Customer Data Platform

What is a Customer Data Platform?

A customer data platform (CDP) is a software tool that helps businesses consolidate customer data from multiple sources into a single, unified database. It accomplishes this by integrating with the company’s entire tech stack, including CRM, website, email marketing, live chat, helpdesk tools, and any other system that’s part of the customer journey.

The CDP acts as a single source of truth for sales, marketing, and support teams.

• Sales teams use its insights to send targeted content and personalized offers during lead nurturing and conversion processes.
• Marketers use them to segment audiences, personalize shopping experiences, and retarget customers with relevant campaigns.
• Support teams use them to route tickets to the right personnel, anticipate customer needs, and provide timely support based on next-best actions.

The first-party data CDPs ingest and analyze gives businesses unprecedented insight into their customers’ preferences, buying behaviors, and engagement activities. Using predictive analytics and segmentation tools, it also allows them to create personalized experiences for each customer.

Synonyms

• CDP
• Customer data management platform
• Cloud-based customer data platform

Customer Data Challenges

According to Neilsen’s annual marketing report, 86% of marketing leaders at medium- and large-sized organizations consider first-party data to be the single most important aspect of a company’s media strategy. 64% of customers expect personalized engagement based on their previous interactions with a brand. And data-driven businesses are 23x likelier to acquire new customers.

But, especially as companies grow their customer bases, the sheer amount of data is enough to cause issues.

Lack of Data Synchronization

Unfortunately, many companies struggle to use their customer data effectively due to siloed systems and isolated datasets. This makes it difficult for them to identify trends, pinpoint new sales opportunities, and understand what motivates their customers.

The main reason for this is that many companies struggle with integration problems. Different departments use different (often disparate) systems. Without a tool to synchronize them, customer data remains scattered and fragmented.

Data Hygiene Issues

Incomplete, incorrect, and duplicate data makes it impossible to create a unified customer profile. Part of the reason so many companies struggle with their data management is that they lack an efficient way to reconcile customer data across multiple sources.

The biggest business risk when it comes to data hygiene issues is misinformed high-level busienss decisions. When company leaders trust analytics results from poorly maintained data, they head in the wrong direction. They end up making decisions that are completely misaligned with customer preferences.

Lack of Actionable Data Insights

A June 2022 Forrester study commissioned by Amperity revealed dozens of things about how organizations utilize their data. Data shortage wasn’t a noteworthy problem. The ability to glean insights from it, however, was.

77% of decision-makers believe they underutilize the customer data they currently have access to. 76% struggle to streamline their data in a way that produces actionable insights. And 79% say they struggle to take action on their customer data in real-time.

Misaligned Marketing and Sales Teams

The abovementioned Forrester study also uncovered a critical issue in the modern company’s ability to share data and move forward as a single unit. 64% say they have significant difficulty sharing data across departments and collaborating to create personalized experiences.

The main challenge with personalization in general is the need for consistency for it to work. All customer touchpoints need to be connected with a single customer profile to ensure customers experience consistent, relevant interactions with the brand. When marketing and sales teams can’t share data and cooperate to deliver this type of personalized experience, customer loyalty suffers.

Staff Inability to Interpret Data

Even for small companies with fewer customers and ways of interacting with them, understanding how certain data points work together is complicated. High-level analysts and data scientists aren’t always available — or affordable — to make sense of customer data.

In the absence of these experts, companies must rely on their existing staff to interpret data and generate insights. This is no small task; it requires training in analytics, data science, and marketing technology. Without proper guidance (and, again, data integration), achieving an accurate understanding of customer behavior becomes nearly impossible.

How a CDP Works

Think of a CDP as an extension of a data warehouse or data lake. While those systems store large volumes of raw data, CDPs interpret and analyze that data to deliver meaningful insights. It’s a data enrichment platform that uses APIs to connect all the customer touchpoints in an organization.

Data warehouses and data lakes store all types of data. CDPs are like assembly lines for first-party customer data. They’re systems of movement, not just storage — the end result is action (i.e., personalization in the customer journey). That makes them fundamentally different from other types of data platforms.

To understand how a CDP works from Point A to Point B, let’s break it down into the following three steps:

1. Ingest — Gather and Centralize Data from Multiple Sources

When customers interact with a business’s marketing, sales, and service systems, it creates pieces of information about each customer. Who are they? What are they looking at? What do they look at first, and what do they do next?

CDPs ingest the following types of customer data from these sources:

• Personal data (e.g., contact info)
• Demographics and firmographics
• Sales and opportunity data
• In-person and physical engagement (from POS systems/beacons)
• Digital marketing engagement
• Transactions
• Order fulfillment
• Device usage
• App usage (e.g., a smartphone app or a SaaS)
• Support tickets
• Live chat

Similar to a data warehouse, data continuously flows into the CDP. But data in the CDP is waiting for what’s next.

2. Unify — Make the Data Tell a Story

A common problem marketers grapple with is the inability to access real-time data. Even larger is the challenge of connecting physical and digital touchpoints across the customer lifecycle.

The first step of a CDP is to quickly and accurately collect data from all the sources mentioned above. The second step is to combine the data into one central platform, where it can be integrated with other sources.

After integration into the platform, there are four primary ways a CDP combines it:

• AI/ML — Advanced CDPs sometimes use AI and machine learning algorithms to identify potentially relevant data points, cluster them into segments, and suggest actions.
• Customer segmentation — CDPs can also treat each data point from each source as a distinct record and assign it to a micro-segment based on certain criteria.
• Multi-channel analysis — A CDP uses several analytical techniques, such as reporting, forecasting, and descriptive analysis to understand customer preferences and behavior across channels.
• Person + account matching — Using cross-device tracking and identity resolution, CDPs can tie all the data points to a single customer profile.

During this stage, the exact types of data it joins together are completely up to the user. To build a single view, it’s up to the user to consider the entire customer journey before deciding what kind of story the data should tell.

3. Activate — Feed Insights Back Into Systems

A customer data platform creates a feedback loop that starts and ends with the customer. They are the ones who create the customer data the CDP ingests in the first place. They are also the ones who see the end result of the CDP’s work — personalized experiences.

Events processing happens in near real-time. Organizations connect their CDP with marketing automation to identify events related to customer data points (e.g., when a purchase is made) and trigger specific campaigns or actions in response.

For instance, if a customer adds a product to their cart, but then abandons the checkout process, an effective CDP should be able to detect this event and send them an email reminding them to complete their purchase. Over time, customers should notice higher degrees of personalization as algorithms learn their behavior.

The possibilities are endless — in large part because of the ability to capture unstructured data points. For example, with sentiment analysis, companies can identify when customers are frustrated or dissatisfied and reach out proactively to address the issue.

Benefits of Implementing a CDP

Implementing a CDP offers untold benefits for scaling companies of all sizes. As a complex undertaking from an integration standpoint, implementing it requires a significant amount of planning. But the ability to offer contextualized, relevant experiences to customers means a huge competitive advantage for any business using one.

Provides Better Customer Data and Actionable Insights

Data access is only half the battle. Organizations need it to tell a story if they want to use it for marketing and sales growth. Without software that strings together dozens of digital and physical touchpoints, it’s nearly impossible to make sense of customer behavior.

Through a CDP, organizations gain a better understanding of their target market, which leads to more effective decisions and campaigns. That expansive view across the customer journey helps marketers respond quickly (sometimes automatically) to changes.

Increases Marketing and Sales Efficiency

Less guesswork always equals higher efficiency. With a CDP, marketing and sales teams can spend less time figuring out what targeting and communication tactics work best. They can hop straight into execution.

The value of increased efficiency with a customer data platform is especially clear if a company has previously dealt with data silos. If sales and marketing teams initially struggled to make sense of their customer data, having a system that connects them dramatically reduces the time spent on lead qualification and research for content marketing.

Segments Audiences for Better Customer Experience

Audience segmentation is quite simple for basic identifiers like location, age, or company size. But grouping audience members based on these data doesn’t guarantee personalization. Two 25-year-old males from the same ZIP code probably have vastly different interests and preferences.

Customer behavior reveals these preferences. And the quality of the customer experience depends on how quickly a business can figure out those preferences and respond accordingly.

CDPs segment customers based on their behavior, not just their unique identifiers. They capture every single touchpoint along the customer journey — from anonymous website visits to in-store purchases.

With predictive modeling and automated triggers, CDPs can adjust segmentation (and, by extension, the experience) as they learn more about a specific customer’s behavior. In that sense, it’s the ultimate enabler for personalization at scale.

Increases Sales Revenue

According to insights from McKinsey, personalization can immediately lift sales revenue anywhere from 5% to 15% and improve marketing ROI by 10% to 30%.

In reality, this seems conservative. Considering 91% of customers are more likely to purchase from brands that automatically remember them and provide personalized content and recommendations, it’s really more of a ‘do or die’ scenario.

Companies need to prioritize personalization if they want to see revenue growth at all. Otherwise, they’ll be beaten out by a competitor that does.

83% of customers say they would exchange data for a more personalized experience. And there’s no better way to collect and use that data than with a customer data platform.

Aligns Business Teams

Sales and marketing have different goals, but both need customer data to do their jobs effectively. By having a shared understanding of who their customers are, they can collaborate more closely on campaigns that benefit everyone involved.

Suppose a customer visits the company website to read about product features. They browse and look around, but don’t make a purchase. Now, if that same customer receives an email from sales offering information about the product’s features based on their browsing history, they’re more likely to take action.

By the time they talk to a salesperson, the sales rep already has a thorough understanding of the customer’s interest level and preferences. This improves the quality of conversations, which in turn leads to higher chances of conversions.

A customer data platform is the bridge between sales and marketing. It helps teams learn more about their customers and make better decisions thanks to that shared understanding.

Essential Capabilities of a CDP

Several types of platforms manage customer data, including data warehouses, data lakes, data management platforms (DMPs), and customer relationship management (CRM) software.

The main distinguishing factors between a CDP and other types of software is its focus on all types of customer data and its ability to transform it into custom automations and insights.

Display Customer Analytics

While a CRM system displays customer information like sales interactions, it’s really more of a record-keeping tool for sales, marketing, and customer success analytics. It’s one data source (albeit, a valuable one).

Customer analytics in a CDP are a lot different. They include anything from website analytics to email opens, purchases, customer support tickets, and more. All of this data then gets combined into a single view — an expansive aggregation of all the different touchpoints that make up the customer journey.

360-Degree Customer View Across Channels

A customer data platform captures all customer touchpoints from multiple sources, then organizes them into individual profiles. Since it’s integrated with all of a company’s other software, they receive an experience based on their personal profile.

CDPs are also helpful for macro trend analysis. By packaging customer data into a comprehensive view, companies can also find out which channels are most successful and why. That way, each marketing campaign, feature rollout, or sales approach they try in the future is more effective.

Automate Customer Segmentation

Segmentation is key in personalization because it ensures messages are relevant and useful for customers. The more detailed the segmentation, the better it is for marketing and sales.

A CDP can automate that process by tracking customer preferences instead of just their identity. For example, if a customer frequently clicks on content about a certain product or service, they’ll automatically get added to that segment.

Automations and Next-Best Actions

Based on data the CDP collects, it can also suggest next-best actions to close the sale or provide customer support. If a customer has expressed interest in a product but hasn’t purchased it yet, CDPs can send them an email with information about the product plus offers that will make it more attractive.

Types of Customer Data Platforms

There are four types of CDPs. The kind that works best for an organization depends largely on company size, complexity of its operations, and the current tech stack the organization uses.

CDP Engines

CDP engines are the most complex type of customer data platform. They aren’t fully built tools — they’re toolkits IT teams use to develop their own applications on top of a CDP.

Like any fully custom solution, they allow users to get the most out of their data. However, since they require a lot of technical work to run properly, very few companies actually need a CDP engine.

The main use cases for CDP engines are:

• Enterprises in data-intensive industries, such as healthcare, finance, or logistics
• Organizations with complex and/or highly competitive customer data requirements
• Companies (small or large) in the data science space that use data to build models or uncover trends for their own software

Marketing Cloud CDPs

Marketing cloud CDPs are platforms that are part of a larger ecosystem. Marketing cloud CDP vendors include Oracle, Salesforce, and Adobe, as well as third-party vendors that partner with or integrate exclusively with these large ecosystems.

A marketing cloud CDP is useful if an organization already uses one of these systems for other services, such as CRM, ERP, and email services. Sometimes, these platforms are also customizable, but they aren’t add-ons developed from scratch like CDP engines.

Marketing Data-Integration CDPs

Marketing data-integration CDPs have powerful data governance and manipulation capabilities, but they have non-technical interfaces. They’re primarily geared towards data operations, so they need to be integrated with visualization platforms to be used for marketing and sales purposes.

These platforms are best for organizations that don’t need (or use) a full-fledged marketing cloud but want to take advantage of the wide range of data sources available. This type of CDP is suitable for most small and mid-sized businesses, as well as some larger companies with multiple software tools.

CDP Smart Hubs

CDP smart hubs have easily configurable backend interfaces and integrate with any type of software, from ecommerce to marketing automation. They’re cloud-based, offer automation triggers, and scale quickly.

These platforms are ideal for companies that need to quickly create customer experiences from multiple data sources. They’re more focused on process orchestration, so they don’t have as robust data manipulation capabilities as other CDPs.

However, if an organization just needs to create customer journeys and segment profiles, a CDP smart hub is a smart choice over a marketing data-integration CDP.

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