Customer Data Platform

Customer Data Is Important

Customers nowadays assume that your company knows – and remembers – who they are, what they’ve done, and what they want, at all times and across all mediums but collecting and acting on unified customer information isn’t easy. Actually  only a few companies have actually achieved complete integration. The rest are struggling with various challenges but customers don’t know or care about those challenges. If you don’t meet their requirements, they’ll assume you don’t care about them and take their business to somebody else whom they believe will treat them better.

No wonder so many marketers have made unified customer experience the highest priority.

Why Customer Data Platforms

A unified customer experience is built with unified customer data. Most data originates in silos. Traditional methods for collecting that data into unified customer profiles, such as an data warehouses, have failed to solve the problem. Newer approaches, like “data lakes”, have collected the data but failed to utilise it effectively.

The CDP is an approach that has had great success at smart companies. A CDP puts marketing in direct control of the data collection and unification project, helping to ensure it is marketing focused.

Customer Data Platform  – Definition

  1. IDENTITY RESOLUTION – Combining profiles and related data points from multiple platforms and systems into a single customer record;
  2. EVENT SYNDICATION – Streaming real time data to various analytics systems to enable modeling and insights;
  3. REPORTING – Monitoring data inflow /outflow and reporting anomalies
  4. AUDIENCE ORCHESTRATION – Creating segments and syncing them to various channels;
  5. 360-DEGREE UNDERSTANDING – By combining profiles together, deploying them for insights and also reinforce learning from analytics and engagement systems to create a complete, well-rounded understanding of customer behaviors

How a CDP is different from a DMP

This is the most common and relevant question. First, let’s revisit what a CDP is, courtesy of the one and only David Raab. Raab defined a CDP as “a marketer-controlled system that builds a multi-source customer database and exposes it to external execution systems.” In particular, Raab wrote that the name works because:

  1. Customer shows the scope extends to all customer-related functions, not just marketing
  2. Data shows the primary focus is on data, not execution
  3. Platform shows it does more than data management while supporting other systems.

Despite those high level similarities, here are the key differences:

  1. DMPs store data in two different ways.

CDPs don’t. Whereas the DMP has two different data stores – one for all data and one for really fast

utilization of a subset of that data but the typical CDP database is all one, so there isn’t a subset of the data that lives separately. The key value store and the total insight lives in a single, massive place. The database is massive, and can scale, but it also features lightning fast reading capabilities.

  1. DMPs collect data like everyone else – with Tags / APIs etc

For CDPs, it’s the same mechanism but data is richer. Any good marketing technology should be able to bring in data in the formats it’s most commonly captured. So while a CDP does use all the means of collecting data, it’s more so about the depth and extent of data brought into the database.

  1. DMPs are in the business of labeling people.

But when and how are pretty apparent. Kihn summarized this by writing, “the data provider must organize its data into categories and subcategories. So all the data sent to the DMP is basically a list of users and an associated list of which predefined categories/subcategories they belong to.” But ideally, we think you should be able to bring enormous lists of individual users and their attributes into the platform without predefined categories. You should be able to create a taxonomy with segment definitions you can create before / after loading the data and can help you discover new ways of classifying segments. Individuals move in and out of segments in real-time based on their behaviors.

  1. DMPs use outside partners to help map data to users.

Sure – whatever the customer wants! The cleaning, integration, and organizing data onboard is extensive, and a CDP both enriches that data set and makes that data actionable. In some cases, systems gives marketers insights about their consumers that they had previously relied on an outside partner. Often, a CDP ingests data from the onboarder and put it to use by powering much more relevant engagement.

  1. DMPs were also designed to personalize websites.

Mobile apps, Campaigns, Emails and Managing the delivery. Kihn is absolutely right when he says that it seems like today “everyone does personalization, including landing page optimizers, testing

platforms, content management systems, recommendations engines and so on.” Nonetheless, despite this apparent abundance of options, very few marketers are actually doing personalization well but not across channels for sure. It’s critical to put the tools in the marketers’ hands to bring data and capabilities together that will allow a linked, consistent brand experience as the customer moves through their decisions. The wealth of traits that a CDP collects and the persistence of the profile for that individual across channels and sessions takes “personalization” to a whole new level.

  1. DMPs make many decisions based on predefined rules.

CDPs deal in rules and triggers – “if this, then that” statements, combinations of criteria, A/B tested decisions. But a jump from the DMP’s capabilities is in the fact that actions taken by a user are the only trigger, whereas a user’s identity or other non-behavioral attribute can result in an immediate decision or action. Furthermore, a CDP is happy to share the rules and triggers that are set up with other systems.

  1. DMPs are better at numbers than analytics.

The key takeaway of this, most provocative statement in Kihn’s post is the following: “The purpose of the DMP is to label people,” In contrast, the role of a CDP is not to create an additional or separate source of truth about customers. It is to facilitate the synchronization of customer data so that at any given touchpoint or interaction, the marketer can recognize that person and engage with the fullest extent of insight about that individual possible. A CDP fulfills the idealized ambition of a single customer view with an entirely marketer-driven solution.

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Different types of CDPs

Data collection

Many CDPs are rooted in an email or web-centric paradigm, meaning they aren’t well-suited for mobile and omnichannel marketers. If customer journey includes people interacting via mobile platforms (Android and iOS), or connected devices (AppleTV & Roku), we need a CDP that supports these natively with robust and well-documented SDKs.

Data enrichment

By definition, CDPs enable unification and distribution of “first-party” customer data. However, if we need to learn more about your customers from other sources of data, you’ll need a CDP that facilitates data enrichment from partners and third-parties;

Data activation

Not all CDPs support the same set of marketing scenarios. Some are great at feeding data into data warehouses and reports, but require manual processes to take action. Still others provide executional support, but only across a limited number of channels, like email and sales automation systems. Our CDP supports the newer breed of mobile marketing automation and attribution platforms, in addition to DSPs, DMPs, and more traditional CRM and campaign management applications. This makes it possible to create and orchestrate experiences across the customer journey.

The ROI of a CDP

Marketing effectiveness

he incremental value associated with creating “more bang for the same buck”

Marketing operations cost avoidance. The cost savings associated with operating more efficiently

Risk reduction

The value associated with reducing business, technology, and regulatory risk

Strategic value

The value associated with creating barriers to entry based on customer data advantage, which in turn drives profit margin

Our CDP with AI

  • As more data is created by your customers during their buying journeys across a growing number of devices, legacy data platforms and integration methods will no longer suffice. Every brand will need a customer data platform purpose-built to integrate and orchestrate customer experiences in an omnichannel world.
  • The demand for high-quality, connected data will further increase as machine learning is commercialized and deployed throughout the enterprise. This is because AI can only “learn” through access to large, well-structured data repositories, CDPs will provide the perfect training ground for marketing, sales, and customer service AI.
  • CDPs will have to stay ahead of shifting privacy regulations and increasing security threats. It is essential that they innovate, not only along the dimension of customer experience but also for security, reliability, and identity management.
  • “Customer data platforms will provide the perfect training ground for marketing, sales, and customer service AI.”