\n<\/p>\n
MarTech.org has defined a CDP<\/a> as follows.\u00a0<\/p>\n \u201cCustomer data platforms (CDP<\/a>) are marketer-managed systems designed to collect customer data from all sources, normalize it and build unique, unified profiles of each individual customer. The result is a persistent, unified customer database that shares data with other marketing technology systems.\u201d\u00a0<\/p>\n<\/blockquote>\n That\u2019s a good definition, but it leaves a lot unsaid.<\/p>\n There are, in fact, different types of CDP, and they can be used to implement very different marketing and operational strategies.This review of 10 CDP types will help you fine-tune your CDP strategy and evaluate prospective vendors.\u00a0\u00a0<\/p>\n <\/p>\n While some CDPs are purpose-built from the ground up, many started off as something else \u2013 e.g., a marketing automation<\/a> platform, a tag management solution, a database or an email service provider. The CDP\u2019s origin story is important because it provides a clue about the focus of the CDP vendor and the needs of their customers. When choosing a CDP, you want to make sure the CDP\u2019s business emphasis and its customer base are aligned with your company\u2019s priorities.\u00a0<\/p>\n Knowing the CDP\u2019s origin story is a helpful starting point for understanding your options, but you need to dig deeper to understand what kind of a CDP it is<\/em>. I\u2019ve divided the CDP world into 10 categories. This isn\u2019t the only way to characterize CDPs, and most CDPs are a combination of several of the types I describe.\u00a0<\/p>\n Despite that, understanding these 10 CDP types will help you cut to the chase quickly to determine if any given CDP is likely to meet your needs.\u00a0<\/p>\n Just as there are different types of cars for different transportation needs, there are different types of CDP to accommodate different company requirements. A retail store has different needs than a digital-only news site. Familiarize yourself with these basic CDP types and you\u2019ll have a leg up when evaluating potential vendors. No CDP will fit neatly into just one of these categories, but they may be more like one than another.\u00a0<\/p>\n Other distinctions can be made, such as \u2026<\/p>\n Sorry, it\u2019s a complicated world out there, and I can\u2019t answer everything all at once. Nevertheless, I do believe that these 10 types can help you better understand the landscape.\u00a0<\/p>\n Dig deeper: Beyond the tech: Mastering customer data with a modern approach<\/a><\/em><\/strong><\/p>\n Focus<\/strong>. Primarily on aggregating data from multiple and varied sources. Such CDPs will have lots of proven integrations, and will focus on real-time data management. A data collection CDP will likely prefer deterministic over probabilistic matching<\/a>.<\/p>\n Use.<\/strong> A data collection CDP is ideal for businesses that need to create a reliable single customer record using data from websites, CRM, mobile apps, offline data sources and third-party systems.\u00a0<\/p>\n Sample use case.<\/strong> Integrate customer data from multiple sources to create a single, unified view of the customer to gain insights into customer personas.\u00a0\u00a0<\/p>\n Potential weak spots.<\/strong> Data cleansing, activation and orchestration.\u00a0<\/p>\n Focus.<\/strong> On cleaning, deduplicating, validating and normalizing data to ensure accuracy and usability.<\/p>\n Use. <\/strong>Suitable for companies dealing with large volumes of data from diverse sources that need to ensure data quality, such as financial services and banking.\u00a0<\/p>\n Sample use case.<\/strong> Ensure data is accurate, complete and complies with health data regulations like HIPAA.\u00a0<\/p>\n Potential weak spots. <\/strong>Activation and orchestration. Also, <\/strong>too strong of an emphasis on accuracy may leave some data behind, or create rigid rules that don\u2019t work with the practical necessities of other systems.\u00a0<\/p>\n Focus.<\/strong> Create deep, actionable insights through analytics and data visualization, often incorporating machine learning for predictive analytics.\u00a0<\/p>\n Use.<\/strong> Best for organizations that need to derive complex insights and forecasts from their customer data.<\/p>\n Sample use case.<\/strong> Find patterns in customer data to expose new marketing and advertising opportunities.\u00a0<\/p>\n Potential weak spots.<\/strong> An emphasis on predictive analytics can result in less precision in segmentation and campaign orchestration.\u00a0<\/p>\n Focus.<\/strong> Managing and automating marketing campaigns and customer journeys across various channels through the use of internal marketing tools and\/or through integrations with external tools.<\/p>\n Use.<\/strong> Create a single system from which to orchestrate and coordinate marketing campaigns across multiple platforms and touch points based on a comprehensive view of the customer.\u00a0<\/p>\n Sample use case. <\/strong>Increase the conversion rates from email marketing campaigns by tying them to web and account activity.\u00a0<\/p>\n Potential weak spots.<\/strong> Marketing automation can cause no end of headaches. (See \u201cHow to keep your marketing automation campaigns from ruining your week.<\/a>\u201d) A campaign-oriented CDP needs effective methods to prevent automated campaigns from crashing into one another.\u00a0<\/p>\n Focus.<\/strong> Segmenting customer data into meaningful groups, based on demographics, behavior, purchase history, and preferences.\u00a0<\/p>\n Use.<\/strong> Categorize customers into well-defined segments to create more targeted engagements \u2013 for content, marketing, customer service or product development.\u00a0<\/p>\n Sample use case.<\/strong> Find all users across multiple company-owned domains who share an interest in high-end fashion.\u00a0<\/p>\n Potential weak spots. <\/strong>Segments that make sense in January might not work in November. Too much segmentation can create scattershot, disjointed efforts that don\u2019t reflect a consistent brand message.\u00a0<\/p>\n Dig deeper: The top 10 benefits of customer data platforms<\/a><\/em><\/strong><\/p>\n Focus.<\/strong> Align customer data insights with content management capabilities, focusing on creating a deeper content engagement with customers based on personalization and behavioral data.\u00a0<\/p>\n Use.<\/strong> Deliver highly personalized content to individual customers based on their demographics, behaviors, preferences and other data. The goal is to increase engagement with brand content and improve customer experience.\u00a0<\/p>\n Sample use case. <\/strong>Increase in-app usage by providing a constant stream of relevant videos consistent with past behavior and preferences.\u00a0<\/p>\n Potential weak spots.<\/strong> Effective targeting requires extensive meta data for content and potentially sensitive user information.\u00a0<\/p>\n Focus.<\/strong> Integrate data from online and offline interactions to create a unified, real-time customer profile.\u00a0<\/p>\n Use.<\/strong> Consolidate data across e-commerce platforms, brick-and-mortar stores, mobile apps and social media to understand customer behaviors, increase sales, and create a better customer experience.\u00a0<\/p>\n Sample use case.<\/strong> Integrate in-store and online purchases to create effective product recommendations for email marketing campaigns.\u00a0<\/p>\n Potential weak spots.<\/strong> Effectiveness may be hampered by point-of-sale (POS) systems that do not have the capability of delivering appropriate data.\u00a0<\/p>\n Focus.<\/strong> Handle the complex business structures and longer sales cycles for B2B efforts.\u00a0\u00a0<\/p>\n Use.<\/strong> Consolidate and manage data from various systems, such as CRM, email interactions, social media and direct marketing campaigns to create a unified view of each business account and the stakeholders therein.\u00a0\u00a0<\/p>\n Sample use case.<\/strong> Maintain a consistent marketing and sales message to an entire enterprise across various departments and individual leads.\u00a0<\/p>\n Potential weak spots.<\/strong> Not all B2B businesses are the same. Creating a single structure to accommodate all prospects can be very challenging.\u00a0<\/p>\n Focus.<\/strong> Leverage customer data to provide more efficient, personalized, and proactive customer service.\u00a0\u00a0<\/p>\n Use.<\/strong> Aggregate all customer interactions \u2013 from support calls, chatbot conversations, email exchanges, social media interactions, etc. \u2013 to enable customer support teams to offer personalized and accurate customer service.\u00a0\u00a0<\/p>\n Sample use case.<\/strong> Enrich the information available to phone customer service reps with relevant data from all customer interactions.\u00a0<\/p>\n Potential weak spots.<\/strong> Some customer interactions, such as social media exchanges, can be difficult to capture in a CDP.\u00a0<\/p>\n Focus.<\/strong> Integrate and act upon new customer data in real time to power immediate marketing activations.\u00a0<\/p>\n Use.<\/strong> Personalize the customer experience, advertising, and marketing campaigns in real time on web, app, or customer service platforms.\u00a0<\/p>\n Sample use case.<\/strong> Include an online visitor in a Google Ad Manager campaign based on the user\u2019s navigation menu selections.\u00a0<\/p>\n Potential weak spots.<\/strong> \u201cReal time\u201d can be a fuzzy concept, and often depends on systems that do not operate in real time. See \u201cThe limitations of \u2018real-time\u2019 CDP use cases<\/a>.\u201d\u00a0<\/p>\n The point of these \u201c10 types\u201d is not to pigeon-hole any given CDP, but to give you, as a marketer, a broad understanding of the different features and emphases CDPs might bring to the table.\u00a0<\/p>\n Reviewing these 10 types can also help the marketer think creatively about how a CDP can bring value to the business.\u00a0<\/p>\n <\/p>\n \n<\/div>\n Opinions expressed in this article are those of the guest author and not necessarily MarTech. Staff authors are listed here<\/a>.<\/em><\/p>\n\n
The importance of the origin story<\/h2>\n
10 types of CDPs<\/h2>\n
\n
1. Data collection\u00a0<\/h3>\n
2. Data cleansing\u00a0<\/h3>\n
3. Analytical\u00a0<\/h3>\n
4. Campaign\u00a0\u00a0<\/h3>\n
5. Segmentation<\/h3>\n
6. Content\u00a0<\/h3>\n
7. Retail<\/h3>\n
8. B2B\u00a0<\/h3>\n
9. Customer service<\/h3>\n
10. Real-time<\/h3>\n
Conclusion\u00a0<\/h2>\n