Unified Customer Data View

Achieving a unified view of customers is currently one of the most prevalent objectives for enterprises. This comprehensive perspective can bring about extensive and notable advantages, as it enables various departments such as sales, marketing, customer success, and finance to leverage it for enhancing customer satisfaction, fostering stronger connections with customers, and anticipating their future clientele.

Enterprises have been struggling to achieve the single customer view despite the widespread agreement that it is highly desirable. Although many companies have tried to achieve it by using a wide range of products and services available today, most of them can only provide a part of the picture and require a huge amount of time and effort from internal teams.

This blog explores some of the reasons why the single customer view is so elusive and how to overcome common barriers. The single customer view is the profile created by aggregating all records across the company's systems related to one customer. The challenges with creating a single view arise from preconditioned notions of where to begin, such as modeling data upfront, blending data from various sources, dirty data, and spiraling customer view projects.

Despite these challenges, every organization could benefit from knowing more about its customers. Although a single customer view can be valuable, one must understand that one can never rely on a single golden record as a complete and wholly accurate representation of the customer.

One can use the insights delivered by the data they have instead of knowing everything about the customer.

Customer - Single View

The single view of the customer refers to the comprehensive profile that an enterprise creates by gathering and integrating all the customer-related data from various systems. This profile includes diverse information such as contact details, transaction history, social media interactions, email communications, and meetings. To create a Golden Record or a single source of truth, these records undergo a process of collection, cleansing, and enrichment.

Common Challenges

Organizations often face several challenges while creating a single view of their customers. One of the primary issues arises from conventional master data management practices, which necessitates determining the data model beforehand. This implies deciding what a customer should resemble and how it relates to other domains before even starting to explore what insights the data can provide. This approach appears counter-intuitive since the goal of the project is to uncover hidden characteristics and insights about customers. Creating a model upfront can limit the potential to learn and act upon the intelligence gained.

One should not try to model the single customer view upfront because data is an evolving entity, and one will always be playing catch-up with the data and the customers it relates to. Truly modern Master Data Management platforms enable connecting and relating data naturally, acknowledging that the relationships between the data are as important as the data itself.

Creating a comprehensive view of a customer can also be complicated by various factors. For example, a customer's information can come from multiple sources and combining it into a single view and identifying duplicates can be challenging. Legacy master data management systems and so-called "single view" technologies may struggle with this task. Additionally, dirty data, which refers to incomplete, stale, or inaccurate records, can further exacerbate the problem by causing unrelated records to be combined. Without proper tracking and lineage of the data sources and modifications made over time, it's difficult to have confidence and trust in the resulting customer view.

Creating a single customer view can be a challenging task, particularly as it often involves interconnected data domains such as products, materials and purchases. This can lead to unexpected complexities and potential invalidation of the overall customer view.

In addition, integrating data from various sources and cleaning up incomplete or inaccurate records can be difficult, and legacy master data management systems may struggle to identify matching records. Despite these obstacles, gaining insights into current and potential customers is critical for any organization's success. The more you understand about your customers, the better equipped you are to attract and retain them, and to stay ahead of the competition.

Understanding what is Good Enough?

While a single customer view can be valuable, it is unlikely that there will ever be a single, wholly accurate representation of a customer. However, this is not a problem, as not all data is necessary to effectively engage with customers. For example, it may not be necessary to know every detail of a customer's call log history, but it may be important to know the last few months.

It is also useful to know if a customer has increased their usage of a product or activated a subscription feature, or failed to reorder over a certain period, these present opportunities to engage with them and provide additional value. Ultimately, it is not necessary to have all information about a customer in order to use the data available to gain insights and engage with them effectively.

Can you achieve a single view of everything?

Attempting to predefine what your single customer view should look like before starting a master data management project is a recipe for failure, as mentioned earlier. Data, like your customers, is a constantly changing and evolving entity. Adopting a model upfront approach, with its strict limitations and constraints, will result in missing out on the subtle nuances and hidden insights contained within the data.

It's essential to let data connect and relate naturally instead of forcing it into a predetermined structure. Modern Master Data Management platforms enable this by using Graph databases that recognize the relationships between the data are as important as the data itself. It's similar to jotting down ideas on a whiteboard instead of storing data in columns and tables in Excel, making it easier to visualize and establish connections between data. Graph, a NoSQL, schemaless database, is a critical component of contemporary methods of mastering data.

Expanding beyond the realm of the customer, and applying this concept to every domain, can vastly increase the value of data to your business. For instance, having a single view of the product, transaction, or location can provide a more comprehensive understanding of your customers, as well as the broader aspects of your business operations.

To illustrate, consider an organization that seeks to improve customer satisfaction within their support department. At first glance, a single customer view may seem like the ideal solution. However, to truly understand what factors contribute to customer satisfaction, they must also be able to pivot around other reference points. Since customers cannot provide insights into their overall satisfaction, it is necessary to examine how other data objects - such as dates, transactions, and departments - relate to their experience.

If you haven't established a strong data foundation to support it, attempting to shift your focus to examine other perspectives will be incredibly challenging. This is not because organizations lacked the foresight to do so, but rather that the technology required to accomplish it was not available until recently.

In the past, prior to the advent of Graph-enabled Zero Modelling, we were always constrained in how deeply we could delve into data that is linked to a record. If we only examine records that are directly linked, we cannot attain a complete single view of the customer, as many insights arise from indirect connections.