Customer Data Platform, How it Works

The customer data platform (CDP) is one of the fastest-growing categories of business technology today. To understand why, you have to look at some of the underlying challenges across many industries related to data, AI, and personalisation.

Customers want an experience that’s more tailored to them. They gravitate to companies that understand who they are and what they need. They want companies to engage with them on their terms, on their schedule, with a connected experience across digital and physical interactions.

At the same time, companies are tapping into the power of predictive and generative AI to allow teams to deliver what customers want — with the proper context. Unifying your first-party customer data and making it easier for your teams to take action is the key to delivering on customer expectations and experiencing AI’s full potential.

So how does the CDP fit in? Let’s take a look.

• What does a customer data platform do?
• How does a customer data platform work?
• What are customer data platforms used for?
• 4 keys to success with a CDP

A customer data platform is technology that allows businesses to pull in customer data from any channel, system, or data stream to build a unified customer profile — updating in real time. This allows you to learn more about your customers, their journeys, and how you can give them the seamless experience they’ve come to expect. When a customer messages you on social media, buys something on your online shop, or comes into your brick-and-mortar shop to make a purchase — all that data goes into your CDP as it happens.

We live in an era where the customer is in control. Amazon can predict what products we will buy next. Netflix recommends the shows we like with great accuracy. Customers want personalised experiences and fast service, and expect companies to have an intimate understanding of their preferences. It’s no longer a business advantage to deliver on this — it’s how companies operate.

Customers want the interactions they have on a company’s website to translate to their mobile app experiences and in-store visits. And they want everything updated in real time, so their current needs are being met on every channel. The problem is that, for most companies, those environments operate from different data sets that are trapped in applications, spreadsheets, and data warehouses or lakes — even though the customer is the same.

As customers move from channel to channel and across different departments, they expect their experiences to be consistent and “in the moment.” Most customer journeys involve three or more different channels (email, web, and mobile app, for example). Customers tend to move seamlessly and quickly between these channels, as well as experiences with sales, service, commerce, and connected devices. Many companies, however, don’t have these data environments synced up.

The result is a disconnected experience for consumers and the lack of a unified customer profile for you to bring an individual customer’s context into every moment.

The first thing CDPs do is connect all of a company’s customer data in a single place. That means not only stitching together a single customer ID from many different customer interactions, but also tying together customer identity, transactional, preference, and behavioural data from the customer journey, like marketing, sales, service, and commerce platforms.

The next thing CDPs have to do is reconcile our identities for our known customers (like email and mobile numbers) with what we know about customers before they share their data (anonymous cookies and mobile device IDs, as an example). This way, we can start to associate a journey that started with an email campaign and continued onto the website with the same customer. Bringing all of the information together around an individual or an account, is powered by something we call customer identity resolution.

As the CDP creates and continually updates the unified profiles of your customers, it makes data and insights based on it available in real time. This way,  you can deliver personalised experiences and work smarter with generative AI that can use your data in a way that customers trust.

That enables customer data to flow across different systems, to segment and trigger personalised marketing journeys, power the right commerce recommendations, provide deeper sales insights, and better inform service interactions.

In a nutshell, CDPs govern these primary tasks: data collection, data unification, data activation, and data insights. Additionally, the right CDP can set you up for success with generative AI.

As the amount of consumer data grows, however, companies must be aware of and respect data privacy concerns. Many consumers are willing to have some of their data used to deliver personalised experiences, but they expect that companies will protect this data and use it ethically. These are the building blocks of a trusting relationship with customers, and companies need to use consumer data in the right ways, with the customer’s consent.

Here are a few examples of how customer data platforms can help marketers reach customers in new ways.

With a CDP, you can use first-party data to tailor advertising to your customers’ preferences and purchase history. For instance, using a calculated product interest score from your CDP can help target the right next offers based on your customer’s past transactions and recent browsing on your site.

This is especially valuable to marketers as the “cookieless future” increasingly becomes the present. But, sometimes the best use of data in marketing isn’t used to better target consumers — but to not target them at all. We’ve all had the experience of being targeted online by ads for things we’ve already bought.

The reason companies have a hard time stopping ads for the sneakers (or cars) we have already purchased is disconnected data. A unified profile that connects marketing and purchase data enables marketers to be smarter with their budgets  by suppressing consumers that have already made a purchase, and redirecting those dollars toward prospective customers, or recommending new products to existing customers. The same approach holds true for pausing marketing while a service case is open.

Say someone comes to your website, browses a particular product — say, a new vehicle — and leaves. Wouldn’t it be great if you could tie everything you’ve learned about that customer to a personalised offer via email or push notification? And then you could connect that same offer to when they visit the dealership.

For example, you can send an email with this message: “Test drive that SUV today and get $500 off MSRP!” You can only do that by connecting that consumer’s identity and behavioural data to your marketing engine and sales CRM. Companies like Ford are taking personalisation to an even higher level, catering to customers’ desire for an experience made to them.

CDPs solve this problem. Customers who see content tailored to their interests (test drive this new SUV today!) are much more likely to engage with a brand. And when they do, that level of experience can now power every moment in the brand relationship.

What drives better marketing, commerce, sales, and service? The answer has always been better customer insights. But most of the analytics systems that generate such insights operate in silos.

Marketing engagement data is separate from website analytics data, which is separate from commerce or sales data. Stitching that customer data together, and tying all of those interactions with the same consumer, can be a herculean effort when you are using legacy systems.

What if an outdoor retailer had a customer’s marketing interactions (email and advertising engagement) tied together with their ecommerce data (purchase history) and website interaction data (products viewed multiple times) – and made that information available to a service rep in the call centre?

A bit of data science (customers who purchased this tent online, and opened email promotions for these hiking boots, and spend between $250 and $1,000 annually, usually buy this backpack) can reveal the right product recommendations to the call centre agent, who can make a personalised, relevant offer on the spot.

This type of personalisation can turn a $25/hour call centre rep into a $100,000 a year salesperson. Gucci is an example of a brand transforming their service centre this way to make resolving cases simpler, allowing agents to provide on-brand luxury customer experiences across every channel.

That’s the power of a CDP like Data 360 that connects marketing, sales, service, and commerce in a whole new way.

1. Think holistically about your CDP’s customer profile

Remember, your top goal with a CDP is getting a connected and actionable view of the customer. That means factoring all the customer data that matters, from marketing and all of the key interactions in their journey.

For example, you want to know if the customer had a shipping delay on the commerce side. Or differentiate if they are a long-standing customer or maybe made their first purchase with your company. Or how they use their connected devices for health and fitness. Having all this data in one profile will help you reach the 1:1 personalisation that builds strong long-term customer relationships.

2. Think about how to best use data and AI

AI functionality within a CDP makes it easy to get insights on your customer data. Think about how you can use that data and insight to create an experience for your customer as they move across your teams. That means strategising different channel experiences, plus your website and mobile app.

Again, strategise beyond marketing to your commerce site, sales agents, help desk and analytics team. A CDP can get everyone connected around the customer, right out of the box. Think big about what a unified customer experience can do for your customer satisfaction and loyalty. The right CDP can make it happen.

3. Think about how to get more from existing data, AI, and applications
You may have a data lake to store your raw data and now need to connect that to a CDP to bring into a customer profile so you can deliver a more relevant journey.. You also need to align with your data science team for insights that you want to bring into your customer experience.

Having extensibility (the ability to incorporate new features) in your CDP is really important in the mission to building a complete customer profile. New techniques like Bring Your Own Lake and Bring Your Own Model have been pioneered to bring the value of these tools forward into the customer experience, without even having to move or copy any data into the CDP.

4. Think about how your data can be a foundation for generative AI success
Now that we’re in the generative AI era, large language models (LLMs) are promising to help us work more efficiently and productively than ever. These LLMs can improve how customers interact with your brand, such as through a commerce concierge experience online.

But to get the most from this AI, it needs context about the customer and business, delivered in a trusted way to protect data privacy and confidentiality. Innovations like the Einstein Trust Layer in the Data 360 CDP make this a reality.

CDP technologies can help you in new ways like this, allowing you to do more with your customer data than ever before.