Small and Medium Businesses (SMBs) face several core challenges when adopting enterprise Master Data Management (MDM) solutions, whether this be a Reltio, Stibo, Profisee or one of the even bigger multidomain solutions. These challenges are often due to a misalignment between the complex nature of these tools and the more limited resources and simpler needs of SMBs.
The main challenges are these:
Cost and Complexity: Historically, MDM was seen as prohibitively expensive with lengthy deployments. While modern cloud native SaaS models reduce upfront hardware costs, the total cost of ownership (TCO) and consulting fees can remain high for many SMBs with limited budgets and IT expertise. SMBs often find the full scope of features and complexity of enterprise MDM to be overkill for their simpler needs, such as basic deduplication or customer 360, leading to them paying for advanced features they don’t use.
Time to Value: Implementations of enterprise MDM solutions can stretch for months or even years, which strains SMB patience and expectations for return on investment (ROI). New features and customizations may also have to wait for product-wide releases, delaying time-to-value for SMBs.
Resource Burden and Skills Gaps: Enterprise MDM typically requires deep organization-wide data governance, custom data stewardship, and significant post-launch maintenance, which can be overwhelming for lean SMB teams. Effective use of these tools demands specialized training for administrators, developers, and data stewards, a stretch for smaller teams not solely dedicated to data management. SMBs particularly feel the pain of vendor-dependent workflow changes, as they often lack in-house MDM experts.
Support Responsiveness and Vendor Dependency: SMBs often experience support delays, sometimes waiting days or longer for vendors to deploy or fix critical system components. Even basic customizations, like workflow updates or expanding data domains, frequently require vendor action, slowing SMB agility and innovation.
Usability and Integration Issues: Complex features such as survivorship configuration, match rules, and large data exports can be cumbersome for smaller IT teams, especially when technical resources are limited and the user interface or documentation falls short. Slow large data downloads and JSON formats as default outputs place extra strain on SMBs who lack robust Business Intelligence (BI) or integration resources. SMBs need simple, pre-built connectors and quick integrations, and solutions requiring heavy customization can leave them behind.
Unpredictable Operating Expenses: Many SaaS MDM offerings including Pretectum CMDM, impose quotas or API call limits, which may force SMBs to incur extra costs as their own data volumes and integrations expand, creating unplanned operational expenses.
Despite technical advancements in SaaS solutions, SMB customers commonly complain about excessive complexity, a high reliance on the vendor, support delays, and a fundamental mismatch between what enterprise MDM offers and what SMBs truly value. The “DNA” of enterprise MDM, with its inherent complexity and resource demands, persists even in “SMB-friendly” SaaS wrappers.
This is why we think Pretectum may present as a generally better fit for the SMB market with its equally complex needs for Customer Master Data Management (MDM), but less resources.
Customer MDM (Master Data Management) is a technology-enabled practice and this is often encapsulated in some solution. Pretectum CMDM is a SaaS solution that centralizes, standardizes, and synchronizes customer data across an organization. It creates a single point of reference or a Single Customer View (SCV) by integrating data from various sources, ensuring that all departments, systems, and applications have access to consistent, accurate, and up-to-date customer information.
Pretectum CMDM acts as both a system of reference and a system of entry for customer data. It allows businesses to maintain authoritative and trusted customer master data, which supports operations like customer service, marketing, sales, compliance, and decision-making. The platform features a rich, searchable UI and API access for managing and curating customer data, with strong emphasis on data security, privacy (automatic PII masking), and collaborative governance.
By using Pretectum CMDM, organizations benefit from improved customer insights, personalized customer experiences, operational efficiencies, and risk reduction. It enables continuous improvement of the customer data model, adapting to evolving business needs while maintaining data accuracy and compliance.
Customer Master Data Management (MDM) with Pretectum CMDM means managing customer data in a centralized, secure, and standardized way to provide businesses with a holistic and reliable understanding of their customers, empowering better business decisions and enhanced customer interactions. This makes it a foundational practice to achieve data-driven excellence and personalized customer engagement.
Centralized customer MDM (Master Data Management) offers a range of benefits for organizations by creating a single, trusted source of customer information that is accessible across departments and systems. Here are the key advantages:
360-Degree Customer View: A central repository consolidates data from multiple sources, giving a holistic view of each customer. This enables better understanding of customer preferences, behaviors, and purchase histories, leading to more personalized marketing and improved service.
Improved Data Quality and Accuracy: Centralization reduces duplicate records, eliminates inconsistencies, and standardizes data entry. This ensures every team is working with reliable, up-to-date customer information.
Enhanced Customer Experience: With consistent and complete customer data, organizations can tailor products, communications, and services, increasing satisfaction and loyalty.
Operational Efficiency: Teams spend less time reconciling data or searching for information. Streamlined, automated processes and data governance reduce manual effort and improve productivity.
Stronger Data Governance and Compliance: Centralized MDM supports compliance with data privacy regulations, offering better control, security, and audit trails for customer information.
Better Decision-Making: High-quality, consistent data supports robust analytics, reporting, and predictive models, enabling leaders to make data-driven decisions with confidence.
Cost Reduction: By eliminating redundant data, streamlining infrastructure, and optimizing IT processes, organizations can cut operational costs and avoid unnecessary expenditures.
Agility and Scalability: Centralized data enables organizations to respond quickly to business changes, expand into new markets, and embrace digital transformation with greater ease and less risk.
Breaks Down Data Silos: Makes customer data instantly accessible company-wide, resulting in cohesive strategies and aligned customer experiences across all channels.
Supports Innovation: Clean and unified customer data lays the groundwork for deploying emerging technologies like AI/ML and supports development of new products, loyalty programs, and business models.
Organizations leveraging solutions like Pretectum CMDM benefit from all these advantages, positioning themselves to provide superior customer engagement, operate more efficiently, and adapt faster to market changes.
In this short demonstration of Pretectum CMDM we show Pretectum Cognito supports you in creating or editing your customer master data classification tag metadata #customerdata #customer #AI #MDM #loyaltyisupforgrabs
Pretectum CMDM offers a sophisticated search experience with "Pretectum Cognito Search," which integrates large language models (LLMs) and Elasticsearch to provide intuitive data retrieval.
Here’s a breakdown of its key features:
Triple Combination Search: This allows users to initiate a search by simply entering a string. Under the hood, it leverages Elasticsearch for initial matches.
LLM-Constructed Query Builder: This is the core innovation of Pretectum Cognito Search. When a simple string search might not yield optimal results, especially when users aren’t familiar with specific field names or tags, the LLM-constructed query builder steps in.
It uses its knowledge of the application’s schemas and tags to interpret natural language questions.
It then constructs a more precise query based on this understanding, aiming to deliver better results.
This eliminates the need for users to learn complex query syntax, making data access more accessible.
Interactive Query Builder: If users receive results from the LLM-constructed query and wish to refine it further without posing another natural language question, they can seamlessly switch to an interactive query builder. This builder still doesn’t require knowledge of query syntax, offering a user-friendly way to adjust the search criteria.
Essentially, Pretectum Cognito Search aims to bridge the gap between user intent (expressed in natural language) and the technical complexity of data querying, by leveraging AI to facilitate more accurate and efficient searches within the Pretectum CMDM platform. This is particularly beneficial for achieving a "Single Customer View" by consolidating and making customer data easily searchable.
This isn’t just a dream; it’s the reality offered by Pretectum CMDM (Customer Master Data Management). By providing a single, unified customer view, Pretectum CMDM empowers your teams with an unparalleled edge.
Here’s how it transforms your operations:
Proactive Risk Mitigation: With all customer data centralized and securely managed, the risk of data breaches is drastically reduced. Pretectum CMDM provides robust security features and consistent data governance, giving you peace of mind.
Enhanced System Stability: A unified view eliminates data silos and inconsistencies, leading to more stable and reliable systems. This means fewer system failures and disruptions, allowing your teams to work seamlessly.
Streamlined Compliance: Navigating complex compliance obligations becomes effortless. Pretectum CMDM ensures data accuracy, traceability, and adherence to regulations, transforming a potential crisis into a well-managed process.
Unleashed Efficiency: Imagine your sales, marketing, and customer service teams all working from the same accurate, real-time customer information. This eliminates redundant efforts, improves decision-making, and significantly boosts overall organizational efficiency.
Superior Customer Experiences: With a complete understanding of each customer, your teams can deliver personalized and proactive experiences, fostering stronger relationships and driving customer loyalty.
In essence, Pretectum CMDM allows you to shift your focus from firefighting operational crises to innovating, growing, and serving your customers better than ever before. It’s about empowering your teams with the insights and tools they need to thrive in a fast-paced world, rather than being bogged down by its complexities.
Here’s our hot take on #customer #loyalty – we believe that it is all about trust, satisfaction and an emotional connection with your brand and is often achieved through personalized experiences.
There are of course different types of loyalty, behavioural, attitudinal and transactional.
Developing loyalty is not without its challenges though – developing it may be hampered by fragmented data, a lack of a personalized engagement plan and execution, accompanied by trust and data privacy concerns.
That’s why we think Pretectum CMDM is a perfect complement to your existing tech and data stack.
With Pretectum, you get a single customer view that enables personalized and timely engagement; real-time data integration as a push or a pull – for dynamic loyalty program adaptation and strengthened data security to foster customer trust.
This all achieved with #AI-powered data tags and data classification, deterministic and fuzzy record matching and flexible data duplicated blending, harmonization, merge and survivorship.
For a loyalty program the operational benefits are pretty clear; increased efficiency through automation with data governance.Cross departmental collaboration with consistent customer information and empowered teams that have actionable insights.
The impact for your targeted business outcomes are pretty clear too – improved customer retention and customer lifetime value (CLV); higher customer advocacy and brand loyalty and competitive differentiation through superior data-driven loyalty management.
Our vision is loyalty as a strategic business asset powered by data mastery. Pretectum CMDM’s role is in redefining loyalty in the digital age and leveraging your customer data to build lasting customer loyalty.
Learn more by visiting www.pretectum.com
#loyaltyisupforgrabs
To succeed, businesses must prioritize recognizing and retaining existing customers, not just acquiring new ones.
While the "Customer Recognition Ratio" isn’t a formal metric, the concept of understanding customers through their past interactions is crucial. Metrics like CRR, CLV, and RPR highlight how customer recognition drives profitability by reducing acquisition costs.
Platforms like Pretectum CMDM are vital for achieving this by centralizing and enhancing customer data, enabling a deeper understanding and stronger relationships.
Click on the article to read more.
https://www.pretectum.com/the-customer-recognition-ratio/
To truly drive informed decision-making and unlock the full potential of your customer relationships, a well-structured and comprehensive customer master data model is absolutely essential. This model, at the heart of your Customer Master Data Management, acts as the blueprint for how you understand and interact with your customers. With the Pretectum Customer Data Management solution, organizations have the flexible SaaS platform to build and manage such a sophisticated model, ensuring data accuracy, accessibility, and the ability to generate valuable insights.
A adaptable customer master data model, built within Pretectum’s adaptable framework, can go beyond just basic contact information. It may encompass a wide array of attributes that paint a complete picture of your customer.
Pretectum’s ability to define one or more data models (schemas) for data drawn from diverse sources, coupled with its strong data typing and data validations, makes it the ideal environment for constructing such a detailed and dynamic model. While the primary intent of Pretectum is people master data management, its flexibility means data can even be transactional, allowing for a truly holistic view.
Consider then, some of the most common customer data attributes and how you might set them up. These also become a foundational aspect of how you might think about data tags and classifying your attributes through metadata.
We’ve grouped these but they could appear at any point in the evolution of your customer profiles.
These are the foundational elements for uniquely identifying and contacting your customers. Pretectum’s schema definition allows for precise data typing to ensure these critical fields are always accurate.
CustomerID: A unique identifier for each customer. Pretectum’s ability to ingest data from various sources means it can consolidate and manage these identifiers across disparate systems.
FirstName & LastName:Â Essential for personalized communication.
FullName:Â A consolidated field often derived or assembled for display purposes.
Email:Â A primary digital contact point. Pretectum supports email as a content data type.
Phone:Â Another crucial contact method.
DateOfBirth:Â Important for age-based segmentation and compliance.
Gender:Â A demographic identifier that can be validated against business area data in Pretectum.
Demographic Attributes
Demographic information provides context about your customers, enabling segmentation and targeted marketing. Pretectum’s flexible schema can easily incorporate these, and business area data can be used for validation.
Address, City, State, ZipCode, Country:Â Geographical identifiers vital for localized marketing, shipping, and understanding regional trends. Pretectum supports text and ISO codes for country validation.
Language: Key for delivering content in the customer’s preferred language.
MaritalStatus:Â Can be valuable for specific product or service offerings.
Transactional Attributes
These attributes capture the history of customer interactions that involve purchases or financial exchanges. Pretectum, while primarily for master data, can ingest transactional data if defined in the schema, making these attributes manageable. There are a number of use cases that benefit from Transaction Attributes.
TotalSpent:Â Cumulative spending over time, indicating customer value.
AverageOrderValue:Â Insights into spending habits per transaction.
NumberOfOrders:Â Frequency of purchases.
LastOrderDate & FirstOrderDate:Â Markers for customer engagement and longevity.
HighestOrderValue & LowestOrderValue:Â Indicators of purchasing range.
Behavioral Attributes
Understanding how customers interact with your digital properties offers deep insights into their preferences and intentions. Pretectum’s flexible schema allows for the inclusion of diverse data types like text for search history or clickstream data. There are a number of use cases that benefit from Behavioral Attributes.
NumberOfVisits:Â Frequency of website or app engagement.
AverageTimeOnSite:Â Indicates engagement depth.
MostViewedPages:Â Highlights product or content interest.
SearchHistory:Â Reveals specific customer needs or desires.
These attributes measure how customers interact with your brand beyond transactions, focusing on communication and loyalty. Pretectum’s schema can hold text-based communication preferences or numerical feedback scores. There are a number of use cases that benefit from Engagement Attributes.
CommunicationPreferences:Â Opt-in/out status for various communication channels.
SubscriptionStatus & SubscriptionStartDate/EndDate:Â Relevant for recurring service models.
FeedbackScore:Â Direct sentiment from customer surveys or reviews.
LoyaltyPoints:Â Track participation in loyalty programs.
Derived Attributes
These are not directly collected but are calculated or inferred from other attributes. They offer powerful predictive capabilities and segmentation opportunities. While Pretectum ingests the raw data, these derivations would typically happen in a connected analytics layer, leveraging Pretectum’s high-quality output. There are a number of use cases that benefit from Derived Attributes.
CustomerLifetimeValue (CLV):Â A projection of the total revenue a customer is expected to generate over their relationship with your business.
ChurnProbability:Â The likelihood of a customer discontinuing their relationship.
NextBestAction:Â Suggested actions for sales, marketing, or service based on customer profiles.
SegmentMembership:Â Assigning customers to specific segments for targeted strategies.
Best Practices
Beyond simply identifying attributes, the success of any customer data management platform hinges on how you design and maintain your data model. Pretectum is engineered with capabilities that directly support these best practices, transforming theoretical ideals into operational realities.
Ensure Data Quality Through Regular Cleansing, Validation, and Enrichment
Data quality is paramount. Pretectum addresses this head-on:
Validation: Its data models can be enhanced with strong data typing and data validations. During manual record entry, bad data is actively blocked. For data brought in via CSV imports, integrations, or streams, lightweight ETL processes use Excel-like syntax for attribute-level transformation, and data is then flagged as matching validation or not, allowing immediate identification of quality issues.
Quick Entry Screen Validation in the Pretectum CMDM
Cleansing: Pretectum features a powerful batch-based duplicate matching process that can identify commonalities across one or more business areas and datasets. These identified matchsets can then be used to nominate a “survivor” record, with a second batch process allowing users to merge or purge records based on configurable survivorship rules, effectively cleaning duplicates.
Self-Service Validation: Pretectum uniquely offers a self-service data validation and consent granting process. This empowers the customer directly to receive a one-time use email, allowing them to edit, redact, and self-consent to the data held in the system, ensuring data accuracy from the source and fostering trust.
Implement Data Governance and Stewardship to Define Standards and Ownership
Robust data governance is crucial for consistent and reliable data management. Pretectum provides the tools:
Data Tagging/Business Glossary: The platform’s flexible and configurable data tagging functionality serves as a business glossary, allowing attributes of the models to be classified. This aids in defining clear data standards and understanding data meaning across the organization. AI prompts can even accelerate the creation of these focused tags.
Role-Based Access Controls (RBAC): Pretectum offers a sophisticated and configurable permissions matrix. Users can have view-only access, or specific permissions for editing. Importantly, PII masking is automatic, and unmasking requires re-entering credentials for users with the specific “PII data unmasking” permission, with all such actions logged in an audit log. This ensures clear ownership and controlled access to sensitive information.
Integrate Data from Various Touchpoints to Provide a Unified View of the Customer
A complete customer view requires integrating data from diverse sources. Pretectum is built for this:
The platform is designed to draw data in from diverse sources, including CSV imports, integrations via JDBC or REST APIs in batches, or via subscribed streams.
Its ability to define multiple data models across partitioned business areas allows for the consolidation and harmonization of customer data, truly working towards that single, accurate, and complete view of your customers across all touchpoints and systems.
Enable Data Security and Privacy to Protect Sensitive Information
Protecting sensitive customer information is non-negotiable. Pretectum prioritizes this:
Automatic PII Masking: If data is marked as PII in the schema, it’s automatically masked once it lands in the dataset.
Controlled Unmasking: Revealing masked data requires specific user permissions and re-authentication, and this crucial action is logged in an audit log, providing a clear trail for compliance and security monitoring.
Granular Permissions:Â The RBAC matrix allows for precise control over who can view, edit, or unmask data, ensuring adherence to privacy regulations.
Foster Cross-Functional Collaboration
Align Customer Data Usage Across your whole Organization
Effective CMDM is a collaborative effort. Pretectum facilitates this by providing a common platform and shared understanding:
The data tagging functionality acts as a shared business glossary, ensuring everyone uses the same definitions and classifications for data attributes.
The AI-powered elastic search allows users across departments to build complex searches using natural language questions and contextual tags, reducing the need for specialized knowledge and fostering broader data utilization and collaboration. The search results are scoped by user permissions, ensuring relevant and secure access.
Continuously Monitor and Improve the Model Based on Evolving Business Needs and Industry Trends
A master data model is not static; it must evolve. Pretectum supports continuous improvement:
Flexible Data Models: Data models (schemas) can be enhanced and every dataset can be edited, appended to, or replaced according to the needs of the business, allowing for agile adaptation.
Audit Logging:Â The comprehensive audit log for sensitive actions (like PII unmasking, self-service consent) provides insights into data usage and changes, supporting ongoing monitoring.
Validation Feedback:Â The flagging of data against validation rules during ingestion provides continuous feedback on data quality, guiding improvements to the model and ingestion processes.
By incorporating these attributes and leveraging Pretectum’s inherent capabilities and data modeling best practices, organizations can create a truly comprehensive customer master data model. This empowers them to make informed decisions, deliver personalized customer experiences, and achieve data-driven excellence, ensuring their CMDM practice is not just robust but also future-ready. Contact us to learn more. #LoyaltyIsUpForGrabs
A FTC Report: “Data Brokers: A Call for Transparency and Accountability” is now ten years old but remains highly relevant today. Over the past decade, the role of data brokers has seemingly expanded, with these entities collecting, analyzing, and selling even greater amounts of personal information. The report initially highlighted the opaque nature of data broker operations and called for greater transparency and accountability. Despite some progress, many of the concerns raised in the report still persist.
Data brokers have even more avenues to gather and monetize information, often without the explicit consent or knowledge of consumers. The regulatory environment has struggled to keep pace with environment change, while legislative efforts have aimed to enhance data privacy at the local and regional level. In the US there is no adequately broad national or Federal legislation and many consumers are still unaware of the extent of data collection and the potential implications for their privacy and security.
In the face of the numerous calls for transparency and accountability, policymakers, industry leaders, and consumer advocates are trying to work together to create robust frameworks that protect individuals’ privacy rights while still supporting innovation. This includes developing clearer regulations, promoting ethical data use, and providing consumers with greater control over their personal information.
Though the FTC report is a decade old, its message continues to resonate.
Big Data is big business
Data brokers continue to amass enormous quantities of information from a multitude of sources, both online and offline. The FTC reported that one particular data broker that they studied had “3000 data segments for nearly every U.S. consumer.” Another’s database covered “one trillion dollars in consumer transactions,” and yet another added “three billion new records each month.”
Data brokers of 3rd party data combine and analyze seemingly unrelated data points to create detailed consumer profiles. They make inferences about consumers, including potentially sensitive ones. “Data brokers combine and analyze data about consumers to make inferences about them, including potentially sensitive inferences.”
Interestingly, even government agencies make extensive use of commercial data brokers’ repositories to gather, analyze, and leverage vast amounts of information for various purposes.
These purposes can include enhancing national security, conducting criminal investigations, tracking financial crimes, and even monitoring public health trends.
By accessing the extensive datasets maintained by commercial data brokers, government agencies can supplement their own data collection efforts and gain insights that may not be available through traditional means. This collaboration can enhance decision-making processes, improve the efficiency of operations, and provide a broader understanding of complex situations.
However, this practice also raises concerns about privacy, data security, and the oversight of how such information is used and shared.
Where they get the data
Data brokers obtain information from a variety of public, commercial and private sources. Federal Data like Census Bureau, Social Security Administration’s Death Master File, U.S. Postal Service, federal courts for bankruptcies and state/local governments such as professional and recreational licenses, real property records, voter registration, motor vehicle records, court records, vital records all form a part of the picture. Some of this information, like voter registration and driving records, has restrictions on commercial use in some states and under federal law (DPPA) but it how it is used to set supplementary data flags may be far from transparent.
While social media platforms offer a way to connect, share, and express ourselves, not all of them prioritize user privacy to the same extent. Some platforms are designed to encourage public sharing, while others may have weaker privacy protections or data practices that expose user content more broadly. Consider these aspects from the top ten social media platforms in the US.
Twitter (X) has tweets as public, visible to anyone on the network, perhaps less so for those without an account but certainly relatively open and unrestricted. While users can set their accounts to private, the platform is designed for open, real-time conversations, making it easy for content to spread widely. Tweets can also be retweeted, quoted, or screenshotted, making it difficult to control how content is shared. Additionally, Twitter itself has faced criticism for data collection and sharing practices.
TikTok is designed for viral content, and videos are often shared publicly by default. The platform’s algorithm encourages widespread visibility, which can make it hard to control who sees your content. TikTok has also faced scrutiny over data privacy, particularly regarding its parent company, ByteDance, and its ties to China. There are concerns about how user data is collected, stored, and potentially shared.
Instagram allows users to set their accounts to private, but the platform is designed for public sharing, especially for influencers and businesses. Stories, reels, and posts can easily be screenshotted or shared without the original poster’s knowledge. Instagram also collects extensive data on user behavior, and its parent company, Meta, has faced criticism for how it handles user data and targets ads.
Facebook offers privacy settings, its default settings often favoring public sharing. The platform’s complex privacy controls can make it difficult for users to fully understand who can see their content. Plus, Facebook has been involved in numerous data privacy scandals, including the Cambridge Analytica incident, where user data was harvested without consent. The platform also tracks user activity across the web for ad targeting.
Snapchat is known for its disappearing messages, the platform’s public features, like Snap Map and public stories, can expose user content and location data to a wider audience. Snaps and messages can also still be screenshotted or saved without the sender’s knowledge. Snapchat has also faced criticism for its data collection practices and sharing with third parties.
YouTube as a public platform by design, has most videos accessible to anyone. Even if you upload unlisted videos, they can still be shared or discovered through direct links. In addition, YouTube collects extensive data on viewing habits and uses it for ad targeting. Comments and interactions on videos are also public by default.
LinkedIn, though a professional networking platform, much of the content shared (e.g., posts, comments, and profile information) is public or visible to your network. Even private messages can be subject to data collection. LinkedIn also collects and shares user data with third parties for advertising and analytics, and tracks user activity across the platform.
Reddit serves as a public forum where posts and comments are visible to anyone unless they are made in private subreddits. Even then, content can be screenshotted or shared outside the platform. And, while Reddit allows for anonymous usernames, the platform still collects data on user activity and interactions, which can be used for ad targeting.
Pinterest is designed for public sharing of ideas and content, and pins and boards are visible to anyone unless explicitly set to private, and even then, they can be shared or repinned. Pinterest also collects data on user preferences and activity to personalize content and ads, raising concerns about how this data is used and shared.
Twitch as a live-streaming platform has most streams as public and accessible to anyone. Even if you set your stream to private, clips and highlights can be shared widely. Twitch also collects data on viewer behavior and interactions, and there have been instances of data leaks exposing sensitive information.
General Privacy Tips for Social Media Users
Review Privacy Settings: Regularly check and adjust the privacy settings on your accounts to control who can see your content.
Be Mindful of What You Share: Assume that anything you post online could become public, even on platforms with strong privacy features.
Limit Third-Party Access: Avoid linking social media accounts to third-party apps or services that may misuse your data.
Use Strong Passwords and Two-Factor Authentication: Protect your accounts from unauthorized access.
Consider Alternative Platforms: If privacy is a top concern, explore platforms like Signal (for messaging) or Mastodon (for social networking), which prioritize user privacy.
Commercial Data Sources
Merchant and financial service company clients, registration websites, and online advertising networks play a significant role in the ecosystem of data exchange and monetization. These entities frequently engage in negotiations and transactions with data brokers, leveraging their access to vast amounts of consumer data. These interactions often occur on a quid pro quo basis, where data is exchanged for services or insights that benefit both parties involved.
Data brokers, known for their expertise in collecting, analyzing, and selling data, enter into a variety of contractual agreements with these sources. These agreements can take several forms, including data supply agreements, licensing agreements, and reseller agreements. Each type of agreement specifies critical details such as the nature of the data being provided, the methods of data transfer, how frequently the data will be updated, and any restrictions on how the data can be used.
Through these agreements, data brokers gain access to a wide range of data, which they then process and enhance to create valuable products. These products are developed using two main types of data: “actual data elements” and “derived data elements.” Actual data elements refer to the raw, unprocessed data collected directly from the source, such as a consumer’s name, address, or purchase history. Derived data elements, on the other hand, are insights or inferences drawn from the raw data. These might include behavioral predictions, preferences, or interests.
For instance, by analyzing various data points, a data broker might infer that an individual who holds a boating license likely has an interest in boating activities. This inference can be invaluable to businesses in the maritime industry looking to target potential customers with tailored marketing campaigns. By combining actual and derived data, data brokers can offer nuanced and targeted insights that help businesses make informed decisions and optimize their marketing strategies.
Overall, the collaboration between merchant and financial service company clients, registration websites, online advertising networks, and data brokers exemplifies the intricate web of data exchange that fuels the modern digital economy. This system relies on a complex framework of agreements and data processing techniques to transform raw data into actionable insights that drive business growth and innovation.
Data Products
The Federal Trade Commission (FTC) identifies three main categories of data products offered by data brokers, each with distinct characteristics and regulatory considerations.
Marketing Products – designed to help businesses and organizations target consumers with advertisements and promotional offers. The data often includes demographic information, purchasing behavior, interests, and lifestyle preferences.
Regulatory considerations for marketing products typically focus on consumer privacy and consent, ensuring that data collection and use comply with laws such as the Fair Credit Reporting Act (FCRA) and the Children’s Online Privacy Protection Act (COPPA).
Risk Management Products – used primarily for assessing risks associated with financial transactions, insurance underwriting, fraud detection, and identity verification. Data brokers provide information that helps businesses evaluate the creditworthiness or reliability of individuals. Regulatory considerations for risk management products often involve strict compliance with the FCRA, which dictates how consumer information can be used in credit reporting and related activities. These products can also assist in identifying potentially fraudulent transactions or activities by matching and analyzing consumer data.
Some of this used to help clients verify consumer identities, often to comply with regulations like the USA PATRIOT Act customer identification requirements. This can involve confirming information or indicating an individual’s status (e.g., active duty military). Data brokers typically use Social Security Numbers (SSN) internally for these products but do not share them with clients.
People Search Products– generally used to locate individuals and gather information about them for various purposes, such as background checks, reconnecting with lost contacts, or verifying identities. The data collected may include addresses, phone numbers, social media profiles, and other personal details.
Regulatory considerations for people search products focus on ensuring accuracy, transparency, and respecting individuals’ rights to access and correct their information, as well as adherence to privacy laws that restrict the sharing of sensitive information without consent.
Shadow Data Brokers – some data brokers not part of the FTC study reportedly sell lists based on sensitive health conditions or professions. others facilitate targeted advertising online through techniques like “onboarding” (matching offline data with online identities using email addresses and cookies). Such a process involves segmenting, matching, and targeting consumers on various websites and social media platforms; they may also offer services to predict consumer behavior, such as scores indicating the likelihood of responding to specific marketing efforts or having undeliverable mail.
Each category has unique implications for privacy, data security, and ethical use, requiring data brokers to navigate a complex landscape of regulations to ensure compliance and protect consumer rights.
Data Quality & Control
The FTC has outlined concerns about the accuracy of data broker information across all product types (marketing, risk mitigation, and people search) but part of the challenge is that data brokers serve a wide array of clients across various industries, including retail, financial services, marketing/advertising firms, government entities, and more, the lack of rigid regulation has also meant that client screening, contracting, and monitoring practices vary among data brokers, depending on the product type.
For the consumers, some data brokers offer opt-out mechanisms, but these may not be comprehensive or easily accessible. Information might still appear in other contexts or through other data brokers. Consumers generally have fewer controls over data used for risk mitigation due to its connection to legal and regulatory compliance (e.g., identity verification). While some brokers may offer opt-out for inclusion in search results, this doesn’t necessarily remove the underlying public record information.
Consumers also often lack a complete understanding what data is collected, how it’s used, and how to exercise any available rights. Opting out with a slightly different name might for example, not capture/cover all stored records.
The report paints a comprehensive picture of the data broker industry, emphasizing its significant reach, the vastness and potential sensitivity of the data handled, and the considerable implications for consumer privacy and well-being.
The report also strongly advocates for increased transparency and accountability through legislative measures and industry best practices to empower consumers with greater control over their personal information in this complex data-driven landscape. The supplemental information serves as a glaring reminder of the tangible ways data brokers operate and the potential consumer privacy and business (and government) decisioning vulnerabilities their practices can expose.
If your business wants to move away from third party data use and focus on zero and first party data and data management you should look at the Pretectum Customer Master Data management platform with consent today!Â
Data breaches are rising, with 44% of consumers reporting that they have experienced fraud, identity theft or financial loss due to breaches. Almost a fifth of consumers unfortunately take no extra data security precautions unless they have personally experienced direct financial harm. So it is up to businesses that hold customer data to take care of it. One measure, is offering two-factor or multifactor authentication on banking or ecommerce websites. After a breach, nearly three quarters of consumers lose trust in that business. Seventy percent say that they would consider taking their business or money elsewhere after a breach and 73% believe that the companies holding their data should be doing more to protect it. Pretectum CMDM can help. Visit www.pretectum.com to learn how we encrypt and secure customer master data profiles with data quality and consent.
A business that is able to process and utilize customer master data handling in real-time will find it brings great advantages for maintaining a competitive edge. Pretectum’s Customer Master Data Management (CMDM) platform is at the forefront of such capability, offering a robust platform that integrates, transforms, and utilizes customer data in real-time to enhance customer relationships and drive business success.
Integration and Centralization
One of the key benefits of Pretectum’s CMDM is its ability to integrate customer data from multiple sources across the organization. This centralization creates a single, unified view of each customer, often referred to as a “golden record” or “single customer view.” By consolidating data from various systems such as ERPs, CRMs, CDPs, and DMPs, businesses can ensure that all departments have access to the same accurate and up-to-date customer information. Such a unified view is essential for making informed decisions and delivering personalized customer experiences.
At the same time, Pretectum facilitates real-time data syndication, a process of distributing data to multiple channels or platforms simultaneously. Syndication ensures that customer data is consistently updated and available across all integrated systems, whether it’s a website, mobile app, or customer service platform. Consider a boutique retailer using Pretectum to securely integrate customer profile data automatically with customer loyalty program information in real-time, ensuring that customers can receive personalized offers and rewards based on their latest interactions using the best possible data without the associative risks of data leakage so commonly present when multiple applications are in use.
Foundations of CMDM in the wider organizational systems landscape
Data Assessment and Handling
The platform’s data transformation capabilities allow businesses to map and transform data into the required formats for different channels. This process is automated, ensuring that data is always consistent and accurate. When data is loaded into the system, it undergoes real-time transformation to align with the organization’s data standards, reducing the need for manual interventions and minimizing errors. Defined data schema with strong typing, lookup pick lists or defined patterns and masks mean that Pretectum CMDM supports robust data quality assessment that ensures data accuracy and completeness from the moment it is ingested.
The system performs real-time data validation, supports profile de-duplication efforts, and even data enrichment to maintain high-quality customer data. Such capabilities are particularly important during interactive data capture, where incorrect or incomplete information can be promptly identified and corrected. A customer can even update their contact information by themselves, the system validating this data in real-time to prevent errors and ensure that all subsequent interactions are based on accurate information.
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6 Reasons to Rethink Your Use of CRM for Customer MDM
1. Data Quality Issues
CRM systems often lack sophisticated data matching capabilities, leading to duplicate records and inconsistent customer information. Without robust data validation and deduplication features, CRMs can accumulate low-quality data over time, creating "technical debt" that impacts business processes.
2. Limited Data Governance
CRMs typically lack robust data governance features needed to ensure compliance with regulations and maintain strict control over data access and modifications. This makes it challenging to implement proper data stewardship practices.
3. Integration Challenges
CRM systems can become data silos, making it difficult to integrate customer data with other enterprise systems. This limits the ability to create a unified view of customer data across the organization.
4. Scalability Problems
As customer databases grow, CRMs may not scale effectively to handle large volumes of complex data. This can lead to performance issues and increased risk of data errors.
5. Limited Flexibility for Complex Data
CRMs are not designed to handle the complexities of detailed customer hierarchies. They lack the flexibility needed to manage and relate various data entities in sophisticated ways.
6. Inefficient for Non-CRM Data
Using CRM for MDM creates additional work for teams who must manually input or update information that doesn’t naturally belong in a CRM system. This leads to inefficiencies and takes focus away from core sales and service activities.
While CRMs excel at managing customer interactions, they fall short as comprehensive MDM solutions. Organizations should consider dedicated MDM platforms that offer robust data quality management, governance, integration capabilities, and scalability to effectively manage customer master data across the enterprise.
Pretectum’s Customer Master Data Management (CMDM) platform offers several key capabilities for organizations looking to improve their customer data management and enhance customer experiences:
A Centralized Data Repository
Pretectum CMDM serves as a centralized repository for customer data, consolidating information from various sources across the organization. This provides a single source of truth for customer-related data, enabling more consistent and accurate information across departments.
Data Quality and Governance
The platform incorporates robust data quality management features, including:
– Data cleansing and deduplication capabilities
– Validation rules to prevent inaccurate or incomplete information
– Automated quality monitoring and alerts
– Tools for defining data governance policies and quality rules
Security and Compliance
Pretectum CMDM includes features to help organizations meet data protection regulations:
– Encryption and access controls
– Audit trails
– Consent management
– Self-service data verification options for customers
Integration and Scalability
The platform offers:
– Seamless integration with peripheral systems
– Support for various data integration and syndication modalities
– Horizontal and vertical scalability to accommodate growing data volumes
Advanced Features
– Golden Record Management: Creating and maintaining a single, accurate view of each customer
– Data Hub Functionality: Supporting data integration from multiple sources
– Self-Service and Consent Management: Allowing customers to verify and manage their data
– Composable Architecture: Enabling configuration to specific business needs
By leveraging these capabilities, organizations can work towards creating a more customer-centric approach to data management, ultimately driving business success through improved customer experiences and operational efficiencies.
Organizations today are on a quest to enhance their Customer Master Data Management (CMDM) capabilities, striving to reach Level 6 and beyond in their data maturity journey. At this pinnacle, businesses can leverage first-party and zero-party data to foster deeper customer relationships and drive personalized experiences.
Level 1 to Level 5 represent foundational steps in this evolution, where organizations establish basic data governance, integration, and analytics. However, reaching Level 6 signifies a transformative phase where data becomes a strategic asset. Here, Pretectum CMDM stands out by offering a comprehensive framework that not only consolidates customer data from various sources but also ensures its accuracy and compliance through robust governance mechanisms.
At Level 6, organizations can harness real-time insights to make informed decisions swiftly, adapting to customer behaviors as they evolve. This agility is crucial in today’s fast-paced market. Moreover, the self-service data servicing feature empowers customers to manage their own preferences, enhancing trust and transparency.
As companies embrace this advanced maturity level, they will not only improve operational efficiencies but also cultivate loyalty through personalized engagement. With Pretectum CMDM as a partner in this journey, organizations can effectively navigate the complexities of modern data management and thrive in an increasingly regulated environment.
Read more at https://www.pretectum.com/leveling-up-on-customer-mdm/
Data clean rooms (DCRs) are secure, collaborative environments where multiple parties can share and analyze their data while maintaining strict privacy controls. This innovative approach enables organizations, such as brands and advertisers, to leverage aggregated datasets without exposing personally identifiable information (PII) or violating privacy regulations.
Learn more by visiting https://www.pretectum.com/the-customer-data-clean-room-dcr/
Take your customer data on a journey that explores how you source, curate and assess it, and then who and how it is used.
With Pretectum CMDM you get more than a customer MDM you get a composable platform that supports federated customer master data management with continuous data quality management, self service contributions with consent under the zero party data model; deduplication and syndication.
Master Data Management (MDM) reduces data-related friction by centralizing and harmonizing critical data.
It creates a single source of truth, enhancing data quality through duplicate removal and automated validation. This streamlining improves operational efficiency, allowing teams to focus on strategic initiatives rather than manual tasks.
MDM enables personalized customer interactions and seamless experiences across channels, fostering better collaboration and informed decision-making. Additionally, it supports compliance with regulations by maintaining clear audit trails and enforcing data governance.
Ultimately, MDM empowers organizations to respond quickly to market changes and customer needs, driving innovation and improving overall performance.
Learn more by visiting : https://www.pretectum.com/how-can-master-data-management-help-in-reducing-data-related-friction/
Elevating Organizations to Level 6 and Beyond with Pretectum CMDM
Organizations today, are increasingly recognizing the importance of effective customer data management; they are striving to achieve higher levels of data maturity as they face the challenge of transitioning from basic data utilization to advanced strategies that leverage first-party and zero-party data.
Pretectum’s Customer Master Data Management (CMDM) platform offers a robust solution that not only facilitates this transition but also distinguishes itself from generic Customer Data Platforms (CDPs) and traditional data management platforms (DMPs).
Understanding Customer Master Data Management
Customer Master Data Management (CMDM) is a comprehensive approach that centralizes and manages customer data across an organization. Unlike generic data management platforms, CMDM focuses specifically on creating a unified view of the customer, which is essential for personalized marketing, improved customer service, and enhanced decision-making. Pretectum CMDM consolidates your data from your various sources, including CRM systems, ERP platforms, and other repositories, thereby providing a Single Customer View (SCV) that eliminates data silos and ensures consistency in customer interactions and can serve as your Single Source of Truth (SSOT).
The Role of First-Party and Zero-Party Data
To elevate organizations to Level 6 and beyond in their data maturity journey, it is crucial to understand the roles of first-party and zero-party data:
First-party data is information collected directly from customers through their interactions with the brand. This may include purchase history (or summaries), website behavior, and engagement metrics. It is valuable because it reflects actual customer behavior and preferences.
Zero-party data, on the other hand, is information that customers voluntarily share with a brand. This can include preferences, feedback, and intentions expressed through surveys or quizzes. Because this data is provided directly by the customer, it tends to be highly accurate and relevant.
By leveraging both types of data, organizations can create more personalized experiences that resonate with customers. Pretectum CMDM facilitates this by integrating first-party and zero-party data into a centralized repository, allowing businesses to understand their customers deeply. Wherever that data comes from, you can consolidate and create your own party!
Customer Self-Service Data Servicing
One of the standout features of Pretectum CMDM is its ability to enable customer self-service data servicing. This functionality empowers customers to manage their own data preferences actively. By providing a user-friendly interface where customers can update their information, consent preferences, and communication settings, organizations can foster trust and transparency.
This self-service capability not only enhances customer satisfaction but also ensures greater compliance with privacy regulations. Customers are more likely to engage with brands that respect their preferences and provide them with control over their personal information. As a result, businesses can build stronger relationships with their customers while minimizing risks associated with non-compliance.
Consent Management: A Pillar of Data Governance
In an era where data privacy concerns are paramount, effective consent management is critical. Pretectum CMDM incorporates robust consent management features that allow organizations to collect, store, and manage customer consent seamlessly. This ensures that all customer interactions are compliant with applicable regulations.
The consent management capabilities enable organizations to:
Track consent states across multiple channels as you harvest it.
Provide marketing teams with supplementary customer preferences, if you have them.
Provide clear options for customers to withdraw consent at any time.
By integrating consent management into the CMDM framework, Pretectum not only enhances compliance but also strengthens customer trust—an essential component for long-term loyalty.
Real-Time Insights for Informed Decision-Making
Pretectum CMDM excels in delivering high performance real-time access to customer profiles in a secure way. This capability allows organizations to make quick, informed decisions based on up-to-date information about customer behavior and preferences. For instance, if a customer frequently browses specific products but does not complete a purchase, the business can algorithmically respond promptly with some next best action or personalized recommendations based on the customer data profile.
Agility in decision-making is a key differentiator for a business looking to stay competitive in today’s highly competitive environment. Leveraging real-time insights derived from first-party and zero-party data, means organizations can adapt various strategies to swiftly meet changing customer needs and preferences.
Distinction from Customer Data Platforms
While Customer Data Platforms (CDPs) focus on aggregating customer data from various sources for analytics purposes, Pretectum CMDM goes further by emphasizing master data management principles. CMDM not only consolidates and de-duplicates data but also enforces quality standards, governance policies, and compliance measures that are essential for maintaining high-quality customer records.
Pretectum CMDM’s ability to establishGolden Nominal Records, serves as an authoritative source of truth for all customer profiles. These records encompass all relevant information about the customer—ensuring accuracy and completeness—which is often lacking in generic CDPs.
Scalability and Flexibility
Pretectum CMDM’s composable architecture allows organizations to scale their operations vertically and horizontally as their needs evolve. This flexibility is particularly beneficial for mid-sized businesses aiming to enhance their data maturity without incurring excessive costs associated with rigid systems.
The modular design enables businesses to configure solutions tailored to specific requirements while ensuring seamless integration with existing systems. As companies grow or pivot their strategies based on market dynamics, Pretectum CMDM adapts accordingly—supporting sustained growth.
The Pathway to Advanced Data Maturity
As your organization strives to elevate its data maturity levels beyond Level 6, consider embracing Pretectum CMDM. By effectively managing first-party and zero-party data alongside robust consent management capabilities under a comprehensive master data management framework, your business could create a unified view of its customers.
A holistic approach not only enhances personalization but also fosters trust through transparency in data handling practices. Ultimately, Pretectum CMDM empowers organizations to navigate the complexities of modern data management while driving better business outcomes through informed decision-making and improved customer relationships.
Consumer expectations are high, leveraging the power of effective Customer Master Data Management will be key for those looking to thrive in competitive markets while maintaining compliance in an increasingly regulated environment.
Pretectum CMDM integrates real-time data from multiple sources seamlessly, providing businesses with a comprehensive view of their customers. This capability is fundamental for organisations looking to enhance customer engagement and improve decision-making processes.
The integration process begins with the centralisation of data. Pretectum CMDM consolidates customer information from various systems, such as Customer Relationship Management (CRM) platforms, Enterprise Resource Planning (ERP) systems, and other data repositories. By creating a single source of truth, businesses can eliminate data silos and ensure that all teams access consistent and accurate customer information.
Real-time data processing is a key feature of Pretectum CMDM. As customer interactions occur—whether through online purchases, customer service calls, or social media engagements—the system updates customer profiles immediately. This immediacy allows organisations to respond quickly to changes in customer behaviour or preferences. For example, if a customer frequently browses specific products but does not purchase them, the business can send targeted promotions based on this activity.
The platform supports various integration methods to accommodate different data sources. It uses lightweight Extract, Transform, Load (ETL) processes that facilitate the seamless flow of data into the central repository. This flexibility allows organisations to integrate both structured and unstructured data from diverse channels, ensuring a comprehensive understanding of each customer.
Pretectum CMDM also employs advanced search capabilities to derive insights from the integrated data. By allowing you to analyse real-time information, you can identify trends and patterns that inform their strategies. For instance, if a particular customer category experiences a surge in behaviour, you can quickly adjust your marketing efforts to capitalize on this trend.
Cross-departmental contributions benefit the organization significantly due to integration. When marketing, sales, and customer service teams have access to the same up-to-date customer profile information, they can coordinate their efforts more effectively. This shared understanding leads to consistent messaging and support across all touch-points, enhancing the overall customer experience.
Data quality management is another important aspect of Pretectum CMDM’s integration capabilities. The platform includes features for data cleansing and deduplication, which help maintain high-quality customer profiles. By ensuring that only accurate and complete data enters the system, businesses can rely on the insights generated for decision-making.
Real-time alerts and notifications play a role in monitoring compliance and risk management as well. If any discrepancies or potential issues arise within the integrated data, the system can notify relevant teams immediately. Such proactive approaches allows organisations to address concerns before they escalate into larger problems.
The scalability of Pretectum CMDM further enhances its integration capabilities. As businesses grow and accumulate more data sources, the platform can adapt to these changes without compromising performance. This scalability ensures that organisations can continue to integrate new systems as needed while maintaining a seamless flow of information.
In addition to these features, Pretectum CMDM supports regulatory compliance by implementing robust security measures around integrated data. The platform includes encryption, access controls, and audit trails that protect sensitive customer information while complying with data protection regulations.
Overall, Pretectum CMDM’s ability to integrate real-time data from multiple sources seamlessly empowers organisations to manage their customer relationships more effectively. By providing a holistic view of each customer through centralised and accurate information, businesses can tailor their strategies to meet evolving needs and preferences. This capability not only enhances customer satisfaction but also drives long-term loyalty by fostering deeper connections between businesses and their clients.
Static consumer data profiles consist of fixed attributes such as demographic information and account details and they can be utilized in risk modeling, particularly in industries like finance and insurance.
The models used, typically assess risk based on static attributes without incorporating transactional or behavioral data but when you combine the two, you land on a more dynamically calculated set of results.
The challenge, is that most Master Data Management system don’t hold and retain transactional and behavioural data, so the question, is whether there is a way that you can benefit from both?
Risk Models Using Static Data
In the implementation of Static Risk Models, there is a often a reliance on attributes such as Customer or Client type, an attribute that identifies the nature of the individual. Another attribute will be the Geographic location which would be often used to assess risks based on region specific factors.
Risk models aggregate attributes and their underlying meaning to derive an overall risk score, this might be used for compliance, regulatory reporting, and basic risk assessment, but it can also be used to identify customer health factors, churn risk and other types of risk. Static data oftens lack the nuance that comes from dynamic data inputs, leading to potential misclassifications or oversights in risk evaluation. There are ways that this can be addressed when data is consolidated via other methods.
Advantages of Using Static Data
Static models have long been utilized in various fields, including marketing, finance, and risk assessment. However, their reliance on data at a point in time, can lead to significant shortcomings, particularly in dynamic environments where consumer behavior is constantly evolving. If you’re updating customer data profiles regularly then some of these issues are mitigated.
It is worth considering that static data has some dimensions that lend it to being at least a consistent yardstick for assessment, including the fact that the data remains relatively unchanged over time, providing a stable foundation for analysis. Static data also tends to be quite simplistic which makes it easier to manage and implement compared to dynamic models that require continuous updates and monitoring.
Static data also pretty categorically meets minimum regulatory requirements for KYC (Know Your Customer) processes without the complexities of ongoing data changes, there is no inference and no extrapolation from behavior. Of course another factor is cost-effectiveness. Static customer profiles reduces the need for extensive data collection and analysis resources associated with transactional data.
Limitations Compared to Transactional Data
Static models do not account for recent behavior or changes in consumer patterns, which can lead to outdated assessments. Static models are also limited in their ability to capture seasonal variations and sudden shifts in market behavior. Static models do not adapt to new influences or changes in the economic landscape, leaving assessment vulnerable to missed opportunities and unforeseen challenges. Relying exclusively on stale static data may lead to a narrow view of current market conditions, which would be particularly problematic in an era where consumer preferences can change rapidly. This is why allowing consumers to self serve on their own data can be a differentiator.
Pros and Cons of Different Data Types
Data Type
Pros
Cons
Static Data
– Consistent and reliable – Simple implementation – Cost-effective compliance
– More resource-intensive – Complex integration
Industry Alignment
Certain industries may benefit more from static such as Financial Services where there is often a reliance on a combination of both static and transactional data for KYC and credit risk assessments. Here, Static data provides foundational insights while transactional data helps in detecting anomalies and patterns indicative of financial crime or credit risk. In the Insuranceindustry, static profiles are often used for underwriting but increasingly incorporates transaction data to refine risk assessments based on policyholder behaviors. In Retail, utilizing static demographic information alongside transaction history allows one to tailor marketing strategies and more appropriately manage inventory.
So, static consumer data profiles can serve as a useful tool in risk modeling, their effectiveness is significantly enhanced when combined with transactional data. A hybrid approach allows your organization to maintain a balance between stability and responsiveness to changing consumer behaviors. Consider then, how you might bring a union of transactional data together with deduplicated customer data, to a single customer view that can inform and empower your tactical and strategic organizational objectives.
As we look towards 2025, the landscape of consumer data will be profoundly shaped by the demand for hyper-personalization, where consumers expect experiences tailored to their unique behaviors and preferences.
This shift will necessitate greater transparency, as individuals increasingly seek control over how their information is utilized, creating a delicate balance between personalization and privacy.
The rise of zero-party data—information that consumers willingly share—will be pivotal in fostering trust while enhancing personalized interactions. Loyalty programs are expected to play a crucial role in consumer decision-making, with a significant portion of digital users actively participating. Meanwhile, AI-driven data analysis will revolutionize commerce, enabling brands to implement conversational interfaces and deliver customized recommendations.
Companies will need to adopt an advisor mentality, leveraging data to cultivate loyalty through personalized guidance. A robust, centralized data strategy will be essential for brands aiming to create effective hyper-personalized experiences.
Despite ongoing privacy concerns, consumers are likely to continue engaging with social media platforms for entertainment and shopping, providing additional data touchpoints. Furthermore, automated insights derived from data will become commonplace, empowering businesses to make swift and informed decisions. Lastly, the emergence of confidential computing will represent a significant advancement in securely analyzing consumer data while safeguarding privacy, setting the stage for a more responsible and innovative approach to consumer engagement.
For organizations, effective management of your personal Dataome through CMDM practice is important. Not all organizations offer consumers the ability to manage their personal digital dataome, but Pretectum CMDM does.
Being able to curate content about yourself, offers several advantages to you as a consumer, and to organizations too.
Enhanced Personalization: Organizations that offer this capability can more precisely tailor their services based on a comprehensive understanding of your preferences and behaviors.
Improved Decision-Making: Access to accurate and timely data in various engagements will allow organizations to make better informed decisions that benefit both them and the consumer.
Increased Trust: Transparent management of personal data fosters improved trust between consumers and organizations by ensuring compliance with privacy regulations.
By understanding the significance of your dataome and how it is managed through CMDM practices, you can better navigate our complex digital landscape all the while ensuring that your personal information is utilized ethically and effectively, because you have the control and Pretectum CMDM powers it.
Read more at https://www.pretectum.com/our-digital-dataome-we-are-our-data/
The Power of Customer Data Management: Purina’s My Pup Portal and Beyond
The pet food industry is highly competitive, pressures comes from a number of areas. Regulations and safety concerns such as adverse event reports and FDA investigations, can erode consumer trust and lead to regulatory actions. Supply chain disruptions and labor shortages can impact the availability and cost of raw materials, straining manufacturing efficiency.
Pet food manufacturers must also adapt to changing consumer preferences towards premium, fresh, and gourmet pet food to maintain market share in a highly competitive market. Additionally, ensuring the quality and safety of ingredients is crucial to avoid contamination or recalls that could damage the brand’s reputation.
Economic factors like inflation can further increase raw material costs, making competitive pricing challenging. To mitigate these risks, pet food manufacturers must invest in innovation, quality control, and supply chain management to maintain consumer trust and its competitive edge. An innovative approach also involves leveraging customer data to maximize relationships.
Managing customer data effectively is essential when building strong customer relationships, driving business growth, and ensuring customer satisfaction. Purina, a leading global pet food brand, has been at the forefront of leveraging customer data to enhance its products and services.
Customer Master Data Management (CMDM) as a service represents a contemporary approach to data management that enables organizations to streamline and centralize their customer data across various platforms and departments.
Such a service is particularly needed in today’s data-driven businesses, where the ability to access accurate, consistent, and up-to-date customer information is essential for delivering personalized experiences and making informed business decisions.
Read more at https://pretectum-as-44250291.hubspotpagebuilder.com/pretectum/customer-master-data-management-as-a-service
Customer Master Data Management (CMDM) is an essential practice, and sometimes a technical solution for a good many organizations. In its adoption, many would seek to optimize customer data, and particularly consumer data across the many platforms and departments that they have.
Pretectum’s CMDM platform exemplifies a solution that aligns well with many of the needs and expectations of such organizations. Offering a comprehensive cloud-based solution, it addresses many of the complexities of managing customer data in multi-channel environments.
In this blog post, we will explore some of the capabilities of the Pretectum CMDM platform, focusing on integration of unified customer profiles across diverse applications and the potential associated with consumer self-service data verification, and data consent management.
The Need for Centralized Customer Data
Modern times have businesses operating with numerous opportunities for customer touch. These range from e-commerce sites where customers shop for anything from ad hoc buys, luxury purchases, groceries, experiences and even larger spend items like property. There are also service portals, where customers might book appointments or engage with customer service agents to schedule various actions or inquiries related to their needs —having a unified customer profile for the optimization of these experiences has become a necessity.
The customer profile consolidates all relevant customer information, including contact details, transaction history aggregations, and interaction summaries, into a single view that can be accessed by various departments. Up until quite recently, the challenge with many of these systems has been the constrained nature of their data models, limitations in terms of integration and a lack of flexibility in how they can be deployed.
Another challenge that many organizations face, is the potential for duplicative customer profiles due to differing departmental behaviours, needs and data entry practices.
Pretectum CMDM addresses this issue by providing a centralized repository that integrates data from multiple sources, ensuring that all interested parties, are working with consistent and accurate information from a unified customer data profile.
Consumer Self-Service Data Verification
A standout feature for Pretectum CMDM might be its support for consumer data self-service data verification. The capability empowers customers to verify and update their information directly, reducing the burden on customer service teams while enhancing data accuracy. By allowing customers to manage their own data, organizations can minimize errors associated with manual data entry and ensure that profiles are current. Self-service models like this, not only improve data quality but also fosters greater trust in the organization. As individuals, consumers may feel more in control of their personal information through this transparent approach to secure customer data profile handling.
Data Consent Management
In addition to self-service verification, effective data consent management is essential to ensure that an organization is remaining compliant in an increasingly complex data privacy and consumer data handling government regulated landscape. Pretectum CMDM includes features that help organizations more easily manage customer consent for data usage in compliance with various local, regional, national and international privacy regulations.
By clearly documenting consent preferences and allowing customers to modify their choices, an organization can ensure they respect consumer rights while maintaining robust data governance practices.
Integration Across Platforms
The ability to access a unified customer profile is further enhanced through Pretectum CMDM’s integration capabilities with other applications and platforms. Whether it’s an e-commerce platform, cloud database, CRM, CDP, ERP, warranty service portal, or call center application, the CMDM system facilitates seamless data flow between these data repositories. This integration allows organizations to service personalized experiences with the best possible, centrally curated customer data profiles. The Pretectum CMDM serves as a single source of truth in relation to the customer data profile.
For example, when a customer makes an online purchase, their information is automatically brought to the purchase event for reference, and when they complete the transaction, the latest interaction could be updated in the unified profile. If they later contact customer support via phone, representatives access their customer profile complete interaction history in real-time. This level of integration not only enhances customer service but also supports marketing segmentation and targeting efforts by enabling targeted campaigns based on more accurate customer characteristics.
When transactional data from transactional systems, campaign data in the marketing system, and the customer master from the Pretectum CMDM are all brought together, there is a much richer source of content upon which decisions can be made.
Security Considerations
A significant advantage of adopting a cloud-based solution like Pretectum CMDM is also the enhanced security, and logging compared to traditional on-premise or siloed systems. Cloud applications often benefit from advanced security protocols that are often beyond the reach of individual organizations managing their own infrastructure.
With Pretectum CMDM we use high data encryption levels both at-rest and in transit, we provide tagging for PII, data obfuscation, secondary authenticated data reveals, and verbose reveal and change logging all stored in a blockchain.
Regular Updates: Pretectum’s platform is frequently updates to bring product improvements and ensure potential vulnerabilities are neutralized.
Data Encryption:Â Sensitive information is identifiable and all data is encrypted both at rest and in transit, this is a safeguarding measure against unauthorized access.
Comprehensive Auditing:Â Detailed audit trails help provide you with the ability to monitor access events and data changes to customer profiles.
Security measures are vital for maintaining compliance with stringent data protection regulations and for providing consumer trust assurances.
Secondary Benefits of Unified Customer Profiles
Beyond the immediate advantages of improved customer engagement and operational efficiency, centralized management of rich customer profiles offers several secondary benefits across various organizational functions too.
Risk Management: Unified customer data profiles allow for better customer risk assessment. This is achieved by providing a perspective on the customer’s profile and any indicators that might influence risk models.
Marketing Optimization:Â With comprehensive customer profiles readily available, marketing teams can craft campaigns tailored to specific audience segments based on key profile attributes.
Collections Efficiency:Â Access to accurate contact information and aggregated history indicators can streamline collections processes, reduce costs and bring efficiency to teams and processes associated with chasing down arrears and debt.
Strategic Decision-Making:Â Executives can make better use of analytics that are fed from unified customer profiles to formulate cross divisional strategic initiatives.
The integration of these and others benefits are illustrative of how CMDM not only enhances operational capabilities but also contributes to broader organizational goals.
Adoption of Pretectum CMDM could represent a significant strategic move for your organization in its efforts to centralize customer data management. A focus on the capabilities of consumer self-service verification and robust consent management, alongside seamless integration with other applications means you are better positioned to formulate a comprehensive view of customers, a view that supports the driving of increased engagement, retention and operational efficiency.
Security measures inherent to the platform, align with those of other cloud solutions, positioning them as superior choices over traditional or home-grown systems. As your organization continues its’ customer data management journe, being able to maximize the benefits of a digital landscape means that embracing cloud-based CMDM solutions may be almost unavoidable if you want to remain competitive and contain costs all the while being compliant and respectful of consumer data privacy.
The implementation of Pretectum CMDM could address your immediate operational challenges and unlock long-term strategic benefits across diverse business functions. Organizations that recognize the value of centralized customer data in the cloud will be better equipped to foster lasting relationships with their customers while still driving sustainable growth.
