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    There are 10 types of CDP. Here’s why that matters

    There are 10 types of CDP. Here’s why that matters

    MarTech.org has defined a CDP as follows. 

    “Customer data platforms (CDP) 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.” 

    That’s a good definition, but it leaves a lot unsaid.

    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.  

    The importance of the origin story

    While some CDPs are purpose-built from the ground up, many started off as something else – e.g., a marketing automation platform, a tag management solution, a database or an email service provider. The CDP’s 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’s business emphasis and its customer base are aligned with your company’s priorities. 

    Knowing the CDP’s 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. I’ve divided the CDP world into 10 categories. This isn’t the only way to characterize CDPs, and most CDPs are a combination of several of the types I describe. 

    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. 

    10 types of CDPs

    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’ll 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. 

    Other distinctions can be made, such as …

    • Composable vs. packaged CDPs.
    • Pure play vs. suite (i.e., CDPs that are integrated into a larger set of marketing tools).
    • Enterprise vs. SMB (small and medium businesses).

    Sorry, it’s a complicated world out there, and I can’t answer everything all at once. Nevertheless, I do believe that these 10 types can help you better understand the landscape. 

    Dig deeper: Beyond the tech: Mastering customer data with a modern approach

    1. Data collection 

    Focus. 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.

    Use. 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. 

    Sample use case. Integrate customer data from multiple sources to create a single, unified view of the customer to gain insights into customer personas.  

    Potential weak spots. Data cleansing, activation and orchestration. 

    2. Data cleansing 

    Focus. On cleaning, deduplicating, validating and normalizing data to ensure accuracy and usability.

    Use. Suitable for companies dealing with large volumes of data from diverse sources that need to ensure data quality, such as financial services and banking. 

    Sample use case. Ensure data is accurate, complete and complies with health data regulations like HIPAA. 

    Potential weak spots. Activation and orchestration. Also, too strong of an emphasis on accuracy may leave some data behind, or create rigid rules that don’t work with the practical necessities of other systems. 

    3. Analytical 

    Focus. Create deep, actionable insights through analytics and data visualization, often incorporating machine learning for predictive analytics. 

    Use. Best for organizations that need to derive complex insights and forecasts from their customer data.

    Sample use case. Find patterns in customer data to expose new marketing and advertising opportunities. 

    Potential weak spots. An emphasis on predictive analytics can result in less precision in segmentation and campaign orchestration. 

    4. Campaign  

    Focus. 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.

    Use. 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. 

    Sample use case. Increase the conversion rates from email marketing campaigns by tying them to web and account activity. 

    Potential weak spots. Marketing automation can cause no end of headaches. (See “How to keep your marketing automation campaigns from ruining your week.”) A campaign-oriented CDP needs effective methods to prevent automated campaigns from crashing into one another. 

    5. Segmentation

    Focus. Segmenting customer data into meaningful groups, based on demographics, behavior, purchase history, and preferences. 

    Use. Categorize customers into well-defined segments to create more targeted engagements – for content, marketing, customer service or product development. 

    Sample use case. Find all users across multiple company-owned domains who share an interest in high-end fashion. 

    Potential weak spots. Segments that make sense in January might not work in November. Too much segmentation can create scattershot, disjointed efforts that don’t reflect a consistent brand message. 

    Dig deeper: The top 10 benefits of customer data platforms

    6. Content 

    Focus. Align customer data insights with content management capabilities, focusing on creating a deeper content engagement with customers based on personalization and behavioral data. 

    Use. 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. 

    Sample use case. Increase in-app usage by providing a constant stream of relevant videos consistent with past behavior and preferences. 

    Potential weak spots. Effective targeting requires extensive meta data for content and potentially sensitive user information. 

    7. Retail

    Focus. Integrate data from online and offline interactions to create a unified, real-time customer profile. 

    Use. 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. 

    Sample use case. Integrate in-store and online purchases to create effective product recommendations for email marketing campaigns. 

    Potential weak spots. Effectiveness may be hampered by point-of-sale (POS) systems that do not have the capability of delivering appropriate data. 

    8. B2B 

    Focus. Handle the complex business structures and longer sales cycles for B2B efforts.  

    Use. 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.  

    Sample use case. Maintain a consistent marketing and sales message to an entire enterprise across various departments and individual leads. 

    Potential weak spots. Not all B2B businesses are the same. Creating a single structure to accommodate all prospects can be very challenging. 

    9. Customer service

    Focus. Leverage customer data to provide more efficient, personalized, and proactive customer service.  

    Use. Aggregate all customer interactions – from support calls, chatbot conversations, email exchanges, social media interactions, etc. – to enable customer support teams to offer personalized and accurate customer service.  

    Sample use case. Enrich the information available to phone customer service reps with relevant data from all customer interactions. 

    Potential weak spots. Some customer interactions, such as social media exchanges, can be difficult to capture in a CDP. 

    10. Real-time

    Focus. Integrate and act upon new customer data in real time to power immediate marketing activations. 

    Use. Personalize the customer experience, advertising, and marketing campaigns in real time on web, app, or customer service platforms. 

    Sample use case. Include an online visitor in a Google Ad Manager campaign based on the user’s navigation menu selections. 

    Potential weak spots. “Real time” can be a fuzzy concept, and often depends on systems that do not operate in real time. See “The limitations of ‘real-time’ CDP use cases.” 

    Conclusion 

    The point of these “10 types” 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. 

    Reviewing these 10 types can also help the marketer think creatively about how a CDP can bring value to the business. 

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    The post There are 10 types of CDP. Here’s why that matters appeared first on MarTech.

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