Enterprises want to use integrated customer data to determine customer engagement strategies, optimize online journeys, personalize communications, improve customer experience, and extend the most appropriate offers in real time.
However, despite the massive investments of organizations in Customer 360 projects and solutions, most initiatives have failed to deliver on the promise of the single customer view. Customer Relationship Management (CRM), Master Data Management (MDM), Customer Data Platform (CDP), data lakes, and home-grown solutions have all fallen short.
In this paper we'll explain why traditional solutions fail, and present a new, pragmatic methodology, based on the "data as a product" principle. A data product approach quickly completes your Customer 360 initiative by leveraging existing customer data investments.
Customer 360 refers to a trusted, fresh, and integrated dataset, containing all the information important to a company about its customers – which is instantly accessible to authorized data consumers.
From a technical perspective, Customer 360 delivers a single, real-time, trusted view of a customer’s master data (name, address, phone, email, etc.), as well as all relevant interaction data (emails, chats, website interactions, phone conversations), and transaction data (orders, products, invoices, payments, and more).
Customer 360 is the latest incarnation of Customer Data Management (or CDM), which was conceived in the 1980s, predating Customer Data Integration (CDI) and Master Data Management (MDM).
Customer Relationship Management (CRM) emerged in the 1990s, with the goal of tracking and managing existing customer data more efficiently.
Enterprises started to adopt a Data Management Platform (DMP) in the 2000s, to expand their reach to wider audiences. For example, a DMP might ingest the data of potential customers (from data lakes) to deliver targeted ad campaigns to specific market segments.
Customer Data Platforms (CDPs) combined all these data systems in the 2010s, to manage marketing campaigns, customer segmentation, and data orchestration more effectively. This evolution was driven by a single aim: to collect, manage, and leverage customer data to its full potential.
Enterprises are now breaking down old domain-based silos – and combining customer master data, transaction data, and interaction data – in order to better understand customer behaviors, journeys, and touchpoints. Today, we call this “Customer 360”.
Achieving a 360-degree view of the customer is a common objective of organizations seeking to optimize the customer experience (CX).
What is Customer 360, if not providing organizations with a competitive edge? By optimizing customer experience, it can increase customer loyalty and maximize spend.
In practice, this promise has proven difficult to realize.
According to the 2021 Gartner Cross-Functional Customer Data Survey, only 14% of business and IT executives said they’d actually achieved a 360-degree view of the customer – although more than 70% claimed that they’re still working to get there.
Gartner: While a third of all respondents claimed partial progress
in achieving Customer 360, only 14% reported total success.
The overall thinking is that the better you understand your customer, the better the experience you’ll be able to deliver, and the greater the likelihood your customers will be satisfied, and remain loyal. A unified customer view serves multiple business domains with strategic business value, including:
Reduced Average Handle Time (AHT), increased First Contact Resolution (FCR), and reduced churn, personalized and consistent customer experience, improving higher Net Promoter Score (NPS) and customer loyalty
Web and mobile self-service
Personalized customer experience, including cross-sell and upsell recommendations
Fewer redundant customer visits, and increased post-sale services
Real-time customer insights, to drive next-best-action recommendations, maximize revenues, reduce operational costs, and minimize customer churn
Improved customer segmentation accuracy, to target the right customer, with the right offer, at the right time
Increased customer wallet share, with hyper-personalized campaigns and pricing
Enhanced product adoption and profitability
Better compliance with data privacy regulations, to protect the brand, mimimize risk, and reduce costs.
Gartner: The top reasons for Customer 360 are
personalization, retention, reach, and analytics.
Customer-centric businesses need Customer 360 to stay on top of their game. Here are 3 prominent industry examples:
For all these industries, Customer 360 collects, cleanses, enriches, and stores customer data from dispersed source systems into a high-performance, high-scale operational datastore. From there, fresh and reliable customer data can be delivered to any consuming application in real time, to drive “next-best-actions”, for each and every customer, across all channels.
Although achieving a true 360-degree customer view has its challenges, 7 out of 10 enterprises are moving full steam ahead.
Gartner: Almost 70% of those surveyed said that they’re continuing to pursue their
Customer 360 objectives in order to realize a return on their investment in data integration.
Gartner: Of those achieving Customer 360, data quality, definition
consensus, and cross-functional governance are the top requirements.
An ongoing effort is required for continuous data integration, extraction, unification, and preparation – compounded by ever-changing IT landscapes, and data quality requirements.
Customer data is hard to get at, and use. Not only is it fragmented, inconsistent, and often duplicated, it’s also stored across multiple, siloed systems – in different formats, technologies, and terminologies.
Since the data must be extracted from many different sources, it’s hard to keep the Customer 360 view fresh and up to date.
Scale and speed
Delivering trusted customer data in real time – to internal users and to the customer via self-service – requires high scale and speed of data integration and delivery, which is a huge challenge in organizations with high data fragmentation.
IT commonly lacks the resources and tools needed to handle the explosive growth in data, applications, and APIs. Pervasive, company-wide projects, like Customer 360, often require integration expertise, which many enterprises lack.
Emerging local and international privacy regulations make it difficult to keep up and comply with the new regulations while serving the Customer 360 view to different users.
Confusion about tools and technologies
The many different tools and technologies available for collecting, organizing, and analyzing customer data often have overlapping capabilities. These include customer data platforms vs customer data hubs, and more.
Real-time data integration
Integrate and unify all customer interactions, transactions, and master data, from any number of underlying data sources, on premises or in the cloud, in real time.
Match, and normalize customer data, from all the underlying systems, into a single source of customer truth.
Customer data quality
Define and enforce data quality policies for each use case, via data processing, transformation, cleansing, and deduplication.
Data privacy compliance
Enforce data privacy policies and procedures within an adaptive framework by implementing consent management, data masking, data tokenization, and DSAR management – at the customer level – for maximum customer data protection.
Real-time customer 360 accessibility
Make the single view of the customer data instantly accessible to authorized data consumers via governed data services.
Enable domain-based Customer 360, in order to provision the relevant customer 360 view per the needs of each domain, e.g., marketing, customer care, sales, product.
The following table summarizes the major principles behind common solution, and their shortcomings in delivering a 360-degree view of the customer in an enterprise.
Customer 360 Approach
Customer Relationship Management
Master Data Management
Customer Data Platform
All the above solution approaches share an additional, strategic pitfall.
They are all based on a central approach which (1) requires reaching consensus from all business domains on the definition of the customer 360 view; and (2) are highly dependent on central data management teams to extract, integrate, prepare and deliver the customer data. These central teams are becoming a bottleneck in most enterprises and cannot deliver the time to market and agility required by the business.
A data product approach to Customer 360 focuses on the different business needs of the customer-focused data consumers. It enables enterprises to federate control of Customer 360 to individual business domains.
Because the replies to "What is Customer 360?", and the associated expectations, data quality, and data privacy requirements, differ from one business domain to another.
A customer data product makes a trusted customer dataset accessible to authorized data consumers in any method, in real time, and in offline. It encompasses everything necessary to generate value from the customer dataset. Examples include:
Delivering a complete Customer 360 dataset, including transactional, interactional, and master data, to a CRM application
Tokenizing sensitive customer data, for use by operational and analytical systems
Preparing a masked test dataset, and integrating it with a CI/CD pipeline, to support testing a wealth management system
Pipelining customer data into a central data warehouse, for AI/ML analysis
Applying a machine learning model on a real-time customer dataset to predict the customer’s propensity to churn
Customer data is often fragmented across dozens of source systems. And what is Customer 360, if not providing enterprises with the ability to integrate, unify, and continuously sync customer datasets with their source systems?
A Data Product Platform continually provisions, transforms, and syncs customer data – via data products – to deliver a real-time, holistic view of the customer . It discovers, integrates, orchestrates, enriches, and governs all relevant customer data, and then stores it in individually encrypted Micro-Databases™ – one per customer.
Data Product Platform, combined with Micro-Database technology, systematically addresses the pitfalls of other approaches to Customer 360:
Data Product Platform
Although traditional solutions have failed to deliver a Customer 360 view – despite massive investments – IT leaders have not given up on enabling their business to excel and differentiate through a complete 360-degree view of their customers.
Customer data is scattered across hundreds of systems, technologies, and formats, so keeping it unified, clean, and fresh is very hard.
Large organizations need real-time performance, at scale, and cross-functional data governance.
Existing approaches are generally centralized, with a “one-size-fits-all” offering that doesn’t sufficiently address the different business domain requirements of customer 360 use cases: quick time to value, agility, and flexibility.
CRM handles only the data it owns, primarily sales, marketing, and service data, while enterprises have many more customer data sources.
MDM is focused on master data, with no support for the customer interaction and transaction data needed for a 360-degree view.
CDP is a marketing tool, used for segmentation and campaign management. It doesn’t support transactional and master data, or data governance.
Data lakes don’t support operational use cases and require a high degree of technical expertise.
Agile Customer 360 overcomes these challenges by:
Rendering Customer 360 definition consensus a non-issue
Having data quality, security and compliance defined, and enforced centrally or by business domains
Allowing for self-service data provisioning
Realizing a return on the company’s investment in data integration
What is Customer 360, if not the ability to access complete, clean, and compliant customer data in real time – to enhance customer experience, and increase company profitability.
Data products are an emerging data construct, adopted by leading, data-intensive enterprises. Their value comes from quick access to trusted data, combined with in-flight operational insights, to drive informed, timely decision-making.
Data products fuel both operational and analytical workloads, and may be deployed in a data mesh or data fabric architecture – on premises, in the cloud, or across hybrid environments.
Data teams should seek out a Data Product Platform that manages the entire lifecycle of data products, deploys them at enterprise scale, and supports multiple data management architectures, and operating models.