Have you ever thought “Wow, they get me,” when interacting with a brand? It could be as simple as Netflix recommending the perfect show to binge, or Amazon suggesting a product that complements what’s already in your cart. We now expect these personalized experiences from brands. However, with more content, brands, and purchasing channels out there, it becomes difficult to personalize at scale. Enter the customer data platform (CDP).
CDPs aren’t new to the market, but there is still some confusion about what constitutes a CDP.
A CDP, as defined by the Customer Data Platform (CDP) Institute, is “a marketer-managed system that creates a persistent, unified customer database that is accessible to other systems.”
Businesses utilize CDPs to compile a single view of their customers by aggregating data from multiple sources. From there, businesses leverage that data across multiple marketing channels to personalize the customer experience.
G2 has over 600 reviews for CDPs. These reviews include questions regarding features like data enrichment, predictive modeling, and providing a recommendation engine that uses artificial intelligence to give suggestions based on desired outcomes. Out of all the reviews for the CDP category on G2, the average response for the recommendation engine feature is a 6 on a scale from 1-7 (7 being excellent). CDPs continue to steadily proliferate the MarTech market (see graph below). Our most recent Summer 2019 research reports for CDPs reveal leaders in the space based on user review data, satisfaction ratings, implementation metrics, and many other data points.
CDP User Review Count by Year
CDPs & AI
For true personalization, a CDP would need AI and machine learning capabilities. These assist marketers in creating unified customer experiences while optimizing the customer journey by triggering behavior-based messages in real time. CDPs that leverage AI create a cohesive customer experience by pulling data. Then, CDPs use predictive segmentation to tailor content in real-time.
Some vendors that leverage AI:
- Leadspace utilizes AI-driven intelligence to drive recommendations and actions.
- Blueconic uses their AI Workbench to run models in real time across your entire profile database to build smarter segments.
- Lytics’ CDP uses machine learning and AI to understand customers on a 1:1 level and predict the best content and interaction for each customer.
- Blueshift activates your customer data with AI and orchestrates personalized campaigns.
- Evergage’s personalization algorithm, Contextual Bandit, is designed to leverage artificial intelligence (AI) to process vast stores of data.
Additionally, CDPs powered by AI help marketers determine a customer’s lifetime value and which products customers are most likely to buy. Machine learning and AI spot patterns in customer behavior that humans can’t, which enables marketers to invest in marketing strategies that yield the greatest ROI, and make precise predictions about future activities.
We also asked Josh Francia, Chief Growth Officer at Blueshift—an AI-first platform for cross-channel marketing powered by a 360-degree customer view—his thoughts on CDPs that leverage AI. “A key component of AI is a rich set of unified and clean data to train the models. Therefore, CDPs are perfectly positioned to leverage AI and see a dramatic impact on their customer’s underlying businesses. The key benefits of AI in CDPs are identifying the right audience (i.e., who), right product (i.e, what), right time (i.e., when), and right channel (i.e., where).”
Francia also commented on what extent AI-based CDP solutions are available and in use today. “Although many CDPs claim they have AI, the vast majority of CDPs do not have true AI solutions available inside their platform...The leading CDPs and the only ones driving real ROI for their customers have made AI a core component of their platform from the beginning.”
CDPs & compliance
Marketers utilize CDPs to assist in complying with privacy regulations. Regulations like the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA), effective Jan. 1, 2020, impact how marketers handle consumers’ personal information. Both of these regulations require marketers to have increased transparency around customer data.
CDPs enable marketers to create a single view of their customer’s data, which assists them in complying with these privacy regulations. Machine learning lets marketers leverage first-party data, lessening their reliance on third-party customer data and minimize regulatory risk. It wouldn’t be surprising if more states enacted privacy regulations like the CCPA (i.e., Nevada’s new consumer privacy law). With the help of CDPs and machine learning, marketers can consolidate customer data and proactively go through data sets more effectively.
Looking to the future
According to Zimmerman, “we are at the peak of the hype cycle.” As investors pour money into CDPs, more vendors will pop up, resulting in a saturated market, eventually resulting in market consolidation. For now the hype will continue as “CDPs are a legitimate and important technology that addresses a real need in the market.”
The customer data platform market continues to grow year over year, including this year, seen with companies like Salesforce and Adobe announcing their own CDP offerings. Salesforce’s CDP will revolve around insights including segments, personalization, and analytics via Salesforce’s Einstein AI engine. Adobe’s CDP will have the ability to apply AI and machine learning through their AI platform, Adobe Sensei. More CDP vendors have received additional funding this year, including Tealium, Segment, Simon Data, and Amperity. As machine learning and AI advance, we can expect CDPs to continue to expand their capabilities to help marketers analyze and activate customer data.