3 ways to engage customers using deep personalization

Customers expect a personalized experience from your brand – and their expectations around that personalized experience seem to rise every day. 

Half of the respondents in our survey on consumer expectations said they’re annoyed when brands send generic messages. Two-thirds said brands that create a personalized customer experience are more likely to get their business.

Brands today need to go deeper with their personalization efforts, but what does “deep” personalization really mean?

What is deep personalization?

To put it simply, deep personalization comes down to being customer-obsessed. You’re getting to know your customers and looking for ways to segment them so that you can show them that you know them.

Logically, any form of personalization is going to start with the data your brand collects about customers. Most large enterprise brands use a data warehouse to maintain an ever-growing list of data points on their millions of customers.

This may include demographic, geographic, or technographic data. You may collect data on product affinity, purchase and/or browse history, or which channels your customers typically use to engage. The type of data you collect will impact the type of personalized experience you’re able to offer your customers.

Once you’ve collected the data, your next step will be behavioral segmentation, which is just another way to say: group customers according to their behavior when making purchasing decisions.

Combining what you know about your customers and the actions they take and voila! ✨Deep personalization ✨

Before you roll your eyes and think to yourself, “It’s not that easy.” You’re right. Making deep personalization work for a large enterprise brand can be extremely complicated. It doesn’t help that legacy marketing tools aren’t designed for deep personalization. 

Deep personalization doesn’t settle for the marketing status quo

With traditional marketing, every customer will trigger an event that will then take them through a standard journey. For instance, when a brand acquires a new customer, that customer likely starts a new customer “welcome” journey – aka they enter some predefined workflow that you’ve built. They may make a purchase, then three days later they get an email, and four days after that you send them a product-related survey. 

But, this old way doesn’t take into account all of the things your new customer might also be doing in the meantime. Maybe they make a purchase – and then make a second purchase in quick succession. Maybe they visit your physical store location after an online purchase. Maybe they sign up for text shipping alerts. Maybe they immediately return what they purchased. 

The scenarios are as numerous as your customers.  

You might know your customer likes winter jackets, but that’s not enough. To deeply personalize your engagement with them, you need to be obsessed with them and what they’re doing – but you also need to give your customers a fluid experience. You need to move beyond status quo personalization.

To make that move, you’ll need to create dynamic segments and enable fluid journeys for those segments… PLUS react to every decision, behavior, and signal that you get from customers in real time (not to mention predict future actions). This will all allow you to deliver the personalization your buyers expect.

No biggie, right? 😅

Before you decide that deep personalization is a challenge for another day, we’d like to share three tactics that won’t require complicated workarounds.

Tactic 1: Use AI to predict customer behavior

Looking at customer behavior to inform marketing tactics is nothing new, but it is a strategy that has been highly improved using AI predictive modeling.

Let’s think back to our new customer. Maybe this customer is a man between the ages of 55 and 70 who purchased a winter jacket. We know he likes winter jackets, but what else do we know about him? Does he have a predicted high lifetime value? Maybe we should give him early access to our VIP program.

In the past, brands relied on super fancy – but manual – statistical models to make these types of decisions. This meant taking resources from business intelligence and analyst teams… Not to mention the use of complex coding to implement it across systems.

Today the barrier to entry is much lower thanks to AI.

In fact, in response to a recent survey, 97% of marketers told us that AI is effective at helping them deliver personalized content and recommendations to customers.

Marketers also told us that the most helpful uses of AI are (or would be):

• Determining which customers are most likely to make a purchase
• Determining the channel most likely to convert customers
• Determining prospects’ likelihood to engage with your brand
• Determining the best day/time to engage with a customer

It’s clear that AI is helping to drive more meaningful insights for brands, which leads to better experiences for consumers. And, you guessed it, deeper personalization. 

Tactic 2: Engage your customers in really real-time moments 

Not everything has to happen in real time, but when the timing matters, it matters. 

Chick-fil-A is a great real-world example of real-time engagement driving lifetime value. Thanks to the ability to securely access all of their customer data, Chick-fil-A can power deep personalization instantly. If a customer takes an action while in line or at the restaurant, it’s immediately captured and Chick-fil-A can take action in the moment – sending that customer an instant push notification showing an up-leveled loyalty tier or giving them a coupon for $1 off a milkshake before they even leave the restaurant. 

These personalization efforts helped Chick-fil-A see a 5x increase in subscriptions for their app and a 20% lift in conversions.

The travel industry is another place where real-time connections matter. A static journey makes marketing messages nearly unusable if they’re delayed. No customer wants to know their plane changed gates two hours after take-off.

Unfortunately engaging with customers in real time is often made more difficult than it should be.

Many platforms place a premium on a more streamlined connection to your data. They charge you for those behavioral events and conversion events you ingest, and there’s a limit to the amount of your data you have access to. This can add up to significant fees for large enterprise customers and limits personalization.

Even more concerning, we see more and more martech vendors touting a “real-time” connection to data when that connection still requires that data be copied, synced, and mapped. That’s not really real-time, but you might think it is based on their marketing language. Terms like ingested or synced can be tip-offs that a real-time connection isn’t truly real time.

Real-time engagement can only be harnessed through a direct connection to your data warehouse. Meanwhile, data friction due to siloed tools and data movement costs time and money while creating disjointed experiences for consumers.

The Frontdoor team faced this common challenge. To activate their data, first, they had to access it – which often meant delays, inaccuracies, and complicated workarounds. Using MessageGears’ direct data access, Frondoor’s marketing team is now able to create highly dynamic, visual segmentations powered by the live customer data in their Snowflake data cloud.

Tactic 3: Engage with cross-channel cohesion

When we’re talking deep personalization, AI and direct data access (tactics 1 and 2 above) walked so that tactic 3, cross-channel cohesion, could run.

For your customers, engagement with your brand shouldn’t feel drastically different from channel to channel. Yet, while large brands often have lots of data to work with for deep personalization, they also often have siloed teams that can make cross-channel cohesion difficult.

If that’s a challenge for your team, we advise starting small with one cross-channel campaign, then using any data patterns you uncover to inform your next. For instance, insights from your email program such as A/B test results could help to inform your mobile push campaign messaging and frequency.

Looking back at our AI tactic, predictive modeling is also especially helpful when you’re looking to decide which channels to leverage with which customers.

Keep in mind that some campaigns or messages may be more impactful with customers when delivered through a specific channel based on their preferences. For example:

• Online retailers can identify the most appropriate channel for promotions and cart abandonment reminders
• Financial institutions can leverage the optimal channel for customer communications regarding account updates, transactions, etc.

Consider the possibilities

You may dream of a better way to communicate with customers, but limitations are holding you back – whether that’s time, your systems, costs, etc. Or maybe worse, you’ve been told that vendors can get you to those dreams, only to discover that the tools they’re selling don’t exactly work the way you were promised.

It shouldn’t feel impossible to deliver a tailored customer experience, but you can achieve a level of deeper personalization for your brand that doesn’t take weeks or years or multiple teams to get there. 

Looking back to our earlier example with Frontdoor, the team is now working at a speed they could only dream of before – responding immediately to every customer interaction with deep personalization.

Want to implement new ideas for deeper personalized messaging at your brand? Don’t miss our handy crawl, walk, run checklist. You’ll find strategies for personalization and every level of data sophistication.