What is 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.

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:

1. Customer care 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

2. Web and mobile self-service Personalized customer experience, including cross-sell and upsell recommendations

3. Field services Fewer redundant customer visits, and increased post-sale services

4. Customer analytics Real-time customer insights, to drive next-best-action recommendations, maximize revenues, reduce operational costs, and minimize customer churn

5. Marketing Improved customer segmentation accuracy, to target the right customer, with the right offer, at the right time

6. Sales Increased customer wallet share, with hyper-personalized campaigns and pricing

7. Product Enhanced product adoption and profitability

8. Data security Better compliance with data privacy regulations, to protect the brand, mimimize risk, and reduce costs.

Although achieving a true 360-degree customer view has its challenges, 7 out of 10 enterprises are moving full steam ahead.

6 fundamental capabilities to achieving a 360-degree customer view

1. 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.

2. Golden record
   Match, and normalize customer data, from all the underlying systems, into a single source of customer truth.

3. Customer data quality
   Define and enforce data quality policies for each use case, via data processing, transformation, cleansing, and deduplication.

4. 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.

5. Real-time customer 360 accessibility
   Make the single view of the customer data instantly accessible to authorized data consumers via governed data services.

6. Federated control
   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.

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.

Why is this important?

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.

What is a Customer Data Product?

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?

Overcoming the Customer 360 Pitfalls with a Data Product Platform

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:

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.

3 main challenges to reaching Customer 360

1.  Definition consensus

   
   A single, company-wide definition of Customer 360 is difficult to achieve because each business domain has its own expectations and requirements.

2. Data quality
   Customer data is scattered across hundreds of systems, technologies, and formats, so keeping it unified, clean, and fresh is very hard.

3. Enterprise requirements
   Large organizations need real-time performance, at scale, and cross-functional data governance.

4 inadequate approaches

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.

1. CRM handles only the data it owns, primarily sales, marketing, and service data, while enterprises have many more customer data sources.
   

2. MDM is focused on master data, with no support for the customer interaction and transaction data needed for a 360-degree view.
   

3. CDP is a marketing tool, used for segmentation and campaign management. It doesn’t support transactional and master data, or data governance.
   

4. Data lakes don’t support operational use cases and require a high degree of technical expertise.
   

1 solution

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
  
  

Case Study

Fortune 50 company achieves Customer 360 in
3 months with a data product approach

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.