The foundation of most businesses is a singular, critical element; their relationship with customers. Analysing what is and isn’t working, and how to best serve the customer is vital in maintaining the health and growth of a business.


What is a Customer Data Platform?

The larger the business, the harder it is to keep track of communications and touchpoints. Ensuring that customers aren’t repeatedly contacted with the same information and don’t have to repeatedly explain any issues they’re having is good customer relations practice.

Achieving this objective is significantly easier with the creation of a Customer Data Platform, which creates a centralised, integrated database of all relevant customer data, pulled together from all touchpoints. This prevents duplication and inconsistency in the database itself, leading to more accurate and efficient customer relations actions down the line.

Why do you Need a Customer Data Platform?

Customer data may be originated in different places. For example, you may be collecting new enquiries on your website, which may lead to a customer record being created on a CRM or ERP system. At the same time a customer record may be created manually by a service representative over the phone, using a data entry process that is slightly at odds with the automatically generated customer record.

At worst this will cause duplicate records that need to be associated to obtain a 360-degree view of interaction across different systems. Even with controls in place to avoid duplicates, different systems will manage details at touchpoints that needs to be integrated to end-to-end process view.

The following is just one example of such an end to end process:

Banner Advertisement -> Web Enquiry -> Call Centre Outbound Call -> Proposal Sent from CRM System -> Order Fulfilment -> Post-Delivery Survey

How Does a Customer Data Platform Work?

A Customer Data Platform functions as a centralised and consistent location for all user and transaction data. It is in part an orchestration layer, processing the data coming out of the different operational systems, transforming it and adding it into the unified master customer profile, which is then also the source for when data is needed.

Identity resolution is also performed, reconciling and consolidating all the many data points associated with a user to ensure that there is no customer duplication. Having all customer data pass through this layer and have a master record greatly reduces the chance of inconsistent or redundant data points between systems. This ensures complete analytics and serves as a foundation for excellent customer relationship management.

Customer Data Platform vs Single Customer View

This type of data solution can be called by a number of different names, but ultimately signify the same basic concept. The two most common names used are a “Customer Data Platform” and a “Single Customer View”. Both refer to the idea of building a unified, consolidated customer database across an enterprise which is linked to activity detail across the lifecycle of an interactions with an organisation.

What are the Key Challenges in Consolidating Customer Data?

Customer Data Integrity

Aligning Duplicates – What are the rules to detect a duplicate record? Is a combination of fuzzy matching (i.e. overcoming typographical differences) as well as exact matching on unique identifiers (such as drivers license number) required? What are the thresholds of confidence to determine a duplicate? Is there a subset of “soft” matches which need to be manually reviewed before making a determination?

Survivorship – What are the “best” details to represent the customer, often referred to as the “golden record”. This may be broken down by data domain (Name, Address, Demographics etc). For example, if there are several duplicate records detected for the same customer, which is the best address to use? Is it the most complete? Is it the most recently updated? What processes do the source system use to collect / verify the information?

Presenting Cross-System Data – A key outcome sought of a Customer Data Platform is to not only remove duplicate records but to maintain the link of the deduplicated customer record with all of the systems from which the duplicates were derived. This is important to maintain a view of transactions and other activities, and effectively provide the 360-degree view of interactions across different touchpoints. In some cases, the transaction or activity detail itself may also need to be conformed into a common structure to support this type of analysis.

Presentation

Search and Display – Enabling front-office staff to quickly view the entirety of a customer relationship is an often requested feature of CRM systems, however this data may not reside within the CRM system itself. Customer Data Platforms can provide a simple interface for details to be searched and retrieve a snapshot of customer demographics and interactions.

Segmentation – Customer data often needs to be easily divided into groups based on behavioural or demographic data. This is often to support strategic analysis for product design, or support target lists for campaign operations. A customer data platform can provide simpler means for segmentation rules to be saved and reused without unnecessary data movement.

How Loome Can Help Set Up a Customer Data Platform

There is some level of integration between commonly used enterprise CRM systems which comes out of the box or can be established relatively easily. However, a Customer Data Platform is different in scope and implementation, encompassing all of an organisation’s data systems and offering an entirely more advanced set of capabilities.

Loome Integrate has over a hundred connectors with which to pull data from your customer data source systems. With the ability to easily schedule and orchestrate data migration tasks and set up the necessary transformations, it is a powerful and intuitive tool with which to build the foundations of your Customer Data Platform. Contact us today to find out more.

Example Data Environment

A data architecture map visualising a succesfully implemented customer data platform