Why data quality is now a regulatory control

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.