The Ultimate Guide to Building a Customer Data Strategy
Are you looking to build a customer data strategy? This guide will teach you everything you need to know about Customer Data Platforms and how they can help your business. With detailed instructions on selecting the platform that will fit your needs, you’ll be able to increase marketing effectiveness and reduce waste while gaining insights into customer behavior that can help drive profitable decisions.
Today’s interactions are increasingly complex, both in-store and online, and adjusting the right message for the right customer is critical to the success of the customer experience as a whole.
Marketers tend to view the marketplace based on the channel of interaction (email, in-person, SMS, Facebook, etc.) but from the consumer standpoint (the purchaser, the patient, the consumer) it becomes obvious that the entire experience is as important as any of the channels where interaction takes place. Throughout that process, a consumer has researched multiple avenues, and taken steps to decide about a purchase or a course of action. If the brand delivers the customer experience well, with each step in the process consumers feel the message is more targeted to them on a personal level. He or she may wonder how is this personalization achieved in a complex landscape for millions of consumers?
If this level of personalization is achieved, most likely the enterprise will already have implemented a Customer Data Platform (CDP) to unify the customer experience.
A Customer Data Platform is a purpose-built software solution that creates a unified customer source for other systems. It serves as a centralized hub for the relevant customer data that a company has collected. A customer data platform serves as a customer data infrastructure management solution that offers a holistic, integrated and persistent view of the customer.
A CDP can manage any type of customer data. It enables an operational data environment that ingests an enterprise’s data from all sources – whether batch or streaming, internal or external, structured or unstructured, transactional or demographic, personal or general – that provides an always on, always updating Golden Record and makes it continually available at low latency to all touchpoints and users across the enterprise.
Gartner broadly defines a CDP as a marketing system “that unifies a company’s customer data from marketing and other channels to enable customer modeling and optimize the timing and targeting of messages and offers.”
A CDP’s unique role in the martech stack as a platform for delivering an omnichannel customer experience makes it especially important to focus on long-lasting value. When the outcome in question is a personalized, highly relevant customer experience irrespective of channel or engagement touchpoint, the value further deepens over time.
In a customer journey context, a CDP enables a brand to visualize an entire process, to look at past journeys by an individual customer and by customers like them, and to understand the likely outcomes and probabilities of moving along the customer journey in particular paths. This, in turn, allows marketers to prepare highly relevant offers for an individual customer in a precise moment of a journey, which will then improve the customer-centric metrics.
There are several key benefits of using a customer data platform. It can help organizations:
One of the most powerful aspects of a customer data platform is the ability to build and maintain a golden customer record at the speed of the consumer. The single customer view, or golden record, that a customer data platform provides will give a deeper understanding of a customer’s preferences and behaviors. It should include every touchpoint or proxy identity the consumer presents, such as a cookie, a social media handle, or even a smartwatch.
Each customer’s golden record should also include transactional and behavioral data, as well as other triggers. This single central source of customer golden records unifies data across typically siloed internal and external sources, and handles data of every conceivable type, cadence and source. This is radically different from other data sources and elements in the martech stack, which are usually channel-specific and narrowly focused, and often are batch-driven or at least much slower to update than customers’ journeys and actions. By having real-time access to customer data, brands can engage in clientelling and create more personalized experiences that meet the customer’s unique needs and preferences.
The transactional data should include the history of all transactions and interactions that a person represented by a proxy identity has had with the brand. Behavioral and transactional data should be integrated into the golden record in real time to ensure that analytics, personalization and engagement are relevant and up to date.
Consumers expect brands to understand their entire history regardless of channel, which is something most brands aren’t able to do — primarily because of data silos.
These are purpose-built databases of customer or transactional information stored in marketing or sales or accounting. Migrating data from these silos or requiring unification of data structures across large enterprises, which are often made up of multiple organizations that have been acquired, is a costly and inefficient way to access this data.
When customer data is present in disconnected silos, it becomes a barrier to effectiveness and success in marketing and decision-making, and it relegates valuable data scientists to spend most of their valuable time integrating and managing this data. Without the ability to uniformly access this data, marketers lack visibility into the customer lifecycle, cannot understand signals necessary to personalize in real time, and find it difficult to deliver messaging through the correct engagement touchpoint.
A customer data platform cuts through the data silos, serving as a centralized data hub of personally identifiable information (PII) and behavioral data from interactions across offline and online channels including second- and third-party data. A CDP allows marketers to integrate anonymous and known customer data into a single platform to create a persistent, real-time, holistic view of each customer. This enables marketers to orchestrate more relevant and personalized omnichannel campaigns, as well as respond to customers in their moment of need or action.
A CDP ultimately brings together useful data from across the functional and channel-specific silos in your organization to know all that is knowable about your customers, allowing you to build progressive profiles as your customers interact with you over time. Additionally, a CDP helps you recognize individual customers across multiple channels and interaction touchpoints, so you can deliver relevant, personalized offers whether customers are in your physical location, online, or on their mobile device.
For a robust, enterprise-grade customer data platform, managing data entails applying data quality, identity resolution and governance processes at the point of data ingestion, ensuring a single view of the consumer throughout an entire customer journey. Ultimately, a customer data platform can deliver a personalized customer experience based on an organization’s own use cases and can do it in a timely, scalable manner.
A customer data platform can meet a variety of goals, but some of the primary benefits from a CDP include:
The CDP Institute (CDPi) defines a CDP as “a packaged software that creates a persistent, unified customer database that is accessible to other systems.” They identify more than 151 vendors calling themselves CDPs at this time, but there is a great level of variance about how a CDP is defined.
An enterprise-grade customer data platform is designed to meet the complex data management and integration needs of large organizations. Enterprise CDPs are typically used by organizations that have multiple brands, business units, and channels, as well as complex data and infrastructure needs. In addition, enterprise CDPs often have more advanced features such as machine learning, predictive analytics, and data governance capabilities that are essential for managing and leveraging large volumes of customer data across an organization.
An enterprise-grade CDP must be able to:
Once a CDP pulls together customer data, it is important to resolve the identities of the individuals across devices and channels. Identity Resolution is the process of finding, cleansing, matching, merging and relating all the disparate signals (martech touchpoints, enterprise systems, databases/datalakes) about a customer to produce an accurate, complete and up-to-date view of the customer. It is used by marketing and other business functions to analyze, deduplicate and relate customer records in order to provide consistent customer experience.
Many companies evaluating a customer data platform today focus heavily on either the data ingestion or orchestration component of the process in delivering personalization and rely on other vendors to perform data quality and deliver identity resolution (IDR). This is not an ideal approach – identity resolution is a core functionality of what Gartner calls a “Smart Hub CDP.”
Omnichannel personalization as an outcome of advanced identity resolution is a competitive differentiator. Ownership of IDR is key to maintaining customer trust and delivering timely and relevant updates. Probabilistic and deterministic matching are both necessary and the ability to fine-tune the levels of strict matching is critical to finding the appropriate balance between efficiency and accuracy of this information as well.
If we think about a CDP’s purpose as a digital transformation tool that links data management with insights and action, to enable marketers to access the data they need to create useful segmentation models and ultimately to create campaigns based on insights, what’s missing from the majority of CDP’s in the market is the correct approach to data quality.
Managing data quality is one of the most challenging issues facing IT organizations today. At every step of the data lifecycle – entering, storing, associating and managing data – you run the risk of introducing errors. While many vendors say that the insights and engagement are key components of a successful customer data platform solution, ultimately it comes down to the data. If data quality isn’t top of mind and the systems aren’t collecting and perfecting data that is fit for purpose, then the insights are based on flawed information and the engagement can only go so deep.
While many customer data programs focus on the activation and insights, they are not investing deeply in data quality to ensure that the foundations of the customer profile are accurate. It is important to understand the role data quality plays and to invest in the organizations that prioritize getting data right every time to deliver the optimal customer experience.
Seamless data integration and superior data quality are must-haves for any digital transformation. Data veracity allows companies of any industry to establish the foundation that allows them to start executing their visions of the future today, not tomorrow. A digital experience platform that delivers unfettered data integration must meet a few conditions. First, it must welcome any and all data; data from every source and every type all hold important clues about a customer’s behaviors, preferences and patterns – which paints a complete picture of an individual customer.
Volume alone, however, is not enough to ensure an accurate unified customer profile, which is where automated tasks come into play for managing even the most complex data. Cleansing, matching, advanced identity resolution, persistent keys and enrichment are all vital for returning a single customer view that combines every customer identifier with complete behavioral and transactional data.
Data enrichment and data validation are under-appreciated as important components in underpinning the accuracy of a resulting Golden Record: a pristine, unified customer profile that is the basis for delivering a personalized, omnichannel customer experience. Data validation is the process of verifying the accuracy, structure and quality of data prior to processing. Data validation can be as simple as confirming that a U.S. state abbreviation value is two characters. However, data validation can also involve leveraging a trusted third-party to confirm a given dataset.
Data enrichment is not as clear-cut. It can refer to leveraging first-party data to enhance existing data sets (common in more siloed organizations), or the use of one of many third-party vendors for processes such as demographic or geographic overlays, different methods to find out information about a person. However, to maintain data integrity for the end goal of a Golden Record, data validation is critical prior to leveraging any data in a production environment.
Most people are familiar with GDPR and CCPA as government regulations that protect consumers’ data privacy, but other regulations such as HIPAA in healthcare and GLBA in financial services spell out in more detail how those organizations must treat patient and consumer data, respectively. Government regulations aside, a business should also self-regulate in terms of its approach to using customer data. That is, customers themselves are the ultimate arbiter for what’s acceptable or not for how their data is used. Following the “less is more” adage, often the best approach is to only reveal what you know about a customer when it will help guide a customer through a customer journey or give the customer something of value.
Currently, especially in the US marketplace consent and preference management is akin to the wild west, it is exceedingly hard to get right across channels. Consent carries more weight because of compliance laws, but each area impacts today’s consumers. A customer data platform should be a part of the process of data stewardship, maintain customer relationships and respect customer preferences/data within the golden record.
Vendors in the industry typically take an “open garden” approach or “integrated suite” approach to the CDP architecture.
An “open garden” CDP is designed to integrate with a variety of other systems and technologies, regardless of vendor or origin. In this approach, the CDP acts as a central hub for integrating and unifying customer data from various sources such as CRM systems, advertising platforms, email marketing tools, and more. It is typically built with open APIs and integration tools to connect and exchange data with other platforms, allowing for greater flexibility and customization.
An “integrated suite” approach in a CDP refers to a system that provides a complete set of customer data management tools and functionalities within a single platform. In this approach, the CDP provides a unified set of tools for managing customer data across multiple channels, including data ingestion, data unification, segmentation, personalization, and more.
The main difference between these two approaches in a CDP is the level of flexibility and customization they offer. An open garden allows for greater customization and control. An integrated suite approach provides a more pre-built and integrated solution, which may be easier to manage but may be less customizable.
Ultimately, the choice between these two approaches in a CDP will depend on the specific needs and goals of the organization, as well as its existing technology infrastructure and data management processes.
A customer data platform collects and unifies data from disparate sources (internally and externally) to build a single view of each individual consumer. The sources could include:
Data is typically divided into three categories based on how it was acquired: first-party data, second-party data, and third-party data. First-party data is information a company or organization acquires directly from a person or device. Second-party data is data that is acquired as a result of a relationship, typically a partner company, or a co-op partner. Third-party data encompasses everything else including information bought to virtually segment and tar based on lookalike customers to sell to anonymous users that fit the general demographics.
First-party data helps businesses shape interactions based on facts rather than assumptions based on look-alike individuals. It is where insights will often have a more immediate impact on the interpretation of the customer experience. Classically, CDPs were utilized primarily by marketers with only touchpoint data, but as they evolved the inclusion of APIs pushed in additional data. This was still largely transactional/touchpoint forward, but could also include relational data from the call center, warehousing, or insights from a variety of other end users.
Forrester Research talks also about zero-party data, ultimately a deeper level of insight from a customer to highlight preferences and history. This might come from a survey or inclusion in a loyalty program, but it’s a next step in the building of the customer relationship.
There are also many types of non-customer data that can be correlated with the first-party data that makes up a single customer view. These elements are relevant to the customer, such as product inventory levels, pricing, recall messages and situational data like geo-location or weather or time of year could further enhance the overall understanding of the needs of an individual customer.
The ideal is to create “360-degree view” of the customer. Many different types of data make this view possible, although the amount and degree of specific information may vary on the needs of the organization. Some types of data include:
Ultimately, the type of data collected and managed with a customer data platform is largely dependent on the purpose and the use cases for the CDP. A company wanting to tightly manage personalized information like health status may want to have stronger controls around identity data, whereas a retailer or educational organization may want the qualitative data necessary to fine-tune the messaging for optimized personalization.
In the customer experience realm, data, insight and action are the pillars of what it takes to deliver personalized interactions at scale across an omnichannel customer journey. Broadly, anyone implementing a customer data platform solution needs three key elements:
The key to delivering superior customer experiences is creating, maintaining and effectively utilizing perfect data about each customer. The fundamental building block of perfect data is first-party data. Everything else is just a form of proxy for trying to identify and understand the customer.
Perfecting an organization’s own first-party data with advanced identity resolution processes, data cleansing and enhancement at the moment data is ingested from every source is vital to ensuring that organizations interact with the right customer, household or entity. Relevant, valuable interactions depend on the organization really understanding who the customer is, and that requires high-quality data sets that are both accurate and timely.
For the customer, that value is measured by consistent relevance across an omnichannel customer journey. A brand must recognize an individual customer across every channel and engagement touchpoint, demonstrated by the delivery of a frictionless experience.
Achieving and persisting relevance is dependent on a deep understanding of the individual. The key ingredient – perfect data. In return for a superior personalized experience, customers say they are willing to share more about their needs, preferences and behaviors. The insights derived from this data are what will build the relationship and increase opportunities through delivering better personalization.
There are many ways in which customer data can be utilized throughout the customer experience. With a customer data platform, enterprises can:
Multichannel or cross-channel marketing is broadly defined as interacting with customers via multiple direct and indirect channels to sell them goods and services, engaging with customers in their channel of choice. Global multichannel marketing solutions make all channels available to the consumer (in-store, web, mobile, social, phone) for the purposes of engaging with a brand and may cross sell among a few of those channels.The need for a single customer view to engage with today’s always-on, connected consumer across an omnichannel journey highlights one limitation of multichannel marketing; the purpose of developing a single customer view is to understand how a customer interacts with a brand across all channels, not on a channel-by-channel basis. While a CDP can certainly support a multichannel marketing strategy, the technology is more suited to support omnichannel marketing, which integrates channels and eliminates data siloes and data latency to enable an organization to move at the pace of the customer throughout a dynamic, non-linear customer journey.
Omnichannel marketing more accurately reflects the way today’s consumers engage with brands across both physical and digital channels and online and offline touchpoints. The strategy enables a personalized, relevant experience that is always in the cadence of the customer journey wherever a customer chooses to engage. It enables consistent customer experiences where and when the customer interacts with the brand.
Real-time data is the lifeblood that provides brands with the depth of understanding required for delivering an omnichannel customer experience that aligns with consumer expectations. Enterprises must invest in systems that can handle omnichannel orchestration at a speed and scale that makes it invisible to the end consumer.
There is an important distinction to make between having a real-time view with respect to each channel, and a real-time view that spans the entire customer journey. A brand may have a real-time view into a customer’s browsing history, capturing clicks, time on page, opens, views, movements, etc., and use that information for real-time web personalization, but that data is typically not shared with other channels.
A classic example of how such a lack of transparency may introduce friction is an abandoned shopping cart. A customer may follow an abandoned cart up with a buy online, pick-up in-store (BOPIS) transaction, but lacking the real-time sharing of data across channels, a brand will miss out on valuable cross-sell or up-sell opportunities when the customer arrives in-store. Context and timing are crucial components in delivering the customer experience today’s consumers are demanding.
The ownership and management of a CDP can vary depending on the organization’s structure and goals. In some cases, it may be owned and managed by the marketing team, which uses the platform to unify and activate customer data for marketing campaigns. In other cases, the customer data platform may be owned and managed by the IT department, which is responsible for ensuring the security and scalability of the platform.
Regardless of who owns and manages the CDP, it is typically used by multiple departments and teams across the organization, including marketing, sales, customer service, and analytics. The CDP acts as a central hub for customer data, providing a comprehensive view of the customer to all teams and systems. This enables more personalized and effective customer experiences, optimizing marketing campaigns, and gaining insights to inform business strategies. Selecting a solution that can meet the needs for a variety of skill levels, technical requirements and business objectives is critical for the overall success of any the platform.
In the push for digital transformation, many companies wanted to understand how a CDP is a different type of approach than other systems of record already at work within their enterprise. This next section will outline why certain older technologies may not be optimal to solve the customer experience problem and what businesses need to consider when evaluating one customer data platform solution against another.
Ultimately, DMPs and CRMs are channels of interaction that can connect into a CDP. Channels that perform their core tasks exceptionally well, but channels all the same whether inbound, outbound, or both. CRMs are customer-focused to the extent that they can be and are valuable in their designed space, but they can’t be used to enable marketing and the rest of the business in the same way a CDP can.
Choices in the purchase phase – such as private or public cloud, SaaS or hosted, clustering controls, or how the CDP will be configured in the martech stack – all have downstream consequences for the actual implementation phase. Will reporting and campaign activation need to be done inside the CDP, for example? Every choice or decision about what needs to be within the CDP will have implementation consequences.
Those choices are tied to the ultimate use cases. If a use case involves attribution, measurement, or optimization, elements of the implementation will need to reflect those requirements to ensure that the customer data platform does not stop short of the last-mile connection to the customer.
Understanding the use cases and the goals for a customer data platform will help narrow down the options for vendors. An enterprise-grade CDP must match the requirements for your business needs to solve disorganized data and make connections and offer intuitive tools for advanced decisioning, segmentation, targeting and campaign activation, as some of the most robust features.
A few common use cases are:
There are an array of other use cases that are commonly used such as personalization, customer service, product recommendations and variety of other types of use cases.
Implementing a CDP starts with deciding on a specific use case and creating an optimal environment for that use case. The recognition that a CDP can be used to achieve a specific use case is an important distinction to make, even though it may seem counter-intuitive to the aspirational notion that a CDP’s purpose is to generate a 360-degree view of the customer – a single source of truth for customer data. You may not need all the functionality of a customer data platform on day one, but it’s still there when you’re ready to explore more features.
Finding the right CDP for your needs also depends of a few factors:
There are a host of other elements that will be critical for your business specifically, but these are some of the questions we recommend everyone consider when implementing a customer data platform.
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