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  • Leveraging AI to deliver hyper-personalized online customer experiences drives consumer engagement and retention rates.
  • AI-enabled personalization strategies improve scale, context, and customer activation, enabling businesses to become more resilient to changing customer wants and needs.
  • Personalization is transformational. Leveraging AI to digitally tailor your offerings to individual consumers gives you an edge over your competition.



The COVID-19 pandemic led to more dependency on technology, with customers switching to digital channels with high levels of personalization. 

As competition shifted online, businesses found it harder to retain customers, forcing brands to get innovative to retain their business as well as their customer base. With 50% of business coming from 10% customers and 90% of business coming from 20%, it is crucial for the brands to come up with improved strategies to retain their top customers as well as focus on increasing them through personalization at scale. In today’s highly competitive online world, it is increasingly important to deliver a personalized real-time online experience.


As one of the leading trends in technology, artificial intelligence (AI) continues to gain popularity for marketers and sales professionals.

AI is an essential tool for brands who wish to provide a hyper-personalized customer experience. 

Cloud-based platforms and solutions that leverage AI are enabling a new class of customer experience. This offers real-time CX targeted specifically to individual consumer’s needs. From personalized fashion recommendations, based on individual style preferences, to chatbots that use AI to tailor helpful conversations, the possibilities are virtually endless. Moreover, personalization use cases and techniques have evolved dramatically in recent years.

Right before the pandemic, Gartner released a report indicating 80% of marketers who invested in personalization would abandon their efforts by 2025, either due to a lack of ROI, the perils of customer data, or both. Additionally, while personalization accounts for 14% of marketing budgets, Gartner found only 5% have a personalization strategy and roadmap they feel good about. That’s a red flag, as consumers say they overwhelmingly favor personalization. In addition, various studies indicate that hyper-personalization drives consumer engagement and better outcomes since the AI engine considers various factors to deliver last-mile personalized experiences that influence decision making. Other studies also show that AI-enabled personalization strategies not only led the consumers to make the right decision but also reduced buyers’ remorse and returns.

Consumers are more likely to shop with brands they recognize and remember with exciting offers and recommendations


Consumers are more likely to shop with brands they recognize and remember with exciting offers and recommendations.

In fact, per the Evergage study, in conjunction with Researchscape International’s “2020 Trends in Personalization,” marketers reported the following benefits of personalization:

  • Lift in sales revenues (97%)
  • Improved customer experiences (64%)
  • Increased conversion rates (63%)
  • Increased visitor engagement (55%)

Unfortunately, there is often a lack of C-suite commitment to a holistic, long-term personalization framework. To build loyalty, businesses must use personalization to virtually deliver streamlined experiences. This implies to customers that you recognize them and that they can trust your business to fulfill their unique and changing wants and needs.

So, what can brands do to ensure that they are personalizing interactions in a way that customers want?



AI can enhance customer journey and experience through three distinct areas

1) AI-infused data improves scale

Today, with AI, we have access to large amounts of data, be it device data, network data, in-home automation data, or vehicle data. Automation aids in distributing and storing this large amount of data more efficiently. Models that used to be impossible to run (or could only run against limited data sets) are now executed with ease against thousands of variables. The variety, velocity, and volume of data definitely results in personalization at scale.

2) AI-based analytics improves context

Personalization works when it is relevant and based on specific data and analytics.

On the machine-learning side, models such as neural networks or decision trees are a better fit than classical generalized linear models. They provide more accurate results and improved context.

Cognitive computing, natural language processing, sentiment analysis extraction aid in creating pertinent structured data from unstructured sources. It allows them to append customer profiles with data which was not previously used for context and provide insights into customer sentiment, so brands can react accordingly.

3) AI-based insight improves customer activation

Customer activation is the ability to increase customer engagement.

The more knowledge you have about a customer on an individual level, the more you can influence activation or engagement. Machine-learning-based techniques, like natural language processing and sentiment analysis extracts key customer data (i.e., phrases from a voice chat, an instruction to an AI-powered assistant, or a discussion on a social platform) to derive insight.

Personalization is transformational and AI forges powerful customer relationships and enables businesses to become more resilient and adapt quickly to shifting consumer needs. Businesses that leverage the treasure trove of data available to them to digitally tailor their offerings, have an edge on the competition.



Multiple consumer research studies point out the facts that: 

  • 80% of consumers are more likely to make a purchase from a brand that provides personalized experiences
  • 90% of US consumers find marketing personalization very or somewhat appealing
  • 63% of consumers will stop buying from brands that use poor personalization tactics
  • 66% of consumers say encountering content that isn’t personalized would stop them from making a purchase

A personalization playbook can help an organization take a broad, holistic view at the ecosystem of data and technology required to deliver the promise of personalization at scale.

A personalization playbook should include following key elements.

1. Top strategic business objectives and digital priorities

2. Essential core capabilities to deliver personalization at scale. Take a look at your existing tech stack.

3. Industry best practices for 4 key categories for Personalization viz. data, content, decisioning, channel delivery

4. Relevant AI-enabled use cases for repetitive tasks in areas of:

Planning: building intelligent strategies

Production: creating intelligent content

Personalization: powering intelligent consumer experiences

Promotion: managing cross channel intelligent promotions

Performance: turning data into intelligence



The playbook approach addresses essential "building blocks" to power personalized journeys across the full customer lifecycle and helps our clients achieve the goal of personalization at scale.

Following this approach, organizations can:

  • Create or validate a value map to drive and measure business success
  • Review current technology stack across all digital marketing capabilities
  • Identify gaps and overlaps in use cases to develop a phased architecture roadmap
  • Prioritize use cases against business goals, technical dependencies, and future architectural maturity
  • Assess operations and process within use case roadmap
  • Automate delivery of hyper-personalization and new insights, and drive operational efficiencies by leveraging AI

The world we’re living in now is profoundly different than it was just a few years ago – and will likely continue changing at a breakneck pace. But, amid constant change, businesses can find success so long as they address customers’ individual problems through personalized solutions.