Single Customer View (SCV): definition, challenges and use cases

In 2016, Technology for Marketing asked its expert board members for their thoughts on the Single Customer View (SCV). For some, it was a " hype-fueled myth ". For others, SCV was simply " the Holy Grail of customer relationship management ". In the age of all-digital, rising consumer expectations and the explosion of Data, SCV is gradually moving away from the definition of "myth" to that of "Grail".

The Indian fable and the elephant analogy

Let's start this paper on Single Customer View with... an Indian fable. Three blind people try to describe an elephant by touch. Each person touches a different part of the animal: tusks, trunk, ears and legs. Their stories are conflicting, and the descriptions don't match. Eventually, they learn that the elephant is an imposing animal, and that all their observations are true, each constituting a part of the puzzle.

The analogy with the corporate world is obvious. Each department develops a fragmented understanding of customers, with scattered, siloed data that prevents the puzzle from being completed.

Today, 90% of customers navigate across different devices and touchpoints, generating a large volume of granular data that not all CRMs manage to process, synchronize and integrate. Each granular piece of data is "true", but it doesn't stand alone. The value of a complete set of data on a customer is greater than the sum of the individual values of each granular piece of data for that same customer. This paradigm has given rise to the concept of Single Customer View (SCV).

What is the Single Customer View?

The Single Customer View is an aggregated, coherent and holistic representation of the data held by a company on its customers, which can be consulted and explored in a single interface.

In many ways, SCV represents the culmination of "customer knowledge", in that it enables "historical" behavior to be analyzed in order to better target and personalize future interactions with the customer. SCV is also relevant when a company engages with its customers on an omnichannel basis, since the latter expect these interactions to reflect a coherent understanding of their history and preferences in the broadest sense. In some Anglo-Saxon countries, the Single Customer View has become a legal requirement in certain regulated business sectors. This has been the case for British financial institutions, for example, since December 31, 2010.

Single Customer View (SCV) is almost universally accepted in the current data explosion, but it is not necessarily an absolute objective for all companies. In some cases, it may be more relevant to have several sets of data broken down by context or scenario, rather than by individual customer. This is particularly the case for B2C companies selling "mass" products.

SCV: a case study

E-tailers with several hundred or even thousands of customers can use their Single Customer View to identify and target "at-risk" customers (whose frequency of purchase or average basket is declining).

They can use criteria such as the frequency of purchases since the very first order, the time elapsed since the last order and the average value of purchases per customer to detect signals of disengagement and anticipate. At-risk" customers can then be segmented into several groups, according to their average shopping basket. Each group can then be targeted with a different marketing campaign:

• At-risk" customers with a high average ticket will benefit from a substantial commercial gesture to reactivate them.
• At-risk" customers with an average ticket will benefit from a discount coupon.
• At-risk" customers with a low average ticket can be targeted with an email without any incentive to buy.

Single Customer View: the culmination of state-of-the-art Data Management

When representations of a customer are stored in several different sets, it can be difficult to make this Data speak holistically and take advantage of the interconnections between the data. There are two major challenges facing companies wishing to merge these siloed sets into a single view.

#1 Customer identity traceability

The customer's identity must be traceable between the records stored in the various systems... which is not necessarily obvious, especially if the technological stack is not properly integrated. SCV generally feeds on databases, Data Lakes and Warehouses, RSS feeds, APIs, Javascript, Advertising Pixels, etc.

The company must have the means to perform " identity resolution ", exploiting unique identifiers such as email addresses, login IDs and IP addresses to establish unique, unified profiles. The Customer Data Platform (CDP) is the reference tool for this task.

Please note: SCV also involves creating a single view of consent (browsing preferences, privacy, etc.). Without this 360° view of consent, you won't be able to distinguish which data can be activated without contravening the GDPR. Consent orchestration is a major criterion for choosing your CDP, especially if it's edited by a non-European company.

#2 Data Quality, a sine qua non for actionable SCV

Anomalies and discrepancies in customer data must be neutralized (preventively and curatively) as part of a Data Quality Management policy. According to an Experian study, poor data quality is the leading cause of failure in SCV projects (43% of marketers surveyed), followed by the compartmentalization and misalignment of different company departments (39%) and difficulties in integrating in-house technologies (37%). Experian mentions five golden rules in Data for a successful SCV project:

• Implement an upstream validation solution to ensure that data entered by customers is accurate, reliable and compliant with standards. Data Enso has implemented EnsoEmail to ensure the reliability of data entered in contact forms (integration via API with forms) and to make your existing databases more reliable (batch file processing).
• Process "historical" data to clean it up and bring it up to standard. Data Enso offers you its EnsoBatch curative solutions in one-off or recurring mode
• The challenge of SCV is decisive for CMOs and CSOs. They must become the driving force behind Data Management within the company. Large companies can opt to create a Chief Data Officer position.
• Data monitoring to ensure long-term data reliability. Companies that don't necessarily have the resources to deploy real-time monitoring technology can track KPIs such as email campaign response rates, bounce rates(hard and soft bounces), and so on.
• Invest in analytical talent to bridge the gap between raw data and actionable information for decision-making.

In reality, the Single Customer View can only be the potential result of good data management.