There’s a whole landscape of customer data solutions out there, and the new customer data platform (CDP) category is causing confusion amongst marketers. Differentiating between CDPs and other solutions can be a difficult task.
We’ve made this guide to help you differentiate between them. If you’re feeling unclear on what CDPs are, check out our beginner’s guide, otherwise, read on to learn about what separates CDPs from other customer data solutions.
CDPs and DWHs
Unlike CDPs, which are developed specifically for marketing purposes, data warehouses (DWHs) are enterprise-wide solutions. Their main purpose is data consolidation and standardization for business intelligence (BI), which encompasses analytics, data mining, reporting and more.
Although they’re powerful, they aren’t optimized for marketing and a DWH alone doesn’t sufficiently fulfill a marketing team’s needs. In being optimized for BI, DWHs are lacking in areas that are crucial for marketers, namely segmentation, real-time marketing and integration with channel tools for message dispatch, as explained below.
Segmentation is possible in a DWH, however, it’s a long process. Requests typically need to be submitted to the company IT team as DWH usage requires programming knowledge and the results are not instantly available. On the other hand, most CDPs enable marketers to perform in-app segmentation without assistance from IT, with segments and their sizes instantly available.
By design, DWHs don’t assist real-time marketing. As BI is retrospective by nature and does not require real-time data, DWH’s perform ETL (Extract, Transform, Load) processes that have a considerable lag between a customer’s actions and a marketer’s ability to access and activate their behavioral data. CDPs are optimized for real-time marketing, and so store behavioral data that is updated in real-time. A CDP’s UI allows marketers to access and activate data instantly, allowing for automated responses to customer action.
Finally, DWHs are not integrable with engagement channel solutions, such as those for sending emails, push notifications, text messages and more, as this isn’t necessary for BI. Creating messaging automation that utilizes data gathered and aggregated by a DWH is a complex process that requires heavy IT support. In contrast, CDPs integrate with engagement channel solutions to make gathered data instantly actionable, and some additionally act as orchestrators for connected tools, enabling automated cross-channel marketing.
|Purpose:||Business intelligence||Customer retention|
|Business intelligence/IT||Marketing/CRM teams|
|Customer profiles:||Known, detailed||Known, detailed|
|Open access to other systems:||Yes, but not real-time||Yes|
|Integration with engagement solutions:||No||Yes|
How do they interact with CDPs?
CDPs integrate with DWHs, and combine a DWH’s customer data with data from other sources to create 360-degree customer profiles.
CDPs and DMPs
Much like CDPs, data management platforms (DMPs) are built with marketers in mind. However, they differ in purpose and structure.
DMPs enable customer acquisition through ad targeting, and to this end gather temporary, third-party, anonymous cookies. As CDPs enable customer retention, they combine first-party known customer data from multiple sources to create permanent customer profiles.
DMPs segment anonymous customers based on their recent viewing history, and through synchronization with a demand-side platform (DSP) they are targeted with ads that have a probability of being relevant. CDPs, in contrast, segment customers based on a range of differentiators, from buying history to demographic to lifetime value and more. CDPs furthermore integrate with channel solutions to send personalized messages to customers.
|Purpose:||Customer acquisition||Customer retention|
|Display (ads) team||CRM/marketing teams|
|Customer profiles:||Anonymous||Known, detailed|
|Customer engagement:||Programmatic display advertising through DSP integration||Yes, through integration with engagement solutions|
How do they interact with CDPs?
CDPs and DMPs can leverage the ‘cookie matching’ process. Cookie matching allows both solutions to identify the same user. This allows for higher quality programmatic advertising: A DMP’s upper funnel data can be enriched by a CDP’s detailed, lower funnel customer data.
Cookie matching also enables unique use cases. For example, CDP/DMP integration can identify unknown users. An unknown user who has previously logged in as a known user can have their temporary cookies matched to their permanent profile, thus de-anonymizing them. As another example, the synergy allows groups of known customers to be excluded from programmatic ad campaigns – useful for not advertising excessively to loyal or subscribing customers.
CDPs and CRM Solutions
Both CDPs and customer relationship management solutions (CRMs) collect data on known customers and create permanent customer profiles, but do so in different ways and serve different purposes.
A CRM solution’s primary function is to create a database of a company’s customers, primarily with basic profile data and one-to-one interactions (signing up, purchasing, complaining or something else) for bookkeeping purposes, mainly for the benefit of sales teams and customer support. That’s done through a combination of automated and manual input processes. Customer profiles are not detailed 360-degree profiles, as CRMs do not account for all customer data. CDPs gather and aggregate data from any connected data source automatically to create detailed customer profiles that, unlike a CRM’s, are optimized for marketer usage rather than sales and customer support’s usage.
Some engagement automations exist, but CRMs are mainly used to optimize manual one-to-one engagement. CDPs, depending on the provider, can activate their aggregated data through segmentation and cross-channel campaign management for automated and highly personalized marketing campaigns across channels.
|Purpose:||Bookkeeping / customer retention / customer service||Customer retention|
|CRM/sales teams||Marketing/CRM teams|
|Customer profiles:||Yes, not detailed||Known, detailed|
|Customer engagement:||If yes, through native email automation||Yes, through integration with engagement solutions|
|Integration with external solutions:||Limited||Yes|
How do they interact with CDPs?
CDPs are able to integrate with CRM solutions and combine the data they collect with data from other sources for detailed customer profile creation.
Integration allows for interesting use cases. For example, an automation can be set up whereby if a customer has complained, and that complaint has been marked in a CRM system, the connected CDP can be set to exclude that customer from upcoming marketing campaigns before the complaint is resolved, so as not to annoy the already disgruntled customer. Afterwards, an apology campaign can be sent using the CDP, and usual marketing campaigns resumed once the customer is happy again.
CDPs and Marketing Clouds
Of the different types of customer data solution, CDPs and marketing clouds differ the least in purpose. Both aim to enable consistent and personalized marketing and CRM across channels. Differences arise in approach and effectiveness.
Marketing clouds take “all-in-one” approaches, coming with their own data aggregation and engagement tools. A CDP, in contrast, acts as a core in a tech stack, orchestrating the data and engagement solutions that connect to it. CDPs enable the “best-of-breed” approach, allowing marketers to integrate the best or most suitable solutions for each role.
The ‘all-in-one’ approach taken by clouds is ineffective compared to the CDP model due to its lack of flexibility. A clouds’ tools are either developed in-house by dedicated development teams or vendors and their solutions are acquired. These approaches share two inherent issues: Firstly, internally developed or acquired tools may not necessarily be the best tools for the job. There may be more up-to-date, or simply better niche solutions on the market that clouds cannot connect with. Secondly, marketing clouds lag behind shifting market trends. When new engagement channels are developed, clouds have to build tools for that channel themselves from the ground up or seek to acquire them in long and costly processes. CDPs, in contrast, have the flexibility to connect with best-in-class solutions, with marketers benefiting from the ideal solution’s innovation and ability. They can integrate with tools for new channels as soon as they’re made available, thus staying on top of marketing technology trends.
Marketing clouds are furthermore less effective than CDPs for real-time marketing, as their data infrastructures aren’t sufficient for real-time data streaming in most cases. CDPs use big data technology that can log and activate behavioral data the moment behavior occurs. That enables messaging that responds to exact customer needs in real-time.
Due to their closed nature, marketing clouds may not have a connection to sufficient data sources to create detailed customer profiles. CDPs can integrate with every available data source and combine every kind of customer data to create unified, complete, 360-degree customer profiles.
They also differ in how easy they are to implement. A marketing cloud, by design, replaces an entire tech stack in an expensive and risky implementation process. CDPs integrate with and orchestrate a tech stack’s solutions, reducing risk, implementation time and cost.
|Purpose:||All-in-one marketing solutions||Customer retention|
|Customer profiles:||Limited||Known, detailed|
|Customer engagement:||Through natively developed or acquired tools||Through integration with engagement solutions|
|Implementation:||Replaces an entire tech stack||Integrates with an existing tech stack|
How do they interact with CDPs?
A cloud’s capabilities can be enhanced by CDP integration. Detailed customer profiles and segments are provided to the cloud by the integrated CDP, improving upon the cloud’s limited data aggregation abilities, whilst the cloud’s engagement tools are used to engage with customers.
You can read more about CDP and cloud interaction here.
With the possible exception of marketing clouds, CDPs should not be seen as replacements for other customer data solutions. Instead, CDPs work as meta-systems, orchestrating a tech stack’s solutions to create holistic customer views and consistent customer engagement.
For further viewing, we recommend b.telligent’s brilliant talk on the MarTech ecosystem!