What is personalization?

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 Overhead view of cups with different types of coffee. One of the cups sits higher up in the frame with a foam flourish on top.
Overhead view of cups with different types of coffee. One of the cups sits higher up in the frame with a foam flourish on top.

Personal greeting, personal touch, personal best. Close, personal friends. All good things, right? Right, according to consumers in a recent McKinsey survey. When asked to define personalization, consumers associated it with positive experiences that made them feel special. Personalization marketing represents an opportunity for companies to benefit from those warm, fuzzy feelings.

Personalization marketing has real advantages for companies: it can reduce customer acquisition costs by as much as 50 percent, lift revenues by 5 to 15 percent, and increase marketing ROI by 10 to 30 percent. Personalization has also been shown to improve performance and provide better customer outcomes. Companies with faster growth rates derive 40 percent more of their revenue from personalization than their slower-growing counterparts. McKinsey research also shows that personalized experiences drive up both customer loyalty and a company’s gross sales. And the COVID-19 pandemic has only made personalization more urgent for brands: three-quarters of customers switched to a new store, product, or buying method during the pandemic, proving that store and product loyalty is increasingly a thing of the past.

But it’s a tricky needle to thread. Getting it wrong can have lasting consequences for brands. You probably already know the uncanny feeling of being served a too-relevant ad. Sometimes it’s just a little unsettling; other times, it can put us off the product altogether.

Even so, the modern customer does expect a personalized experience—even if they take it for granted. McKinsey research shows that 71 percent of consumers expect companies to deliver personalized interactions. And the story doesn’t end there: 76 percent get frustrated when this doesn’t happen.

How can companies get started with personalization and scale up? What trends can we predict for personalization in the future? And—critically—how can companies toe the line between creepy and helpful? Read on to learn more.

Learn more about McKinsey’s Retail and Growth, Marketing & Sales Practices, and McKinsey Digital.

What do customers value in personalized marketing?

McKinsey asked 60 shoppers to create mobile diaries of their personalized interactions with various brands over two weeks. They made over 2,000 entries, which helped us see what works for customers and what doesn’t. Here are four things customers said they wanted from brand interactions:

  1. “Give me relevant recommendations I wouldn’t have thought of myself.” One common personalization practice is to remind shoppers of items they looked at but didn’t buy. This can be annoying or intrusive if not executed well. Instead, customers appreciate being recommended products or services that complement what they’ve already browsed or bought. Brands should keep track of impressions and stop serving ads to customers who haven’t responded.
  2. “Talk to me when I’m in shopping mode.” A message’s timing is just as important as its content. Perfecting the timing requires a close look at customer behaviors, patterns, and habits. One clothing retailer found that shoppers who visited a physical or online store were more likely to open and respond to messages delivered either on that same day or exactly a week later.
  3. “Remind me of things I want to know but might not be keeping track of.” Brands can become relevant to shoppers by tracking events and circumstances. These can include letting a customer know when a desired item is back in stock or when a new style is launched for a product the shopper has previously bought.
  4. “Know me no matter where I interact with you.” Customers expect communications that seamlessly straddle offline and online experiences. This is challenging for retailers because it requires collaboration between disparate areas of the organization—from store operations to analytics.

How can brands avoid being perceived as creepy?

Customers see value as what they get from a message relative to how much it costs—meaning how much personal information they have to share to get it. To understand how to deliver value while retaining trust, the following questions are helpful:

  • Are you infusing empathy into your customer analytics and communications? Create segmentation based on customer attitudes and prioritize customer satisfaction based on the overall journey rather than individual touchpoints.
  • Are you listening carefully for feedback on customer acceptance? Test and learn constantly to improve engagement. Do this by digging into upstream (likes, clicks, opens) and downstream (conversions, unsubscribes, ROI) engagement metrics. Engage with qualitative listening tools, like an ongoing shopper panel and ethnographic research and observation.

Learn more about McKinsey’s Growth, Marketing & Sales Practice.

How might brands use personalization to achieve their goals?

Most marketers know that personalization is important. But we anticipate that, in coming years, personalization will transform the way companies approach marketing. Here’s what brands should focus on to prepare for the future:

  • Invest in customer data and analytics foundations. These include systems to pool and analyze data, algorithms to identify behavior patterns and customer propensity, and analytical capabilities to feed that information into simple dashboards. This foundation will allow marketers to understand what high-value customers are looking for on an ongoing basis. A recent survey indicates that nearly one in five organizations are already investing in customer service analytics and customer segmentation AI use cases.
  • Find and train translators and advanced tech talent. This technological leap requires a close partnership between marketing and IT. In addition to data scientists and engineers, product management teams will need analytics translators who can communicate business goals to tech stakeholders and data-driven outcomes to the business. The ability to recruit and develop this type of translator will provide a significant competitive advantage for organizations.
  • Build agile capabilities. A successful personalization program requires cross-disciplinary project teams—and hence, a commitment to agile management. Teams should be organized around specific customer segments or journeys and should excel in creative, collaborative problem solving.
  • Protect customer privacy. Data privacy is a big deal to customers: according to a 2022 survey, 85 percent of customers say that knowing a company’s data privacy policies is important before making a purchase. Companies working on personalization are likely to trigger privacy concerns, so proactively managing these will be important. That means showing customers that they take data privacy seriously.

Learn more about McKinsey’s Growth, Marketing & Sales Practice.

OK, but I’m starting from scratch. Can you be more specific?

As with most things, the hardest part of personalization marketing is getting started. Here are four steps companies can take to establish and scale digital personalization, without investing millions in IT:

  1. Use behavioral data to analyze customer journeys. Organize behavioral data by grouping customers, like say, mothers who shop for their children, or fashion-conscious young women who buy new private-label styles. Then understand the customer journey—that’s the series of interactions a customer makes with a brand, from initial consideration to repeat purchases. Combining segments and customer journeys creates microsegments, and that’s a step toward personalization.
  2. Listen to customer signals—and respond quickly. When customers provide signals about their intentions, marketers should be prepared to respond right away with a relevant message known as a “trigger.” Trigger messages can be any combination of images, copy, titles, or offers to match the situation. Developing the right trigger involves combining creative problem solving with analytics. For example, when a mother clicks on a product but hasn’t bought it, a next-product-to-buy algorithm based on machine learning could send a message suggesting a set of related products.
  3. Build a small, dedicated team. Empower a small group of the right people to transition the marketing department to a focus on personalized triggers. The team should be staffed with a fully dedicated campaign manager and creative, digital media, analytics, operations, and IT staff—and should have executive sponsorship to remove roadblocks. The team’s goal should go beyond page views and clicks to actual business results.
  4. Focus on processes and technology that help teams work faster. Agile processes are key here—they enable teams to quickly mix and match copy, creative content, and templates to find out what works and what doesn’t. Mistakes will happen, and that’s OK. Learn lessons and move on.

    The right automation technology is also needed to work at this pace. Too often, automation software spits out messages that customers perceive as spam. It’s the tech team’s responsibility to guide the tech stack to find signals and efficiently deliver triggers that work.

How about an example of personalization marketing in action?

Here’s how a personalization journey might work.

Mary is a mother with two children in primary school. Early last August, she visited a store to buy items for her kids, including several she’d previously viewed online. That’s signal one. The items she purchased were logged and attached to Mary’s profile in the store’s database.

This summer, almost a year later, Mary browses children’s clothes on the same retailer’s website but doesn’t buy anything. This interest combined with her purchases last summer together comprise signal two: Mary might be open to making her first back-to-school online purchase for her children this year.

Within 24 hours of browsing the clothes, Mary receives a trigger message: a personalized email offering a 10 percent discount on some of the items she’s been reviewing if she purchases them online. The message explains how to make the online purchase and suggests additional items she might consider based on her history with the retailer.

Learn more about McKinsey Digital.

I meant a real example.

Sephora is a great example of a real-world brand that has excelled at personalization. The beauty retailer has used personalized experiences that are truly omnichannel, encouraging shoppers to book in-store makeovers and fashion consultations via its online channels, particularly its mobile app. The app lets makeup artists log each product used in makeovers into each customer’s profile, and lets customers virtually try on products and receive personalized recommendations.

Sephora’s personalization program also demonstrates the effectiveness of focusing on the most loyal customers. The retailer’s loyalty program offers its highest-level members perks such as early access to new products, invitations to exclusive events, free custom beauty services, and more. And all of Sephora’s customer communications, no matter the platform, display the customer’s loyalty points.

The results speak for themselves. In 2018, members accounted for 80 percent of Sephora’s total transactions. As of 2020, the loyalty program had about 25 million members. And in 2022, for the fifth year in a row, Sephora ranked first in Sailthru’s Retail Personalization Index.

Learn more about McKinsey’s Retail and Growth, Marketing & Sales Practices.

How can brands scale personalization?

According to McKinsey’s research, four factors—or four “Ds”—drive personalization at scale. These four factors can be further broken down into eight core elements.

Data foundation

Data should be centralized and made available so activity in one channel can immediately support engagement in another—in real time or close to it.

  • Data management. Brands should develop a multidimensional view of the customer to serve as the backbone of analytics. Quality should take precedence over quantity; having the right data is more important than having lots of it.
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Decision making

Marketers should create an integrated decision-making engine that uses machine learning and AI models to score various propensities for each customer.

  • Customer segmentation and analytics. Segment customers, identify value triggers, and score customers accordingly.
  • Playbook. Create a library of campaigns and content that can be matched with customers.
  • Decision-making engine (campaign coordination). Develop a multichannel decision-making engine to prevent conflicting messages and drive maximum value per touchpoint.


Marketers should break content into small pieces that can be mixed and matched for maximum flexibility.

  • Cross-functional team. Assemble a cross-functional, co-located team to manage weekly deployment in a test-and-learn culture for faster results.
  • Talents, capabilities, and culture. Secure the right capabilities and talent, often starting out by setting the right ambition in leadership.


Integrate channels to coordinate communications and react to customer actions.

  • Technology enablement. An optimized technology platform can be complex; start with existing technology and make the most of its potential.
  • Test and learn. Don’t let the perfect be the enemy of the good; get started and iterate over time.

How will personalization shift in the near future?

Advances in AI, analytics, and data over the past few years have created new frontiers for marketers. But to capture the opportunities, marketers need to understand the three main shifts in personalization and build the skills to respond to them.

  • Physical spaces will be “digitized.” Deploying personalization beyond digital channels is a huge zone of opportunity, especially as physical stores continue to build back business in the wake of the COVID-19 pandemic. Offline interactions such as store visits could be the new horizon for personalization. Store employees can use insights from advanced analytics to provide customers with personalized offerings, and personal shoppers can use AI-enabled tools to improve service. Finally, facial recognition, location recognition, and biometric sensors will likely become more widely used.
  • Empathy will scale. Empathy is the basis of all strong relationships. Understanding social cues and adapting to them builds trust. And it’s not easy to do digitally or at scale. Machine learning is changing that. Sophisticated algorithms are allowing programs to extrapolate emotions from data more easily. Ultimately, these advances can help marketers respond to customers’ specific moods.
  • Brands will use ecosystems to personalize journeys. At present, various players contribute to a customer’s in-person experience—for instance, a shopping mall, a retail store, and a brand. Creating connections among these points is a big opportunity for organizations in the retail space to provide customers with more seamless decision journeys.

Learn more about McKinsey’s Retail and Growth, Marketing & Sales Practices and McKinsey Digital. Also check out personalization-related job opportunities if you’re interested in working at McKinsey.

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