The power of personalized marketing: How to engage customers at scale

Personalized marketing is the practice of using what a brand knows about an individual customer, including their behavior, preferences, context, and history, to decide what to say to them, where, and when, rather than sending the same message to everyone in a group.

What that practice involves has changed. It used to mean inserting a first name into a template and sending the same offer to the whole list. Now it means deciding, per person, which message they receive, on which channel, at which moment.

If your brand runs email, push, SMS, in-app, and web programs, this guide covers the data, tactics, and AI behind personalized marketing, with examples from a first personalized campaign to enterprise-scale programs.

TL;DR

• Personalized marketing means using what you know about an individual customer to decide what to say, where, and when, rather than sending one message to a whole group.
• Personalized marketing runs on four kinds of data, all held on one profile with consent stored alongside it, because channels reading from separate records contradict each other.
• Tactics differ by channel, from dynamic content and product recommendations in email to location and milestone triggers on mobile.
• AI does two separate jobs: generating the content variants a program needs in volume, and deciding which variant, channel, and time each person gets.
• Build in order, starting with one lifecycle moment and adding channels as the profile supports them, and measure everything against a control group.

What is personalized marketing?

Personalized marketing is the practice of using what a brand knows about an individual customer, including their behavior, preferences, context, and history, to decide what to say to them, where, and when, rather than sending the same message to everyone in a group. Marketing personalization describes the same practice, and the two terms are used interchangeably across the industry.

Every personalized message draws on three kinds of input:

• Identity. Who the person is, as they have told you: account details, stated preferences, language, loyalty tier, subscription status.
• Behavior. What they have done: pages browsed, items added to a cart, features used, messages opened, purchases made, sessions abandoned.
• Context. The circumstances of this moment: device, location, time of day, position in a journey, what they did 30 seconds ago.

Together, those inputs decide what the message says, which channel it arrives on, and when it lands.

This happens at two levels. Segment-level personalization groups people by a shared trait or behavior and sends that group one message. For example, lapsed buyers get a win-back offer and gold-tier members get early access. Individual-level targeting chooses each person's content, channel, and timing separately, so two customers in the same segment can receive different messages on different days.

Personalization should not be confused with segmentation. Customer segmentation is the act of dividing an audience into groups, and it’s just one method used for personalizing. True personalization is the final outcome: making a message relevant to an individual. While you can achieve this through segmentation, you can also reach it by applying rules to individual profiles or making one-at-a-time decisions for each customer.

Why personalized marketing matters, and what it costs to get wrong

Personalized marketing matters because customers ignore what isn't relevant to them. Each ignored message costs you something: customer attention in the moment, permission to keep messaging later, and revenue in between. Deloitte Digital's 2024 research found that brands with high personalization maturity were 48% more likely to exceed their revenue goals and 71% more likely to report improved customer loyalty than brands with low maturity.

Personalized marketing pays off in several ways:

• Relevance lifts response: A message tied to something the customer did outperforms one written for an average customer who does not exist. It shows up first in open and click rates, then in conversion.
• Fewer wasted sends protect deliverability and attention: Messaging only the people a campaign applies to means fewer irrelevant emails, fewer ignored push notifications, and less of the disengagement that erodes sender reputation.
• Consistent recognition builds retention: When the app, the inbox, and the SMS thread reflect the same understanding of a customer, the relationship accumulates instead of restarting with every campaign. If you're looking to grow, the ability to respond with relevance, at scale, shapes long-term brand growth as much as it shapes any individual campaign.

Yet personalization done badly can be more damaging than not personalizing at all. Watch out for these three common pitfalls.

1. Stale data: A profile that hasn’t been updated in six weeks will produce the wrong message, like recommending a product that the customer already bought or a store in a city the customer just left.
2. Over-personalization: Referencing something a customer did without realizing they were sharing data can come across as creepy surveillance. Consider whether the customer would recognize where the information you’re using came from.
3. Personalizing only on a single channel: If you’re personalizing emails, but the customer is also getting push notifications, they may feel the difference. It shows that your brand has the information, and isn’t consistently using it.

What data powers personalized marketing

Marketing personalization only works when it's based on meaningful signals. The right data helps marketers understand who someone is, what they care about, and how they interact across touchpoints. Here are the main types of data that make it possible.

• Profile and preference data: This includes who the customer is and what they’ve told you, including preferences shared directly through quizzes, surveys, or onboarding flows. Most of this arrives through a preference center, a page where customers pick the channels they want to hear from you, how often, and about what. It’s helpful to give customers specific choices. For example, someone who’d unsubscribe from a daily newsletter may stay for delivery updates. That consent makes the data usable, and first-party data from people who chose to give it beats anything inferred about people who didn't.
• Behavioral events: This includes what consumers browse, click, or tap, and how often they return. This is what indicates intent and journey stage: three views of the same product page within a week triggers a reminder notification that includes that product.
• Purchase and lifecycle data: This data shows what customers bought, the product worth, and where they sit in their relationship with your brand. When you know a customer’s replenishment cycle, then you know the timing for a message to prompt the next purchase as their supply runs out.
• Contextual signals: Use device, location, and time information to offer things like a prompt to visit a store within travel distance, or to hold back a push until the hour that a person usually opens your app.

That said, all of this data needs to live on one profile. If email and push read from different records, the same customer exists twice, and each record holds something that the other is missing. The customer then gets two versions of your brand: one that knows about yesterday's return, and one that doesn't. A single profile is also what makes customer segmentation workable, because it groups people by what they do rather than by what you assume about them.

Personalized marketing tactics by channel

Personalization looks different depending on the channel. The strongest marketing strategies understand that customers need a cross-channel, cohesive approach, so that their journey never feels disjointed. This means brands meet customers where they are, adapting content, timing, and delivery based on behavior, context, and preferences across email, mobile, web, and in-app experiences. The tactics below are grouped by channel, with the data each one needs.

Personalization strategies in email marketing

Email carries the most content and tolerates the most variation, which is why most personalized marketing campaigns start here.

• Dynamic content blocks: One template with modules that resolve differently per recipient, so that loyalty tier, region, or category affinity changes what renders. Needs profile data and a template system that supports conditional logic.
• Product recommendations: Items are chosen from browsing and purchase history rather than a merchandising calendar. Needs a product catalog connected to behavioral events.
• Behavioral triggers: Browse abandonment, cart abandonment, price drops, and back-in-stock alerts, sent from what the customer did rather than on a schedule. Needs event data ingested quickly enough to still be accurate when the message sends.
• Send-time optimization: Choosing each recipient's send hour from their own open history instead of picking one hour for the list. Needs enough message history per person to detect a pattern.

Best for: teams sending their first personalized campaigns, such as a retail brand growing its subscriber list and launching a loyalty scheme. There's more on the thinking behind personalized email marketing if that's where your program lives.

Personalization strategies in mobile marketing

Push notifications and SMS are interruptive, so relevance carries more weight here than anywhere else. Both messages are also short, which means personalization is more about who gets the message and when, than in the copy.

• Location-aware messaging: An offer tied to the store, venue, or city the customer is near. Needs location permission and consent.
• Milestone and status triggers: A message when a streak breaks, points expire, or a delivery reaches a new stage. Needs event data and a clear trigger definition.
• Behavior-based re-engagement: A reminder built from what the person already did, like the music they've listened to before or the category they browsed last week. Needs behavioral history tied to the profile.
• Frequency control by person: Capping how often an individual receives a push based on their own engagement, rather than applying one cap to everyone. Needs per-user message history.

Best for: teams already running lifecycle journeys and ready to act on behavior as it happens, such as a streaming service winning back lapsed subscribers before they cancel. Anything that fires from what a customer is doing right now depends on real-time personalization and how fast that data arrives.

Personalization strategies for web marketing

The web is where anonymous and known visitors mix, so tactics here often have to work before you can identify who someone is.

• Visit-history content: Showing a different homepage module to a fourth-time visitor than to a first-time arrival. Needs session history, which can be anonymous.
• Progressive profiling: Asking for one preference at a time across several visits instead of a long form on the first. Needs a consented identifier to attach answers to.
• External context: Pulling data from outside your systems, such as local weather or live conditions, into the page or message as it renders, without building a separate campaign per location. Needs an integration that can fetch and render live values.

Best for: teams whose website and messaging already recognize the same customer, such as a travel brand where someone browsing destinations on a laptop gets a follow-up that reflects what they looked at. Retail sites layer catalog, merchandising, and checkout mechanics on top of all this, which is where ecommerce personalization becomes its own discipline.

Personalization strategies for in-app messaging

In-app messages reach people who are already engaged, which makes them better suited to guidance than to promotion.

• Onboarding paths by observed behavior: Branching what a new user sees based on the first action they take, not the segment they were assigned at signup. Needs event data from the first session.
• Feature prompts tied to use: Introducing a capability at the point it becomes relevant to that person. Needs feature-level usage data.
• In-session offers: A message that appears while someone is browsing a category, reflecting what they're looking at. Needs behavioral triggers that fire during the session.

Best for: teams with enough event tracking to tell one person's path from another's, such as a fintech app guiding a new user toward the feature they came for.

Personalization strategies for WhatsApp and conversational channels

WhatsApp, RCS, and similar channels carry rich media and support two-way exchange, which suits updates and questions customers reply to.

• Transactional updates with context: Order, delivery, and appointment messages that carry the specific detail the customer is waiting on. Needs order or booking data connected to the profile.
• Reply-driven follow-ups: Branching the next message on what the customer sent back. Needs conversation state stored against the profile.
• Template variants by segment: Approved templates with variables that change by language, market, or status. Needs consent captured per channel and templates cleared in advance.

Best for: teams already personalizing across several channels and ready to add one with its own rules, such as a delivery brand handling order updates and the questions that follow them.

How AI changes personalized marketing

AI does two separate jobs in a personalization program, and treating them as one thing is the most common source of confusion about what these tools do.

Generative AI makes content variants. It writes subject line variants, drafts body copy for different audiences, cuts a message down for a shorter channel, and produces image variations. Volume is the reason it matters. Eight segments, four lifecycle stages, five channels, and you're looking at 160 pieces of content before anyone has written a word. Most teams simply don't have the hours, so they personalize less than they meant to.

AI-driven selection decides what gets used. Given a set of variants, it chooses which one an individual receives, on which channel, at which hour, based on what happened when similar choices were made before. Every send produces an outcome, that outcome feeds back in, and the choices sharpen as evidence accumulates. Producing content and choosing between content are different capabilities, and it's the decisioning that moves a program from segment-level to individual-level.

Of course, both families of AI have limits. Generated content needs human review and brand guardrails so that what goes out is accurate and sounds like you, and selection needs volume and a measurable outcome to learn from.

What selection saves you is in the rule writing. Someone has to decide which message lapsed customers get, on which channel, at which hour, then keep that decision current as the catalog and the audience change. Do that for every segment and the rules pile up faster than anyone can maintain them. Selection makes the choice based on what happened last time, and decisioning keeps the program running.

How to build a personalized marketing strategy

You don't need to personalize everything at once when you’re building a personalized marketing strategy. A strong strategy starts by focusing on the moments where relevance has the most impact, then scaling from there with the right data, tools, and internal support. What you can do next depends on how mature your program is, not how big your company is.

1. Unify the profile and get consent right

Start by bringing your consumer data into one place, so that every system that holds a piece of it feeds the same profile. Store channel preferences and consent on that profile too, including what each customer agreed to, when, and through which channel. Check legal requirements for consent as well, because they can differ between channel, country, and state, and as you scale, non-compliance risks get higher. Audit what you have before you start to build anything, and fix any siloed data, as that creates the foundations you'll move forward with.

2. Start with one high-value lifecycle moment

Map the turning points in your lifecycle marketing, like first visits, sign-ups, purchases, drop-offs, and renewals. These are the moments where relevance makes the biggest difference. Then choose one, such as a welcome, first purchase, or first sign of lapse. Choose the one with a clear success metric and enough volume to teach you something within a few weeks. You'll learn more from personalizing that single moment specifically than from personalizing multiple moments at once.

3. Add channels only when the profile supports them

Match the channel to the job. Email is ideal for detailed content, while in-app messages work well for guidance and feature education. Push notifications can drive urgency, and SMS is great for time-sensitive offers or confirmations. Add a channel because your customers are already there, not to have one more. Before you do, check that it can read the same profile as the others. More messages across more channels can start to feel invasive, so set frequency caps per person and hold to them. The unified profile you built in step one does the rest of the work here, keeping the same customer from getting a message that contradicts one they received an hour ago, or an offer for something they bought last week.

4. Move from rules to selection as volume allows

Use dynamic content blocks, conditional logic, and send-time optimization to tailor the experience without building dozens of variations by hand. Stay with rules while you have few variants and a clear reason for each. When the combinations outgrow what your team can keep current, and you have enough traffic for outcomes to mean something, hand the choosing over to selection. Expect to do this channel by channel rather than all at once.

5. Measure with control groups

Treat personalization as something you keep testing. Use A/B tests, multivariate experiments, and continuous experimentation with AI to compare personalized and non-personalized experiences. Always keep a holdout, because without one a rising conversion rate could be the personalization, the season, a price change, or the campaign that ran the week before. Lift measured against a control group gives you a more accurate picture of what drove a sale.

Personalized marketing examples from consumer brands

From sports leagues to global retailers, examples of personalized marketing look different for every brand. But the underlying goal is the same: create value by making each interaction count. These five brands found ways to do that, across retail, travel, quick service, sport, and ecommerce, and published the results.

Retail: Sephora SEA wraps up a 132% purchase lift

Sephora SEA is the Southeast Asia division of the beauty retailer, which operates more than 2,600 stores across 34 countries and carries close to 300 brands.

The challenge

In a saturated beauty market, a discount alone carries little signal. The team needed something more memorable to stand out over Lunar New Year, one of the busiest promotional periods in its Malaysia market.

The personalization strategy

Sephora SEA built a campaign around the Malaysian tradition of giving Ang Paos, or red packets. Between January 15 and 17, on the eve of the 2020 Lunar New Year, customers in Malaysia received a virtual red packet at noon as an in-app message. Tapping it revealed a prize that differed from customer to customer, from loyalty points to a cash voucher to a Dyson hair dryer, pulled in by an API trigger as each person opened their packet. The message itself was built as custom HTML and connected to a Google Sheet through a JavaScript API. Once someone had won, a second in-app message invited them to fill in personal details, which were added to their profile for later campaigns.

The results

• A 132% uplift in purchases between customers who played the campaign and those who didn't, tracked over 30 days
• Most of the increase came from people who won the lowest-value prizes, which points to the game driving purchases rather than the incentive
• One campaign built and launched within a month, using in-app messages alone

Best for: growing programs that want to personalize the experience itself rather than the offer inside it. By collecting profile data while it runs, the next campaign starts with even more to work from.

Travel & hospitality: Luxury Escapes upgrades its members and lands at 142% of the signup goal

Luxury Escapes is an Australia-based travel brand with more than eight million members worldwide, built on delivering value with every booking.

The challenge

The brand launched LuxPlus+, a paid VIP travel club, and needed every offer in its emails to show the right thing to the right reader. Members needed to see the benefits they'd paid for. Non-members needed to see enough to want those benefits. And subscribers in the 27 markets where LuxPlus+ hadn't launched had to be excluded.

The personalization strategy

Luxury Escapes rebuilt its email suite so that each offer block had three versions, decided by membership status, with Braze Catalogs controlling whether LuxPlus+ appeared at all in a given market. Advanced Liquid Logic pulled in each subscriber's status at the moment of send to pick the right version. Members saw member-only pricing, bonus inclusions, early access, and hidden offers. Non-members saw the same trips framed around what joining would unlock. Cloud Data Ingestion connected the setup to the company's data warehouse, and Content Blocks meant a single Catalog update changed every email at once, rather than template by template.

The results

• 142% of the membership signup goal reached in the first month of the campaign
• A 10% increase in clickthrough rate on the daily newsletter emails
• A 100% increase in CTR on the LuxPlus+ launch email, a full database send, compared with the benchmark for other full database campaigns

Best for: expanding email programs personalizing by customer status, where one offer needs to say different things to members and non-members. By reading status at the moment of send, the message stays right even for someone who joined that morning.

QSR: Dutch Bros brews a 230% ROI increase, one order at a time

Dutch Bros is an Oregon-based drinks brand with more than 800 locations across the US, and it considers itself to be in the relationship business first and the coffee business second.

The challenge

Fragmented channels and platforms were getting in the way of consistent messaging, pushing costs up and producing experiences that didn't match the in-person one. The CRM team wanted to speak to each customer individually at scale, and to simplify a tech stack full of overlapping tools while doing it.

The personalization strategy

If two people open the app while standing in the same line, Dutch Bros wants them to see different things. The team built campaigns across email, SMS, push notifications, and in-app messaging using Braze Canvas, with product recommendations drawn from each customer's purchase history and app behavior, including tipping, loading stored value, and digital badge engagement. One group was offered blended drinks and another iced, based on individual taste profiles. Braze Catalogs pulled in the relevant products for each participating location, and messages were further personalized by customer name and the location that person visited most within a given timeframe.

The results

• A 230% increase in ROI from CRM campaigns
• 31% cost savings from platform consolidation, after eliminating multiple software subscriptions and overlapping functionality
• Higher engagement and retention rates, with more app engagement and revenue from the personalized campaigns

Best for: large-scale programs personalizing on purchase history across several channels at once. By drawing every message from the same record, the recommendations agree with each other wherever they land.

Media & sports: The NBL sinks every ticket with an assist from fan data

The National Basketball League has been building Australia's basketball audience since 1979, with a stated ambition to become the biggest, most engaged global fan base in Australian sport.

The challenge

Scottie Pippen was arriving as the league's season launch ambassador, and the NBL had premium meet-and-greet tickets to sell on a short turnaround. Traditional marketing wasn't moving them. A second goal complicated things further: a competition to win one of Pippen's signed Chicago Bulls jerseys, aimed at die-hard basketball fans who weren't in the NBL database at all.

The personalization strategy

The team passed its existing Google Cloud Platform user data, including lifestyle segments and other customer attributes, into Braze to build segments and messaging matched to each one. Braze Audience Sync then exported those audiences straight into Meta Ads Manager, so first-party customer data could be combined with Meta's native interest targeting to build more relevant ad sets. For the jersey competition, the team excluded anyone with a known email address from receiving ads, so the spend went only toward fans who weren't already on the database. The campaign ran on email and mobile app messaging alongside the ads.

The results

• 100% of event tickets sold for the Scottie Pippen campaign events
• More efficient ad spend, by targeting audiences better matched to each goal
• 25 hours of team time for the whole campaign, including ticketing and competition landing pages, creative, and copywriting, with the Braze component taking around 20 minutes

Best for: mature programs extending cross-channel personalization beyond owned channels into paid ones. By letting the same segments decide who sees an ad and who doesn't, first-party data becomes useful for finding customers as well as keeping them.

On-demand: Bloom & Wild pulls Mother's Day messaging and watches goodwill grow 5X

Bloom & Wild launched in 2013 and has become the UK's leading ecommerce flower delivery service.

The challenge

Mother's Day is one of the biggest commercial moments in the flower business. For some customers, though, the holiday is a painful reminder rather than a shopping occasion, and they’d shared with the brand that they preferred not to be messaged about it.

The personalization strategy

Ahead of its 2019 Mother's Day campaign, Bloom & Wild emailed every customer a link to a simple opt-out landing page. Anyone who chose to opt out was added to a single segment and removed from the upcoming campaign, so the messaging never reached them. The approach worked well enough that the brand repeated it for Father's Day, Valentine's Day, and Grandparent's Day, then launched the Thoughtful Marketing Movement in 2020, a pledge more than 170 businesses have signed.

The results

• Positive feedback through phone calls and emails spiked to 1,500 responses, five times the brand's average
• Close to 18,000 customers opted out of the 2019 Mother's Day campaign
• Interactions on Twitter (now X) rose from an average of 4% to 5% up to 20% on the day the opt-out campaign began, an increase of more than four times

Best for: programs at any maturity level, including teams running their first personalized campaigns. By personalizing through what you withhold rather than what you tailor, a campaign can still respect a sensitive moment.

How Braze helps teams personalize at scale

Adding channels, segments, and variants can turn a working personalization campaign into an unmanageable program. That's why the more you scale, the easier your personalized marketing platform should make it for you.

Why choose Braze as your personalized marketing platform?

The data layer is warehouse-native, so personalization runs on the first-party data already in your warehouse or CDP with zero-copy access, and no migration project between the plan and the first campaign.

Execution is native across email, push, SMS, in-app, WhatsApp, and web, rather than bolted on channel by channel. Creative Studio centralizes assets, templates, and reusable content blocks. BrazeAI™ personalizes what goes out across all of it, including content, offers, channel, timing, and frequency.

For enterprise programs, BrazeAI Decisioning Studio™ makes 1:1 decisions for each customer, optimizing toward the business results you care about rather than clicks and opens. Results are tracked as uplift against a random control, holdout, or business-as-usual group.

Guardrails run through all of it. Brand guidelines can be applied to content created in the platform, and BrazeAI Decisioning Studio™ shows which customer attributes drove each decision.

See how consumer brands personalize every message across email, push, SMS, and in-app with Braze.