The Customer Data Platform Institute (CDPI) recently surveyed its members about their customer-facing systems and CDP deployment plans. 

A complete predictive analytics solution combines a sound understanding of your target market and multiple sources of intent data and real-time engagement data to accurately predict and target new accounts. Target market data includes current customer intelligence and lookalike modeling, plus firmographic data derived from organization characteristics and technographic data that looks at organizations’ current solutions to glean information about purchase behavior. Real-time engagement data comes from responses to various sales and marketing tactics, including direct mail, display advertisements, inside and field sales outreach, and email campaigns to help round the solution out.

Intent data can build on target market intelligence with first-party and third-party data by helping uncover the content research and engagement trends for solutions in your stack. This type of data includes first-party data such as website traffic monitoring that companies can already access internally, and can be an invaluable advantage for a predictive solution. True intent data incorporates third-party data such as intelligence from the B2B web, making it even more powerful as a contributor to a predictive strategy.

Combined, this internal and external intent data provides a framework from which sales and marketing teams can begin to characterize the accounts that make up their current and prospective customers. Intent data forms part of a solid groundwork from which a predictive customer acquisition strategy can build if it has broad coverage of the target market. Real-time engagement provides the final piece to a true predictive solution.

60 per cent of enterprise B2B marketers are using a DAM but only 23 per cent are actively using a content marketing platform to support their editorial processes. Net, just over half even have a tool to managed their valuable content assets and a very small number have codified processes that happen in something more formal than an excel spreadsheet and email. 

Atomic content and personalisation is only possible if we, marketers, shift our mindsets, build some new skills and use the tools available to us to attend to the care and feeding of our content library.  A few months ago I wrote a very nerdy post about metadata and attributes. It sparked a lot of conversations about how hard it is to do this today. But trust me, it’s getting easier.  More tools – some CMPs and some re-imagined DAMs – are making it simpler to store and retrieve assets. The infusion of artificial intelligence (AI) into some of these systems will augment work done by marketers and creatives – taking the drudgery out of tagging and unlocking the value in our content – even making recommendations about which assets may work best for a given situation.

iPaaS (integration-platform-as-a-service) and similar kinds of integration services were built from the ground up to be hubs. You plug all your marketing applications (particularly SaaS products) into the iPaaS service — most of which have hundreds of application connectors ready for you to pick from a simple menu — and then it handles piping data between them via their APIs.

Most iPaaS solutions initially focused on making it easy to integrate all your different marketing cloud services. They were the Data Plumbing layer in the above diagram. Many then added Data Parsing/Routing Logic that gave their users — “citizen integrators” — the flexibility to create more intelligent, multi-step recipes for reacting to events from any one connected product and triggering actions in response through other connected products.

The reason I call iPaaS solutions “virtual platforms” is because as long as they focus only on the Data Integration layer, they don’t serve as a System of Record for the data that passes through them, and they don’t provide Marketing Execution or Marketing Intelligence.

Indeed, this is exactly what customer data platforms (CDPs) have done. Many of them have an iPaaS-like background or capabilities, but they also serve as a System of Record, storing and even normalizing the data that passes through its engine. Several have now started to implement their own built-in Marketing Execution and Marketing Intelligence capabilities too — while still making it very easy for marketers to plug in alternate execution and intelligence services.

A Customer Data Platform needs to:

Function for marketers
Unify data
Track individuals
Connect with external systems
Segment customers
Let’s look at how that works in a little more detail.

1. A CDP is marketer managed

2. A CDP breaks down data silos, from any data source, and unifies it in a single database

3. A CDP can track individuals, not just data sources

4. A CDP is accessible with external systems

5. A CDP can segment your customers

From one side, the Customer Data Platform seamlessly integrates with each of the SaaS product you’re using in your stack as the primary way to get data, from the other side it has advanced automation capabilities to actually operate.

A sample of questions that can be answered by Customer Data Platforms:

• Is this user active or not?
• Did they ever pay for my product?
• Where do they prefer to be contacted, via email or with browser notifications?
• How many tickets did they open in the last month?
• What’s the NPS score for my product?
• What are the chances of churning for this user?

But that’s not all, you can actually combine all those data to refine segments and audiences and deliver remarkable journeys to your customers.

Here's a list of common CDP questions and answers.  These apply to CDPs in general but details may vary for specific systems.

This is a challenge that has continued to plague marketers, despite the promise of solutions such as customer-relationship management (CRM), master-data management (MDM), and marketing-resource management (MRM). These solutions can help companies consolidate and streamline data, manage segmentation, organize workflow, and improve customer relationships. But they don’t take full advantage of digital signals customers provide. Instead, relying on antiquated “list pulls,” basic segmentation, and campaigns, all lack the automated decision making, adaptive modeling, and nimble data utilization to scale personalized interactions.

Enter the Customer Data Platform (CDP)—a data discovery and “decisioning” (i.e. automated decision making) platform. The CDP makes it possible for marketers to scale data-driven customer interactions in real time. And while CDP hasn’t really broken into the Gartner Magic Quadrant or Forrester Wave, it is gradually becoming an industry-standard concept, with a small but growing cadre of third-party platforms emerging that will soon shape the category.

If you’re not already familiar with CDPs, the important points to know are:

• They are software or software-as-a-service products that ingest customer data from many source systems and combine it into a unified customer database — also known as the Single Customer View. 
• They are designed to be run by marketing departments with little or no support from corporate IT. That’s important because IT groups often don’t have the resources or understanding to do what marketing needs as quickly as they need it. 
• They make their data available to all external systems. That’s important because marketers today have so many different systems that need complete customer data to deliver the best results. 

I do feel I can safely publish statistics for three groups within the industry.  This gives some additional insight without exposing any proprietary or misleading vendor data.  The groups are based on each vendor's original business.  They are:

• Tag managers.
• Campaign managers. 
• Data assembly systems.

How it works is this: Hull has connectors for major customer-facing SaaS systems, such as Salesforce, Optimizely, HubSpot, Mailchimp, Facebook custom audiences, Slack, and Zendesk. Users connect with those systems and specify data elements or lists to synchronize. When data changes in one of customer-facing products, the change is sent to Hull which in turn sends it to other products that are tracking that data.

But, unlike data exchanges such as Zapier or Segment, Hull also keeps its own copy of the data. That’s the “persistent” bit of the CDP definition. It gives Hull a place to store data from enhancement vendors including Datanyze and Clearbit, from external processes called through Javascript, and from user-defined custom variables and summary properties, such as days since last visit. Those can be used along with other data to create triggers and define segments within Hull.  The segments can then be sent to other systems and updated as they change.

In other words, even though the external systems are not directly reading the data stored within Hull, they can still all work with consistent versions of the data.