The real Customer Data Platform revolution is in public cloud
by Jan Hendrik Fleury, on Sep 14, 2021 3:32:29 PM
In the overviews of CDP Institute and other Martech overviews you will hardly find the large cloud platforms. They are not 'packaged' and therefore do not fit their definition of customer data platform software. At the same time, public clouds in unified customer data analytics are growing by 30% year on year, on revenues of tens of billions.
What is happening and why?
If we see public platforms at all in the Martech lists it is with an analytical datastore like Google Big Query, Snowflake, AWS Redshift or a measurement and activation product like Google Analytics. What they don't show is that the real revolution is in the combined set of natively integrated components that manage the entire data lifecycle of collection, analytics and activation for you. Not less important, these clouds offer essential supporting functions like governance, orchestration of processes, and security. All functions can be accessed through one console, this is the environment where you can access a cloud.
An increasing number of companies like Coolblue, BOL, Rituals Cosmetics, Ebay, Philips Healthcare are building their CDP foundations, including martech features and tools, in Google Cloud Platform. You can think of a global customer identity graph and audience segmentation. This is built on top of the entire data lifecycle management of Google Cloud. Soon I will publish an article on how these tools and features are made.
The cause is twofold, creating a paradigm shift:
- Big Martech suites didn’t keep their promises to deliver open, flexible and natively integrated platforms
- Cloud is delivering on these CDP requirements and add orchestration of all functions as a bonus
Big Martech didn’t keep their promise
The major marketing tech players such as Adobe, SAP, Salesforce promised to deliver platforms. However, they didn’t keep their promise. None of these players was in fact building a real platform and a real ecosystem. Their CDPs weren’t designed as frameworks on which third-party applications could run. In other words, their Customer Data Platforms were software suites, not platforms. And even in these suites not all components are natively integrated; offering an API for a company they took over as an example. And don’t forget the massive cost for licenses, storage and computation. See also my article on ‘Should I bring my data and analytics to Salesforce?’
In addition, there are many packaged CDP’s (from data, to analytics to activation and hybrid) from smaller vendors such as Segment, Lytics, Tealium and 100+ others. These tools can profit from cloud but they might suffer because of substitution by the cloud platforms as well. This segment is growing at a fast pace too.
The secret of competitive advantage
The competitive axis is not the data itself alone. Data means nothing if you can’t execute properly. To win the hearts of the external and internal customers it takes something different:
- Flexible, integrated technology
- Ability to execute and orchestrate business and technical processes
Cloud is delivering flexible, integrated technology and the ability to orchestrate technical processes.
Soon I will write an article about Data Ops, Marketing Ops and other Ops principles that covers orchestration of business processes.
The cloud as the center of martech engineering
My general advice for mid- to large size companies with large customer databases: build, compose and buy in the public cloud what is possible. Integrate with tools that these clouds don't offer natively. For example a component as a user interface for creating customer process flows like Salesforce Einstein and Selligent offer. This way you can get the best out of both worlds: integration, orchestration and adding these tools from other vendors you want to use.
Don’t turn your back to the customer
Why would you use tangled tooling from various vendors in your data and analytics cycle? Suppose you keep working this way. Then you have to integrate all those products that are not naturally designed to talk to each other. You probably end up with an unnecessarily complex system. And if you don’t implement it well, your central (customer) view and the possibility to model on all data instead of data in silos gets wasted. Also, aspects such as privacy, security, orchestration of processes, checks on data flows, etc. become too complicated and error prone. You don’t want to focus on technical issues. You want to serve the customer!
The art of finding the optimum between cloud and the thousands of martech apps available is the way to go if you ask me. This is one of the aspects that make our work fun.
Finding the equilibrium in cloud platforms and martech apps/tools
- Cloud platforms are vertically integrating towards building and buying features and tools for marketing tech and apps
- This is increasingly competing with buy / point solutions because cloud is natively integrated in one console / platform and offering orchestration
- Centralized architecture is a prerequisite for easily testing, buying buy / point solutions
- Centralized architecture enhances performance of point solutions, i.e. central data model, central decisioning, no migrations of data; easy switching
- If well architected, you will have the best of both ‘worlds’ in buy and build
Cloud platforms are much more than a 'data lake' and a feed for tools put 'on the cloud'. In addition to the aforementioned data lifecycle, marketing related functionality is increasingly being created in the cloud so that cloud integrates up into the martech software world. And, as a result, cloud competes with the big martech vendors and also with some point solutions/apps. Example of establishing an identity graph that starts with Google Analytics where a customer gets an Google ID. Delivering this ID in natively integrated Datastore Big Query enables you to establish a global customer ID where all customer interactions are linked. You can define your set of rules and data doesn’t have to leave Google Cloud (data gravity principle). I will cover examples like this in another article soon.
Centralized architecture as a common ground for martech
Centralizing core functions such as data, analytics, campaign management, identity management and content management on a common ground in cloud, adding orchestration is a prerequisite for easily testing and productionalizing new martech tooling.
Having core data functions such as a common data model centralized and centralized persistent data in the cloud, you can easily switch from martech apps. So you can become flexible and try out the packaged tooling from the SAAS vendors you like!
Centralizing feeds the point martech solutions with the right fuel thus enhancing the performance of these tools. If you implement this properly you have the best of both worlds in building and composing CDP features and tools in the cloud and the joy of buying those martech tools that have a specific added value.
The real CDP revolution is in the cloud and not the martech suites. Sure, Adobe, Salesforce and the entire longtail of other logos in the packaged martech world are growing fast. However, the core of centralizing functions and orchestrations is in public clouds that integrate with buy components. Both are growing fast and are reinforcing each other precisely if you implemented it properly.
Get started on your data strategy, engage your stakeholders, take the lead and get good advice from smart and experienced solution architects.
What are your thoughts and experiences? Let’s have a coffee over it.
Crystalloids helps companies improve their customer experiences and build marketing technology. Founded in 2006 in the Netherlands, Crystalloids builds crystal-clear solutions that turn customer data into information and knowledge into wisdom. As a leading Google Cloud Partner, Crystalloids combines experience in software development, data science, and marketing, making them one of a kind IT company. Using the Agile approach Crystalloids ensures that use cases show immediate value to their clients and frees their time to focus on decision making and less on programming.