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How to Use Personalization to Grow in 2022
This playbook was created in partnership with mParticle—the customer data platform (CDP) powering Venmo, Airbnb, and Gymshark. DC community members can get up to one year free.
Data. Privacy. Personalization.
Businesses used to skate by without putting much thought into these three words. In the 2020s, that doesn’t work anymore.
Data is now the lifeblood of consumer-focused businesses. And the tools that use that data are companies’ vital organs.
Smart companies are using data to personalize their interactions with customers in a way that enhances their experience while also respecting their privacy. But we don’t need to tell you how important personalization is.
Instead, we can simply look at Spotify Wrapped—Spotify’s annual year-in-review slideshow highlighting users’ listening stats.
The mechanics are simple: Spotify collects user data and then uses it to create a personalized experience with curated “Top Songs” playlists and other interesting insights.
It’s a big deal because:
- In 2019, Wrapped playlists earned almost 3 billion streams. And roughly 1.2 million tweets mentioned the campaign within its first month of launching.
- In 2020, the campaign increased Spotify’s downloads by 21% in the first week of December.
The takeaway here: Personalization leads to engagement, and engagement leads to growth.
In fact, today’s customers expect personalization. According to a 2021 McKinsey report, 71% of consumers expect personalized experiences from companies. And when companies fail to deliver, an even larger number (76%) get frustrated.
In other words, personalization is no longer a “nice-to-have”—it’s the new standard for customer experiences.
Personalization and growth
Spotify has millions of daily active users, engineering teams that can figure out what data to collect and how to get it, and growth and product teams that know how to use that data to grow.
Most businesses don’t have these resources. But with the right setup, any business can collect and leverage data for personalization. We’ll show you how in this playbook.
First, let’s define personalization.
Personalization means respectfully collecting relevant user data and then using that data across different touchpoints within the marketing and sales funnel. A few examples:
- Last week, you browsed sweaters on Patagonia’s site. Today, you go back to the site and see tailored sweater recommendations based on the products and colors you eyed last time.
- You order a bag of coffee beans from your favorite coffee shop using its app. Two weeks later, as your supply is running low, the app sends a push notification with a 10% off promo code.
- You’re having trouble accessing a feature on the SaaS tool you use for work. Since you’re a premium-tier customer, you’re routed straight to a customer service rep and get your problem solved quickly.
Besides creating a better customer experience, personalization also drives 10-15% lifts in revenue. The effect is even higher—as much as 25%—for some online companies, like DTC brands.
Why does this happen? Personalization creates a flywheel effect that drives customer lifetime value (LTV) and loyalty. It amplifies marketing spend and ROI, especially when invested in for the long term.
Simply put, companies that use personalization tactics win more customers and grow faster than those that don’t.
Still, many companies and startups struggle with personalization, especially when it comes to more granular tactics. Unless your company has loads of data (like the Spotifys, Amazons, and Netflixes of the world), personalizing beyond basic info like someone’s name or location can feel difficult.
Not to mention, companies like Apple and Google are increasingly limiting marketers’ ability to use customer data. Consumers’ (rightfully) growing privacy concerns mean that companies must work harder to be respectful and avoid coming across as creepy or intrusive.
This playbook tackles all of these challenges.
We’ve worked with data and personalization experts to understand how you can use customer data to improve conversions without breaking privacy laws or anyone’s trust. In other words, we’ll show you how to use personalization to grow in 2022.
Specifically, we’ll cover the three key pillars of personalization:
- Privacy—why data governance and compliance are important, and the type of data needed for optimal personalization.
- What tools you can use to collect customer data, and how to maximize their value.
- Then on to the fun part—we’ll show you how to execute personalization tactics across different channels, including email, ads, and SMS.
Let’s dive in.
Privacy and data governance
Privacy should be viewed as a fundamental human right.
While it might seem contradictory to advocate for privacy in a playbook about personalization, privacy and personalization actually go hand in hand. Consumers value both—think of them as two sides of the same coin.
The key is personalizing data in a way that respects customer privacy. This all comes down to having a well-defined data governance strategy—the policies and rules about what type of data you collect, how you collect it, and how you manage it.
Your strategy should follow these three guidelines:
- Collect customer information responsibly. Customers are increasingly wary of how companies collect and use their data, so you need to be transparent about it. Tell people how you do it and why.
- Adopt a customer-centric mindset. There’s more to data governance than simply following regulations like the GDPR and CCPA. Rather than thinking of data in terms of legal compliance, you’re better off embracing a customer-first mindset all around.
- Don’t overstep. Respecting your customers’ privacy will earn you trust. Break that trust and you’ll find it hard to recover.
Personalization with both privacy laws and customer boundaries in mind will ultimately position your company for faster, more consistent growth.
What kind of data should my company collect?
To avoid breaking the law or compromising your customers’ trust, you should collect only the data you need and use tools that manage this data responsibly.
For starters, here’s a quick breakdown of the different types of customer data:
- First-party: Data that customers provide directly to you on your channels, like your site or app. This includes info given directly and intentionally, like forms and surveys, plus behavioral data from customer interactions like email engagement and website activity.
- Second-party: Another organization’s first-party data. You can buy this from another company without any middleman.
- Third-party: Customer data collected and managed by data aggregators that don’t directly interact with customers. They buy first-party data and combine it into one dataset that they then sell on marketplaces and exchanges.
Since first-party data comes straight from your audience, it’s the most accurate and relevant type of data for any business. And because privacy regulations like the CCPA and GDPR restrict the use of third-party data, first-party data is quickly becoming the only viable option.
Not to mention, it’s often (though not always) the type of data that customers are comfortable with you having. Knowing that you’re working only with the information they’ve provided, customers will appreciate your personalization rather than feel violated.
At this point, we’ve covered how personalization can drive growth, and why privacy matters. Now we’ll explain what tools can help collect and manage your data, and then execute personalization tactics to grow your business.
How to collect and manage customer data quality
Great personalization hinges on clean and accurate customer data—but data is notoriously hard to collect in a clean manner. Since many companies have data coming into their ecosystem in all sorts of ways, it’s tough to keep it standardized.
Imagine trying to work with bad data:
Your marketing campaigns would come off as irrelevant or out-of-date. Your analytics would be wrong. More than likely, your entire digital strategy would fail.
The trouble is, without a strong infrastructure in place, the actual data collection process can quickly lead to “data chaos”:
- Different departments like product, marketing, and customer support might collect the same data in different ways without sharing it. So you wind up with data silos—separate, inconsistent collections of information.
- On the customer side, users toggle between logged in and logged out states, anonymous and known. They might even engage across multiple devices.
The thing about data is that it creates compounding effects over time. So while high-quality data improves performance, poor data quality increasingly degrades it.
So if your data quality isn’t improving, it’s degrading.
The good news? There are tools that can help you manage your customer data. Using these, you can deliver personalized experiences that resonate with your users—without violating their privacy.
CDPs as a foundation for personalization
Customer data platforms (CDPs) collect and consolidate customer data from a variety of sources like websites and mobile apps in real time. They create and update customer profiles that can power marketing and analytics tools instantly.
By collectively using customer data across all of your tools rather than relying on a series of one-off integrations, a CDP can be the source of truth when it comes to customer data.
Note that a CDP isn’t the same as customer relationship management (CRM) software like Salesforce or HubSpot. Here’s how they differ:
- CDPs unify fast-moving data from different online and offline channels, devices, and software tools to create complex profiles of individual customers. They can identify people you’d otherwise have very scattered information about, or who you might think of as separate users.
- CRMs focus primarily on collecting first-party customer data from sales and marketing. They’re often used to help salespeople (not just marketers) understand known contacts so they can deliver more customized interactions.
CRMs can keep your sales data organized, and they’re helpful in many marketing use cases, too. But if personalization is the goal, you can’t beat a quality CDP. That’s why we’ll focus mostly on using CDPs to not only gather data but use it for actionable insights.
The best CDPs help with this by capturing clean, real-time first-party data at the source, storing it, and delivering it across tools. There's less of a need for second- or third-party data enrichment since the first-party data is clean, organized, and centralized.
But not all CDPs are privacy-forward. We’ll help you make sense of this.
Should my company invest in a CDP?
A CDP helps capture quality, real-time data on your site and mobile apps, plus data from other partners and vendors—like your email marketing platform and your analytics tools. So you can easily see how different personas and audience segments are interacting with your business.
It’s a must for any company that wants to build and maintain a relationship with customers over time.
Most companies we’ve helped grow fall into one of three stages when it comes to data strategy:
- Beginner: Startups that lack a formal first-party data strategy. At this stage, companies should consider an analytics tool like Indicative to measure usage and create unified customer journeys.
- Informed: Startups in the process of creating a holistic data strategy. There’s clear direction, leadership is thinking about standardizing data, and there are dedicated team members working on data management. Companies at this stage acknowledge the challenges around data and want to execute it better.
- Advanced: Startups that have a full-blown data strategy and are already running a high volume of quality experiments. Companies at this stage are almost definitely already using a CDP.
If your company is moving into or already at the informed or advanced stages, you’ll benefit from having a CDP. But which one should you use?
There are a handful of CDPs on the market, but we recommend mParticle.
There’s a common misconception that setting up a CDP is complicated—but that’s not the case with mParticle. Installation is simple: it involves a single API for tracking customer data so you don’t need to swap out code again and again.
All you need to get started is a simple data strategy that answers the following questions:
- Goals: What business outcomes are you trying to achieve?
- KPIs: What KPIs map to these goals?
- Tools: What tools will you use to accomplish these goals?
- Data: What data needs to be collected to power these tools?
- Segmentation: How do you want to use customer segmentation and what data needs to be collected to segment successfully?
While some CDPs only store and process data, mParticle enables you to automate complex marketing processes using rules and conditional logic.
What’s more, mParticle takes care of privacy with its data collection and identity resolution processes. Because it records all opt-ins and opt-outs, plus other contact points and preferences, you’ll never hit someone with an unwanted piece of messaging. So you’ll spend far less time managing privacy regulations.
Let’s say you run an ecommerce company.
Your main goal is to increase conversions, so you plan an email campaign for users who’ve performed a specific set of activities like adding products to their cart.
You use your email marketing tool to create and execute the campaign, looking at email marketing KPIs like click-through rate (CTR) and conversion rate to track its results. Depending on which (if any) link your subscribers click in the first email you send, you’ll segment them to personalize the email flow they’ll get.
Pretty straightforward campaign, right?
Here’s the catch. Without a CDP, you’d run this campaign directly in your email marketing tool. But all of the data you collect would be stuck in your email tool—you wouldn’t be able to use it across the rest of your marketing stack. Most of the data would go to waste.
But with a CDP, you’d collect the relevant data from the campaign and send it to all of your other systems—your analytics tool, business intelligence system, and data warehouse. You’d have a real-time profile for each of the subscribers in your campaign. And you could easily share customer insights with other tools like Google Analytics and Intercom.
CDP-compatible marketing and measurement tools
CDPs collect and streamline first-party data from all the other tools in your marketing stack so you don’t have to deal with scattered pieces of info. Here are some popular platforms that we recommend and that commonly integrate with CDPs.
Personalization tactics—how to use customer data
How your customer data is managed ultimately determines your business’s personalization opportunities. So for more effective personalization, you simply need a CDP like mParticle and a marketing tools stack to support it.
Now we’ll dig into the next step: Using your collected data to personalize your marketing.
As you think through personalization, consider these three important questions:
- What data do I currently have?
- What data can I get?
- Where can this data be applied?
The rest of this playbook covers tactics you can use to ethically leverage customer data and create a personalized experience for more conversions.
We say ethically because all of these tactics index on using a privacy-forward CDP to execute.
Below, we’ve categorized different tactics based on channel—but this actually brings us to our first personalization tip. Use customer data to prioritize which channels and touchpoints to focus on.
Different personas have unique preferences for how they engage with brands, and CDPs can capture these preferences. So look at the channels your top customers and other segments use most. If your top customers engage more with your emails than via SMS, prioritize email personalization.
Here are the different channels we cover:
Customize on-site search results
Best for: Ecommerce brands
How it works: If you’ve ever collected customers’ demographic information (whether from email surveys, user profiles, or something else), you can easily use it to personalize users’ search results on your site. This way, shoppers can find what they’re looking for even faster.
Example: Because of its running and training apps, Nike has both demographic info and workout data about its customers.
So if a woman who uses the Nike Run Club app searches “shoes” on Nike’s website, the results could return women’s road running shoes rather than men’s shoes or shoes for another activity.
Personalized search results make for a better shopping experience—people don’t have to scroll through irrelevant products to find what they want. And if they’d rather not, they can always opt out of these targeted product recommendations on Nike’s app or website.
Show in-stock merchandise based on customers’ preferences
Best for: Ecommerce brands
How it works: By looking at purchase history, CDPs can capture granular info about customers’ shopping preferences, like their most frequently purchased sizes. Using this data, you can personalize how products are shown. For instance, you could arrange the order of your products based on customer interest or make it so that only in-stock products that match a customer’s size (or another preference) appear.
This shortens the typical buyer’s journey because customers won’t spend time browsing all of your items. Instead, they’ll only see those that are in stock and most relevant to them.
Example: A sports equipment retailer like Academy Sports + Outdoors could create and implement a point system—using a CDP—to understand which sport (or category of products) shoppers are most interested in. Using this system, a purchase would count as a point toward a specific sport. So a customer’s shopping history could generate data output like this:
- Tennis: 5
- Cycling: 3
- Volleyball: 1
- Golf: 0
Academy could use this data to personalize the order of products shown on its site, placing tennis equipment ahead of golf equipment. It could also remove from display any currently unavailable products that match the customer’s interests so that customers only see the in-stock options at hand.
The result: less friction for shoppers since out-of-stock products and products they’re not interested in are automatically removed from display. And with a smaller chance of being overwhelmed by decision fatigue, shoppers may be more likely to convert into repeat customers.
Upsell and cross-sell only to high-AOV customers
Best for: B2C companies and any company where repeat customers are possible, e.g., ecommerce shops, food delivery apps
How it works: Average order value (AOV) is a strong indicator of how much a customer is willing to spend on an order. So if a customer has a consistent history of ordering above a certain threshold, you could automate upsell and cross-sell offers on the checkout page whenever their current order is below that amount.
Example: The meal delivery company HelloFresh could categorize recurring customers based on AOV, with those spending an average of $150 labeled as high-AOV customers. Whenever returning customers are about to check out with a smaller order, HelloFresh’s site could automatically upsell them with gourmet protein options or add-ons like sides and desserts.
This kind of personalization wins for two reasons:
- You’ll drive more revenue from a proven source—your high-value customers.
- You’ll create a better shopping experience for customers with a lower AOV. This personalization avoids futile cross-selling or upselling attempts with these customers.
Give product fit recommendations
Best for: Ecommerce brands, especially apparel companies
How it works: Online-only apparel companies often face more issues with incorrect sizing than their brick-and-mortar counterparts. While size charts and quizzes help customers get a better idea of fit, they often don’t account for differences in wearing preferences. Some customers prefer loose-fitting clothing while others prefer slim fit. In this case, two customers of the same proportions might choose different sizes for comfort.
To reduce return rates, companies can use customer data to find out their wearing preferences and give tailored recommendations. For instance, it can specify on its product pages how true to size products are and give personalized messages like “Buy one size up for a more relaxed fit.”
Example: When shoppers create a user profile for the apparel brand Lulu’s, they have the option to enter fit details like their body type and measurements.
Combined with their purchase history and size chart, Lulu’s can better understand shoppers’ fit preferences. So as customers browse its product pages, a popup might appear to suggest, “Even if you usually buy M, we’ve found this fit to look great in one size up.”
How does this level of personalization benefit growth? It reduces the likelihood of returns, especially among “serial returners” who buy multiple sizes of the same product to identify their best fit. Over time, customers might even develop greater trust in a company’s products and sizing suggestions.
Send hyperspecific email offers based on buying patterns
Best for: All companies
How it works: One of the most useful functions of a CDP is to consolidate data from all of your marketing tools in a centralized location. This way, you can easily see comprehensive customer profiles instead of trying to put together scattered info from separate marketing and analytics tools.
You can then use these complete profiles to create more targeted email offers to granular slices of your audience—like segments based on customers’ purchase frequency and time of purchase.
Example: Apart from different levels of enthusiasm, fans of professional sports teams come from all kinds of backgrounds—location, age, income, etc. But without a CDP, it’s hard for teams to manage or even collect customer data since customers bounce between team websites and ticket marketplaces like Ticketmaster.
Using a CDP, marketers for a specific sports team can identify individual fans’ preferences for attending games. This way, they can create and send hyperspecific email offers. For example:
- One-time ticket buyers receive promotional deals that don’t apply to season ticket holders, like free drink specials.
- Fans with a history of buying tickets for weekend games only receive offers for weekend games.
- Male fans receive emails about “Guys Night Out” while female fans receive emails about “Ladies Night Out” deals.
Consolidating your data and then using it to segment your audience ultimately reduces the likelihood of sending redundant or irrelevant offers. And since people will find value in your emails, they’ll actually open and read them, rather than ignoring them or unsubscribing. You’ll see more conversions as well as improved email campaign results.
Tailor and send messages based on company size and user role
Best for: B2B companies, companies that serve different tiers of customers, products with a variety of use cases
How it works: Not all customers find value from your product in the same way. Because of this, a general onboarding email sequence may be ineffective in showing users how to get the most from it.
This is especially common among B2B software companies that serve different industries—consider how educators, software developers, and finance professionals all use Zoom. In cases like this, info like company size and a user’s job title can be used to create and send more targeted content.
Example: During the signup process, a software company could ask new users for information like the size of their company and their department. For instance, the project management software Clickup asks users how many people they’ll be working with.
The company could then send tailored messaging based on this info, plus additional user behavior that gives clues on what users are looking for. In Clickup’s case, here’s how that might look:
- For people who indicate that they’re working alone, ClickUp could send emails with resources like ClickUp for Freelancers. People working in teams, on the other hand, might receive emails with collaboration tips, like ClickUp’s Teams: How to Create User Groups article.
- ClickUp could automatically enroll new users in email workflows based on their job role, with different sequences for recruiters, product managers, software developers, etc.
- Specific actions could trigger even more targeted emails. For instance, as product managers explore different task features, ClickUp could automate a message about creating work sprints for product management.
Targeted content that caters to a user’s role and their purpose for using your product helps to maximize their experience with it. They’ll be more likely to stick around, unlike users who miss out on important features because they’re not applicable to every audience.
And besides retention, an improved user experience can even drive more referrals—satisfied customers will be happy to share your product with friends and colleagues with similar needs.
Match your retargeting ads to the content users visit
Best for: Companies that invest in content marketing
How it works: Whether it’s SEO or virality, many businesses invest in content to drive website traffic and improve overall awareness of their brand. Depending on the number of unique personas your company serves, you can use customer insights from your content to enhance your retargeting campaigns.
Specifically, you can create retargeting ads that match and complement the content that users visit, and then only target those users.
So you won’t just have one mass retargeting campaign that tries appealing to everyone who’s ever visited your site—instead, you’d have targeted ads for each unique visitor segment.
Example: The SEO content strategy for a B2C company that sells supplements for joint pain might involve creating blog posts targeting different customer personas based on sport. For instance, there might be separate posts about joint pain for weightlifters, golfers, and runners. The idea here is to attract a wide variety of visitors suffering from joint pain using niche content.
Using insights from traffic analytics, the same company could create separate ads for each persona and then retarget users based on the blog posts they visited. So someone who read a blog post about knee pain while golfing might only see ads specifically about joint pain in the context of golfing—like the ad on the right below.
This is far more effective than a generic, catch-all retargeting ad like the one on the left. After all, each of your customer personas has unique needs. By speaking to their individual preferences, personalized retargeting can help drive more conversions.
Test a wider variety of retargeting ads for lapsed customers
Best for: All companies
How it works: Not all inactive customers are unsatisfied with your company. Research suggests some lapsed customers may love and even identify as being loyal to your brand, but shop elsewhere because of convenience, budget, or other reasons.
Because of this, a “We miss you!” ad with a discount may not be enough to reactivate them.
Beyond customers’ order count or last date of purchase, your retargeting ads should also consider additional data points that factor into a customer’s decision to buy from your business again. This could be location or type of product, for instance.
While it’s difficult to pinpoint a customer’s exact reason for lapsing, CDPs make it easy to create detailed audience segments and test retargeting ads more efficiently.
Example: A local business might have lapsed customers because of physical distance—either because their customers have moved or they simply don’t want to drive so far when closer alternatives are available. It may even be that when they do make the trek and shop there, it’s crowded or the lines are long.
In this case, it’s worth testing several retargeting strategies based on your customers’ location. For instance, you could retarget customers within your region with ads that:
- Emphasize low shipping fees or free delivery within a certain mile radius
- Promote local pop-ups in the same city as your customer
- Offer curbside pickup as a more convenient way of shopping
Doing so can help shed light on why different customers lapsed—and what offers might compel them to return. You’ll also be able to gather more customer insights on the value props that speak to your audience, which you can then use to optimize your business’s messaging.
Create and update complex negative audiences instantaneously
Best for: All companies
How it works: To get a better return on ad spend (ROAS), marketers often create negative audiences for their ad campaigns. Negative audiences are groups of people you don’t want your ads to reach, whether because they’ve already bought something from you or they simply don’t belong in your target demographic.
You can create and upload negative audiences without a CDP. But the challenge with them is that you need to regularly update them; otherwise, your suppression lists quickly become outdated. CDPs can automate this—and also create more complex negative audiences because they consolidate data from more sources, like the rest of your marketing tools.
Example: To develop traction, a photo-sharing app like FamilyAlbum might set a goal for increasing user downloads and run ads on Facebook and Instagram.
Since it would be redundant to show ads to users that already have the app, FamilyAlbum could create a negative audience made up of registered users. This way, users that have already downloaded the app and created an account won’t see any of its ads on social media.
Using negative audiences means not only delivering more relevant messaging to your target audience but also doing so cost-effectively. In the example above, you’ll save money by excluding people who’ve already downloaded and registered for your app.
In-app messaging and SMS
Request product reviews and referrals from engaged users at the right time
Best for: All companies
How it works: When asking for a product review, marketers often rely on the passage of time to determine when to make their request. For instance, they wait two weeks after purchase to ask for a review—regardless of whether the customer has actually used the product.
In reality, to get positive reviews, both user engagement and timing are critical. Rather than blasting all customers with a message asking for a review, you can automate these requests based on how engaged customers are and when they interact with your business.
Combining these data points will get better results than if you were to only use one. Here are different data points you could use to trigger your requests:
- Engagement: Specific page views; video views; social media likes; minutes spent on an app; number of app sessions
- Timing: Completion of a certain action in your app (e.g., reaching the next level of a game); after a high-value order has been fulfilled; after your customer renews for another billing cycle
The same strategy can also be applied to asking for referrals. By catching engaged users at a moment when your business is top of mind, they’ll be more likely to actually share it with a friend.
Example: The language learning app Duolingo could look at product usage to determine which customers to ask for reviews or referrals. It could trigger a request for users who have used the app for 10 consecutive days, and it could even time this request for after the user has completed a daily language exercise.
After all, it wouldn’t make sense to interrupt users in the middle of a learning session or to reach out to those who barely use the app.
By targeting users based on both engagement and timing, you’ll reach customers who have a strong rapport with your company and are in the right frame of mind to give a positive review or refer a friend.
Notify traveling customers about locations in their destination city
Best for: Businesses with brick-and-mortar locations in different cities
How it works: Use geolocation data from sources like Foursquare to find out where customers are traveling. If they travel to a city where your business has a location, send a text or push notification encouraging them to visit. Alternatively, entice them to visit with local discounts and promotions.
Example: The salon chain Drybar uses the tool Radar to detect when customers are traveling. Traveling to an area where Drybar has a location triggers a push notification asking if customers want to book an appointment for a blowout.
Drybar shares that not only did these push notifications increase its full-price bookings by nearly 3x, but they amplified brand awareness. So customers who weren’t already aware that their destination city had a Drybar location learned about the new location and could make plans to visit.
Besides simply driving more conversions and improving brand awareness, this kind of personalization could potentially grow customer loyalty. Imagine a business professional who travels frequently—either to a new place or the same city—and wants consistency in her haircare. Since people prefer familiar brands, these push notifications make a great case for going to Drybar both at home and when traveling.
Target users struggling to make progress
Best for: Mobile app companies, especially those that drive revenue through in-app purchases (e.g., games and dating apps)
How it works: When users struggle with an app, chances are they’ll get discouraged and stop using it. After all, no one wants to continue using an app when it doesn’t feel like it’s working or that they’re not good at it.
This struggle is an opportunity to provide personalized support—helpful tips, links to resources, or even a discount on a helpful upgrade to make things easier.
The exact details depend on your app, but you can automate this content based on event triggers like the number of games lost or total matched connections. Providing personalized support helps to retain users for longer, and in the case of offering discounts on premium features, can also increase in-app purchases.
Example: On the dating app Coffee Meets Bagel, users can buy “beans,” its in-app currency, to unlock special features.
To identify struggling users, Coffee Meets Bagel could calculate the number of matches each of its users receives, and then compare it against the average number of matches across all users. It could then deliver personalized content to the users receiving a relatively low number of matches, like:
- Give tips on how to improve their profile (“People tend to get X% more matches when they upload more than one photo!”)
- Offer a discount on beans to get a better app experience (“Want to unlock more Priority Likes? Get 20% off our beans”)
Providing support to struggling users ultimately motivates them to stay, and by doing so, reduces customer churn.
Prioritize high-value customers
Best for: Companies that serve different tiers of customers
How it works: Companies with freemium or tiered business models may struggle with prioritizing customer support requests. But with consolidated user data and ID recognition, you can automatically prioritize high-value customers. For instance, on your site, you could default to using AI chatbots for self-serve customers while immediately directing enterprise clients to live agents.
Example: Unexpected cancellations and delays can lead to a lot of customer phone calls—more than an airline company’s support team can handle. But, with a CDP that recognizes customer phone numbers, agents could automatically prioritize calls from first-class customers.
This doesn’t mean economy travelers would have to wait endlessly for in-person support, though. The airline company could instead connect these customers with AI-powered callbots as an immediate solution, and route them to agents as they become available.
In this way, personalization helps to triage customer support, placing high-value customers first so that you can retain their business for longer.
Route customers to the most efficient solution—and deliver better service
Best for: All companies
How it works: A single customer may reach out to a company for support in a variety of ways—by email, phone, chatbot, and online contact forms, to name a few. CDPs can consolidate this activity in a single user profile, making it easier to understand the wider context of a specific customer’s product use or needs.
This way, support can be delivered faster and more efficiently, without having to ask the customer to repeat themself.
This not only sheds light on where exactly a customer is struggling, but also which customers need more support. For instance, a customer who’s submitted multiple helpdesk tickets and emailed support in the past month probably needs more help than a customer creating their first ticket over the last year. Feeding this info to your support team helps them both prioritize users and deliver more tailored responses.
Example: Looking at data about customer support requests, a SaaS company like Shopify might notice two distinct audience segments: tech-savvy users and users with basic computer literacy. The tech-savvy customers tend to ask more nuanced questions about software capabilities while the other users ask about common features already covered on your site.
Here are a few ideas for delivering better customer service based on these observations:
- Whenever a tech-savvy user contacts a chatbot, connect them with a live support agent. Their questions will probably be more complex than a chatbot can handle, and a support agent can intervene to prevent them from getting frustrated.
- For users with basic computer literacy, automate an email that links to helpful resources, like tutorials and FAQs.
- Flag customers with frequent support requests so that once a minimum of support interactions is reached, someone from your support team will reach out to offer a guided training session.
Some users will struggle with your product more than others, and they’ll probably seek support in different ways. A CDP helps to combine these separate streams of data so you can deliver support faster and more effectively. By enhancing your support, you’ll preempt further frustrations and improve the overall product experience—meaning greater customer satisfaction and loyalty in the long run.
Companies that collect customer data without leveraging it are missing out on the benefits of personalization—that is, faster growth and more conversions.
But delivering personalized experiences comes with two big hurdles: data quality and privacy concerns. Without a solution for either, you’ll quickly lose customers.
Fortunately, a robust CDP like mParticle can help you execute a sharper personalization strategy with privacy at the forefront rather than as an afterthought.
By focusing on data quality, governance, and connectivity, CDPs help teams to execute a privacy-safe personalization strategy across multiple channels—helping you not only build but also sustain quality customer relationships for the long run.
Our CDP of choice
The team at mParticle provided top personalization insights for this playbook. If you're in the market for a CDP, we highly recommend checking out their special offer for Demand Curve community members. You'll get $25k of mParticle credits—completely for free.
mParticle is the customer data platform (CDP) powering Venmo, Airbnb, and Gymshark. It captures real-time data and delivers it across your marketing tools. The result? Powerful, timely, and respectful personalization. Demand Curve community members can claim either one year of free mParticle or $25k in credits here.
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