Single customer view

What is a single customer view?

A “single customer view,” or SCV, is a compilation of everything a company knows about a specific customer or prospect, gleaned from the broadest range of touchpoints possible. 

Other terms for “single customer view” include:

• Unified customer view

• Unified customer profile

• Golden profile

• 360-degree view or 360-degree customer view

• Full-view user data

• Customer 360

All these terms refer to the attempt to gather as much data on every individual customer and prospect as possible and consolidate it in a single profile or database record. The purpose of a single customer view is to provide a team with a deep understanding of the needs, habits, and intentions of your prospects and customers.

The touchpoints for building an single customer view include: 

• Marketing channels used to reach out to the customer

• How the customer purchases and interacts with a company’s product

• A company’s one-to-one interactions with each customer 

As marketing channels, customer touchpoints, and data sources multiply, creating a single customer view has become more challenging: Having so much data on so many customers can be difficult to parse, especially when it’s scattered across multiple platforms.  

To organize and consolidate large amounts of customer data, organizations often create a single customer view within a customer data platform (CDP) or in an enterprise-wide cloud-based data warehouse, with a single record for each customer.

Single customer views and targeted marketing

While SCV profiles are used to build audiences for marketing campaigns, their granularity also makes hyper-personalized marketing possible. With information on each customer’s purchasing, content, and marketing channel preferences, teams can customize customer journeys, messaging, service, and product recommendations by individual. 

Technologies like tracking pixels and geo-location make it possible to update single customer views and execute hyper-personalized advertising in real-time. By leveraging machine learning with single customer views, it’s now possible to predict a customer’s future shopping preferences and behavior and tailor marketing accordingly.

Why marketers are failing to create a single customer view (and how to fix it)

Where does the data in a single customer view come from?

Ideally, single customer views comprise information from every customer-facing channel, touchpoint, and interaction. They can include: 

• Website activity as collected in a back-end analytics platform

• Online activity, such as interaction with a brand’s social media channels and digital advertising

• In-store sales, self-checkout systems, and point-of-sales systems (POSs)

• Responses to omnichannel marketing campaigns, including emails and content downloads, driven by marketing automation platforms

• Interactions with sales or customer service teams recorded in customer relationship management platforms, or CRMs

Single customer view data is usually first-party data: information collected directly from your customers and prospects. It is sometimes supplemented with second- or third-party data licensed from data partners or data vendors and then reconciled with the first-party data collected by a company.

What type of data is collected for a single customer view?

A single customer view should include as much data about a customer as possible. Ideally, the data is collected in real or near-real time and stored in a central location that’s easily accessible for non-technical members of every team and on every relevant platform in use enterprise-wide. 

Customer data can be broken into five broad categories:

• Identity data is information like name, demographics, location, and contact details that paint a picture of who the person is.

• Descriptive data is information about what a person does — career information, hobbies and interests, product preferences, and so on.

• Quantitative data is mainly related to a customer’s interaction with a company’s products: purchase date, cost, and billing information, for example.

• Behavioral data covers anything the customer does online, from website visits to social media activity and engagement to the type of devices they use.

• Qualitative data describes a customer’s preferences: their favorite color or the size they usually buy, as well as their opinions about products, customer service, and other company touchpoints. 

The data in single customer view profiles can include:

• Contact information such as email address, phone number, and through what channel the customer prefers to be contacted

• Device information, including the type of hardware (smartphone, desktop computer, laptop, or tablet), the operating system, the browser, and whether they’ve used more than one device to interact with a brand

• Transaction history, including what and when the customer has browsed and purchased, what’s still in their shopping cart, and what purchases they’ve abandoned, returned, or canceled

• Demographic and geographic information such as age, race, ethnicity, gender, marital status, income, education, personal interests, and travel patterns

• Firmographic and professional information such as their profession, employment history, position in their company, and whether they have purchasing-making authority

• Website behavior, including content viewed and downloaded, demo videos viewed, visiting patterns, typical user journeys, form submissions

• Online behavior, including what ads they’ve viewed, what sites they’ve visited, what social media platforms they’ve posted on, and what company-sponsored webinars they’ve attended

• In-person behavior (also called offline data) gathered at events like trade shows and user conferences or at brick-and-mortar retail stores through POS systems

• Purchasing lifecycle stage and brand engagement 

• Communications with sales or customer service teams

• Loyalty program participation, including purchasing patterns, points or benefits earned, and how they are spent

• Product usage, including in-app purchases, length of time logged in, and which features they use most often

Benefits of a single customer view

Customers expect content to be personalized and every stage in their purchasing journey to be as frictionless as possible. But inaccurate or incomplete customer data can lead to marketing errors that erode confidence in a brand — for example, by sending the same email offer to a customer multiple times, or a discount coupon for a product they just paid full price for. Poor data can also lead to a poor return on ad spend (ROAS) after advertising a product to customers who have already purchased it. 

Use cases for single customer views

Personalizing cross-channel marketing campaigns for unique, enjoyable, experiences is perhaps the best-known benefit of organizing data in single customer views. But, the data in a single customer view can also inform efforts such as:

• Improving customer service and support - Teams can solve problems faster based on contextualized information about previous customer support interactions, and follow up with advice about products or services.

• Cultivating customer lifecycle marketing efforts - Knowing which customers are active, inactive, lapsed, or prospective helps calibrate sales, upselling, and cross-selling activities, as well as loyalty program offers.

• Gauging the success of ad spend - Marketing teams can adjust spend amounts and priority in real-time based on conversion rates.

• Developing accurate multi-channel attribution - Even when users jump between multiple devices or browsers, or navigate to the same page via different pathways (a Google ad and organic search, for example), SCVs can bring clarity to the customer journey and highlight on which channels are successful. 

• Increasing sales - Use SCVs to target product recommendations and abandoned shopping cart reminders.

• Reducing churn, boosting loyalty, and increasing user satisfaction - Understanding the reasons for failed conversions or abandoned purchases can help teams erase pain points in the customer journey and design frictionless, productive, and personalized brand interactions that enhance the customer experience. 

How to build a single view of the customer

There are six top-level considerations to address in any data strategy for building a SCV database.

1. Establish business goals

Any data strategy should begin with top-level business objectives: How does each team currently use customer data, and what do they want to achieve with that data? This process involves input from teams across an organization, as the data will ideally be available to all stakeholders. So begin by ensuring everyone understands how single customer views can benefit their team and elicit input on how they envision using the tool.

2. Determine data sources

Teams should log all potential data sources for the single customer view and how data from those sources can be centralized. Data sources can include information from platforms that handle customer relationship management, e-commerce, social media, sales, website hosting and analytics, marketing automation, in-store sales, and product usage metrics. Industry-specific platforms such as those that handle ticketing, reservations, and financial transactions are often added to the mix. Remember, the more customer data sources collected, the more accurate and useful single customer views will be.

3. Set compliance guidelines

Data collection is governed by various state and national laws, as well as industry regulations. Companies must ensure the way they collect and store customer data complies with the relevant standards, such as the General Data Protection Regulation (GDPR), the California Consumer Privacy Act (CCPA), and the Health Insurance Portability and Accountability Act (HIPAA).

4. Ingest, model, and clean data

Single customer views are typically housed in a centralized database such as a data warehouse or packaged customer data platform (CDP). That means finding a way to link the source data platforms with the centralized database so the data can be ingested and aggregated. 

Teams make use of data modeling to address data hygiene, ensuring that the data is accurate, deduplicated, consistently formatted, and free from irrelevant information. 

Then the data must be integrated, ensuring that each record represents a unique customer and that all the data related to that customer is included in that record, a process known as identity resolution. Identity resolution reconciles different records about the same customer. While it is a complex process, the functionality is built into most customer data platforms.

After the initial data is ingested into a database of single customer views, it must also be updated regularly. This involves a bi-directional connection between the database  and its data sources through a process called “extract, transform, and load” (ETL), or its sister process, “extract, load, and transform” (ELT).

5. Access, analyze, and activate data

A single customer view database is only useful if teams across an organization know how to use it. Ideally, the user interfaces for analyzing and using — or “activating” — the data for marketing activities will be intuitive and easy for non-technical users to work with. That means its integration with existing platforms should be seamless.

6. Institute data governance

A single customer view is only as good as the data it contains. Data governance is the set of procedures for collecting, storing, accessing, and using data. Data governance models document how data will be kept up to date, compliant, and accurate, and that it’s stored securely. Whether SCVs are stored in a data warehouse or a traditional CDP, platform administrators will apply these procedures as new data sources are added and old ones are deprecated. 

Using a customer data platform to build single customer views

Customer data platforms

Customer data platforms (CDPs) are purpose-built for achieving a single view of the customer.

CDPs automate the data collection and unification of data from disparate sources and aggregate it into separate records that each contain a single customer view profile by identifying and eliminating duplicate entries and inconsistencies. Their native functionality often includes:

• APIs for linking to external data sources or a data warehouse as well as marketing automation systems

• Identity resolution tools for unifying disparate datasets

• Intuitive interfaces for nontechnical users

• Flexible analytics tools for slicing and dicing data.

Marketers use CDPs for market segmentation, audience building, journey orchestration, and other tactics that form the backbone of personalized or targeted marketing campaigns. 

Traditional vs. composable CDPs

The key difference between a traditional and composable customer data platform is where the data is stored.

A traditional or packaged CDP is a standalone platform that stores customer data internally, while a composable CDP connects to a cloud-based data warehouse.

Traditional CDPs are less flexible: The platform dictates the data points, models, and sources, as well as the activation channels. And because the data is stored locally, updating it is more cumbersome and often involves manual data dumps from multiple systems that must be performed by IT staff.

A composable CDP connects to an existing data warehouse, giving companies more control over their data. This control is especially important for businesses with complex data and those in highly regulated industries such as financial services, healthcare, and education. 

Composable CDPs also enable teams to use the martech stack — and marketing channels — they choose, providing faster time-to-value and integration with best-of-breed technology. And since the composable CDP sits on top of the data warehouse, it can quickly activate campaigns to any channel based on the single customer view in the data warehouse — no need to wait to copy information from a separate database. 

Challenges to creating a single customer view

Data silos

A data silo is a database that’s populated and controlled by one department or team but isolated from the rest of the organization. Data silos tend to arise organically as large or fast-growing companies add new channels and platforms without planning for how the data will be shared company-wide. 

Customer data silos become detrimental when one silo contains data describing the same customers in another silo, but the data between them differs. Silos also make exchanging information between departments difficult, and reconciling the information across silos is often a manual and time-consuming process that can introduce errors. Data silos are the single biggest obstacle to developing a single customer view.

Using a cloud-based data warehouse and composable CDP is the ideal way to tear down the data silos. Because every team’s data is uploaded to the data warehouse, every team is working from the same set of accurate customer data, putting enterprise-wide strategy decisions on a surer footing.  That data can be combined to build single customer views and also put to use by other teams for various purposes.

Compliance, legal, and privacy concerns

Many companies have been collecting customer data for years. The data collection methods, and even the data itself, may no longer be in compliance with laws and regulations governing the collection and storage of the data in various platforms. Embarking on an SCV project is the perfect opportunity for a legal review of the provenance of legacy data.

Legacy data formats and technology

It’s not just reconciling and aggregating the information in data silos that poses a hurdle to building comprehensive customer profiles. The data in those silos may be in disparate or outdated formats, or housed in older or proprietary technologies that make it difficult to connect to other platforms so the data can be extracted. Some customer data may even have been maintained manually or in simple applications like spreadsheets or flat-file databases. Finding a way to unlock data from many different sources and reformat it so that it can be maintained properly going forward can be a formidable challenge.

Emerging trends in use cases for single customer views

Artificial intelligence and machine learning

Generative AI has already had a profound impact on the way marketers work. Machine learning is now being applied to data modeling and analysis to make real-time predictions about future customer behavior based on historical customer data. This, in turn, enables marketers to anticipate what prospects and customers want, allowing them to provide personalized experiences that yield more conversions and sales. 

Because a CDP powers real-time advertising platforms connected to it, retargeting campaigns will attract new customers with a similar single customer view through lookalike modeling. As industry-specific machine learning models proliferate, the accuracy of this functionality will continue to improve. 

AI can also be applied to data hygiene tasks to make building and maintaining single customer views more efficient.

Privacy and data security

As privacy protections evolve, single customer views have the potential to power sophisticated encryption methods, better consent solicitation workflows, and integrations with more flexible data privacy technologies.

Channel integrations

The number of online and offline marketing channels for reaching out to prospects and touchpoints for customers is always growing. In the future, single customer views will incorporate information from emerging technologies such as Internet of Things (IoT) devices, augmented reality platforms, and even biometric devices, all through integrations with the data warehouse.

Warehouse-centric technology

As the cloud data warehouse becomes commonplace in enterprise companies, more technology will be developed that centers around the warehouse and the single customer view stored there. Warehouse-native applications for event collection, processing, and AI-powered data modeling will build off of and enhance the SCV data.