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ExperianThis is the citation

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ExperianThis is the citation
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In the latest episode of “The Chrisman Commentary” podcast, Experian experts Joy Mina, Director of Product Commercialization, and Ivan Ahmed, Senior Director of Product Management, explore how lenders can navigate a tight mortgage market, from rates to equity. “Current rates exceed their locked in rates,” said Ivan. “Homeowners are choosing options as alternatives to selling with the refinancing for cash outs and HELOC." Listen to the full episode for all the details and check out the previous episode to learn how mortgage companies can optimize their business expenses and protect prospects. Listen to podcast

For businesses across all sectors solutions that improve productivity are more important than ever. As technology advances, organizations across industries are looking to capitalize by investing in artificial intelligence (AI) solutions. Studies have recently shown that productivity is a leading measure of how well these AI tools are performing. About 60% of organizations surveyed are using “improved productivity” as a metric to measure the success of implementing AI solutions.[1] Experian research shows it takes an average of 15 months to build a model and put it into production. This can hinder productivity and the ability to quickly go to market. Without a deep understanding of key data points, organizations may also have difficulty realizing time to value efficiently. To improve upon the modeling lifecycle, businesses must examine the challenges involved in the process. The challenges of model building One of the most significant challenges of the modeling lifecycle is speed. Slow modeling processes can cause delays and missed opportunities for businesses which they may have otherwise capitalized on. Another difficulty organizations face is having limited access to high-quality data to build more efficient models. Without the right data, businesses can miss out on actionable insights that could give them a competitive edge. In addition, when organizations have inefficient resources, expenses can skyrocket due to the need for experts to intervene and address ongoing issues. This can result in a steep learning curve as new tools and platforms are adopted, making it difficult for organizations to operate efficiently without outside help. Businesses can combat these challenges by implementing tools such as artificial intelligence (AI) to drive efficiency and productivity. The AI journey While generative AI and large language models are becoming more prevalent in everyday life, the path to incorporating a fully functional AI tool into an organization’s business operations involves multiple steps. Beginning with a proof of concept, many organizations start their AI journey with building ideas and use cases, experimentation, and identifying and mitigating potential pitfalls, such as inaccurate or irrelevant information. Once a proof of concept reaches an acceptable state of validity, organizations can move on to production and value at scale. During this phase, organizations will select specific use cases to move into production and measure their performance. Analyzing the results can help businesses glean valuable information about which techniques work most effectively, so they can apply those techniques to new use cases. Following successful iterations of an efficiently functioning AI, the organization can then implement that AI as a part of their business by working the technology into everyday operations. This can help organizations drive productivity at scale across various business processes. Experian’s AI journey has been ongoing, with years of expertise in implementing AI into various products and services. With a goal of providing greater insights to both businesses and consumers while adhering to proper consumer data privacy and compliance, Experian is committed to responsibly using AI to combat fraud and foster greater financial access and inclusion. Our most recent AI innovation, Experian Assistant, is redefining how financial organizations improve productivity with data-driven insights. Introducing Experian Assistant Experian Assistant, a new GenAI tool announced in October at Money20/20 in Las Vegas, is helping organizations take their productivity to the next level by drastically speeding up the modeling lifecycle. To drive automation and greater intelligence for Experian partners, Experian Assistant enables users to interact with a virtual assistant in real time and offers customized guidance and code generation for our suite of software solutions. Our experts – Senior Director of Product Management Ankit Sinha and Director of Analyst Relations Erin Haselkorn – recently revealed the details of how Experian Assistant can cut down model-development timelines from months to days, and in some cases even hours. The webinar, which took place on November 7th, covered a wide range of features and benefits of the new tool, including: Spending less time writing code Enhancing understanding of data and attributes Accelerating time to value Improving regulatory compliance A case study in building models faster Continental Finance Company, LLC’s Chief Data Scientist shared their experience using Experian Assistant and how it has improved their organization’s modeling capabilities: “With Experian Assistant, there is a lot of efficiency and improvement in productivity. We have reduced the time spent on data building by almost 75%, so we can build a model much quicker, and the code being generated by Experian Assistant is very high quality, enabling us to move forward much faster.” For businesses looking to accelerate their modeling lifecycle and move more quickly with less effort, Experian Assistant provides a unique opportunity to significantly improve productivity and efficiency. Experian Assistant tech showcase Did you miss the Experian Assistant Tech Showcase webinar? Watch it on demand here and visit our website to learn more. Visit our website [1] Forrester’s Q2 AI Pulse Survey, 2024
By Erik Hjermstad, VP of Product Management for Experian Automotive In today's digital landscape, where consumers increasingly turn to connected TV and addressable TV for entertainment, precision targeting has become more critical than ever for automotive advertisers. By delivering highly relevant ads to the most likely potential customers, advertisers can maximize campaign effectiveness, increase conversion rates, and ultimately drive a better return on investment (ROI). The following is a summary from a recent article I wrote for Ad Age. The Power of Precision Targeting Precision targeting allows advertisers to focus their efforts on the right people with the right message at the right time. This targeted approach helps minimize wasted ad spend and ensures that every dollar invested makes a meaningful impact. By understanding an audience's demographics, interests, and behaviors, automotive marketers can tailor their messaging to resonate with specific consumer segments and pointedly reach those ready to purchase. Leveraging Data Intelligence for Competitive Advantage To stay competitive, automotive marketers must stay on top of the latest data intelligence. Agencies and marketers have turned to data-driven strategies to set their campaigns apart and increase the likelihood of a consumer purchasing a vehicle. When choosing their consumer audiences, automotive marketers turn to companies like Experian Automotive for data like license, registration, and title. They utilize models based on actual vehicle sales and ownership to target the right audience and measure effectiveness more accurately. Measuring Success: Key Metrics and Omnichannel Measurement To ensure that advertising campaigns deliver results, automotive marketers must have a robust measurement strategy. This involves tracking key metrics such as: Sales: Did the buyer purchase a vehicle? Impressions: The number of times an ad is seen. Clicks: The number of times an ad is clicked on. Conversions: The number of people who take a desired action, such as purchasing. Cost per acquisition (CPA): The cost of acquiring a new customer. Return on ad spend (ROAS): The revenue generated by an ad campaign divided by the cost of the campaign. Experian Automotive offers advertisers a robust measurement solution that provides omnichannel measurement, connecting website visitation and ad exposures to automotive sales. Analysis includes make, model, and vehicle class reporting, competitive analysis, and 30-, 60-, and 90-day vehicle sales projections. The Importance of Omnichannel Measurement In today's interconnected world, automotive consumers interact with brands across multiple channels, including TV, digital, social media, and in-store. Advertisers must adopt an omnichannel measurement approach to truly understand their audience and measure the effectiveness of their campaigns. By tracking consumer behavior across all channels, advertisers can gain a more complete picture of the customer journey and identify opportunities for optimization. Omnichannel measurement integrates data from various sources, such as website analytics, social media metrics, and point-of-sale data. This allows advertisers to understand how consumers interact with their brand at different stages of the buying process and attribute conversions to specific channels and touchpoints. By leveraging omnichannel measurement, advertisers can make more informed decisions about their marketing investments and deliver a more cohesive and personalized customer experience. Measurement is critical to advertising success for several reasons: Optimization: By tracking key metrics, advertisers can identify what is working versus what is not and make necessary adjustments to improve campaign performance. Attribution: Measurement helps determine which marketing channels and tactics drive the most conversions. ROI analysis: By measuring the ROI of their campaigns, advertisers can justify their marketing investments and demonstrate the value they bring to the business. Navigating the Challenges of the Streaming Era While precision targeting and measurement are essential in the streaming era, advertisers face unique challenges. The fragmented nature of the streaming landscape, with its multiple platforms and devices, can make it challenging to track consumer behavior and measure campaign effectiveness. However, advancements in data technology and measurement tools provide advertisers with many new opportunities to overcome these challenges. By leveraging data intelligence, using deterministic data models to understand their audience, and tracking key metrics, advertisers can deliver highly relevant ads that drive results and maximize their ROI.
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