Tag: CLV

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Customer Master Data Management is strategic

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Putting a price on data is a tough task to perform if your business is not “in the business” of selling data. The real question though, is does your organization recognize its arguably most valuable data asset – customer master data, as a valuable resource? The true value of that data more than likely hides in how you use it.

Customer data holds enormous economic potential if managed well and appropriately leveraged. The challenge though is that many companies struggle with proper handling and syndication of their data. Further, they don’t think about their customer data strategically. They don’t take what they have and convert the data that is in hand, into actionable information. Worse, they may not be managing their customer data in any meaningful way, they may have data all over the place in systems, spreadsheets and even email.

By implementing some data management best practices around customer data, these same businesses could be thinking more strategically especially if they are ensuring that their data is accurate and aligned with their business objectives.

The practice of data governance has many facets. Master Data Management is just one of those and certainly, your master data management practice doesn’t have to cover all of these, particularly if the maturity of a given organization is not appropriate to an over-investment in policies and procedures, but there are some basics that you could and should consider.

Data governance includes all of the activities relating to the planning, implementation, development, and control of the information generated by an organization.

In other words, data governance is the development and realization of all the aspects that you might consider for data definition, creation, collection, storage, access, quality, change management and data distribution. Data governance also covers both the master and transactional data.

Pretectum sees data governance and the broader discipline of data management as covering many areas including data security, sharing, integration, architecture, MDM (Master Data Management), RDM (Reference Data Management), Insights and Intelligence, change management, storage, retention and retrieval

The best data is the data that is available and usable at exactly the right place and time, in the right format. While that is easily said, achieving this goal is a little more challenging depending on the maturity of your business practices, technology, the characteristics of the data itself and the nature of your industry.

There are key critical strategies and best practices that are generally accepted for improving the way companies manage their data, consider these aspects.

Forget the Rule of Thumb

We use the term frequently, and many businesses actually run their data governance program, if they have one, using the “Rule of Thumb”, but this approach is downright wrong.

At best, “Rule of thumb” is what you might consider an approximate method for data governance, sure, it is based on practical experience, but it is tribal in nature and really only as good as the individuals and the experience that they have with the maintenance of the program.

Having its roots in seventeenth-century trades where weights and measures were almost non-existent and certainly not measured and prescribed, quantities were measured by comparison to the width or length of some sort of loosely defined measure. A thumb, for example often being used as the baseline. These rough swags are fine when you don’t need more control and precision but unfortunately with the growing interest in compliance and meeting regulatory policy, managing data in a loose way is no longer acceptable.

Apart from any business rules you might have in place, there may be a raft of additional rules that are imposed by regulators specifically because of the nature of the data.

Having a robust approach to data governance provides some assurance that data meets the needs of the business and the legal obligations of the environment in which the business operates.

Data governance programs have several hurdles to overcome in order to be part of the DNA of the way the business views and deals with its data. There are cultural challenges, organizational politics and the simple mechanics of basic data control and management.

The technology aspects may be the least of your concerns if you have the right expectations defined in terms of requirements and the right level of complexity and flexibility in a given selected vendor.

The Pretectum CMDM affords you a number of different ways to meet your organizational objectives for Customer Master Data Management as part of a larger data governance program. Reach out today to learn more.

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Collecting and using retail customer data

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Customers expect a personalized retail experience when they are shopping. However, effective personalization as part of your retail operating model has to have some core elements. Gathering retail information is about gathering details on how your channels are performing, including physical stores and e-commerce. You also need to examine your customers, their demographics, behaviour, attitude, and specific actions.

Modern retailers will use this data to tailor the supply chain, the outbound message to audiences, pricing, merchandising and the whole retail experience for the customer.

Highly personalized customer experiences used to be something only available to the higher-end retailer with a degree of exclusivity but by using the power of customer data retailers now have the ability to offer this kind of experience to many millions of individual customers.

Some of the data will be proprietary zero-party data (0PD) that will be difficult for competitors to imitate. When the use of this data is considered and executed well, businesses not only successfully differentiate themselves but also gain a sustainable competitive advantage that can augment the customer’s lifetime value and extend the relationship in perpetuity. Both drive higher profitability and ensure brand loyalty.

Amazon may have set the minimum standard in terms of polishing the e-commerce experience but many other retailers like Sephora and Nike are blazing their own trails with innovative approaches to personalization and customer relationship management according to McKinsey.

“Retailers should collect information about who their customers are and what their shopping patterns are, so as to develop demographic and psychographic segment profiles,’

AR Rao, General Mills Chair in Marketing – Carlson School of Management.

Retailers have struggled to gather this kind of information in the past, for a number of reasons. Amongst them, getting the data is sometimes harder than it should be, locked away in the inner logic of ERP, CRM, CDP, POS and e-commerce platforms. Whole teams are often required to just prepare the data for use and for many retailers, the way that they are set up organizationally means that information sharing, particularly about customers, is difficult if not impossible. For many, the tools available for use, are also misaligned with the intentions that the business has for the data.

Successfully overcoming these hurdles requires two important facets to be considered. The first of these is the types of customer data. There is a lot of information you could be collecting but really the most useful ones are the personal data, and the demographic aspects, like age, ethnicity, socioeconomic status and education – these factors often have a strong influence on tastes and interests.

In terms of a retailer’s brand, there is value in understanding customer preferences. Consumer preference theory suggests that consumers have preferences for certain products and services and this can be a valuable tool for planners, product managers and marketers in understanding what customers want. This might even include factors like, whether they prefer physical stores over online shopping.

Customer Master Data should include personal preferences

When you combine the personal data with the preference data you know something about your audience, but you can push this further if you’re able to tie transactions back to the specific customer. Behavioural data can be suggestive of future behaviour and can also assist your representatives in having more meaningful conversations with the customer.

The Pretectum CMDM supports you in many aspects of this data-gathering exercise, from the consolidation of the many sources that you have to the establishment of a golden nominal that provides a digital twin representative of each customer, through to the support for active data governance in the creation and maintenance of the customer record.

All of this is achieved through the SaaS customer master data management platform that is available to you and your teams via interactive use or through integration.

Contact us today to find out how we can assist.

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Customer Master Data Quality is relative

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Data are not fuel, not energy, not a life force. In itself, customer data, in particular, holds no innate value to your business beyond the purpose for which your business acquired it and uses it.

However, all that said, the decisions that an enterprise takes based on insights from customer data can lead to different outcomes some of which can be very detrimental to the health of the business. For this reason, the quality of the customer master data may be the one thing that you really want to focus on, in relation to customer master data overall.

In the absence of good data quality, data-driven initiatives are sub-optimal and may even be rendered useless. This is why, ensuring that you have quality customer master data is pretty foundational to avoiding missteps, reducing risk and harvesting meaningful benefits from your customer master data.

Absolute data quality is probably an unattainable goal and quite honestly, the value of absolute data quality is relative to the nature of the data itself. Data that is sourced as zero-party and first-party data should typically be of better quality but the effectiveness of capturing good quality 0PD and 1PD is dependent on the data capture and collection methods and the control mechanisms put in place to provide data quality assurance.

One of the ways that the Pretectum Customer Master Data Management (CMDM) platform helps, is with the ability to capture and edit new and existing records through interactive screens with built-in data validations. Another way is to leverage the Pretectum CMDM APIs and integrate these with your business applications to provide added data quality assurance at the time of customer master data capture and edit. Rules and data quality measures are all driven by the schema definitions that overarch a given dataset.

The $1-$10-$100 Rule

Capturing the best possible data at the time of origination is the first prize in approaches to customer master data collection. According to Gartner and D&B, the costs associated with the collection of a single record can be as much as US$1, this cost not including other acquisition costs. But, if there are any fatally bad data elements, the resolution costs, which are basically investigations and workarounds, could be as much as US$10 per record. Correcting those records at the sources reaches a whopping US$100 per record.

This is commonly known as the 1-10-100 rule. It is a rule-of-thumb model illustrating the hard costs to an organization of chasing mistakes and reinforces the argument that failure to take notice and correct mistakes early on, escalates costs the later they are realized.

The counter-position is that a shared source prevents the time and costs of rekeying and verifying information entered into separate disconnected systems.

A single source for customer master data also eliminates the costly and embarrassing mistakes that are created with disconnected systems and the absence of real-time or near real-time synchronization and integration.

All this comes down to the simple calculation that if your company holds 1M consumer records, it would have cost you potentially US$1M to acquire them over their lifetime. If there is 10% inaccuracy in any customer master data dataset, you’ll be spending just as much on data issue triage during the lifetime of those records and millions more on correcting those same records to avoid the triage costs.

A couple of studies cited in the MITSloan Management Review based on research by Experian plc, as well as consultants James Price of Experience Matters and Martin Spratt of Clear Strategic IT Partners Pty. Ltd. estimated the cost of bad data to be 15% to 25% of revenue for most companies. That’s astonishingly high but seems to align with the high cost to triage and remediate just customer data alone.

Multifaceted data quality

What becomes pretty clear the moment you start reviewing data quality across your systems estate, is that data quality is multifaceted. Consistency, accuracy, recency, completeness, and de-duplication are obvious aspects, but when you consider the typically siloed nature of systems you quickly come to the realization that consistency, for example, doesn’t carry quite the same weight for all business use cases.

Many organizations field ten or more systems of record. These range from ERP, CRM, CDP, POS, Service, Support, and warranties through to the many spreadsheets and Access databases as well as other specialist systems of record that a given organization might have.

The data quality problem is further compounded when you examine data ownership and who is designated as the most responsible person for customer data and which systems are considered the true authority in relation to the customer master.

A customer master data management platform like the Pretectum CMDM provides an organization with the ability to define what good customer master data should look like and then, is able to assess data added or loaded into the system in relation to its conformity with the customer master data quality definitions.

The adoption of the CMDM platform affords the average organization not just a centralized customer master collation and insights point, it can also serve as the hub to a multispoked approach to servicing customer master data to different systems with different needs and usage.

Through the combination of manual data stewardship and automation, a CMDM like a Pretectum Customer MasterData Management platform can also reduce the cost of triage and remediation, depending on the implementation approach.

The relative quality of customer master data records can be assessed holistically and compared and contrasted with data sourced from other upstream data collection systems and repositories.

Most importantly, the records, even with variations in their content, can be consolidated and converge on a single source of truth. To learn more about how Pretectum can help with your Customer Master Data Management challenges, contact us today.