Personalization has been a buzzword in the business world for years because — when done well — it works. Deloitte research found that companies doing personalization effectively are 48% more likely to achieve revenue goals, and 71% more likely to report greater customer loyalty.

But businesses have often struggled to gain clarity on what personalization should look like in practice and how to do it effectively at scale. Part of the answer is selecting the right tech. Here's what you should know about what tools support marketing personalization at scale.

Start with your personalization use cases and pain points

At its most basic level, personalization is getting the right message to the right person at the right time and place. But in practice, that's a pretty broad concept. Before you can figure out what technology will best support your personalization efforts, you have to clarify what you want to accomplish.

Personalization involves three main levels that each build upon the last in maturity:

• First level: Who  - The most basic level is identity resolution, knowing who someone is, so you can connect the data you have about them to their identity.
  
  
• Second level: What - The next level up is understanding what they're interested in and their activities, like their past transactions and engagement data.
  
  
• Third level: Why - The most sophisticated level is trying to understand the why, so you can predict future behavior. For example, if you know someone has recently hired movers, you can surmise they may be interested in offers for items like house paint and curtains.

These categories can guide you in thinking through what kind of personalization you want to achieve, and what information you need about your customers to accomplish it. 

Common personalization problems that prompt a tech assessment

In addition to clarifying your use cases, you also want to consider some of the main personalization problems you've faced so far. Some common issues businesses face with personalization include:

• “Frankenstein” data - If your data isn't well organized, you could risk applying the information you have to the wrong customers. That could mean matching the wrong last name to the first name, or mixing up the data for different contacts linked to the same account.
  
  
• Inaccessible data - If your customer data lives in multiple disconnected products or is stuck in departmental silos, you'll struggle to access the information you need for effective personalization. At best, your marketing team will be able to access the data, but finding it manually will slow down workflows and cause avoidable delays.
  
  
• Slow reaction times - The faster you can act on behavioral insights, the faster you’ll reap results from personalization. But you need the ability to analyze behavioral data in real time, and also make changes to your marketing campaigns based on what you learn right away — a tall order for marketers doing the work manually.

The tech stack that powers personalization at scale

To achieve effective personalization in marketing, you must prioritize a few main types of technology:

The foundation: Clean, unified customer data in the cloud

A cloud-based data warehouse that stores your customer data in a central location is the core of a personalization strategy. With all your data centralized in one cloud warehouse, you'll gain functionality that's crucial to successful personalization, most notably:

• One single source of truth for all customer information
• Real-time access to all your data across departments 
• A clear foundation for any machine learning (ML) and AI features you use to power your personalization efforts

The right architecture: Why composable is the answer

The next tech tool many organizations find essential for personalization is a customer data platform (CDP) — what Deloitte calls the "gold standard" for using customer data to create personalized experiences. But you don't want just any CDP. For best results, look for a composable solution, such as a Compound Marketing Engine or composable CDP.

This composable solution works as an activation layer on top of your existing data warehouse that also integrates with your marketing tech stack. That means you can access and act on your customer data in real time. In comparison to a traditional CDP, this offers significant benefits:

• Faster time to value - Composable architecture takes the work of managing system integrations and wrangling data off marketing's plate, so they can start creating audiences, building journeys, and applying data insights to campaigns — and see results — faster.
  
  
• More user-friendly - Getting updated data into a traditional CDP requires leaning on the expertise of the data team. That can mean tickets with long wait times that slow down your campaign momentum. Composable solutions give marketers an easy interface to build audiences and journeys directly from the cloud data, no specialized technical knowledge required.
  
  
• Better data - As long as the data in your warehouse has been cleaned and structured correctly, you don't have to worry about duplicates and errors. You get the right data in the right format, in real time.

The role of AI: Velocity of experimentation

When paired with a composable data cloud-native architecture, AI is a valuable tool for scaling your marketing personalization efforts. When you have a comprehensive centralized source for data, AI has ample information to pull from to make smart insights and recommendations. And a key benefit of AI personalization marketing is that it's fast.

Agentic AI can analyze and process data at much faster speeds than humans, producing data-backed suggestions that marketers then use to make better decisions. Even better, it can help fuel experimentation at scale. You're not stuck with one campaign at a time. You can run dozens of variations, collect data on their performance, and iterate in real time to boost your results. 

By significantly scaling the amount of data you can collect and analyze on customers and campaigns, agentic AI can shorten the time it takes to experiment and iterate within the marketing cycle. Ultimately, this helps drive compound marketing growth.

Misconceptions about personalization

Before you embark on updating your martech stack for better personalization, you want to have clear eyes about what's possible. There are many myths and misconceptions about personalization in marketing:

• “Personalization means using a first name.”

That's one (basic) form of personalization, but it's a small fragment of what's possible. True personalization leverages context, lifecycle stage, and customer behavior.

• “More personalization is always better.”

Not necessarily. One Gartner study found that 53% of consumers reported negative experiences with personalization, although McKinsey found that 71% of consumers expect personalization. That suggests that it all depends on how you do it. You have to focus on providing value, and not crossing any lines around consumer privacy.

• “Personalization is just for marketing.”

Personalization is valuable across many points of the customer journey — from marketing (obviously), to sales, and up through customer support. Providing a tailored, relevant experience to people through the entire customer lifecycle is part of providing an exceptional customer experience.

Fixing your personalization and CX starts with the right tech foundation

If your customer experience is broken, odds are your data strategy is, too. If you want to increase sales and boost customer loyalty, personalization is an important part of the equation. And building out the right tech foundation is paramount to powering effective personalization at scale. 

Investing in a composable, cloud-first marketing engine that includes AI features to help you put your data to work is a necessary early step in achieving effective personalization. 

Set up a GrowthLoop demo to see how for yourself.