relevant for the consumer (e.g. lawn care for people living in a high-rise apartment). This makes consumers take their business to places where they feel recognized, appreciated and valued as an individual. So brands are now leveraging AI and machine learning to create better hyper-personalized experiences for consumers.

Machine learning has changed the game of personalization for marketers. By leveraging machine learning (ML) technology, marketers can process vast amounts of data in real-time, to make the best decision about what to offer to each individual. Marketers can now make a shift from conventional rule-based personalization to using machine-learned algorithms and predictive analytics to deliver more relevant experience to each and every customer.

So, how does ML-based personalization work?

Building customer personas

The first step for delivering hyper-personalized experience is to collect and analyze right set of data. By using ML-powered predictive analytics technology, brands can leverage consumer data from various sources to build buyer personas. ML system makes use of historical customer data for all the customers along with aggregate usage behavior of the customer for each specific product to derive an expected usage pattern corresponding to each product. Using 360-degree customer profiling and machine-learned ‘Next Best Offer’ capabilities of customer engagement products, brands can determine the right offer to be extended over different touch points fitting each customer’s demographic profile, lifetime events and his predicted needs and intent.

Learning customer preferences

Machine learning algorithms play a crucial role in determining customer preferences. Enterprises can learn user preferences in two ways. Firstly by observing their usage patterns and secondly by observing preferences expressed by the customer at a touch point like POS. For e.g. If an offer is accepted or rejected at a touchpoint, the customer profile can be dynamically updated in real-time and used for future decisioning.

Examples of hyper-personalization from BFSI and commerce industries

According to a Salesforce study, 51% of consumers expect that companies will anticipate their needs and make relevant suggestions before they even make contact. One of the best examples is of Amazon, which creates a unique, hyper-personalized experience for customers. Amazon uses data points like full name, search query, purchase history, average time spent on search, average spend amount, etc. They gather this data using predictive analytics and create a 360-degree customer profile for a deeper understanding of the customers and their shopping habits. This helps them in increasing customer satisfaction with hyper-personalized marketing techniques. For example: A customer searches its database for a pair of headphones, and when they click on the product, the interface automatically recognizes the search and a “Frequently bought together” section will appear on the page. (Source)

Within the banking, insurance or telecommunications sectors, brands are now offering tailor-made solutions to customers based on their contextual needs. An interesting example is that of a European bank that altered its retention strategy because of an interesting insight it noticed. Their initial strategy — which focused on targeting inactive customers — failed to translate into anything significant. The bank then turned to machine learning algorithms to predict currently active customers that were likely to reduce business with the bank based on an analysis of their current transaction patterns. What came out of it was a targeted campaign that focused on high-risk but currently active customers, reducing future churn by 15 percent. (Source)


Gartner predicts that organizations that excel in personalization will outsell companies that don’t by 20%. B2C enterprises need to consistently deliver products and offers tailored to individual customer’s needs for ensuring personalized experience over touch points. However, they often end up delivering broadly segmented and contextually irrelevant offers which leads to customer dissatisfaction. Offline analysis and heuristic decision making limits their capability to analyze all possible data points and recommend personalized offers at scale and in real-time. Enterprises need a more sophisticated decisioning engine with machine learning capabilities like Offer-X that can enable personalization at scale, recommending next best offers for each customer based on historical behavior, contextual needs and business objectives of enterprises.


Pravin Vijay

Assistant Vice President-Marketing

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