Data Capabilities for Customer Centricity
The Need for Customer Centricity
The challenges of moving from a policy to a customer orientation are well known in the Insurance world but this movement is now a strategic driver for much of the Financial Services industry. Customers now expect financial services providers to orient their business operations around them; and not the products they sell.
Many Financial Services organisations developed their customer base based upon the individual products they brought to market. Changes in consumer experience, from industries such as online retail and telecommunications, has shown consumers that organisations can put them at the heart of the experience and financial providers need to adopt similar approaches to stay relevant.
New entrants into the market are causing churn as keeping existing customers is getting harder as these new entrants don’t have the operational legacy traditional providers have. Customer Centricity is easier to achieve when you have little or no legacy and a small number of very modern systems.
The major advantage for traditional providers is that they have a sizable customer base but this has to be nurtured and maintained as it won’t remain valuable on its own.
For traditional providers, the provision of great data is a key capability in providing an outbound facing set of Customer Centricity capabilities whilst behind the scenes the business is transforming its operations. This is where Customer Centricity meets Digital Transformation. The latter is a necessary but resource and time consuming process, so Customer Centricity programmes are using data to meet the needs of the customer and to protect the organisations during Transformation.
Customer Centricity Data Capabilities
Here are some topic areas Financial Services organisations are investing in, around great data, as part of a Customer Centricity programme:
- Using technology to create a single, accurate and encompassing view of all data related to a customer from data residing in a number of business systems. This could include personal details, product/services purchased, transactions executed, contacts made, communications, preferences and segmentation.
- Pool internal and external sources of data covering both small and big data as well as structured and unstructured content.
- Assessment of the quality of data across a range of business systems so remediation can ensure the raw data is accurate and up-to-date. Standardisation of addresses, addressing missing or incorrect data and addressing duplication of data are common activities
- Having an agreed set of Business Terms across the enterprise where definitions and rules of the language of the organisation are defined and governed. These terms are used by business and IT consumers, and because they are linked to source systems then finding the right data becomes much simpler and quicker
- Push data updates out to all relevant systems quickly to ensure business unit specific benefits are achieved
The Role of Big Data
Some Customer Centricity programmes are utilising Big Data technologies to help deliver the underlying set of requirements. Large amounts of Customer data are being loaded into Data Lakes, or similar large scale data capabilities, as an approach to overcome the challenges associated with changes to enterprise data warehouses – the organisational default for a location to store cross organisation data.
Whatever the underlying technology solution, the same set of data capabilities are still required to deliver Customer Centricity. Data is still data regardless of how big, how fast, how frequent, how complex. The same underlying set of principles and capabilities are required to manage data regardless of the technology platform employed.
Customers are looking for Financial Services providers that make their lives easier & simpler by providing them the right decision making information at the right time and in the right way. As part of wider Customer Centricity programmes, great data provides value during business transition phases and ensures on-going operations keep that data in a good state and prevent the creation of a new legacy system.