CDP Round Table: Forget Composable, Are Cloud Databases a Threat to CDPs?

On July 16 and July 18 2024, the CDP Institute hosted a pair of online roundtable discussions for industry vendors. Here are summaries of the conversations.

 

July 16 Roundtable - U.S. and Europe participants.

We started with a quick list of trends that the Institute is watching. These include Composable CDP, CDP integration with advertising channels, third-party cookie deprecation, vertical (industry-specific) CDPs, and AI applications in CDP systems.

The discussion quickly focused on Composable CDP. Key points included:

•  Replacing the term “composable” with “warehouse native”. This is more accurate and resonates in particular with data and IT teams, who want to get the most use from their warehouse investments. “Composable” is still more commonly understood among marketing users.
• Composable CDP comes up most often at large enterprises, which have the most mature IT and data resources.
•  Composable also has appeal in healthcare and financial services, where regulatory concerns make companies reluctant to copy data into a separate CDP database. (Note: I have also heard the opposite, because IT and data teams don’t want to give business users direct access to their entire warehouse and would rather provide them with a limited extract.)
• Digital native companies are also good prospects for Composable because they tend to have well managed data already in place.
•  Composable promises a faster start than building a separate CDP, which is appealing to all potential users. Whether this really happens depends on the state of existing data sources and warehouses.
• Composable CPDs have an advantage because the company’s existing warehouse will include both customer and non-customer data elements tailored to company and its industry. Standard CDPs often must build a custom data model for each new industry, and may struggle to include non-customer information such as product data. Vertical CDPs are appealing because they have industry-specific data models already in place.

Other observations:

• The fast growth of cloud databases, and Snowflake in particular, is a threat to packaged CDPs because they make it easier for IT to build their own products. So far, the cloud databases haven’t built in key tools such as data quality and ID resolution, but there are indications that will change. Such tools are already available as pre-integrated applications in the cloud database vendors’ marketplaces. This is worth calling out as a separate trend from Composable CDP.
• The data engineers who buy cloud databases are not aware that CDP systems exist. They see the value when it’s explained to them, but are still likely to want to expand use of their warehouse systems to justify the investment. This is true even if the warehouse doesn’t already have rich customer data features and customer data has been a low priority. “Data-in-place” and “zero-copy” are strong selling points against putting data into a separate CDP.
•  Regulatory changes, such as anti-redlining rules for banks and Sunshine laws for healthcare marketing, have driven investments in customer data in the past. Privacy regulations may play a similar role in the nature future. This is also worth calling out as a trend.
• There is limited convergence between CDPs and privacy systems. Few CDP vendors have invested in privacy features beyond ingesting consent data, possibly because privacy is complicated so it would take a major investment. In addition, privacy systems have different buyers from CDPs, and privacy managers feel it’s safer to buy from OneTrust than lesser known vendors. But basic privacy and security always requirements in CDP RFPs.
• Journey orchestration is available in many CDPs, which means they overlap with existing journey orchestration systems. But very few clients will replace a mature journey orchestration system with a CDP, due to the effort involved in training staff and migrating programs. Most CDPs with extensive journey orchestration and messaging capabilities are vendors that started as journey orchestration and messaging products and added a CDP capability. Journey orchestration and messaging systems that connect directly with a warehouse may pose another threat to CDPs.
• Advertising integration is often the first CDP application in Italy and elsewhere in Europe.

July 18 Roundtable - APAC participants

Industry trends

• Overview: composable, cloud database, advertising integration, cookie deprecation, privacy & compliance, vertical industry CDPs, AI
• Seeing lots of requests for clean room and consent management, and how CDPs can merge those to streamline work for customers. Some parts of consent can be managed in CDP, others should be in separate platform. Privacy is often first use case.
• Among companies with CDP already deployed, often see extension beyond marketing to other departments. CDP often leads to teams within marketing working together that before kept separate.
• When a new CDP is deployed, people become aware of it organically and through analytic reporting that uses CDP data.
• Cloud databases and composable CDPs introduce new buyer personas from IT and CTOs, who have different use cases and technical concerns from marketers. 

Other items

• Understanding of CDP is relatively low in SouthEast Asia (SEA), compared with Australia, India and Japan.
• Composable is mostly raised by IT teams, who are looking for easiest path to moving data from one platform to another, without understanding the strategy behind the CDP project. Applies to both small and large organizations. Companies like Salesforce with a lot of installed products can co-exist with composable.
• Companies can easily identify many use cases for a CDP. Vendors spend time helping them to prioritize. The sales process is sometimes slowed because buying teams are overwhelmed by the number of use cases.
• Greatest interest among marketers is performance marketing use cases, where results can be directly measured, such as data activation, segmentation, and pipeline-to-paid. This is limiting because there are so many other use cases that don’t have immediate results, such as brand building.
• Advertising is often an early use case, since simple things like suppression and lookalike audiences from CDP can generate immediate, measurable result.
• It’s usually easy to build a use case for CDP looking at almost any part of the lifecycle, from acquisition through churn reduction.
• Confusion about CDP definition can slow down sales, because there are so many different vendors that are hard for buyers to differentiate. The products have changed over time, starting from data capture and now moving to integration with cloud data warehouses and activation in multiple engagement channels, including those outside of marketing.
• It’s not clear whether growth in composable and cloud databases has caused a drop-off in selling stand-alone CDPs. CDP Institute industry update report shows that growth has definitely slowed in the past 18 months, but we don’t know the reason. The slowdown might just mirror the general tech employment slowdown post-covid, and it’s possible that the companies buying composable and cloud databases would have built their own solution anyway, so they were never the companies fueling growth of stand-alone CDP.
• Composable in APAC is having more of an impact in the small to medium sector than with big enterprises.
• AI requires higher data quality.
• Companies feel competitive pressure to invest in AI but are moving slowly, in part due to privacy and security concerns. CDP vendors have added both predictive and generative AI to their systems. Predictive AI deployments are much further along and we’re now seeing initial deployments of generative AI.
• Generative AI is being used to automate existing tasks but not yet to transform tasks. Expect to see more impact next year in terms of benefiting system users, developers, and ultimately at touchpoints where it will change the customer experience itself.
• Generative AI will eliminate some jobs and create others, probably for a net positive impact. People can view AI as a centaur that helps them do their work or a cyborg that takes over their job completely. There are many issues to work out from an enterprise perspective before generative AI becomes widely adopted.