The Composable CDP Approach to Customer Engagement

A woman is sitting at a computer, she's using composable approaches to CDP and customer engagement to do more with her data

Doing more with less has always been a top priority for leadership teams. Today’s organizations are facing heightened financial scrutiny as they feel pressure to maximize ROI and do more with fewer resources.

At the same time, consumer expectations for personalized, relevant, data-driven experiences have never been higher. Consumer brands, in particular, are competing for customer loyalty, relevance, and share of wallet. The ones that come out on top will be those that excel the most in engaging their customers.

Amid these challenges, marketing, martech, and data analyst teams are facing a dilemma. They’re asking themselves:

“How can we collaborate more effectively to deliver outstanding customer experiences while also controlling costs, minimizing risks, and avoiding unnecessary technology complexity?”

These conversations naturally lead to discussions about customer data platforms versus customer engagement platforms.

Composable CDP and customer engagement

To address these challenges, data-driven brands are adopting a new approach to customer data and engagement, often described as “composable” or “warehouse-native.”

This composable model has its roots in the significant advancements made in the world of data storage, particularly in data clouds like Snowflake, which is a leader in this space. These modern data clouds are powerful and flexible, capable of handling large data queries and scaling up or down as needed. Older solutions like Teradata and Netezza couldn’t match this performance without incurring high costs.

Snowflake leads the market in data sharing and data clean room capabilities. It enables secure, real-time applications in areas such as identity resolution, data enrichment, and secure data collaboration with partners. This has given rise to a new breed of warehouse-native applications that either connect to a centralized data cloud owned by the brand or directly to it as their primary data source. The brand’s data cloud becomes the central hub, powering everything related to data.

Simplifying data management with composable CDPs

What’s crucial is that a true warehouse-native connected application, like MessageGears, doesn’t require data mapping or copying to a third-party provider’s data cloud. This eliminates data friction and the associated costs. Any deployment model that involves mapping and copying data, even via data sharing, is known as a traditional SaaS or packaged application model, which can be more expensive.

The composable, modular, connected app model delivers disruptive value when implemented effectively through cross-functional collaboration. It’s agile, secure, and cost-effective, leading to transformational improvements in customer engagement and experience.

Components of a Composable CDP and customer engagement model

So, what does this new composable CDP model look like? Here’s an overview of a typical structure influenced by actual data and customer engagement models in place across enterprise brands. Let’s discuss each pillar in more detail.

Warehouse-native, composable CDP ecosystem

The brand’s data cloud

The data cloud is at the core of the composable CDP model. All customer, transactional, and behavioral data lives here, with transformations, data model standardizations, and universal identifiers.

Chances are, your brand’s data team already has some level of a centralized customer and transactional data cloud in place. Other parts of the organization rely on this kind of data for planning and analytics, so it’s important for marketing teams to understand how they can leverage it.

1. Data collection

Most brands with millions of customers have data teams responsible for collecting transactional and behavioral data from point-of-sale, websites, and mobile apps using ETL (Extract, Transform, Load) and behavioral data tools. In some organizations, capturing behavioral data might be a missing piece. Luckily, solutions like Snowplow and RudderStack can fill this gap effectively.

2. Identity, transformation, and enrichment

This area can be transformative compared to traditional methods of identity resolution and data enrichment. Best practices are emerging, where brands manage some of the deterministic identity resolution natively in their data cloud using tools like DBT, Coalesce, or Truelty. For more advanced identify resolution use cases, brands can tap into third-party identity graph providers via the Snowflake Marketplace, leveraging data sharing and data clean room capabilities.

Data enrichment and appending can also occur in near-real-time through the Snowflake Marketplace. With this approach, the brand’s data cloud remains the single source of truth for all first-party customer data. 

3. Machine learning, AI, and decisioning

Many brands with a large customer base already have data science teams using tools to perform propensity, predictive, next-best offer, or next-best-action models. These models operate on first-party data and store their outputs within the data cloud.

4. Segmentation, orchestration, campaign management, and channel activation

For these functions, a stand-alone solution like MessageGears’ segmentation tool deploys as a modular, connected app running on top of the brand’s data cloud. It offers a user-friendly audience builder, segmentation data append capabilities, and the ability to activate audiences across third-party channels such as Meta, Google, and direct mail, as well as traditional Saas marketing clouds and customer engagement platforms. The term “reverse ETL” is often used for the third-party channel activation component.

5. Native channel execution (email/push/in-app/SMS)

Enterprise-grade alternatives to traditional SaaS marketing clouds, ESPs, and customer engagement platforms exist today that function as warehouse-native connected apps built to deliver personalized, timely messaging on a massive scale. A prime example is MessageGears Message, a solution leveraged by some of the largest brands in the world.

Adopting a Composable CDP model

You don’t need to implement the entire composable model at once or have high levels of maturity in every area to achieve significant business impact and reduce operational costs. Taking a phased “crawl, walk, run” approach is common.

Phase 1: Build a strong foundation

Begin by ensuring your brand’s marketing and technology teams are on the same page. Confirm that a customer data cloud foundation is either in place or in progress. Establishing a collaborative partnership between these teams is crucial.

Phase 2: Take your first steps

Once you set the foundation, start your composable journey by introducing visual audience segmentation and activation tools. For instance, you can leverage MessageGears Segment, which integrates seamlessly with your brand’s customer data cloud as a connected app.

MessageGears Segment

By adopting this approach, marketers can create advanced visual audience blueprints and nodes, provide for audience metadata enrichment, and activate audiences on any third-party endpoint.

Phase 3: Evolve your composable model

As you continue to evolve, you can layer in components of the composable model over time. You don’t need to rush to implement every aspect at once. This process allows you to gradually phase out and replace traditional SaaS tools with innovative solutions to reduce costs and enhance customer engagement.

Navigating the composable model landscape

Leverage what you have: Composable models harness data cloud and data ingestion foundations that typically already exist within your organization. Connect with your technology and data teams to understand the current state and future plans for your composable CDP approach.

Collaborate across teams: If your organization is planning or already has a customer data cloud foundation, encourage collaboration between data and marketing teams so everyone can leverage the investment.

Avoid duplicate investments: Marketing teams, reach out to your data counterparts. They might already have a customer data cloud foundation in place that other parts of the organization are using. Avoid significant investments in third-party CDP platforms when you might already have best-of-breed foundations within your data and analytics team.

Unlock agility and efficiency: This new B2C martech approach is agile, secure, and cost-effective. It can have transformative impacts on customer engagement and experience.

Connect with us: Reach out to MessageGears to delve deeper into composable CDP models. We’ve helped some of the largest brands in the world adopt this approach so we can guide you in the right direction for your organization. 

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About the Author

Walter Rowland

Walter Rowland is the SVP Growth and Partnerships at MessageGears. Walter has worked in Sales, Marketing and Partner leadership roles at high-growth technology firms his entire career. Walter holds an MBA in finance from Columbia Business School, and an undergraduate degree from Harvard.