How does personalization change the face of marketing?

What is personalization in marketing?

Put simply, personalized marketing is a strategy in which a company delivers tailored content to customers through data collection, analysis and automation technology. The goal is to engage customers and prospects by communicating with each of them individually, not with the average member of a large group.

A simple example: two people open the same online shop. One has bought running shoes twice and reads the trail-running guides, the other has only looked at children’s clothing. With personalization, the homepage, the recommended products, the next newsletter and even the discount each of them gets are different. The same logic works in B2B, where a software company shows a manufacturing prospect case studies from factories instead of from retail.

Three terms are often mixed up:

• Segmentation splits customers into groups, such as new and returning buyers, regions or spending levels, and sends each group its own message. It is where most programs start.
• Personalization goes down to the individual: the company uses data to decide what each person sees, often automatically and in real time.
• Customization puts the customer in control: they choose the settings themselves, such as the topics of a newsletter or the colors of a shoe they design.

Most companies use a mix. Rules written by marketers (show this banner to visitors from Texas) sit next to models that predict the next best product or the best time to send. The more decisions a model makes, the more the marketer’s work shifts from writing campaigns to feeding the system good data and checking its results.

How personalization in marketing has changed

The idea is older than the internet. In 1993 Don Peppers and Martha Rogers published The One to One Future, which argued that companies should compete for their share of each customer rather than their share of the market. Online shops made it practical: Amazon was already recommending products based on what other buyers of the same items purchased in the late 1990s.

When this post was first written in 2019, personalization mostly meant a first name in an email subject line, product recommendations and rule-based website banners, much of it fed by third-party cookies. Several things have changed since then:

• Privacy law. The EU’s GDPR has applied since May 2018. California’s CCPA, passed in 2018, was extended by the CPRA with rights to correct data and to limit the use of sensitive data from January 2023. Consent and data minimization are now part of every personalization project.
• Tracking limits. Since iOS 14.5 in April 2021, Apple’s App Tracking Transparency requires apps to ask for permission before tracking people across other companies’ apps and websites.
• Cookies. Google planned for years to remove third-party cookies from Chrome, then dropped the plan. In October 2025 it confirmed that Chrome users would keep the choice, and it retired most of the Privacy Sandbox replacements, including Topics and Protected Audience. Brands that had built their own customer data were ready either way.
• Expectations. A 2021 McKinsey study found that 71% of consumers expect companies to deliver personalized interactions and 76% get frustrated when that does not happen.
• Generative AI. Since late 2022, language and image models have been able to write and adapt copy, images and product descriptions for each segment or person. The bottleneck moved from producing variants to deciding which one to show.

There was also a reality check. In December 2019 Gartner predicted that by 2025, 80% of marketers who had invested in personalization would abandon their efforts because of a lack of ROI, the difficulty of managing customer data, or both. The prediction was too gloomy about the idea but right about the risks: the projects that stalled were the ones without clean data or a way to measure results.

What data personalization runs on

Personalization is only as good as the data behind it. Marketers usually sort that data by where it comes from:

• Zero-party data: what customers tell you on purpose, such as quiz answers, preferences saved in an account or the reason for a return.
• First-party data: what you observe on your own channels, such as page views, searches, purchases, email clicks and app use.
• Second-party data: another company’s first-party data shared with you under an agreement, for example by a retail media partner.
• Third-party data: data bought from brokers or collected by cookies on other sites. It is the least reliable and the most restricted by law and by browsers.

Within your own data, the most useful signals are behavior (what someone looked at and when), transactions (what they bought, how often and for how much), context (device, location, time of day, weather, stock levels) and stated preferences. In 2026 more and more teams treat first-party and zero-party data as the foundation, because it is collected with consent and does not depend on other companies’ tracking decisions.

What benefits does personalized marketing bring?

Personalized marketing has advantages for both businesses and consumers. For customers, the most important one is a better experience: fewer irrelevant messages and faster access to what they need. People are more comfortable sharing personal information with their favorite brands when they get something in return. They fill in forms, download white papers, answer polls, collect discounts and state their favorite products or preferences. In exchange, they expect the brand to protect sensitive information and to offer a more personal experience when they come back.

When consumers provide information, they expect to be treated as individuals with specific preferences. Companies that put time and resources into a well-run personalization strategy gain an advantage in both brand loyalty and customer satisfaction.

For companies, revenue matters most. By identifying and using each customer’s preferred channel, companies can increase their return on investment. With the right automation technology, marketers can see which channels each customer responds to and coordinate them automatically. McKinsey’s 2021 research puts numbers on it: personalization most often lifts revenue by 10 to 15%, and companies that excel at it generate 40% more revenue from those activities than average players.

It is also worth keeping the sales channels coherent. Consumers interact with brands by email, on mobile, on social media and in stores, often all in one day. That is why consistency between different channels matters more than ever: the experience in the store should match the one in the app, which should match the emails.

For what this means in a single campaign, with brand examples such as Coca-Cola, Spotify and Netflix, see the benefits of personalized campaigns.

Challenges facing personalization in marketing

Like every marketing strategy, personalized marketing comes with obstacles.

One of the biggest is technology that does not fit the way people shop. Personalization depends on data collection and automation, and with them on an intelligent algorithm. Many marketers still struggle to find a smart personalization engine that meets their needs, or they buy one and cannot feed it, because product, customer and behavior data sit in separate systems.

Software is not enough: companies also need a dedicated team, and not every company is ready to give personalization the time and resources it needs. The next challenge is building a single customer view. By finding common threads across channels and combining data into one unified profile, marketers can tell who a customer really is. In practice, matching an email address, a phone number, an app login and an anonymous browser to one person is hard. Segmentation is a problem too: many teams still rely on broad segments and simple grouping.

Three newer challenges have joined the list:

• Privacy and consent. Every data point needs a legal basis, and customers can ask what you hold about them or ask you to delete it. A program that only works with data you are not allowed to use is a liability.
• The creepiness line. Relevance stops being welcome when it reveals more than customers expected you to know, or when it pushes them at the wrong moment. A June 2025 Gartner survey of 1,464 B2B buyers and consumers found that personalization can triple the likelihood of purchase regret at key points of the journey, and that it leaves people feeling overwhelmed or rushed when it arrives in the middle of a hard decision.
• Measurement. A personalized email that sells well may only take sales from another channel. The reliable answer is a control group that does not get the personalization, a step many teams skip.

How to build a personalized marketing strategy

When introducing a personalized marketing strategy, work through these steps in order:

• Set one goal you can measure, such as repeat purchase rate, average order value or the rate of leads that turn into meetings, and agree on limits: how often you will contact people and which data you will not use.
• Compare personalization engines. If you already use a platform, check it against other solutions that may give more value from the same data.
• Add the tracking code to your site and apps so you capture behavior: clicks, time on site, searches, abandoned carts, purchase history and more. Ask for consent where the law requires it.
• Join the data into one profile per customer, so the website, emails, push notifications and ads draw on the same facts.
• Start with a few high-value use cases: a welcome series, abandoned cart reminders, product recommendations, a homepage that changes for returning visitors.
• Create the content variants. Generative AI makes this cheaper, but a person should still check the tone and the facts.
• Validate the system against a control group with an A/B test, then expand what works.

Capturing data, building analytics and creating responsive websites all help, but they are most valuable when they work together. After a few weeks of collecting data, your personalization solution should be able to show customers relevant content recommendations.

The future of personalization

Customer expectations and marketing technology keep moving, and both point toward more personal marketing. Customers expect communication tailored to them and a consistent experience across channels. As companies adopt new technologies, brands will more often show people what they need before they go looking for it.

The next wave of personalization also reaches offline contact. Smart marketers use predictive analytics and buyer and account-level signals so that their sales representatives know a prospect needs a product, sometimes before the prospect does. Brands that use these technologies and read all the important purchase signals gain a real advantage over competitors.

Four developments are shaping what comes next:

• AI that acts, not only predicts: systems that pick the audience, write the message, choose the channel and adjust the budget, while marketers set the goals and the limits.
• Experiences generated on the spot: product pages, emails and images composed for one person at the moment of the visit, which raises the stakes for brand guidelines and fact checks.
• Shopping through AI assistants: when a customer asks a chatbot to compare running shoes, the brand’s product data and descriptions shape the answer as much as its own personalization does.
• Privacy by design: more processing on the customer’s own device and more reliance on what customers choose to share, as tracking across other companies’ sites keeps getting harder.

Nobody wants to feel like a face in the crowd. Focusing on each customer’s needs makes people feel heard, and they expect that attention through the whole buying journey. If your strategy treats everyone the same, customers feel less involved and are less likely to buy from you. With clean data, clear consent and honest measurement, personalization remains one of the most dependable ways to grow sales and keep customers loyal.