Discover how AI, customer data and personalization are helping brands create more relevant and memorable customer experiences.

Why Personalization Has Become So Important

Customers interact with brands through websites, apps, social media, email, stores, customer service platforms and increasingly through AI-powered interfaces.

With so many choices available, generic experiences can easily become forgettable.

A customer may not want to receive the same recommendation, advertisement or email as everyone else. They increasingly expect businesses to understand their interests and provide information that is relevant to their situation.

This is where hyper-personalization comes in.

Hyper-personalization goes beyond simply adding someone's first name to an email. It uses customer information, behavior, context and artificial intelligence to create experiences that are more relevant to individual users.

McKinsey describes the next stage of marketing as moving from traditional segment-based personalization toward one-to-one journeys in which AI systems can determine the next message, offer or experience for an individual customer.

The goal is simple:

Give people a more useful experience at the moment it matters.

What Is Hyper-Personalization?

Traditional personalization might divide customers into broad groups.

For example:

• New customers

• Returning customers

• Frequent buyers

• Occasional buyers

• Customers interested in a particular product category

Hyper-personalization goes further.

Instead of asking only which group a customer belongs to, a business can consider factors such as:

• Previous purchases

• Browsing behavior

• Content preferences

• Location, where appropriately collected

• Device and channel

• Purchase history

• Customer service interactions

• Responses to previous messages

• Current browsing or shopping intent

AI can then help analyze these signals and determine which content, product or communication may be most relevant.

The Difference Between Personalization and Hyper-Personalization

Personalization can be relatively simple.

An online store might recommend men's shoes to a customer who has previously purchased men's clothing.

Hyper-personalization could consider a much wider set of signals.

A customer who recently searched for running shoes, viewed several products, abandoned a particular pair and returned through a mobile device could receive a different experience from someone who has never shown an interest in running.

The distinction is therefore not simply about collecting more information.

It is about using information more intelligently and in context.

First-Party Data Is the Foundation

Hyper-personalization requires reliable customer information.

One of the most important sources is first-party data — information collected directly through a company's own interactions with customers.

This can include:

• Website activity

• Purchases

• Account information

• Loyalty programs

• Email engagement

• App activity

• Customer service conversations

• Preferences customers voluntarily provide

Salesforce describes first-party data as information collected directly from customer interactions and notes its importance in creating personalized experiences.

This is particularly important as businesses place greater emphasis on consent, privacy and direct relationships with customers.

AI Changes the Scale

A marketing team cannot manually study millions of customer interactions and decide what each person should see next.

AI changes the scale of the problem.

Machine-learning and AI systems can analyze large quantities of customer signals and identify patterns that would be difficult to process manually.

They can help businesses:

• Recommend products

• Suggest relevant content

• Segment audiences

• Predict customer needs

• Personalize messages

• Identify potential churn

• Adjust offers

• Determine appropriate communication timing

McKinsey's current marketing research describes AI-powered personalization as increasingly dependent on clean customer data, real-time decision systems and mechanisms for determining the next-best action.

E-Commerce Is One of the Clearest Examples

Online shopping provides an obvious example of personalization.

Imagine two people visiting the same online store.

The first customer regularly buys running equipment.

The second frequently searches for casual fashion.

Showing exactly the same homepage to both customers may be less useful than adapting the experience to their interests.

An intelligent system can potentially change:

• Product recommendations

• Search results

• Promotional messages

• Homepage content

• Email suggestions

• Related products

The objective is not simply to display more products.

It is to reduce the amount of searching customers have to do.

Streaming Services Changed Expectations

Entertainment platforms have helped make personalized recommendations part of everyday digital life.

Streaming services can recommend movies, television programs, music and playlists based on previous behavior.

That has changed what consumers expect from digital platforms.

People have become accustomed to interfaces that adapt to them.

The same expectation is spreading into shopping, banking, travel, customer support and other industries.

Personalization Can Improve Customer Service

Hyper-personalization is not limited to marketing.

It can also improve customer support.

Imagine contacting a company about an order.

Instead of starting from scratch, a customer-service system could already understand:

• The customer's recent order

• Previous support requests

• The status of the shipment

• The customer's preferred communication channel

• Relevant account information

This can reduce unnecessary repetition.

McKinsey has described this type of connected customer view as a way for organizations to understand interactions across channels and respond using a customer's broader history and context.

Personalization Across Multiple Channels

Customers rarely use only one channel.

Someone might:

1. Discover a brand on Instagram.

2. Visit its website.

3. Read product reviews.

4. Subscribe to email updates.

5. Download the company's app.

6. Purchase from a physical store.

7. Contact customer service later.

If every channel operates independently, the customer may experience the brand as a collection of disconnected systems.

A stronger personalization strategy attempts to connect these interactions.

The customer should not constantly have to explain who they are or what they are trying to accomplish.

Real-Time Personalization

One of the biggest developments in personalization is the move toward real-time decision-making.

Traditional campaigns might be planned weeks in advance.

Modern systems can react to customer behavior much faster.

For example, a travel website might recognize that someone is repeatedly researching flights to a particular destination.

Instead of continuing to display generic travel information, the platform could surface destination guides, relevant accommodation options or useful planning content.

The experience changes according to the customer's current intent.

Personalization Is Moving Toward Intent

The next stage is not simply recognizing what customers did in the past.

It is understanding what they are trying to accomplish now.

This distinction is becoming increasingly important as AI systems become more conversational.

Salesforce describes emerging "agentic personalization" as a shift toward understanding intent, context and timing rather than simply reacting to past behavior.

For example, a customer saying:

"I need a lightweight laptop for university."

has communicated considerably more intent than someone who simply visited a laptop category.

An AI-powered experience could use that information to provide a more relevant response.

The Risk of Going Too Far

More personalization is not automatically better.

A customer may appreciate a useful recommendation but become uncomfortable if a company appears to know too much about them.

This creates an important balance between relevance and privacy.

Salesforce's latest customer research reports that 73% of customers say companies treat them more like individuals, while 71% say they are increasingly protective of their personal information.

That tension is central to the future of personalization.

People want useful experiences.

They also want control over their information.

Trust Has Become Part of the Experience

A personalized experience is only valuable if customers trust the organization providing it.

Companies therefore need to be clear about:

• What information they collect

• Why they collect it

• How it is used

• Which choices customers have

• How long information is retained

• When AI is involved

Transparency can become a competitive advantage because customers are more likely to engage with personalization when they understand its purpose.

Personalization Should Solve a Problem

The best personalization is not necessarily the most complicated.

A business should first ask:

What problem are we solving for the customer?

If personalization makes it easier to find a product, complete a purchase, receive support or discover useful information, it can improve the experience.

If it simply creates more notifications, advertisements and recommendations, it can create the opposite effect.

Personalization should therefore be useful before it is impressive.

Personalization in Email Marketing

Email remains a strong application of personalization.

Instead of sending every subscriber the same message, brands can adapt communications based on interests and previous interactions.

For example:

• A sports customer can receive sports-related offers.

• A frequent buyer can receive loyalty benefits.

• An inactive subscriber can receive a re-engagement message.

• A customer who abandoned a purchase can receive relevant follow-up information.

First-party data can help companies make these communications more relevant without relying entirely on broad audience assumptions.

Personalization in Content

Publishers can also personalize content discovery.

A news or media website may learn that a visitor frequently reads:

• Technology

• Football

• Entertainment

• Business

Instead of presenting exactly the same content to every visitor, the site can highlight topics that appear more relevant.

For publishers, this can make large content libraries easier to navigate.

It can also help readers discover articles they might otherwise miss.

Personalization for Small Businesses

Hyper-personalization is not only for global corporations.

Small businesses can start with relatively simple systems.

A small online store might track:

• Customer purchase history

• Product categories

• Email preferences

• Repeat purchases

• Frequently asked questions

Even these basic signals can improve communication.

A small business does not need a sophisticated AI platform before it can begin making customer experiences more relevant.

The Importance of Good Data

AI cannot compensate for poor information indefinitely.

If customer records are inaccurate, incomplete or duplicated, personalization can produce bad recommendations.

For example, a customer who purchased a product once as a gift may not actually be interested in buying it again.

This is why effective personalization requires more than collecting large amounts of data.

Businesses need systems for maintaining accurate information and interpreting it responsibly.

Measuring Whether Personalization Works

Companies should also measure outcomes rather than assuming personalization is successful.

Useful metrics can include:

• Conversion rate

• Customer retention

• Repeat purchases

• Engagement

• Click-through rate

• Customer satisfaction

• Average order value

• Support resolution time

Different businesses will have different objectives.

The important point is to connect personalization to measurable customer or business outcomes.

The Future of Hyper-Personalization

The next generation of personalization is likely to become increasingly conversational and AI-driven.

Instead of navigating menus, customers may simply explain what they want.

An AI system could then understand the request, consider available information and guide the customer toward an appropriate solution.

McKinsey's 2026 research describes this broader movement toward AI systems that continuously analyze customer signals, personalize experiences and determine next-best actions.

This could make digital experiences feel less like static websites and more like intelligent services.

The New Challenge for Brands

The challenge will not simply be creating more personalization.

It will be creating better personalization.

Brands will have to answer difficult questions:

• Is the recommendation genuinely useful?

• Did the customer consent to the relevant data use?

• Is the AI making a reliable decision?

• Can customers understand why they received the recommendation?

• Can they control their preferences?

• Is personalization improving the experience or simply increasing sales pressure?

These questions will become increasingly important as AI becomes embedded in customer journeys.

The Bigger Picture

Hyper-personalization represents a major shift in how businesses think about customer experience.

The old model was largely built around broadcasting one message to many people.

The emerging model is more adaptive.

A brand can use customer information, AI and real-time signals to create experiences that respond to individual needs and circumstances.

But the most successful personalization will not necessarily be the system that knows the most about a customer.

It may be the system that uses just enough information to be genuinely useful while respecting the customer's trust and control.

That is the real opportunity behind hyper-personalization: not simply making customers feel recognized, but making every interaction more relevant, useful and effortless.

Tags: Personalization, Customer Experience, AI