This is the second part of a three-part series. The first part, on composable CDPs is here<\/a>.<\/em><\/p>\n The CDP space has been overtaken in recent months by talk of composability. Misnomer or not, the term has stuck, and in essence means the possibility of CDPs activating data within a customer\u2019s data warehouse (or \u201clakehouse\u201d). There are many ways to do this, but what\u2019s important to note is that it\u2019s a step away from the traditional model of the CDP itself ingesting customer data, creating and storing a persistent database of customer profiles.<\/p>\n While the noise about this has been coming primarily from the CDP space, a similar trend is detectable in customer engagement platforms \u2014 platforms that emphasize messaging customers in the moment across multiple channels. \u201cBe absolutely engaging,\u201d is the motto of customer engagement platform Braze.<\/p>\n But while platforms like Braze and Iterable are taking advantage of the growing trend of pulling data from data warehouses, that doesn\u2019t mean some data doesn\u2019t reside in their platforms, at least for a certain amount of time. <\/p>\n We spoke with Kevin Wang, Braze\u2019s chief product officer, about composability just as Braze launched a new offering, the Braze Data Platform<\/a>. \u201cThe ability to pull data from a data warehouse directly with the very seamless, automated transitions we facilitate, that for us is relatively new,\u201d Wang said. Braze has also long been able to activate data held in CDPs.<\/p>\n \u201cThe overall thinking here is that there\u2019s more data than ever; the data needs to go somewhere; companies are becoming much more data-driven; and computers, systems, networks need to support all of that data flowing through.\u201d The CDP is one solution for this; an alternative, growing in popularity, is the use of data warehouses like Snowflake, Databricks, Google Big Query, Amazon Redshift and so on.<\/p>\n \u201cThe general trend of companies wanting to leverage the data warehouse more and more to be that central organizational point, connecting out to different platforms and systems, we certainly see that trend happening,\u201d said Wang. \u201cThe CDPs have seen that trend and as a result a lot of them have very strong integrations into data warehouses as well. Because data warehouses are really built for storing massive amounts of data and keeping it well-organized, they\u2019re less optimized for a lot of what we do. For example, that ability to have a user take an action or make a purchase and immediately respond with a message to a browser or email inbox. \u201c<\/p>\n What Wang sees is Braze customers who have centralized their data in a data warehouse syncing those elements relevant to personalized customer journeys into Braze and setting up customer journey orchestration within Braze. Does that mean the data is being copied into Braze?<\/p>\n \u201cThe way that it works with a data warehouse is that we set up a sync where at very high frequency \u2014 a matter of minutes \u2014 we actually sync data into our user profiles,\u201d Wang explained. \u201cAll of our real-time action and performance and reponsiveness all operate in terms of our user profiles. Our cloud data ingestion allows customers to define segments, pull data directly from the data warehouse, then use the segments in Braze and they\u2019re off to the races.\u201d<\/p>\n The way the customer action triggers a response can vary from brand to brand. Some brands have the customer action data flow direct to Braze where a response will be automatically determined. Some channel it to the data warehouse first; that means the response is not triggered quite as fast. Some brands, indeed, have a mixture of needs. An ecommerce site or a dating app, to take two examples, will see transactions that require an instantaneous response. \u201cBut they might also have a newsletter or weekly recommendations; that\u2019s a use case that is more data heavy but the real-time need is not central for that use case.\u201d<\/p>\n The newly announced Braze Data Platform has extensions to Amazon Redshift and Databricks (they already had Snowflake) but the data ingested by Braze for activation does linger in Braze. \u201cWe can store data for a long amount of time,\u201d said Wang, \u201cbut some of it will eventually get archived. We\u2019re not designed to store every single data point in perpetuity but we do store data long enough for customers to re-target off of it.\u201d<\/p>\n Dig deeper: What the composability revolution means for CDPs<\/a><\/em><\/strong><\/p>\n Traditional CDPs are starting to struggle to demonstrate value, especially set against the trend towards data warehouses. That\u2019s the view of Heather Blank, SVP GTM strategy and partnerships at Iterable. \u201cModern players like Hightouch, those have been the ones to coin the term \u2018composable CDP,’\u201d said Blank, \u201cbecause they saw an opportunity with customers who had data warehouses or data lakes where they were storing not just customer data but all of their business data. They saw that you do not need all the bells and whistles of a CDP, nor do you need to duplicate your storage of data, so we\u2019re just going to lay down some rail tracks for data to move back-and-forth on.\u201d Hightouch is a partner of Iterable. It now calls itself a composable CDP but has been primarily known for its reverse ETL capabilities.<\/p>\n \u201cWhere Iterable fits in is, we\u2019re not interested in storing all data for our customers. We want to make sure that customers have the ability to easily pull the appropriate data into our platform from those central data sources and then have a place to marry that data with the data we have \u2014 who clicks on an email, what channels are they most responsive in, all the things that we observe.\u201d The aim is to drive the most personalized content possible in the optimum channel, at the right time and with the right frequency, Blank explained. <\/p>\n Iterable has integrations with a number of established CDPs. \u201cThose guys are going to market with a composable CDP as well. While Iterable has these integrations, Blank is seeing more and more customers who want to pull their data out of warehouses directly and to have a CDP as a hop in-between to get to us. That\u2019s why we have the integration with Hightouch.\u201d<\/p>\n The challenge faced by traditional CDPs, according to Blank, is similar to the challenge that confronted DSPs. While CDPs dealt in first-party data, DSPs dealt with audiences for paid media, but they both tended to become rail tracks between a brand\u2019s data and where that data is activated. What\u2019s more, there was a time when multiple applications were needed to activate data \u2014 Iterable was once only email, Braze was in-app, for example. <\/p>\n \u201cWhat you had was this really complex stack where you had to bring data in, log into all these different places to design your campaigns, segment, personalize and send your campaigns. We call that problem the \u2018activation gap,’\u201d said Blank. Iterable is solving the problem by plugging directly into data respositories, via reverse ETL. \u201cThen we\u2019re a single source platform for you to compose all of your messages, do all your decisioning and deliver across all the channels that you want.\u201d Iterable also has partners like The Trade Desk where data can be activated on the paid side.<\/p>\n Blank was keen to emphasize that she was not denigrating CDPs but rather observing their evolution. \u201cIf you have to pull your data out of somewhere that\u2019s not a data warehouse, or from multiple places because you haven\u2019t been able to centralize it all in a data warehouse, then [traditional] CDPs are still the answer.\u201d<\/p>\n But the data has to be in Iterable at some point in order for it to be activated, right? In fact, Iterable does have data repository capabilities. \u201cWe have some customers for whom we are their CRM system. It sort of depends. You can store as much or as little data in our platform as you want.\u201d And that depends, again, on the need to respond in real-time. \u201cYou don\u2019t want to wait for data to move back-and-forth. As soon as somebody buys something, you don\u2019t want them then to get a cart abandonment email.\u201d<\/p>\nOff to the races<\/h2>\n
Solving for the activation gap<\/h2>\n
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