It is very commonly said that successful companies listen to the voice of their customers. For example, Amazon’s CEO Jeff Bezos, has repeatedly said that Amazon’s obsession with their customer and their commitment to fulfilling their needs is what makes the company so unique. Furthermore, there is evidence that customer-centric enterprises outweigh their competitors in market capitalisation. The importance of customer-centricity is the reason why enterprises invest in IT systems that support them in targeting the individual needs of their customers in processes like sales, marketing, and customer services. As a prominent example, many enterprises are employing CRM (Customer Relationship Management) systems, which help them plan, automate, and deploy efficient customer segmentation, personalized marketing, and tailored customer service strategies.
CRM systems are among the most popular enterprise applications. However, there are cases where they fall short, especially when it comes to collecting, consolidating, and processing very large volumes of data from many different sources. In recent years, CRM systems are complemented with new types of enterprise platforms, namely Customer Data Platforms.
Introducing Customer Data Platforms
Customer Data Platforms (CDP) combine data and information from many different software modules towards creating a unified customer database, which is accessible to all the enterprise systems of an organization. In the scope of a CDP customer data from different touchpoints are collected, cleaned, and aggregated, to enable accurate profiling of every customer. In most cases, the customer’s profile is based on structured data, which provides accuracy and clarity to other enterprise systems that make use of it.
One may argue that most of the above-listed functionalities of a CDP are already offered in the scope of a Customer Data Warehouse (CDWH), which is a core element of every CRM system. A CDWH is an analytical database, which consolidates customer information from many different sources, including all customer interaction touchpoints. On top of a CDWH, a CRM system enables the execution of data mining techniques (e.g., clustering of customers), which facilitate customers’ profiling. Likewise, it also enables sophisticated analytical processes like OLAP (On-Line Analytical Processing). However, CRM/CDWH solutions are associated with some proclaimed limitations that CDPs come to alleviate:
- CRM/CDWH systems rely on complex ETL (Extra Transform Load) processes towards transferring data from operational systems to the analytical database. ETL processes are cumbersome and usually performed overnight rather than in real-time. CDPs alleviate this limitation through richer and real-time integration of customer data sources, including both data sources internal to the organization (e.g., corporate databases, operational systems) and data sources outside the organization (e.g., the social media channels of the company).
- Data warehouses are developed and maintained by internal IT teams, which limits their capacity and scalability. On the other hand, CDPs’ services are available as cloud-based SaaS (Software as a Service) services i.e. they leverage the capacity, scalability, quality of service and pay-as-you-go properties of the cloud.
- CRM systems collect and analyse information about known customers or prospective customers known to the company. CDPs are much more versatile: They collect and analyse data from anonymous customers as well i.e. customers that interact with the company through on-line channels without providing identity information.
In practice, CDPs integrate CRM systems as one of their main sources of customer data. However, CDPs integrate customer information from other systems as well, including social media platforms, e-mail servers, e-commerce systems and other databases that comprise customer data. In this way, CDPs facilitate tracking of entire customer journeys, rather than being limited to sales and marketing processes.
Fundamentals of Data Management in CDPs
The rise of CDPs is largely due to the explosion in the amount and variety of customer data, as well as due to their rising importance for sales, marketing, and other business operations. In principle, CDP collect, aggregate and manage the following types of customer data:
- Customer Identity Data: Prior to interacting with a company’s system a customer must usually register to the system and validate its identity. This enables the company to access important information about their customers, including for example their name, address, demographics, as well as contact details (e.g., e-mail, phone, social media accounts).
- Extended Customer Description: Beyond basic identity data, enterprises are also interested in acquiring and managing additional descriptive data. The latter provide an initial foundation for a better profiling of the customer. The extended customer description varies from industry to industry and from company to company. For instance, banks and retailers are interesting in spending habits of the customer. Likewise, manufacturers attempt to collect data associated with the customer’s engagement with their products or with competitive products.
- Behavioural Data: Behavioural data are more dynamic and help enterprises monitor the customers’ interactions with the company over time. For example, in the scope of a customer’s interaction with a bank, behavioural data include the payment transactions, as well as the customer’s engagement with other banking products such as deposits, bonds, stocks and other assets. Similarly, retailers are interested in a customer’s past purchases. The analysis of such data enables the implementation of marketing, sales, and loyalty strategies, such as assigning the customers to loyalty clusters and segmenting marketing databases using strategies like RFM (Recency, Frequency, Monetary Value). The latter identifies high-value customers that are most likely to respond to marketing and loyalty campaigns, based on the recency, frequency, and monetary value of their past purchases.
- Customer Context Data: These data provide additional context about the customer, which enables more accurate profiling and customer analytics. As a prominent example, contextual data include information about the personality of the customer such as his/her likes and dislikes, favorite colors, hobbies and more.
Finding a reliable CDP provider is a key prerequisite for a successful CDP deployment. In many cases, companies must employ a CDP consultant as well. This is because there is commonly a need for modeling customer data, defining customer data sources, and planning for the right customer analytics. Hence, it is always a good practice to look for CDP vendors and consultants with a proven track record and good reputation.
If you truly believe in customer centricity it is worth understanding CDPs and how they can elevate the productivity of your customer-oriented processes. It is also important to understand how CDPs are different from legacy CRM and marketing automation systems. Once you do this, the merit and need for investing in CDPs will become crystal clear. We hope to have motivated you to research more about CDPs and the business benefits they can deliver.
