Tag: CLV

Posted on

Customer data customization for personalization

I think we all appreciate that little touch of personalization or customization of an interaction with a company or brand as long as we think it was thoughtful, non-intrusive and not tacky.

The question, though is how do you do that exactly in a tasteful and respectful way?

In days past one of the things that certain kinds of retailers were very good at, was the concept of the “personal shopper” or the knowledgeable customer service consultant who knew something personal about you and your tastes.

Consider it somewhat akin to having a tailored shirt or suit, or a custom pair of shoes.

A fundamental question, is why do we like that personalized and custom experience? It’s not for everyone, and definitely not appropriate for every random retail engagement but the evidence suggests that customers are ready to pay more for customization.

Customers also recognize that customization services require dedicated resources in the realm of personalized product development with niche dedicated resources.

As a small or medium-sized business your vision for retention of customers and to draw them into coming back for more, you want to think about how you can strategically plan a customization engagement to maximize the customer experience. The benefits will often far outweigh the expenditure you make in support of personalization.

Nordstrom, Inc. is an American luxury department store chain. Founded in 1901 by John W. Nordstrom and Carl F. Wallin, it originated as a shoe store and evolved into a full-line retailer with departments for clothing, footwear, handbags, jewellery, accessories, cosmetics, and fragrances. Wikipedia

One of the distinguishing traits of Nordstrom Inc is that for their high-end customers they offer a Personal Stylist service. Despite the prevalence of “fast fashion” on the main street personal stylists are important as brand ambassadors and ensure that you maximize customer lifetime value (CLV).

“Customers are assets to be cared for and nurtured.” 

Jeanne Bliss – Founder and President of CustomerBliss

Armelle Ferguson is someone who should know. Armelle frames herself as “an Ethical Personal Stylist & Sustainability Coach for women with ethical values, who want to live a better, fulfilled life, and reach their goals while looking good in clothes that reflect them and their values. ” Armelle does this by working with clients on an 8-week program developing around what she calls a “6C Framework”. For certain kinds of consumers this is a perfect way to meet a number of objectives, namely, look good and feel good, and actually do good.

Not every consumer can afford a personal stylist though, and not every consumer has the patience, and indeed, there are probably not enough stylists to go around for all the consumers that are out there. At scale, your business probably also cannot afford to offer it as a service.

What Armelle is doing though, is through meetings and discussions and show and tell, and above all ‘listen’, is that she is able to formulate a vision of that customer that she can then leverage to personalize and customize the fashion acquisition statement around.

That gathering of data is likely the same for pretty much every customer but for scale, the trick is to maintain a centralized customer data repository that is continuously maintained and accessible to all the important parts of your business.

Older generations may be more familiar with personalization offers by merchants but that doesn’t mean it doesn’t hold appeal for Millenials too. In fact, if anything, if you work on personalizing the experience for this generation of customers you’re targeting longer-term retention and lifetime value growth.

This next generation will enjoy the personalization of goods and services but they’re savvy enough to see through the faux customization of you simply attaching a name to an email! You need to entice the millennial consumer, doing that requires data to inform your decisions.

Customer master data management is all about gathering the right data and the right levels of data to support business choices about how you are going to tackle the customization and personalization challenges.

Customer experience personalization has traditionally been a time-consuming process, the cycle involves understanding customers’ requirements, collecting samples, and sending them back and forth to the manufacturing team – chances are you are not going to do that, but what you can do is ask the customer what they like, what they prefer and all the vital statics that can drive an optimized eCommerce or in-store experience.

You could do this in a number of ways. But we think that the Pretectum approach to customer master data curation is one of the most flexible and affordable. Supporting many ways to gather, house, and maintain the data opportunities that personalization requires. Reach out today to learn more.

Posted on

Data Governance: Master Data is but one facet

Here is an outline of some facets of a robust data governance program that you might want to consider as part of your overarching approach to customer master data management.

Start with goals and strategy

Put aside the data for now and think more holistically about your business objectives. A good business strategy that understands its dependence and relationship with data helps your business to understand what data you need to have and for what purposes. This in turn will inform your data strategy.

Answer these fundamental questions to guide your strategy:

  • What is the problem you are trying to solve with data?
  • What kind of data do you need, where and when?
  • What is the data literacy of your organization and do you need to improve it?
  • What is your data worth? Justify this proposition with CLV or other measures.
  • How do you exchange, collate, disseminate and control the data you have?

A considered and well-formulated data strategy has a strong vision, clear goals, well-defined success metrics and compelling business rationale.

Roles and responsibilities

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.

Answer these fundamental questions to guide your organizational definition:

  • Will you have dedicated people for data management or will this be an integral part of the role of existing peoples’ day jobs?
  • Have you committed to your initiative being a business-led initiative as opposed to technology and It led one?
  • Do all the participants in the process understand their roles and responsibilities with respect to proper and appropriate activities and decisions around the data?
  • Do you have assigned data owners?
  • Do you have a decided escalation or triage process for contentious issues?
  • Do you subscribe to the concept of “Data Stewardship”?

Recognising that part of the responsibilities of people undertaking data stewardship is the creation and handling of the Data Management Body of Knowledge (DMBoK); defining, evaluating and resolving data quality assessment; documenting policies and procedures ultimately help with transitioning to a more evolved state of organizational data governance.

Defining and measuring success

There is an old adage, often attributed to W Edwards Deming, statistician and QC expert, that “you can’t manage what you can’t measure”. The same is true of your data management program. If you have not defined measures and are not evaluating those measures, are you in fact managing your data?

Very little data enjoy a static existence, more often than not, data evolves and changes according to the needs and usage of the business. Setting up monitoring of data is therefore one of the key tasks for the teams responsible for data management.

These are the common measures you might expect to see

  • Assessment of Accuracy
  • Evaluation of Relevance
  • Completeness checks
  • Consistency checks
  • Rights and permissions

What you use to perform these assessments and checks is less important than ensuring that they are undertaken regularly and consistently and reported on.

Remediation plans can only be put in place and be executed if you are performing these checks.

Data quality measures can include different things such as information like the number of completeness checks, root cause analysis, and recurrence relative significance of certain types of issues encountered. Your metrics may vary when compared with other organizations but in some instances, there are standard measures for industry sectors and your peers. What’s crucial is to establish key metrics for assessing data quality and follow through on leveraging them.

Communication and education

A well-implemented data governance program avoids being labelled as bureaucratic and instead demonstrates effective management and use of data through a comprehensive and elaborate communication programme.

This means that those people with the assigned responsibilities of data governance need to bring strong communication skills to the table when dealing with people and process issues. Since data governance is often accompanied by business process change or digital transformation, many of the communication approaches will be common.

If this is a new initiative for the business there are a couple of critical communication touchpoints that might be beneficial.

  • Develop a timeline and a schedule for your communication activities and channels.
  • Introduce others to the initiative by way of an executive sponsor in the town hall, internal newsletters or communique.
  • Make use of social media and print media on noticeboards, corporate screensavers and email banners.
  • Clearly articulate business expectations in terms of outcomes – why are we doing this and what expected benefits will we or our customers all enjoy
  • We talked about roles and responsibilities already but reiterate these so everyone understands especially those not a part of the team.
  • Avoid buzzword bingo and jargon in conveying the message and building organizational understanding.
  • Communicate on a regular basis.

The more others understand what you are doing and why you are doing it, the greater the likelihood of success.

Fitting MDM into the big picture

Master data management encompasses the processes, standards and tools that are used to support the creation and management of master data.

MDM is a cornerstone of data management and data governance. At its core, MDM is the maintenance of the relationships between the master file and all other masters and transactions with the Masterfile for the customer in particular being a key point of reference.

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

The long game

Things that are worth doing, often take time, and the same is perfectly true about data governance and the implementation of master data management as part of a more broadly focused data management program. It isn’t something you do once, and then you’re done.

Over time your organization will determine that the true value of data is often hidden in how it is used in ways that were perhaps less obvious at the outset.

Fast-evolving data and data needs mean that the job of data governance never really ends and so your business will need to keep pace with these changes both in terms of how the data itself is managed and also in how teams think about the data.

We view Pretectum C-MDM as a flexible and adaptable complementary technology to any data governance program with our special focus on customer master data management, contact us today to find out how we can be of benefit to you in your data governance initiative.

Posted on

Customer Master Data Management is strategic

gold padlock locking door

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.