The Shift: From Storage-Centric to Cloud-Centric CDPs

The classic CDP model revolves around a central repository where customer data is ingested, unified, and stored. This approach makes sense if you believe real-time activation isn’t a competitive differentiator. Tealium never subscribed to this approach but more about that later. Today, consumers expect experiences that are not only personalized—but instantaneously relevant. Every delay, every missed signal is a missed opportunity.

At the same time, Data Clouds like Snowflake, AWS, and Databricks have become the new standard for enterprise-scale data management and AI. These platforms are infinitely scalable, accessible, and most importantly already house vast amounts of data and insights. Why duplicate all that into another storage solution?

The Rise of Data Hunger: Fueling the Data Cloud in Realtime

Shifting away from traditional storage based CDPs, the future is in real-time streaming customer data that has been consented, standardized and enriched with identity from every customer touchpoint.  Organizations are hungry not only for event level data but also the highly contextualized and labeled intelligence created through real-time enrichment. This gives the Data Cloud access to 3 essential data scopes in event, visit and visitor. Data wrangling slows down Data Cloud innovation, and with Tealium data is delivered at the highest possible fidelity at the point of engagement in a ready to use format.

The value of this newly realized asset is a massive improvement in overall Data Cloud cost and time to value.  Most importantly real-time means Agentic and ML initiatives get access to in the moment context needed for the absolute best outcome at the highest confidence level.

A Flexible Composable Architecture: Power Without the Bloat

Tealium’s data strategy is delivering in a very unique manner. Instead of requiring organizations to duplicate data into a new isolated warehouse or re-ingesting what enterprises already maintain in their data cloud, Tealium integrates natively with these environments. That means organizations can activate insights directly from their data cloud infrastructure, with no performance tax and no new data silos to manage. By eliminating the need to copy data, Tealium reduces operational complexity, lowers data governance risks, and dramatically shortens the time to value. Zero Copy alone however leaves a large gap in orchestrating real-time experiences as Zero Copy and other Reverse ETL options only access the historical data.

The Real-Time Profile Cache: Precision at Speed

What sets Tealium apart is its high-speed, event-driven profile cache, which updates in milliseconds with every single customer interaction. This lightweight asset unlocks all of the real-time use cases.

Every click, tap, swipe, purchase, or interaction a user has with your brand immediately updates their profile in Tealium’s cache. This live profile view acts as a hyper-relevant, continuously evolving source of engagement for personalization engines, AI models, and customer service systems. Best of all it’s not just the events that are captured but also calculated enrichments that are adding deep intelligence to the customer profile. Now we have a realtime streaming intelligent pipeline and the sky is the limit in how powerful this is for AI and CX. Rather than housing all of this in isolated storage, Tealium streams data back to the Data Cloud for AI and Segmentation.

Think of it as a real-time edge profile—lightweight, ephemeral, but immensely powerful. It ensures that every moment with a customer is powered by the most up-to-date understanding of who they are, what they want, and how they’re engaging right now.

In-the-Moment Activation: The New Gold Standard

With this dynamic infrastructure in place, Tealium enables true in-the-moment customer experiences with no lag between insight and action. Whether it’s triggering an AI enabled email based on a product engagement, surfacing personalized content in an app, fueling a GenAI agent, or routing a customer to a VIP support line, Tealium delivers the real-time intelligence to make these experiences possible.

And because this real-time engine is supported by integrations with thousands of turnkey AI, martech and adtech platforms, activation isn’t just fast—it’s everywhere.

A New Era for Customer Data Platforms

The rise of the Data Cloud has fundamentally changed the rules of the CDP game. In this new era, storage-heavy CDPs are quickly becoming obsolete. Enterprises are demanding tools that are cloud-centric, real-time, and tightly integrated with existing data ecosystems. Tealium isn’t just evolving to meet this demand—it’s leading the charge.

With a native-warehouse friendly architecture, the ability to activate data without loading or copying, a real-time profile cache updated in milliseconds, and deep integration with the modern enterprise stack, Tealium is redefining what it means to be a CDP. The result? Smarter data, faster insights, and more meaningful customer experiences—when they matter most. Now your CDP can be the perfect complement to your Data Cloud investment. Why choose one method over the other when you can have the Best of All Worlds!

The post How Tealium is Redefining the CDP Market in the Age of the Data Cloud appeared first on Tealium.

Features and Benefits of Tealium’s Events, Functions, and Attributes

• Tealium’s platform enables data activation through Consented Events, Functions, and Attributes, enhancing customer interactions and insights.
• Events capture user interactions, providing valuable insights into behaviors and preferences without complex IT dependencies.
• Functions is a serverless data stream processing environment, enabling developers to apply custom JavaScript logic to transform, clean, and enrich data in stream. Functions can help developers save time, reduce errors, and improve data quality.
• Attributes enrich customer profiles dynamically, allowing for personalized marketing and segmentation based on real-time data.
• All of this results in the ultimate modern real-time data supply chain.

First-party data is a crucial asset, driving smarter decisions, personalized marketing, AI models, and seamless customer experiences. However, data is only as valuable as it is actionable, and making it accessible to teams across the organization is key. This is where Tealium’s Customer Data Platform shines, enabling data activation with an intuitive UI. By leveraging Consented Tealium Events, Functions, and Attributes, businesses can create powerful, data-driven customer use cases without needing complex development or IT cycles. Let’s explore how these features can help unlock the full potential of customer data.

Tealium Events: The Foundation of Customer Interactions

What Are Tealium Events?

Tealium Events are the building blocks of customer interactions and are captured according to every user’s consent preferences. Every time a user interacts with your website, app, or channel of engagement, it generates a consented event. In Tealium, these events can range from page views and clicks to more complex interactions, like form submissions, mouseovers, rage clicks, or product and video views.

Why Do Consented Events Matter?

Events provide insight into user behaviors, preferences, and engagement patterns. You can gain a more complete picture of each customer’s journey by capturing these moments.

Are Tealium Events Easy To Use?

Yes, Tealium Events is easy to use. With Tealium’s no-code interface, you can set up and manage events easily—no need for complex implementation processes or IT dependencies. This accessibility means that marketing, AI, and analytics teams can quickly activate or modify events as business needs evolve.

Tealium Functions: Transform and Enrich Data in Real Time

What Are Tealium Functions?

Tealium Functions allow you to manipulate and transform incoming data in real-time, unlocking more complex use cases and allowing you to tailor data for specific actions or analysis.

Use Cases for Tealium Functions

For example, you can create a function that categorizes users based on frequency of engagement, transforming raw data into actionable customer segments. Or, you can use functions to calculate metrics, like cart value thresholds, which might trigger targeted messages to high-value customers. You can even use a function to call another system and receive back a payload such as an AI/CDW or identity service.

What Is The Value of Tealium Functions?

Typically, data transformations require third-party software and customization and never act in real-time. Tealium’s functions make this possible with its easy-to-use UI. This empowers teams to create custom data transformations and conditions without waiting for developer resources. Soon users will be able to leverage natural language to define and create Functions making it even more powerful and accessible across the entire organization.

Tealium Attributes: Building Rich Customer Profiles

What Are Tealium Attributes?

Tealium Attributes are characteristics of your users, like demographics, behavioral patterns, or transaction history. The most powerful aspect of Tealium Attributes is that they can calculate and enrich data in real-time as behaviors and events are captured. Tealium allows you to define and leverage attributes to build rich, detailed customer profiles that can be used across various touchpoints. These attributes constitute the profile and all remain intact and fully up to date in real-time.

What Are Examples of Tealium Attributes?

Tealium offers attributes—such as badges, metrics, and tallies—that help you better understand and categorize customer behavior. For instance, you can create an attribute to identify VIP customers based on lifetime purchase value or to flag users who abandon carts frequently. There are hundreds of attribute models available out of the box leaving no use case behind.

Are Tealium Attributes Easy To Use?

Yes, Tealium Attributes is easy to use. In the no-code UI, adding or modifying attributes is straightforward. You can define, update, and activate these attributes as your understanding of your customers grows, which allows for a truly dynamic customer profiling experience. Remember, these attributes calculate and resolve in sub-200 ms so they are always the absolute in-the-moment activation elements.

Putting It All Together: Customer Data Use Case Examples

With Tealium’s Events, Functions, and Attributes, you can create a wide range of powerful customer data use cases. 

Here are some examples:

• Fuel AI Projects: Collect consented engagement data (Events) and then hash or obfuscate PII (Functions) to leverage this clean correlated data to create AI-ready insights that can be streamed in real-time to any CDW or AI application. This can be an explicit call out to an external AI model for things such as fraud detection, propensity scoring, or next-best-action.
• Personalized Product Recommendations: Track users’ product interactions (Events), calculate the most-viewed categories (Tealium Functions), and then assign these preferences as attributes. Use this data to dynamically personalize website content and emails.
• Real-Time Cart Abandonment Alerts: Trigger an event when a customer begins a conversion process but leaves the site. Use a function to calculate the cart or stage value, and if it exceeds a threshold, send a personalized message to the customer encouraging them to complete their purchase.
• Customer Lifetime Value Scoring: Track all customer purchases and use functions to aggregate total spending over time. Assign a high-value badge as an attribute once they pass a defined spend threshold, allowing you to reward loyal customers with special discounts.

Why Tealium’s Customer Data Approach is a Game-Changer: Turning Data Into Action

The intuitive design in Tealium democratizes access to customer data insights, allowing more teams within an organization to be directly involved in creating and activating customer data strategies. It reduces dependency on technical resources, shortens time to market, and enables a more agile approach to data-driven decision-making and AI outcomes.

With Tealium’s Events, Functions, and Attributes, powered by a user-friendly low/no code UI, organizations can swiftly turn data into action. Whether it’s real-time personalization, customer segmentation, or targeted messaging, Tealium empowers you to bring customer data use cases to life, maximizing value for both the customer and your business.

The post How Tealium Events, Functions, and Attributes Create the Modern Real-Time Data Supply Chain And Turn Data Into Action appeared first on Tealium.

To survive in the modern data-driven world organizations must rely on actionable real-time AI insights to make informed decisions, personalize experiences, and drive outcomes. However, understanding user behavior requires using different dimensions of data from the same event. Data dimensions serve specific purposes, which is where Tealium’s data model shines– by offering three distinct scopes: Event, Visit, and Visitor. Each scope provides a unique lens on customer data, allowing businesses to unlock diverse use cases and better understand their audiences. Adding to its impact, all three scopes are available in real-time, processing data with sub-200ms latency. This rapid access fuels AI systems, empowering machine learning models to deliver actionable insights at every stage of the customer journey. Let’s dive into why these three scopes are so valuable and how they enhance AI applications for brands.

Event Scope: Real-Time, Contextual Actions Fueling Adaptive AI

What It Is
The Event scope focuses on individual interactions or actions taken by a user on a website, app, or other digital platform. Every click, view, and engagement is treated as a separate event, capturing details in real-time.

Why It’s Valuable
Event-level data is critical for understanding real-time behavior and responding immediately to user actions. With sub-200ms access, Tealium’s Event scope provides AI systems with a stream of real-time event data that is crucial for immediate decision-making in machine learning algorithms, enabling a dynamic response to user actions, precisely as they happen.

Key Use Cases for AI with Event Scope:

• Adaptive Personalization: AI models use Event data to instantly adjust recommendations or content suggestions based on real-time behavior, such as the current page a user is viewing.
• Predictive Triggers: Machine learning algorithms can predict likely next steps, adjusting experiences or offerings immediately if, for instance, a user hovers over a purchase button or exits a product page.
• Automated Anomaly Detection: Event data allows AI to detect anomalies in user actions (e.g., repeated checkout errors), and flag issues in real-time to mitigate churn or optimize the experience.

With Tealium’s Event scope, AI models can respond in real-time, providing the personalization and contextual insights that today’s users expect.

Visit Scope: Session-Level Data Enabling Cohesive AI Modeling

What it is:
The Visit scope aggregates actions taken by a user during a single session, providing a snapshot of all activities within a specific visit or session.

Why it’s valuable:
Visit-level data provides session-based patterns, making it ideal for AI models that focus on understanding user journeys, measuring session quality, and analyzing behaviors across a session. With Tealium’s sub-200ms data processing, Visit scope data powers AI-driven insights on session behavior in real-time, enabling AI models to identify engagement trends, recognize session-specific patterns, and optimize the user journey at a session-wide level.

Key Use Cases for AI with Visit Scope:

• Session-Based Journey Analysis: AI models can evaluate session data to understand pathways through a site, optimize the customer journey, and guide users to key interactions.
• Session Scoring and Segmentation: Machine learning algorithms can assess session quality by scoring actions and interactions, helping identify high-intent visits and prioritizing experiences accordingly.
• Exit Prediction and Retention Models: AI can leverage Visit data to predict session exits and trigger automated responses, like chatbots or exit intent pop-ups, designed to keep users engaged.

With Visit-level data, AI models develop a more cohesive understanding of each user’s journey in a session, improving user experience in real-time and delivering more effective personalization.

Visitor Scope: Long-Term Behavior Data for Enhanced Predictive AI

What it is:
The Visitor scope captures data that spans multiple sessions, providing a holistic view of an individual user over time.

Why it’s valuable:
Visitor-level data is essential for training AI models that focus on long-term behavior and customer lifetime value. Tealium’s sub-200ms latency means that Visitor data is not only comprehensive but also available instantly, empowering AI to refine its predictions on evolving user behaviors and to optimize experiences based on historical patterns. AI algorithms that consider long-term behavior data can make more accurate predictions, allowing brands to engage customers in ways that increase loyalty and enhance lifetime value.

Key Use Cases for AI with Visitor Scope:

• Predictive Lifetime Value Modeling: AI can leverage Visitor data to predict customer lifetime value, identify high-value users, and allocate resources to retain or engage them.
• Long-Term Personalization and Content Curation: By understanding historical preferences, AI models can deliver curated content and offers aligned with each user’s unique history and preferences.
• Churn Prediction Models: AI algorithms trained on long-term Visitor data can recognize behaviors linked to potential churn, allowing businesses to engage with at-risk customers proactively.

With the Visitor scope, AI models go beyond immediate actions, enabling brands to deepen relationships with their audience by anticipating future behavior based on a history of interactions.

The Power of Combining Event, Visit, and Visitor Scopes for AI

The combined power of Tealium’s three scopes—Event, Visit, and Visitor—unlocks transformative potential for AI. Together, these scopes allow machine learning models to build a layered, nuanced understanding of user behavior, offering real-time adaptability, session-based optimization, and predictive insights for long-term personalization. When the data from these scopes is fed into AI systems with Tealium’s real-time processing (sub-200ms latency), brands can optimize the customer journey across every level of engagement, from immediate reactions to long-term relationship-building.

For example:

• Enhanced Customer Support: Event data can fuel instant troubleshooting, Visit data provides context on the session, and Visitor data offers a history of past interactions, allowing AI to assist with greater accuracy and relevance.
• Advanced Attribution Models: Event, Visit, and Visitor data work together to improve attribution insights, with AI parsing real-time ad clicks, session patterns, and multi-session journeys for a complete attribution analysis.
• Multi-Stage Personalization: AI can leverage Event data for in-the-moment suggestions, Visit data for session-aware personalization, and Visitor data to offer evolving content recommendations based on historical trends.

How To Maximize AI-Driven Decision-Making with Tealium’s Scopes

Tealium’s Event, Visit, and Visitor scopes provide an essential structure for harnessing customer data at every level. By isolating, analyzing, and acting on insights from each scope, brands can fuel AI models to personalize, optimize, and predict with unmatched precision. The ability to access all three scopes in real-time, with sub-200ms latency, enables AI to respond dynamically at every touchpoint, allowing businesses to elevate their customer engagement strategies, strengthen loyalty, and drive growth. With the combined power of Tealium’s data scopes and AI, brands are well-equipped to transform their customer experience for the better.

For more information, we recommend you check out Tealium for AI.

The post How To Maximize Artificial Intelligence (AI) Insights with Tealium’s Event, Visit, and Visitor Data Scopes appeared first on Tealium.

7 Artificial Intelligence (AI) Examples for Insurance

Examples of AI in insurance include claims processing, risk assessment, fraud detection & prevention, product development, and regulatory compliance.

Automated Claims Processing

Claims processing is one of the most labor-intensive aspects of insurance. Traditionally, it requires significant human involvement, from collecting and verifying customer information to assessing claims and issuing payments. AI streamlines this process through automation. Machine learning algorithms can sift through data, analyze the validity of claims, and even assess damage through image recognition technology. For example, AI-powered systems can analyze photos of damaged vehicles or properties, compare them to past cases, and make fast, accurate decisions on whether a claim is valid.

This automation speeds up the claims process, reduces operational costs, and minimizes human error. It also leads to quicker resolutions for customers, enhancing their experience.

Risk Assessment and Underwriting

Risk assessment and underwriting are core functions of any insurance company. AI can improve these processes by analyzing vast amounts of data to create more accurate risk profiles. With the help of machine learning algorithms, insurers can assess a broader range of risk factors than traditional methods allow, from behavioral patterns to real-time data feeds.

For example, in auto insurance, AI can analyze driving behavior using data from telematics devices installed in vehicles. This data allows insurers to offer personalized policies based on an individual’s driving habits, rather than relying solely on demographic data or past claims history. Similarly, in health insurance, AI can predict the likelihood of future medical issues based on lifestyle patterns, genetic predispositions, and medical history.

Fraud Detection and Prevention

Insurance fraud is a persistent issue that costs the industry billions of dollars annually. AI plays a critical role in identifying and preventing fraudulent activities. By analyzing vast datasets, AI can detect anomalies and suspicious patterns that human agents might miss. Machine Learning (ML) models can be trained on historical data to flag potentially fraudulent claims based on abnormal trends, behaviors, or inconsistencies.

For instance, AI-powered systems can analyze the frequency of claims, the consistency of customer information, or even social media activity to detect red flags. These systems can operate in real-time, ensuring that fraudulent claims are caught before payouts are made, saving insurers both time and money.

Customer Experience and Personalization

In the digital age, customers expect fast, personalized service. This is an area in the Insurance vertical that needs to be more robust. AI enhances customer experience by offering tailored interactions and improving response times. AI-powered chatbots, for example, can handle routine inquiries, such as providing quotes, updating policy information, or guiding customers through the claims process. These chatbots are available 24/7, ensuring that customers receive immediate assistance, even outside of business hours.

Additionally, AI can analyze customer data to offer personalized insurance products and pricing. By analyzing lifestyle, demographic, and behavioral data, AI systems can recommend insurance policies tailored to individual needs, whether it’s a customized health plan or personalized home insurance coverage.

Predictive Analytics for Customer Retention

Customer retention is a major challenge in the insurance industry. Predictive analytics, powered by AI, helps insurers identify customers who are at risk of leaving and develop strategies to retain them. By analyzing data such as customer behavior, interactions, and policy history, AI can predict when a customer may be likely to cancel their policy or switch to a competitor.

Insurers can then use this information to take proactive steps, such as offering special discounts, personalized offers, or improved services to retain at-risk customers. This predictive capability not only helps increase retention rates but also improves overall customer satisfaction by addressing concerns before they escalate.

Enhanced Product Development

AI enables insurers to develop new and innovative insurance products based on emerging risks and customer needs. By analyzing market trends, social data, and customer feedback, AI can identify gaps in existing offerings and predict future demand for specific types of insurance products.

For example, as the gig economy grows, insurers can use AI to create tailored policies for freelancers and contract workers, offering coverage for situations that traditional policies may not address. AI can also be used to design usage-based insurance products, such as pay-per-mile auto insurance, which adjusts premiums based on actual driving habits. In addition, AI can create real-time bundles and present them at the moment improving upsell opportunities. 

Regulatory Compliance

Compliance with regulatory requirements is a significant challenge for insurers, especially in an environment where regulations are constantly evolving. AI can help insurers stay compliant by automating compliance processes and ensuring that all necessary documentation is in place. Natural Language Processing (NLP) algorithms can review policy documents to ensure they meet regulatory standards, while AI-driven auditing tools can monitor transactions for any signs of non-compliance.

By automating these processes, insurers can reduce the risk of costly penalties and focus on delivering value to their customers.

As technology evolves, we can expect even more innovative AI applications in the insurance space, making the industry more agile, responsive, and customer-centric. Insurers that embrace AI will not only enhance their operational efficiency but also be better positioned to meet the changing needs of their customers in an increasingly digital world.

The post 7 Artificial Intelligence (AI) Examples for Insurance: Transforming the Future of Insurance with AI appeared first on Tealium.

What Is Customer Experience In Insurance?

Customer experience in insurance refers to the overall perception and interaction that a customer has with an insurance company throughout their entire journey. This includes every touchpoint, from the initial research and purchasing of a policy to filing a claim, receiving customer support, and renewing or canceling a policy.

How To Enhance Customer Experience In Insurance

Create A Seamless New Customer Experience As An Insurance Company

Tealium, a leading Customer Data Platform (CDP), can offer significant advantages to insurance companies by enabling them to better manage, unify, and activate their customer data all in real-time. Here’s how Tealium can help insurance companies:

1. Unified Customer View

• Centralized Data: Insurance companies often deal with vast amounts of data across multiple touchpoints (website, mobile app, call centers, agents, etc.). Tealium consolidates this data into a single customer view, making it easier to understand customer behavior and preferences.
• Omnichannel Data Integration: Tealium integrates data from all customer interactions, whether it’s online, offline, or across devices, providing insurers with a holistic view of their customers. Breaking down these traditional data silos unlocks tremendous value in both real-time experiences and AI. 

2. Personalized Customer Experience

• Segmentation and Targeting: With Tealium, insurers can segment their customers based on demographics, behavior, and preferences. This allows for highly personalized interactions, improving customer satisfaction and retention.
• Real-Time Personalization: Tealium’s real-time data processing capabilities allow insurers to offer personalized recommendations and offers, such as insurance policy upgrades or cross-sell opportunities, at the right moment.
• AI Driven Experiences: Now that insurance companies have a consented, clean and enriched data set they can fuel and activate AI initiatives far more efficiently. For more, see our product, Tealium for AI.

3. Improved Marketing Efficiency

• Audience Management: Insurance companies can use Tealium to build more effective marketing campaigns by identifying high-value customer segments, enabling targeted and efficient marketing strategies.
• Marketing Attribution: By tracking customer interactions across various channels, Tealium helps insurers better understand which marketing efforts are driving conversions, allowing for smarter budget allocation.

4. Compliance and Data Security

• Data Privacy Management: Insurance companies deal with sensitive customer information, and Tealium provides tools for managing data privacy regulations. Tealium’s consent management capabilities ensure that customer data is handled in compliance with legal requirements.
• Data Governance and Consent: Tealium offers robust data governance and consent orchestration tools that help insurance companies control who can collect and access customer data, ensuring that sensitive information is only available to authorized personnel. Furthermore consent orchestration guarantees that only customer data that is fully consented can be used to activate campaigns, create experiences and fuel AI initiatives. 

5. Customer Retention and Loyalty

• Churn Prevention: Tealium’s predictive analytics and data insights help insurance companies identify customers who are at risk of leaving. By understanding customer behavior, insurers can proactively engage these customers with tailored offers or support to increase retention.
• Customer Journey Orchestration: Insurers can map out and optimize customer journeys across channels and devices, ensuring that each touchpoint (from initial inquiry to claim handling) provides a seamless and positive experience, fostering long-term loyalty.

6. Actionable Insights for Product Development

• Customer Feedback Analysis: By unifying data from customer interactions and feedback, Tealium enables insurance companies to better understand customer needs and preferences. This insight can guide the development of new insurance products or policy enhancements.
• Predictive Analytics and Next Best Action (NAB): Tealium’s Machine Learning (ML) integrations allow insurers to analyze trends and predict customer needs, enabling the creation of proactive solutions and product offerings.

The Best Tool For Customer Experience In Insurance

Tealium empowers insurance companies to harness their data more effectively, resulting in improved customer experiences, operational efficiency, and competitive advantage in a highly regulated industry. Let’s look at our case study with Income Insurance, one of Singapore’s largest composite insurers, with over 1.7 million customers. 

Like many insurance companies, customer and prospect data originated from a variety of disconnected sources, both online and offline. By integrating Tealium’s technology with Merkle’s consultative expertise, Income was able to transform its inbound and outbound data streams through a centralized intelligence hub. This enabled the insurer to deliver the right message to the right individual at the optimal time.

After adopting Tealium’s CDP, Income Insurance saw a remarkable 40% reduction in cost per acquisition and an impressive 92% improvement in click-through rates. The company also successfully transitioned customers from offline to online channels, with early online renewals for car insurance rising 28% year-over-year (YoY). Additionally, the use of Tealium CDP nearly doubled the number of activated campaigns, leading to an extraordinary 452% YoY increase in online-generated revenue.

The post How To Enhance Customer Experience In Insurance appeared first on Tealium.

Let’s start with some basic concepts, and then get deeper with a guide to enhance customer experience in banking, and the best tools for a better customer experience in banking.

What Is Customer Experience In Banking?

When it comes to superior customer experience in banking, we’re referring to the overall perception and satisfaction that a customer (or prospective customer) experiences while interacting with your bank’s services, products, and touchpoints. 

This can mean online and offline interactions, like visits to a local ATM or checking an account balance in a digital portal. Additionally, customer experience in banking can incorporate the usage of banking products (such as savings accounts, loans, or investment services), and the support a customer receives throughout their relationship with the bank (like chatting with a Customer Service representative).

4 Challenges That The Banking Industry Is Facing

It’s important to understand the challenges the banking industry faces today. These include siloed data, regulatory compliance, personalization, and omni-channel experiences.

1. Siloed Data: Banks often have customer data spread across multiple systems—Customer Resource Management (CRM) platforms, transaction records, customer service databases, and more. This fragmented data makes it difficult to create a single customer view.
2. Regulatory Compliance: The financial industry is heavily regulated, and banks must ensure that their data practices comply with various legal requirements, such as consent, GDPR, CCPA, and other data protection laws. For more information on these laws, see our blog, De-Mystifying Data Privacy: The Scoop on Data Privacy Regulations.
3. Personalization Expectations: Modern customers expect personalized experiences similar to those provided by tech giants like Amazon. Banks must meet these expectations to retain and attract customers.
4. Omni-channel Experience: Customers interact with banks through multiple channels—online, mobile apps, branches, ATMs, and call centers. Ensuring a consistent experience across these channels is challenging without a unified data strategy. Of special interest is Tealium’s unique mobile solution set that ensures enterprise-level engagement because today’s banking customer demands next-generation mobile experiences. 

How To Enhance Customer Experience In Banking 

To enhance customer experience in banking, you should use a tool like a Customer Data Platform (CDP) to unify and activate customer data in real-time. 

A CDP is a tool that unifies customer data from various touchpoints, enabling organizations to create a real-time and actionable view of their prospects and customers. This unified data can then be used to deliver personalized experiences, improve customer engagement, and ultimately drive business growth.

The Best Tool For Customer Experience In Banking

Tealium’s CDP is the best tool for creating a better customer experience in banking. 

Tealium’s solution is enterprise-ready and offers features and solutions to the challenges faced by the banking industry. Here’s how we do it!

Breaking Down Data Silos

Tealium’s ability to integrate with over 1,300 vendors and FinServ systems allows banks to unify data from disparate sources. Whether it’s transaction data, teller, ATM, or CRM systems, Tealium pulls everything together into a single, cohesive platform. This unified data layer provides banks with a complete view of each customer, enabling more informed decision-making and personalized customer experiences.

Enhancing Regulatory Compliance

With stringent data privacy regulations, banks need to be meticulous about how they handle customer data. Tealium offers advanced data governance features that help banks maintain compliance with global regulations. The platform allows for detailed tracking and auditing of data, ensuring that banks can demonstrate compliance with GDPR, CCPA, and other regulations.

Enabling Personalization at Scale

Tealium’s CDP enables banks to create detailed customer profiles based on real-time data. These profiles can be used to segment customers and deliver personalized content, offers, and services. For example, a bank can use Tealium to identify high-value customers and offer them personalized loan products or investment opportunities, thereby enhancing customer satisfaction and loyalty.

We recommend exploring our case study with ABN AMRO to see how we helped them offer more personalized experiences to their customers during their banking journey.

Providing an Omni-channel Experience

Tealium excels in providing consistent customer experiences across multiple channels. By integrating data from all customer interactions, Tealium ensures that a customer’s interaction with a bank is seamless, no matter what the touchpoint. This consistency is crucial in building trust and loyalty among banking customers.

A leading bank that implemented Tealium to streamline its customer data management saw major results. Before Tealium, the bank struggled with siloed data, leading to disjointed customer experiences. After implementing Tealium, the bank was able to unify its data, resulting in:

• A 20% increase in cross-sell opportunities and 
• A 15% improvement in customer satisfaction scores
• Full compliance with GDPR, avoiding potential fines, and enhancing its reputation for data privacy

What Is The Future of CX In Banking?

The most important part of the future of CX in banking is the importance of data-driven decision-making. Although nobody has a crystal ball for the future, we know that future-proofing with consistent and reliable data will remain important. 

Tealium offers a future-proof solution that not only addresses current challenges but also equips banks with the tools needed to adapt to future trends. By investing in a robust CDP like Tealium, banks can stay ahead of the curve, delivering exceptional customer experiences while maintaining the highest standards of data governance.

In today’s competitive banking environment, the ability to leverage customer data effectively is a key differentiator. Tealium empowers banks to break down data silos, ensure regulatory compliance, and deliver personalized, omnichannel experiences that customers now expect. By implementing Tealium, banks can not only meet but exceed customer expectations, driving growth and loyalty in an increasingly digital world.

The post How To Enhance Customer Experience In Banking (Plus, The Best Tool For Customer Experience In Banking) appeared first on Tealium.

In response to recent OCR guidelines, some vendors use proxies to block IP addresses and Protected Health Information (PHI) when using a service like YouTube. While this approach may seem like an easy fix at first glance, it poses several serious risks and may ultimately do more harm than good.

Here are some reasons why using a proxy video service (like embedded YouTube videos), in the context of patient data protection can lead to negative consequences:

1. Risk of Re-Identification: Proxies are often used to mask IP addresses and anonymize some traffic, but they are not foolproof or comprehensive for blocking all Personal Identifying Information (PII) and PHI. Sophisticated tracking mechanisms employed by platforms like YouTube can still identify users and track their activities, even if you block IP addresses from being shared with these platforms. This means that such proxies may not effectively prevent data tracking and could create a false sense of security.
2. Proxy Server Security: The proxy itself can become a target for attacks. If compromised, it could lead to unauthorized access to all data passing through it.
3. Risk of Misconfiguration: Improper configuration of the proxy can lead to leaks of sensitive information. Proxy servers only protect traffic that is explicitly directed through them. This means they do not automatically secure all embedded content such as YouTube videos unless each one is specifically routed via the proxy. This setup requires careful configuration and routine web security audits to ensure comprehensive coverage.
4. Potential Violation of HIPAA Regulations: While the intention behind using a proxy video service to continue embedding YouTube videos on a healthcare organization’s website may be to protect patient privacy while leveraging existing video players without a Business Associate Agreement (BAA), the method itself could inadvertently lead to HIPAA violations for the reasons listed above. 

How To Be HIPAA Compliant

A Case for Avoiding Workarounds for HIPAA

As we discussed, YouTube is a popular and quick solution, but it may open your business to risks. Your strategy for hosting educational videos should consider a service willing to sign a BAA. This would give you the assurance that any PHI or PII that is not meant to be shared, will be blocked. Redirecting visitors to YouTube is not considered a HIPAA violation, because by redirecting the user, your site is no longer collecting or transmitting PII or PHI.

Relying solely on proxies to block specific vendors is not a comprehensive or effective solution. It is more like a workaround. Instead, healthcare organizations should focus on implementing data security measures that address the broader aspects of patient privacy and confidentiality. These measures include BAAs, encryption, access controls, employee training, migrating to HIPAA-friendly platforms, and regular security audits to identify and mitigate potential vulnerabilities.

Using workarounds adds a lot of risk, because it opens up the door for non-compliance and downstream issues. By embracing robust security measures and forward-looking solutions, healthcare organizations can better future-proof the safeguarding of sensitive patient information going forward in the changing digital landscape. For more resources, explore our web pages, HIPAA Compliance and Customer Data (full of details on HIPAA compliance) and our Tealium for Healthcare.

The post Think Twice Before Using Proxies to Block YouTube From Tracking Patient Data appeared first on Tealium.

Personalization at Scale

When it comes to delivering tailored experiences at scale, you’ll benefit from harnessing the power of AI and a CDP. By analyzing vast amounts of customer data, AI algorithms can discern patterns, preferences, and behaviors, helping to provide customized recommendations, content, and services in the moment of consideration. Whether it’s suggesting products, customizing communication, or curating user interfaces, AI-driven personalization enhances customer satisfaction and loyalty.

Let’s say a customer is browsing online. They are a shoe collector, and spend time browsing shoe brands that they often purchase from. The customer clicks on an ad for your company’s limited-edition shoe line, but they don’t check out. With real-time data from Tealium’s CDP, you can instantly retarget them on social media! Now, let’s take this example a step further with AI. As your shoe company gains popularity and visitor sessions increase, you can leverage AI algorithms to automatically enable personalized recommendations like color, size, style, and offers. By delivering consistent, delightful experiences across touchpoints, AI combined with a CDP help with building lasting relationships with customers.

Predictive Insights Fuel Proactive Engagement

Predictive analytics, a cornerstone of AI, enables businesses to anticipate customer needs and behaviors. By forecasting trends and understanding potential pain points, companies can proactively engage with customers, addressing concerns before they arise. Predictive insights help optimize inventory, predict service issues, and recommend timely solutions, enhancing overall customer satisfaction.

Here’s an example. As customers browse an e-commerce website or mobile app, their actions can be observed in real-time with Tealium’s CDP. As they navigate, an AI model dynamically generates personalized product recommendations based on their real-time behavior. For example, if a potential customer adds a camera to their cart, a “recommended products” section could suggest compatible accessories for that particular camera model, informed by the customer’s previous purchases. With personalized recommendations, AI-driven innovations captivate customers, fostering emotional connections and brand loyalty. 

Enhanced Customer Support

Real-time AI-powered chatbots and virtual assistants revolutionize customer support by offering immediate, round-the-clock assistance. These intelligent systems use Natural Language Processing (NLP) and Machine Learning (ML) to understand inquiries and provide relevant solutions. With continuous learning, they evolve to offer more accurate, efficient, and human-like interactions, resolving issues swiftly and enhancing customer experience.

Think about this example… A patient is on a healthcare website and chats with a virtual assistant, getting information about a consultation. The patient gets a call to schedule a visit to the doctor, and in real-time the information they filled out with the virtual assistant is available to the doctor’s office. The patient doesn’t have to repeat themselves and the experience is efficient. Being able to respond to customers’ behavior faster and with more relevance unquestionably leads to happier customers and better business outcomes.

Data-Driven Decision-Making for Enhanced CX Strategies

AI unlocks the potential of data, providing actionable insights that shape Customer Experience (CX) strategies. For example, Tealium’s CDP could contain customer information such as a user’s language preference based on the language of their previous interactions, or the language of the device they are using. Using Tealium’s CDP and AI, you could launch regional marketing campaigns that resonate with the cultural and linguistic nuances of each audience segment, increasing the effectiveness of promotional efforts.

By analyzing customer feedback, interactions, and behaviors, businesses gain a deeper understanding of their audience. This data-driven approach guides decision-making, allowing companies to adapt offerings, marketing strategies, and service delivery methods in real-time, optimizing the overall customer journey.

Key Takeaways

Activating AI in combination with a real-time CDP like Tealium is a strategic imperative for businesses aiming to thrive in a customer-centric landscape. The power of AI is best utilized in combination with accurate, reliable first-party data. AI’s capacity to humanize interactions, anticipate needs, and create memorable experiences will create large-scale market changes.

The post How To Capitalize on AI with CDP Use Cases appeared first on Tealium.

Data fuels AI algorithms, serving as the raw material that shapes their learning, understanding, and decision-making capabilities. Without high-quality data, AI systems may falter, leading to inaccurate predictions, biased outcomes, and flawed decision-making. Understanding the significance of data collection and compliance in AI is critical when considering these five pivotal reasons:

Ensuring Accuracy and Consistency

Quality data ensures accuracy and consistency in AI models. Just as a house needs a strong foundation, AI systems require accurate and comprehensive data to learn patterns and make informed predictions. Inaccurate or incomplete data can misguide AI algorithms, leading to flawed conclusions and unreliable outputs.

Avoiding Fines and Breaches

Without the appropriate safeguards at the point of data collection, it is too easy to feed sensitive or personal information into an AI platform. To achieve AI compliance, companies must apply consent and data transformation before data ever enters the data supply chain.

Enhancing Performance and Efficiency

Good quality data optimizes AI performance. Clean, well-organized data streamlines the learning process for AI algorithms, enabling them to operate efficiently and produce more accurate results. This efficiency translates into better user experiences, cost-effectiveness, and improved business outcomes.

Building Trust and Credibility 

Trust is essential for the widespread adoption of AI technologies. Reliable, high-quality data enhances the credibility of AI systems. When users and stakeholders trust the accuracy and compliance of AI-generated insights or decisions, they are more likely to embrace and leverage AI-driven solutions.

Enabling Innovation and Progress 

Quality data acts as a catalyst for innovation across the organization. Access to robust data sets fuels the development of advanced AI models. This fosters groundbreaking solutions to complex problems. Companies embracing AI will benefit immensely from AI innovation driven by quality data.

Key Takeaways

In the realm of AI, data quality is the bedrock upon which successful models and applications are built. To harness the true potential of AI and ensure its responsible and compliant use, a relentless focus on collecting, transforming, and filtering high-quality data is indispensable. Organizations and stakeholders must prioritize data quality initiatives, understanding that the quality of data directly correlates with the ultimate success of AI systems.

In the journey towards a more AI-driven future, acknowledging the pivotal role of quality data collection and compliance is not just a necessity but a moral imperative. Only by cultivating a culture that values and prioritizes data quality can we unlock the full potential of artificial intelligence at scale.

The post 5 Reasons Why Data Collection and Compliance Matter for AI appeared first on Tealium.

The Golden Rule of AI

The old adage of if you put garbage in, you get garbage out applies to AI as well. Imagine a scenario where an AI algorithm is trained on data that is not AI compliant. The output becomes flawed and dangerous, mirroring the distortions inherent in the input it receives. Just as a house built on a shaky foundation is prone to collapse, AI that is built on erroneous and sensitive data is destined to yield flawed results. Data collection should be a strategy, not an afterthought. 

Diminished Accuracy and Reliability

AI’s primary strength lies in its ability to analyze vast amounts of data and make predictions or decisions. However, when fed with inconsistent, sensitive, or non-standardized data, its outputs become unreliable and unusable. 

Let’s look at an example. A self-driving car that relies on faulty data might misinterpret traffic signals, leading to potentially dangerous situations like collisions. Now, let’s examine this from a customer trust perspective. Imagine your AI insights lead your marketers to make the wrong decisions in the moments that matter. This could not only erode customer trust but cost your business in other aspects like profits, resources, and reputation.

As AI integrates into various aspects of our lives, trust becomes a pivotal factor. Trust in AI systems erodes rapidly when they produce poor quality or non-compliant outcomes. 

When consumer confidence diminishes, it limits the adoption and acceptance of AI-driven solutions (even in situations where AI might prove beneficial). While AI feels ‘new’ it has been around for a long time and the fact that it is suddenly so accessible could lead to major mistakes that have massive consequences. All it takes is one mistake to lose consumer trust.

Legal and Ethical Issues

The ramifications of AI acting upon bad data extend into legal and ethical realms. Who bears responsibility when an AI-driven system recommends an erroneous medical message due to flawed training data? How about a financial services company offering a financing option to a consumer that does not meet the requirements? Determining accountability and liability in such scenarios becomes a convoluted and contentious issue. Legal and privacy-related implications will quickly stifle any AI endeavors being fed data that is not AI compliant.

Mitigation Strategies: Navigating the Minefield

Addressing the challenges posed by feeding AI bad data requires a multi-faceted approach:

1. Data Standardization and Transformation: Rigorous data curation and preprocessing are imperative to weed out PII, PHI and other sensitive data types before training AI models. Think consent, collection, standardization, and filtering. 
2. Inclusive Data Sources: Using a single standardization platform for various data sources will provide a single source of AI compliant data. Think websites, mobile apps and any other streaming sources of customer data. One data layer and associated rule set will help mitigate treacherous waters. 
3. Constant Monitoring and Auditing: Implementing mechanisms for continuous monitoring and auditing of AI systems can help detect and rectify issues as they arise. Any changes to the data layer or identification of new potential PII risks is key. 
4. Transparency and Observability: Making AI processes transparent and enabling observability can aid in understanding how decisions are made and defended, fostering trust and accountability.

The Path Forward

In the ever-evolving landscape of AI, the consequences of feeding it bad data cannot be overstated. As creators and stewards of AI, the responsibility lies with us to be vigilant custodians of the data we feed into these systems. The path toward leveraging AI for positive transformation necessitates an unwavering commitment to standard data practices, continual improvement, and a collective resolve to mitigate the detrimental effects of bad data on AI systems.

Ultimately, the relationship between AI and data is symbiotic. Just as AI relies on trusted, consented, enriched, and filtered data to learn and evolve, it falls upon us to ensure that the data we provide is of the highest quality. Only then can AI truly fulfill its potential as a force for progress and innovation, empowering us to find a path forward with real-time customer engagement. 

To help companies navigate the complexities of AI, we created Tealium for AI. Tealium for AI is designed to help companies get consented, filtered, enriched data in real-time for AI models and activate the results. Whether training data, data to feed the model, data for fine-tuning, or activating the results, Tealium connects your AI models to all your tools and customers. 

With Tealium for AI, businesses can:

• Dramatically improve the time to model and value 
• Apply data preparation, transformation, and encryption to incoming data for immediate data availability 
• Directly send consented, organized, and filtered data in real-time to major AI platforms
• Provide a real-time activation engine for AI insights and scores 
• Integrate AI models and platforms with the rest of your marketing tools 
• Reduce risk by blocking any non-consented or non-compliant data from AI models

Tealium offers the perfect solution for IT, CIO, CDO, and Governance teams, providing them with the necessary tools for a strong data pipeline tailored for AI. Our goal is to empower organizations to effectively utilize all their data, with proper consent, exactly when required. To get started, schedule a demo.

The post What Happens When You Fuel AI With Bad Data? appeared first on Tealium.