Customer MDM

Data quality and consistency says Pretectum Customer MDM, should not be neglected, market research suggests that poor quality, data redundancy, and data inconsistency are what most businesses struggle with on a daily basis. Customer data is amongst the most common of the data that represent challenges to marketing, sales, service, support and billing, and collections functions. There has never been a better time to implement a more disciplined approach to customer data management than today!

Customer Master data management (“CMDM”) itself is the convergence of people process and technology to maintain a disciplined approach to the definition, creation, maintenance, and distribution of customer data. Customers can be consumers or businesses and when you consider consumer customers these encompass those that visit brick and mortar stores and locations as well as those that your business engages with via eCommerce or via phone, mail, or home visits.

Brands and retailers need to reevaluate how they think about customer data

If you believe that data can fuel your digital transformation journey then you will also recognize that it offers the potential for more interaction and communication consistency. By leveraging centrally stored well-managed detailed customer information, all manner of services and support can be made use of in the honing of the customer message to provide a personalized and distinctive customer experience.

Master Data is only one facet of Data Governance

All successful data governance and data management programs including those that only consider Master Data Management as a first step, have to be implemented by people. These could be stakeholders from the business, members of IT, a specialized group of people that form a data management organization (DMO) or they could be external consultants or service providers.

A well-implemented and maintained MDM practice avoids duplicates, redundancy, and inconsistencies.

Customer master data management data types

Depending on the nature of your business and the relationship that you have with your customers you may have several different types of customer master data that you choose to manage and maintain. There are other types of customer data that you may need to manage too, data types that you don’t necessarily always think of as master data but which may benefit from being stored and retrieved centrally as required.

Pretectum’s Customer Master Data management system (C-MDM) doesn’t prescribe what you should or should have as that basic data definition, it is entirely up to you. While we may offer some standard models (schemas) and your systems may have specific minimum requirements, those can be supported but the end decision is up to you.

Call Centres & Customer Data Management

As 2020 ended as we now conclude 2021 it is worth looking back on the increase in calls that call centres will have fielded but there was also a significant increase in the rate of difficult calls and the need to escalate them. That’s at least according to a 2020 study by Harvard Business Review. Difficult calls increased by 50% overall.

The solution to some of these challenges may lie in Customer Master Data Management.

Cloud vs On-Premise Customer MDM

Cloud-based Customer MDM presents itself in the shape of software as a service (SaaS). This means that organizations do not incurring a cost of the hardware and software associated with it and have no application or hardware maintenance to pay. The organization and financial cost of provisioning on-premise and captive hardware is sometimes prohibitive enough for organizations to effectively ditch capital expenditure on a system and instead invest capital budgets elsewhere in the business and instead choose a pay as you go model which SaaS will support.

Maximizing CRM & ERP Master data Value

Many companies think that their shiny new CRM, ERP or CDP will solve all their customer master data issues but without a solid understanding of customer data and a strategy for how it will be managed. When you move to those new systems you can often compound the problem rather than solving it by creating yet another system with its own data repository and governance rules.