Tag: data management

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How do we leverage customer data for competitive advantage?

a hyper realistic photo style image of how consumer data contained in a saas cloud platform can generate value-based dividends to the business as a result of the high level of detail and data quality of the data in the saas cloud platform

Organizations can leverage customer data for competitive advantage by creating personalized experiences and tailoring offerings and communications to individual preferences, which increases customer engagement and loyalty.

Sounds simple enough, right?

Effective data management allows businesses to respond swiftly to market changes, adapt strategies based on customer insights, and introduce innovative products or services that align with customer needs; however, all of those ideas and initiatives need accurate customer data. Quality in the customer data is crucial for meaningful analysis and decision-making, whilst proper data management processes improve data quality and resource allocation. So how might you go about getting more advantage? Here are a few ideas for you to mull over.

Building a Custom Customer Data Platform (CDP): Instead of buying a pre-built CDP, CRM, DMP or CMDM solution, organizations can explore the option of building a custom customer data platform, one that allows them to tailor the platform to their specific needs and gain a competitive edge through unique data management capabilities.

The idea of building a Custom Customer Data Platform (CDP) stems from the need for organizations to have a more personalized and effective way of managing their customer data. Such a system aggregates and organizes customer data across a variety of touchpoints and is used by other software, systems, and marketing efforts; collecting and structuring real-time data into individual, centralized customer profiles. 

This would allow for a unique, unified view into customers’ minds and needs. The concept of a custom CDP comes into play when organizations want to tailor the platform to their specific needs and gain a competitive edge through unique data management capabilities. This would involve selecting best-in-class componentry for each layer of the customer data journey.

Building a custom CDP is not a simple task though. To engage in such an undertaking requires It requires clarity on design, the right resources, a deep enough budget, and a considerable amount of time. The benefits of having a custom CDP that aligns perfectly with an organization’s needs might meet or fail to meet expectations.

Foundations of CMDM in the wider organizational systems landscape

Implementing Privileged Insights: The concept of “Implementing Privileged Insights” comes from a Harvard Business Review article written by Ian Kahn, Paul Leinwand, and Mahadeva Matt Mani. 

The authors suggest that companies can gain a competitive advantage by creating and integrating unique and relevant information about customers into their decision-making processes. This way, companies can gain a competitive advantage by creating and integrating unique and relevant information about customers into their decision-making processes, which competitors do not have access to.

This can be achieved by observing and interacting with customers while they use products and embedding privileged insights into existing customer touchpoints. That’s fine if you’re running a webshop, have a mobile app or have the ability to serve up those privileged insights. You’ll still potentially have a data quality problem and you’re still building a custom CDP.

Radically Different Approach to Digital Experience (DX): Organizations can take a new approach to digital transformation by focusing on organizational buy-in, metrics that matter, categorizing the customer, democratizing the data, and leveraging the SEO benefits of personalization. This idea is a synthesis of several concepts in the field of digital transformation and customer experience management.

Composable DX: This concept emphasizes a modular and flexible approach to delivering digital experiences as suggested by CEO and co-founder of Pimcore Dietmar Rietsch. This allows organizations to create custom digital experiences that align with their unique business needs and customer expectations. Such an approach requires organizations to prioritize agility, flexibility, adaptability, effective communication, composable thinking, and empathy toward customers.

Digital Transformation (DX): uses digital technologies to create new or modify existing business processes, culture, and customer experiences to meet changing business and market requirements. The need for digital transformation has been highlighted by the disruption caused by events like the COVID-19 pandemic.

Customer Experience Management: This involves providing a seamless user experience, fixing any issues quickly, and understanding and adapting to user behaviors.

In the end, it isn’t about technology or radical ways of doing different things beyond making better use of customer data; most of the time it is about having a better understanding of customer behaviour and preferences, by analyzing customer data. That’s assuming you have the data and it is of good enough quality.

You can gain insights into customer behaviour and preferences with almost any customer data repository but if you want it to help you tailor your products, services, and marketing efforts to meet the specific needs and wants of your customers you need to ensure that the data is broad enough, deep enough and of sufficient grade to meet to analytics needs.

Customer data can also be used to identify inefficiencies in your operations and improve processes. This can lead to cost savings and improved customer satisfaction. Again, the dependency here is the breadth and quality of the data.

By gathering your data on individual customers into unique profiles and storing them in a single location like the Pretectum CMDM, you can match customer interactions with the right contact profile. This in its turn allows you to deliver personalized marketing messages that pique your customer’s true interest.

Superior data analysis can confer a competitive advantage. However, it’s important to note that the data must offer high and lasting value, be proprietary, lead to improvements that can’t be easily imitated, or generate insights that can be quickly incorporated

By embedding a data-led approach in all customer-focused efforts and goals, organizations can achieve customer-centricity by maintaining clarity on customer value, understanding the customer journey, and fostering exceptional customer experiences at every stage. This radically different approach relies heavily on data as a solid foundation for customer success. A CMDM is perfectly positioned to support such an approach.

Organizations can balance the need for data collection with customer privacy concerns by implementing the following strategies:

Gather Only the Essentials: Collect data that is essential for business purposes and inform customers about how it will be used. This in its turn promotes transparency and helps in building a mutually beneficial relationship.

Obtain Consent for Sensitive Information: When obtaining sensitive information from customers, seek their permission before using it. If a customer refuses to provide consent, refrain from using the data.

Data Analysis and Customer Consent: Utilize data analysis to understand customer behaviour and preferences. Additionally, communicate with customers about the intended use of their information, ensuring that their consent is obtained before proceeding.

Understand the Value and Risks of Data: Organizations should comprehensively understand the value and risks associated with the data they collect. This understanding will help in prioritizing data collection and drawing additional insights from the available data.

Establish Clear and Transparent Communication: Communicate clearly with customers, employees, and partners about the collection, storage, and use of data. Respect their preferences and consent, and collaborate with other businesses, organizations, and authorities to share best practices and insights.

Design for Transparency and Trust: You should design products and services with transparency and data privacy in mind. You must provide customers with appropriate value in exchange for data, educate them about how it is collected, and allow them to have control over it.

Not prioritizing customer privacy when collecting data can lead to several potential consequences.

Non-compliance with data privacy regulations can result in severe penalties, including hefty fines. Prioritizing data privacy ensures that businesses adhere to the necessary legal requirements. Failure to comply with data privacy regulations could result in steep fines, potentially costing a company a significant amount relative to sales turnover.

Data breaches and privacy violations can shatter customer trust in an instant. High-profile data breaches have made headlines, exposing the personal information of millions of individuals. Prioritizing data privacy is essential for safeguarding customer trust and maintaining a positive brand reputation.

Not prioritizing customer privacy can lead to financial and reputational risks for businesses. Data breaches and privacy violations can have a significant impact on a company’s financial standing and reputation.

If you recognise data as a valuable asset, you then prioritize data privacy and gain a competitive edge. Failing to prioritize customer privacy can lead to a competitive disadvantage. Failure to prioritize data privacy can result in a loss of customer confidence, leading to weaker relationships and reduced customer loyalty.

Organizations can gain a competitive advantage by leveraging customer data for personalized experiences. Effective data management allows businesses to respond to market changes, adapt strategies, and introduce innovative products.

Building a custom Customer Data Platform (CDP), implementing privileged insights, and adopting a radically different approach to Digital Experience (DX) are ways to enhance data management. Balancing data collection with customer privacy is crucial, involving strategies like gathering only essential data, obtaining consent, and designing for transparency. Failure to prioritize customer privacy may result in legal consequences, loss of trust, and financial risks.

Recognizing data as valuable and prioritizing privacy is key for sustained success and customer loyalty.

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The Integration of Pretectum CMDM in Business Intelligence Competency Centers

Information is a currency in modern business and strategic decisions that are tied to it, can make or break the enterprise.

The relative significance of efficient customer data management cannot be overstated. There has been a sustained surge in demand for business intelligence (BI) tools for years now, and this is a testament to the competitive business landscape, where the ability to access timely and relevant information is essential for good decision-making.

At the center of some, is the Business Intelligence Competency Center (BICC). For some businesses, the BICC is a pivotal entity that ensures organizations can deploy BI solutions with sustainability, efficiency, and strategic precision.

Often a BICC operates as a driving force, establishing policies, governance structures, methodologies, and support models that foster just-in-time decision-making. The mandate of the BICC extends beyond mere functionality, encompassing the transformation of insights into tangible assets, thereby providing a competitive advantage and fueling business growth. That’s the theory at least.

We think of the BICC as a cross-functional team within an organization that focuses on promoting and implementing best practices in business intelligence, data management, and analytics. The BICC develops the overall strategic plan and priorities for Business Intelligence (BI) and defines the requirements including data quality and governance, and fulfils the role of promoting the use of BI. Organizations with a BICC claim to have seen increased usage of Business Intelligence, increased business user satisfaction, better understanding of the value of BI, increased decision-making speed, and decreased staff and software costs.

Often there is a significant role played by data warehouses in managing business information. Organizations increasingly recognize that technology as an enabler means more effective information management. Recognition of this underscores the need for a centralized approach to information management that integrates policies, technologies, programs, and people to deliver BI efficiently.

BICCs are a growing trend in industry and are made up of both technical and business experts, they signal a departure from the conventional integration of BI with IT. Instead, a more holistic approach is adopted, where BI experts equipped with technical, analytical, and business skills collaborate with the business to align knowledge strategies with broader business objectives. In some organizations, the head of the BICC is often even part of C-level management resulting in an expectation of higher returns on investment for BI projects.

BICCs can be multifaceted and contribute significantly to the effective integration of BI into organizational processes. From conducting assessments for strategic services to actually implementing BI software, administering systems, providing training, mentoring users, and overseeing development activities, the BICC ensures that every facet of BI is optimized. This optimization, in turn, leads to a better understanding of the value of BI, increased user satisfaction, faster and more accurate decision-making, adaptability to changes, and enhanced collaboration between IT and business entities.

One of the key dependencies for many BICCs is accurate and complete Customer Master Data and systems like the Pretectum CMDM can be of assistance here. A CMDM system will often play a pivotal role in streamlining the process of referencing and analyzing customer data across the organization. The integration of CMDM within any BICC framework ensures that customer data is accessible, timely, of good quality and relevance and s highly suitable to be leveraged for strategic decision-making. Pretectum CMDM, with its advanced capabilities, facilitates the creation of a unified and accurate view of customer information, enabling organizations to not only enhance customer experiences and drive targeted marketing initiatives but also elevate the quality of the output of the BICC.

Integration of the Pretectum CMDM into the BICC ecosystem supports a new dimension of data insights and reporting by unifying the customer data in such a way that organizations can derive deeper insights into customer characteristics, behaviour, preferences, and trends. Such a comprehensive understanding enables businesses to tailor products and services, adjust tactically and strategically and also personalize customer interactions, and ultimately build longer-lasting customer relationships.

The centralized control and standardization of customer data provide greater assurances about customer master data quality, integrity, and consistency across the organization. This, in turn, leads to more reliable and accurate BI outcomes, providing decision-makers with a solid foundation for their strategic initiatives.

Integration of a Customer Master Data Management system like Pretectum CMDM within the broader framework of a Business Intelligence Competency Center elevates the capabilities of those organizations looking to customer data to advance BICC capabilities. Such an approach not only ensures more precise and efficient management of customer data but also amplifies the impact of BI initiatives. Modern organizations must navigate what is now a very dynamic business environment, the fusing of the BICC and CMDM systems holds great potential for unlocking the great capabilities and insights from customer data, driving innovation, and staying ahead of competitors.

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Best Practice: Cleansed Customer Master Data

broom sweeping up

Poorly managed and unclean customer master data can give rise to numerous issues and hurdles for businesses. At its core, these challenges often manifest as inaccuracies in data, encompassing incorrect contact details, addresses, and other vital information. Such inaccuracies can lead to misunderstandings, miscommunications, and failed business transactions, alongside the accumulation of duplicate records in the system. The presence of duplicate customer entries can sow confusion, waste resources, and introduce errors in analysis and reporting processes.

Additionally, the absence of crucial details may impede effective customer communication, hinder marketing efforts, and adversely affect overall customer relationship management. This, in turn, can impact decision-making processes, resulting in faulty strategic decisions, ineffective targeted marketing campaigns, and misallocation of resources. The repercussions extend to reduced productivity, with employees expending more effort and time in managing data.

Furthermore, the ramifications of poorly maintained customer data extend to a subpar customer experience marked by irrelevant communications or difficulties in accessing services. This dissatisfaction can potentially lead to the loss of business. Finally, inaccurate or outdated customer data poses the risk of non-compliance with data protection regulations, legal issues, fines, and damage to the organization’s reputation.

Outdated and irrelevant customer data creates a messy and purposeless digital landscape. This demands immediate attention. Far from being acceptable, this situation necessitates a decisive move toward comprehensive bulk data cleaning; because nearly all aspects of an organization’s information are intricately linked through the customer master and this may reside in CRM, a CDP, or some other customer data management platform but likely is not in a CMDM (Customer Master Data Management) system.

The imperative here is clear then – preserve this invaluable data asset not by choice, but by necessity. Unkempt customer data not only raises questions about your organization’s credibility but also poses a tangible risk to operational efficiency and compliance.

The rate at which customer data becomes obsolete can vary greatly depending on the industry, the type of data, and how often it’s updated. However, it’s important to note that data can become outdated quite quickly. The Data Genomics Index found that over 40% of stored data has not been touched in over three years, and is considered ‘stale’.

A significant portion of stored data is considered redundant, obsolete, or trivial (ROT). A 2016 Veritas Global Databerg Report found that 33% of data is considered ROT and provides little or no business value at all.

All this serves to underscore the importance of regular data cleansing and updating to maintain the accuracy and relevance of customer data. It’s also crucial to have a robust data governance framework in place to manage the lifecycle of data and ensure its quality and compliance.

Establishing a Single Source of Truth (SSoT) is considered the “brass ring” of data capability in that it refers to a definitive and centralized repository for customer information that acts as the authoritative source for all customer-related data across the organization.

Having regularized standard Data Governance Processes provides some assurances around data accuracy and completeness, and integrating Customer Data with Other Systems and Data Sources allows for a more comprehensive view of the customer. The presence of Data Lineage and Auditing in all your data management activities helps you to track the origin of data and changes made to it over time.

Providing Self-Service Capabilities for customers to manage their data empowers the customer. It can lead to more accurate zero and first-party data which is preferred over second and third-party data.

Regularly Evaluating and Improving Processes for Customer Data Management is part of continuous improvement and is key to maintaining high-quality data. Taking Security seriously and ensuring data privacy should be integral to all your organization does with customer data to build and maintain customer trust.

Customer data master Data Cleansing isn’t just a clean-up; it’s a strategic process meticulously executed to purge irrelevant and corrupted records, creating a pristine canvas for accurate data entries. The significance of doing this extends beyond driving operational efficiency – it is a cornerstone for optimizing data management operations, with the further promise of access to invaluable customer insights.

The clutter within customer data masters, if left unchecked, becomes a bottleneck in customer data management. The domino effect is felt most acutely through the inefficiency of incomplete and duplicated records that may be present in the data landscape.

A daily ritual of data cleansing should be not just a routine; but a habit that holds all data-using teams together. Any deviation, be it due to data inaccuracies or outdated information, sends compounding waves of chaos through normally streamlined processes, derailing promises to upsell, marketing, service, and support.

Data Cleansing Action

Data cleansing shouldn’t be viewed as a pointless chore; it’s a catalyst for unlocking the new, freeing teams from broken, outdated, and duplicated data. Success in outreach and communications campaigns hinges on a hygienic robust database of customer data. Cleaning the customer data master allows teams to channel focus toward accurate accounts, bypassing the wasteful exercise of re-verifying incorrect information through emails or calls.

Cleaning the customer data master is not to be trivialized, at scale, in particular, it can be a challenge. Fortunately, systematic automated data cleansing is possible, liberating companies from a historically time-consuming activity.

Understanding the nuances of data cleaning is paramount, irrespective of the chosen method.

Clear out Duplicates: Duplicates are stealthy infiltrators, entering customer data masters through various channels. Identifying and eliminating them is not just a best practice; it’s a necessity to prevent potential customer loss. Automation takes centre stage to proactively block duplicates and fortify data integrity.

Archive the Old Data: Archiving old data in the customer data master recognizes its latent value. Though seemingly dormant, certain data holds perpetual importance, adapting to the context of the moment.

Data Cleansing Tools: Choosing the right data cleansing tools is a strategic decision. Opt for tools equipped with smart duplicate data detectors, data formatting capabilities, and automation functionalities. The aim is not just to prevent bad data but to fortify customer data masters against inaccuracies.

Customer data master data cleansing transcends the realm of a mere cleanup tool; it emerges as the catalyst propelling your company toward sustained growth. The strategic elimination of obstacles – duplication, data overload, or inaccuracies – accelerates overall growth and amplifies your company’s performance. The focus should not merely be on eliminating unwanted data; it should encompass the optimization of data quality, ensuring the CMDM system operates as a beacon of efficiency within your organizational framework.

The key lies in eliminating the unwanted and embracing the ethos of better quality and efficient data management – the essence of customer master data cleansing.