There’s no question that shopping has transformed into an effortless act. Long gone are the days of rolling out of bed, starting up your car, and mentally preparing to stand around waiting in long checkout lines. So, it should come as no surprise that a large percentage of Americans now engage in the convenience of buying their goods online. Yet given the rise in virtual shopping, many eCommerce businesses still confront the roadblock of getting their customers to finalize their transactions. If you’re like most business leaders, this leaves you with one very important question—how do I optimize the checkout process for my clients? In this article, we’ll delve deeper into shopping cart abandonment and help you identify quick and easy strategies to weave into your business plan to maximize online sales by perfecting the shopping experience. Why do customers abandon at checkout? If a client lands on your page, browses your online products, and adds them to a virtual cart, all signs point to them being interested in your company and what it has to offer. So why, then, do they go through this effort just to abandon your website at checkout? The truth is, there are dozens of factors at play that may cause prospective customers to leave your website prematurely. While you can’t be certain without an exit intent survey, here are a few common problems: Complicated or confusing checkout process: If your customers have to fill out contact information without an autofill option, they may find the checkout process to be too inconvenient—especially when your competitors may have a simplified checkout process on their site. Concerns surrounding data security: Does your website seem spammy or unsecured? If so, users may be hesitant to pass over valuable personal information, including their name, phone number, and address—key data components that are often required at checkout. Technical issues: When users shop online, they anticipate a quick and effortless experience. If your website often presents slow-loading pages that fail to let the user checkout in a reasonable amount of time, it may be the reason behind your higher shopping cart abandonment rates. Optimizing the eCommerce checkout process Despite the common issues we outlined above, there are a few strategic ways to finalize virtual sales and help your business nail its performance KPIs. Here are several key points to keep in mind when mapping out your eCommerce strategy: Data management equals happier customers Data quality automation and investment can have a far-reaching impact on the customer experience. Businesses that prioritize accurate, well-managed data are better positioned to reduce friction, minimize errors, and build stronger relationships with their customers. In simpler terms, investing in data management is a fundamental step in optimizing the checkout process. But how? With an organized and streamlined data management plan, you can segment your audiences more effectively, maintain secure and up-to-date information, and reduce errors that negatively affect fulfillment and communication. Streamlined checkout simplifies the shopping experience Shopping should be a stress-free act. When users check out on your site, do they have an effortless experience with autofill options and intelligent prompts? If not, investing in real-time data solutions can significantly streamline the process while helping you capture accurate information at the point of entry. Consider leveraging address verification to reduce the number of keystrokes required from the user, allowing for a simplified checkout process. Once integrated into your system, address verification provides users with a drop-down selection of validated addresses as they begin typing. Not only does this reduce data entry time, but it also helps ensure proper spelling and formatting. Accurate contact data provides peace of mind for delivery Optimizing what happens after a user checks out is just as important as what happens during the checkout process. To maximize ROI and reduce unnecessary risk, you want to increase the likelihood of successful delivery while keeping customers informed along the way. Help ensure your business captures accurate data at the point of entry with tools such as real-time address verification, phone verification, and email verification. With validation processes in place, you can capture valid, correctly formatted, and legitimate customer data from the start. Human error can prevent users from receiving order confirmation emails, text updates, or even their packages entirely. If a prospective customer mistypes their contact information, real-time error messaging allows them to correct the issue immediately—reducing downstream complications. Data management and shopping cart success go hand in hand While eCommerce continues to grow, not every online shopping experience will result in a sale. To decrease shopping cart abandonment rates, implementing a strong data management strategy is essential. Accurate contact data allows you to precisely reach your audience, fulfill orders successfully, and provide a seamless checkout process. When customers experience ease, accuracy, and reliability, they’re more likely to complete their purchase—and return again. Interested in getting started? Reach out to a data quality specialist to explore your options and create a customized plan suited to your company’s needs.

Retailers aren’t short on personalization tools. If anything, the complete opposite is true. With the rise of AI-driven segmentation, everything needed for sophisticated and data-driven personalization is seemingly already in place. And yet, the results often fall short. Campaigns miss the mark. Product recommendations feel irrelevant. Customer experiences lack cohesion across channels. The technology is working, but the outcomes simply are not. So, what’s actually going wrong? More often than not, the issue isn’t the strategy. It’s the data supporting it. The promise of personalization (and why it falls short) Personalization in the retail industry is built on one simple premise: the more you understand your customer, the better you can serve themThat understanding is built on data. It flows in from ecommerce platforms, CRM systems, loyalty programs, mobile apps, and in-store interactions. When that data is accurate, complete, and connected, it enables experiences that feel intuitive and almost effortless from the customer’s perspective.This is the vision retailers invest in: higher conversion rates, stronger loyalty, and more efficient marketing. But that promise depends on something often overlooked and less visible to the consumer: data quality.Because when the underlying data is flawed, even the most advanced personalization strategies begin to unravel. How the problem develops Data problems rarely start as obvious failures. On the contrary, they build up quietly over time. A customer creates multiple profiles across different channels. Address details are entered inconsistently. Records go stale. Duplicate identities begin to stack up. Over time, what once looked like a rich dataset becomes fragmented and unreliable. This is a familiar pattern in modern retail environments, where data is constantly moving across systems that weren’t necessarily designed to work seamlessly together. On the surface, everything still appears functional. But beneath that surface, inconsistencies grow and eventually surface in the customer experience. When data issues reach the customer Personalization doesn’t fail all at once. It erodes gradually. An email promotes something the customer already purchased. A recommendation feels slightly off. A promotion arrives just a little too late to matter. Each interaction seems minor on its own, but together they create a disconnected experience. Outdated profiles lead to mistimed messaging. Fragmented data creates disjointed journeys. Instead of feeling understood, customers are left navigating experiences that don’t quite add up. Why AI alone isn't enough AI is everywhere right now, so it makes sense that companies have woven it into their general business operations. But the truth is, AI doesn’t solve data problems. It scales them. Take, for instance, a customer that exists within your CRM as multiple profiles. AI doesn’t realize this is the same person and instead treats every version separately. Essentially, when the underlying data is flawed, you’re not just making mistakes— you’re actually making them faster and across more channels at once. So, AI alone is not enough to scale retail personalization. If the data is strong, AI can unlock real value. But if the data is weak, it amplifies the gaps and makes the impact harder to ignore. The hidden cost of getting it wrong The impact of poor data quality on personalization isn’t always immediate. There is rarely a single point of failure. Instead, the impact builds over time. Engagement declines. Campaign performance weakens. Teams spend more time correcting data than using it. Opportunities are missed. Eventually, confidence in personalization efforts starts to drop. Not because the strategy is flawed, but because the data cannot support it. Fixing personalization starts with fixing data Improving personalization outcomes doesn’t always require another platform or a more advanced model. In many cases, it requires revisiting the fundamentals. It starts at the point of entry, validating customer data as it’s captured. Ensuring accuracy early prevents errors from spreading across systems. Tools like address validation and email verification help maintain clean, usable data from the start. From there, consistency becomes critical. As data moves across systems, it must remain standardized and enriched to stay reliable. Finally, there’s unification. Retailers need a single, trusted view of the customer. That requires resolving duplicates and connecting fragmented records across channels. Capabilities like identity resolution and data matching make this possible. And importantly, this isn’t a one-time fix. Data quality requires ongoing monitoring and governance. When these elements are in place, personalization becomes easier to execute and far more effective. Customer profiles become more reliable, messaging becomes more relevant, and the experience starts to feel connected instead of fragmented.

One factor consistently determines whether an organization's adoption of artificial intelligence (AI) succeeds: data. AI-ready data is central to building reliable, high-performing AI systems that avoid inaccurate, biased, or unusable results. AI systems rely on information to learn patterns and make predictions that allow them to automate decisions. However, raw data alone is not enough. It must meet strict AI data requirements, adhere to defined standards, and undergo careful preparation. In this guide, we’ll break down what AI-ready data is, why it matters, and how businesses can make sure their data supports strong AI outcomes. What is AI-ready data? At its core, AI-ready data is properly prepared, structured, and validated data that AI and machine learning models can use. Unlike raw data that is incomplete, inconsistent, or unstructured, AI-ready data is optimized for analysis and model training. The primary difference between raw data and AI-ready data is usability. Raw data is collected while AI-ready data is refined and standardized, ready to deliver insights. Why AI-ready data matters for success The quality of your data directly impacts your AI model’s performance. Even the most sophisticated algorithms cannot compensate for poor-quality inputs, which is why creating data quality standards for AI is seen as more important than the model itself. AI-ready data matters for a few reasons: Models can train faster and perform more accurately More reliable insights Faster deployment of AI solutions Saves time Encourages the scalability of your organization's AI initiatives Ultimately, AI-ready data enhances the overall model’s effectiveness. From a business perspective, AI-ready data enables improved automation and more efficient operations, delivering meaningful value to your business. AI data requirements for effective data usage To transform raw data into AI-ready data, organizations must meet several essential AI data requirements. These requirements ensure that the models can use the data effectively throughout the AI lifecycle. Accuracy and completeness Missing values, incorrect entries, or outdated information can significantly impact model performance. Verifying that datasets are as complete and accurate as possible helps produce consistent and trustworthy results. Consistency and standardization Consistency across datasets allows for AI systems to interpret data correctly. AI data standards for formats, units, naming conventions, and structures make sure that data from different sources can be analyzed without errors. Accessibility and integration AI models need access to your data across different departments within your organization. Breaking down data silos and creating unified data pipelines allows AI systems to operate efficiently and at scale. Security and compliance As AI systems often handle sensitive information, security and compliance are critical. Your organization needs proper data governance to protect data privacy and comply with regulations, guaranteeing your AI initiatives remain ethical and legally sound. AI data standards and governance AI data standards provide the necessary framework for consistency and reliability across different datasets. These standards define how data is formatted, labeled, stored, and managed for the AI systems to analyze. In addition to standards, strong data governance is essential. Governance frameworks establish policies for data usage, access, and quality control. These frameworks help organizations maintain data integrity over time so that your data aligns with business and AI objectives. Without clear standards and governance, data can quickly become fragmented and unreliable, limiting the effectiveness of AI systems. The role of data quality for AI Data quality for AI directly influences model performance, as high-quality data produces accurate, consistent, and meaningful results. There are several key dimensions of data quality for AI, including: Accuracy: Verifies that the data correctly represents real-world conditions Completeness: Checks that all necessary data is available Consistency: Guarantees uniformity across datasets Timeliness: Certifies that your data is up to date When data quality is poor, AI models can produce biased or incorrect outputs that result in flawed insights and poor decision-making. AI data preparation: Making raw data AI-ready Creating AI-ready data requires a standard and structured approach to AI data preparation. This preparation process transforms raw data into a format that AI models can analyze and use. You can follow these AI data preparation steps for the best results: Data Collection and Aggregation: Gather data from multiple sources to build a comprehensive dataset your organization can analyze. Data Cleaning and Validation: Remove errors, duplicates, and inconsistencies to make sure the data meets predefined quality standards and improves the AI model’s reliability. Data Labeling and Annotation: Label or annotate supervised learning models by assigning meaningful tags or categories to data points so that the model can learn from examples. Data Transformation and Formatting: Data must be in a structured format that AI systems can process, which involves normalizing values, encoding categorical data, and organizing data into specific schemas. When moving through this process, your organization may run into a few challenges, especially when first starting with AI data preparation. Common challenges in creating AI-ready data Despite its importance, building AI-ready data comes with several challenges. Understanding these challenges with data quality for AI can help you protect your raw data from becoming unusable. These challenges include: Data silos: Information stored in separate systems that the models cannot easily access can lead to inaccurate data. Inconsistent data formats: These types of inconsistencies can create difficulties when combining datasets. Lack of governance: A clear framework improves data quality, leading to more reliable outputs. Additionally, preparing data for AI can be time-consuming and resource-intensive, especially at scale. To overcome these challenges, organizations should adopt best practices for building AI-ready data pipelines. Automated data quality tools can help identify and resolve issues quickly. Continuous monitoring with up-to-date and valid data also helps maintain quality. Building a foundation for AI success with Experian Without AI-ready data, even the most advanced AI systems cannot deliver reliable outcomes. By meeting AI data requirements, maintaining high data quality for AI, and investing in proper AI data preparation, organizations can build a strong foundation for AI success. As AI continues evolving, the importance of data quality will only grow. If your business is ready to focus on data quality, talk to our Experian data experts to learn how we can help you improve your data strategy.

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Email remains one of the most important ways businesses communicate with customers and prospects. From account notifications and service updates to marketing campaigns and sales outreach, organizations depend on accurate email addresses to reach the right people. But an email address appearing in a database doesn’t necessarily mean it’s valid—or that a message sent to it will be delivered. Invalid, outdated, or incorrectly entered email addresses can increase bounce rates, waste marketing spend, and negatively affect sender reputation. Email validation helps businesses identify these issues before they interfere with customer communications What is a valid email address? A valid email address is an email that follows the correct format and can be assessed for whether it is associated with a domain capable of receiving email. A typical email address contains three basic components: name@example.com Local part: The unique name before the @ symbol, such as "name" @ symbol: Separates the local part from the domain Domain: The domain associated with the email address, such as "example.com" Correct formatting is an important first step, but syntax alone doesn't tell you whether an email address is usable. An address can look perfectly legitimate and still be inactive, mistyped, associated with a nonexistent domain, or otherwise unable to receive messages. That's where email validation becomes important. What makes an email address valid? Email validation can evaluate an address using multiple checks to determine whether it appears accurate and deliverable. Depending on the validation process, those checks can include: Syntax: Does the address follow accepted email formatting rules? Domain: Does the domain exist and appear capable of receiving email? Mailbox: Is there evidence that the mailbox can accept messages? Typographical errors: Does the address contain common mistakes or misspellings? Risk indicators: Are there characteristics that could make the address less useful or appropriate for business communications? Checking email addresses at the point of collection can help prevent inaccurate information from entering your database in the first place. Existing customer and prospect records can also be validated periodically to identify email data that may have become outdated. What does an invalid email address look like? Some invalid email addresses are easy to recognize. Others are much harder to spot without automated validation. For example, john.example.com is clearly invalid because it doesn't contain an @ symbol. Other problems can be more subtle, such as a misspelled domain. Common signs of an invalid or inaccurate email address include: A missing @ symbol A missing local part or domain Incorrect email syntax Misspelled domains Accidental spaces or invalid characters A domain that doesn't exist An address that is no longer active or deliverable These errors often originate from simple data-entry mistakes. Customers may mistype an address when completing a form, employees may enter information incorrectly, or previously accurate information may become outdated over time. At scale, even a relatively small number of bad email addresses can create significant data quality problems. Why is email validation important? An email address is often a critical link between a business and its customers. When that information is wrong, communications may never reach their intended recipient. Maintaining accurate email data can provide several business benefits. Reduce email bounce rates Sending messages to invalid or inactive addresses can result in hard bounces. Identifying problematic addresses before sending can help organizations reduce unnecessary delivery failures. Protect sender reputation Repeatedly sending email to invalid addresses can negatively affect email performance and sender reputation. Maintaining a healthier email database can support stronger email deliverability practices. Improve marketing efficiency Every message sent to an unusable address consumes resources without creating an opportunity for engagement. More accurate email lists help marketing teams focus their efforts and budgets on contacts they can actually reach. Create better customer experiences A mistyped email address can mean a customer never receives an order confirmation, account notification, password reset, or other important communication. Validating an address while a customer is entering it can give them an opportunity to correct mistakes immediately, helping create a smoother experience. Improve overall data quality Email validation isn't only an email marketing concern. Email addresses frequently connect records across CRM, customer data, ecommerce, service, and analytics systems. Improving email accuracy can therefore contribute to better contact data and more dependable customer records throughout the organization. How to improve email data quality Accurate contact data requires ongoing maintenance. Even a correctly entered email address can eventually become outdated as people change jobs, switch providers, or stop using an account. Organizations can strengthen email data quality by: Validating email addresses during data capture Regularly reviewing existing email databases Monitoring hard bounce rates Correcting common domain and formatting errors Removing or suppressing addresses confirmed as undeliverable Establishing consistent data-entry standards across systems Integrating contact data validation into CRM and customer workflows These practices can help prevent inaccurate email data from accumulating and affecting future communications. Build more trustworthy email data with Experian Reliable customer communication starts with reliable contact data. Experian's email validation capabilities help organizations assess email addresses for accuracy and deliverability, whether data is being entered in real time or already exists within a database. By identifying problematic email addresses earlier, businesses can reduce unnecessary bounces, protect sender reputation, and get more value from their customer communications. Email validation can also be part of a broader contact data quality strategy that keeps customer information accurate, complete, and ready to use across the organization. With better email data, businesses can spend less time dealing with preventable data quality problems and more time connecting with customers. Fill out the form to learn more:

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In today’s economy, data fuels every strategic decision. Poor data quality can lead to costly mistakes, inefficient operations, and missed opportunities — which can be a liability. Experian empowers you to trust your data at every stage. We help businesses manage, cleanse, and connect their information with confidence. The result? Smarter decisions, stronger customer experiences, and measurable growth. Here’s what makes Experian different. 1. We know data Experian is a trusted leader in data quality, analytics, and decision intelligence. For years, leading organizations have relied on Experian to help them turn raw data into meaningful insights that drive smarter decisions and stronger customer relationships. Our approach is built on a deep understanding of how data flows through your business today and into the future — from customer onboarding and marketing to compliance and analytics. 2. We simplify complexity Data lives in many places — from customer relationship management (CRM) to marketing tools and internal databases. Experian’s suite of data management solutions brings all of this information together. Whether it’s: Cleansing to remove inaccuracies Matching to eliminate duplicates Profiling to understand data quality Enriching to add valuable insights Aperture Data Studio – Experian’s unified data quality solution – curates accurate customer data views into a single platform for data management. 3. We delivery accuracy Our data management solutions are built on powerful technology, proven methodologies, and high-quality reference data that work together to keep your information complete and dependable. Through advanced validation, enrichment, and continuous monitoring, Experian helps organizations maintain data they can trust — to drive improved customer experiences and smarter business decisions. 4. We scale with you Whether you’re managing millions of customer records or integrating data across multiple systems, Experian provides the flexibility and performance needed to keep information accurate and accessible at scale. Our cloud-based platforms and powerful API integrations make implementation seamless, so teams can focus on driving outcomes — not maintaining systems. 5. We drive results Take IKEA as a concrete example. By using Experian’s real-time address validation and enrichment solutions, IKEA increased their match rate from 83% (with a competitor) to 95%, enabling better segmentation and more effective marketing. That uplift translated into more confident targeting, improved campaign ROI, and a better customer experience. This is the kind of result we aim for with every engagement—across retail, finance, healthcare, and beyond. Your data journey starts here Data management isn’t a one-time project — it’s an ongoing journey. Experian is more than a solution provider; we’re a long-term partner in your success. With dedicated support, expert consultation, and continuous innovation, Experian helps your organization evolve with data — not fall behind it. The bottom line: Experian turns your data into a competitive advantage In a world where data drives everything, Experian gives you the clarity and confidence to move forward. From data accuracy and enrichment to governance and compliance, Experian provides the tools and expertise you need to make data your most valuable asset.

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Anyone can set a goal of saving money, but it is impossible to reach that goal if you don’t know when to put money away, don’t monitor your spending, or never review your bank statements. Any time you set a goal, monitoring your path to success is crucial to achieving it, and achieving effective data governance is no different. Establishing metrics and performance indicators allows you to measure progress and adjust your data governance strategy as necessary to maximize success. Once you establish proper data governance metrics and KPIs based on your business goals, you can take your data governance strategy and success to the next level. What are data governance metrics and KPIs? Data governance is the process used to ensure data follows strict rules and regulations as it enters a database and is used throughout an organization. Data governance metrics are indicators put in place in order to measure the effectiveness of an organization’s data governance processes. Key performance indicators (KPIs) provide additional ways to measure progress.Data governance centers around three key areas—people, process, and technology. Because data governance is a comprehensive process and is often more difficult to measure, organizations need to establish metrics in each of the three areas to experience success.Effective data governance strategies are crucial for organizations to stay in compliance with regulations, protect consumer data, and ensure that data is reliable for gaining insights and informing future business strategy. By using data governance metrics, organizations and businesses can ensure they remain in compliance and maintain quality, valuable data. Why are data governance metrics important? Data governance metrics and key performance indicators (KPIs) are vital for measuring success and monitoring progress. Measuring progress in data governance is important for determining whether or not your current data governance practices are effective and how to improve them if necessary.When you focus on KPIs for governance and compliance, you are able to determine how effective your processes are and how they may need to be adapted over time. After all, setting a strategy and hoping for the best is not a recipe for success, as you can never address issues that you are not aware of.Data governance metrics are powerful tools for any data-driven business, as they allow businesses to:  Highlight the effectiveness of a data governance strategy on areas like data quality and accuracy Demonstrate the success of data governance initiatives to stakeholders Uncover areas of improvement in your data governance and data management strategies Identify the need for a change of approach or priorities in your data governance initiatives Without focusing on data governance metrics, it can be easy for your best processes and strategies to fall by the wayside. Above all else, metrics and key performance indicators help provide accountability and ensure that your business maintains strong standards in its data governance procedures.  How to measure success in data governance One of the first steps in measuring success in data governance is determining what success looks like for your business. To do so, you must consider your business objectives, what operations data governance will improve, the desired data governance outcomes, and what stakeholders stand to benefit. Generally, to reach success in data governance, you should focus on four key areas to measure your business’s effectiveness in getting there. Data quality Data quality is a crucial part of any data governance strategy, and fortunately, it is not difficult to measure. Strong data quality is a direct consequence of effective data governance and data management. Some measurable aspects of data quality include: Data accuracy Data completeness Data validity Data integrity The easiest way to track your data quality is through the use of a data quality platform, which can provide a breakdown of your data’s accuracy and completeness through a dashboard and highlight areas of improvement. You can also improve data quality by using tools like email, address, and phone number verification. Each of these tools can be used in real-time or through batch cleansing methods to prevent errant data from entering your database and remove the errors that sneak in over time. People People are one of the driving forces of proper data governance in your organization. Your organization should consider who is accountable for managing data and what resources they need to succeed. For many businesses, this involves establishing set roles and teams. Some of the measurable components in this area include: How many hours of training stakeholders receive The percentage of employees trained on data literacy The percentage of employees trained on data governance strategies The number of stakeholders trained in a given time period The percentage of employees that engage with data management meetings and events Establishing these metrics allows your organization to ensure that data management teams are adequately prepared to handle tasks surrounding data governance. Process The processes within data governance help describe how your organization approaches the strategy. Processes reflect how data is collected, moved, stored, accessed, and secured. Establishing effective processes is vital for ensuring that data remains accurate and valuable throughout its life in an organization.  Measurable metrics and KPIs for your business include: The number of passed data audits carried out The number or percentage of areas and standards where your business is compliant The number of employee logins to a data insights tool or dashboard Technology Once your organization has the right systems and a strong team in place, technology is what really seals the deal. Data governance technology helps to support the process in several ways, saving time and energy for the teams responsible for managing and maintaining its quality. Data governance tools can: Assist with data profiling Identify and correct errors in data entries Assess the validity of data that enters a database Securely store and maintain data Seamlessly collect and transfer data throughout the organization Given the potential functions of data governance technology, some measurable metrics in this area can include: How often you run batch cleansing processes The number of attributes profiled The number of data entries corrected by a tool The percentage of accurate data in a database Using data governance metrics to monitor and assess the effectiveness of your technology makes it easier to determine whether your tools are achieving the desired outcomes. If the metrics are not providing the returns you would hope, then you can consider if there are better ways to use the tools or if you simply need better tools. Develop your data governance metrics for sustained success It's one thing to take strides in your data governance processes, but it is another thing to make sure that these strides are actually effective. Data governance metrics and KPIs play an important role for any data-driven business by ensuring that a data governance program is effective and achieving the desired outcomes. Establishing a strong data governance strategy is even easier with the right tools, and Experian can help. Our data quality solutions are geared toward helping you get the most out of your data, from ensuring its accuracy and completeness to gaining valuable insights and analysis from it. Contact us to learn more about how to take your data governance strategies to the next level.