Full Block Accessibility Test

In the last decade, electric vehicle registrations have increased by 3,600%, and the demand for alternative fuel vehicles continues to soar. Manufacturers are rapidly expanding alternative fuel operations to keep up with the demand from consumers that has expanded across all generations. Target in-market EV consumers Today’s automotive marketers understand that finding targeted consumer audiences is critical to a successful marketing strategy. With more electric vehicle model options available and improved infrastructure driving popularity, we’re seeing automotive marketers wanting to target in-market EV consumers as well as current alternative fuel vehicle owners. Applying data-driven insights to find targeted consumer audiences is critical to today’s marketing strategies. For example, as of Q2 2022, 23.5% of plug-in hybrid owners that returned to market, migrated to an electric vehicle As a marketer, would it be helpful to select In-Market Likely Segment Switchers as your target audience for your marketing campaign? Or Hybrid owners as a whole? Experian Automotive has a variety of alternative fuel owner audiences and in-market consumer audiences to help marketers target the right consumer with the right message on the right channel. The Experian Marketing Engine Syndicated Auto Audience portfolio includes 70+ audiences focused on likely buyers and owners of Electric Vehicle (EV) and Plug-In Hybrid (PHEV) vehicles. Of Experian’s 750+ syndicated auto audiences, we offer a subset of over 25 audiences focused on individual EV/PHEV vehicle models. How to find EV audiences on your preferred platform Experian electric vehicle audiences are available in the Auto Audience area of your preferred platform.  Simply navigate to Experian Automotive’s Audiences to find Electric Vehicle related audiences, as well as all of Experian’s Auto Audiences.  To learn more about Auto Audiences for Electric Vehicles, contact our Subject Matter Expert, Gary Meteer.

For a credit prescreen marketing campaign to be successful, financial institutions must first define their target audience. But just because you’ve identified your ideal customers, it doesn’t mean that every individual within that group has the same needs, interests or behaviors. As such, you’ll need to use data-driven customer segmentation to create messages and offers that truly resonate. Customer segmentation example Customer segmentation is the practice of dividing your target audience into smaller sub-groups based on shared characteristics, behaviors or preferences. This allows you to develop highly targeted marketing campaigns and engage with individual groups in more relevant and meaningful ways. What role does data play in customer segmentation? When it comes to segmenting customers, there isn’t a one-size-fits-all approach that works perfectly for all campaigns and markets. However, regardless of the campaign, you’ll need accurate and relevant data to inform your segmenting strategy. Let’s walk through a customer segmentation example. Say you want to launch a credit marketing campaign that targets creditworthy consumers in the market for a new mortgage. Some of the most influential data points to consider when segmenting include: Demographics Demographic data allows you to get to know your customers as individuals in terms of age, gender, education, occupation and marital status. If you want to create a segment that consists of only middle-aged consumers, leveraging demographic data makes it easier to identify these individuals, refine your messaging and predict their future buying behaviors. Life stage Life event data, such as new parents and new homeowners, helps you connect with consumers who have experienced a major life event. Because you’re targeting consumers in the market for a new mortgage, using fresh and accurate life stage data can help you create an engaging, event-based marketing campaign relevant to their timeline. Financial Financial data segments go beyond income and estimate the way consumers spend their money. With deeper insights into customers’ financial behaviors, you can more accurately assess creditworthiness and make smarter lending decisions. Transactional Transactional data segments group your customers according to their unique buying habits. By getting to know why they purchase your products or their frequency of spend, you can gain a better understanding of who your most engaged customers are, segment further and find opportunities for cross-sell and upsell. Why is data-driven customer segmentation critical for your business? With data-driven customer segmentation, you can develop relevant marketing campaigns and messages that speak to specific audiences, enabling you to demonstrate your value propositions more clearly and deliver personalized customer experiences. Additionally, because customer segmentation enables you to tailor your marketing efforts to those most likely to respond, you can achieve higher conversions while cutting down on marketing spend and resources. Ready to get started? While data-driven customer segmentation may seem overwhelming, Experian can help fill your marketing gaps with custom-based data, audiences and solutions. Armed with a better understanding of your consumers’ patterns and journeys, you can start targeting them more effectively. Create highly targeted credit marketing campaigns

From chatbots to image generators, artificial intelligence (AI) has captured consumers' attention and spurred joy — and sometimes a little fear. It's not too different in the business world. There are amazing opportunities and lenders are increasingly turning to AI-driven lending decision engines and processes. But there are also open questions about how AI can work within existing regulatory requirements, how new regulations will impact its use and how to implement advanced analytics in a way that increases equitable inclusion rather than further embedding disparities. How are lenders using AI today? Many financial institutions have embraced — or at least tested — AI within several parts of their organization. The most advanced use of machine learning (ML) models tends to occur with credit card and unsecured personal loan underwriting.1 However, by late 2021, nearly three-quarters of businesses had used AI and machine learning, and 81 percent felt confident in using advanced analytics and AI in credit risk decisioning.2 READ MORE: AI and Machine Learning for Financial Institutions Today, lenders are implementing AI-driven tools throughout the customer lifecycle to: Target the right consumers: Lenders can sift through vast amounts of data to find consumers who match their credit criteria and send right-sized offers, which enables them to maximize their acceptance rates.Detect and prevent fraud:  Fraud detection tools have used AI and machine learning techniques to detect and prevent fraud for years. These systems may be even more important as fraudsters invest in technology and conduct increasingly sophisticated attacks.Assess creditworthiness: Machine learning-based models can incorporate a range of internal and external data points to more precisely evaluate creditworthiness and create a 10 to 15 percent performance lift compared with traditional linear and logistic regression models.3Automate decisions:  More precise evaluations can increase how many applications flow into your automated approval and denial process rather than requiring a manual review.Manage portfolios: Lenders can also use a more complete picture of their current customers to make better decisions. For example, AI-driven models can help lenders set initial credit limits and suggest when a change could help them increase wallet share or reduce risk. Lenders can also use AI to help determine which up- and cross-selling offers to present and when (and how) to reach out.Improve collections: Models can be built to ease debt collection processes, such as choosing where to assign accounts, which accounts to prioritize and how to contact the consumer. Additionally, businesses around the world have recognized improving customer acquisition and digital engagement as top priorities. In a recent Experian survey, companies ranked investing in AI second, behind investing in decisioning software, as the best way to improve their digital experiences.2 The benefits of AI in lending Although lenders can use machine learning models in many ways, the primary drivers for adoption in underwriting are:1 Improving credit risk assessmentFaster development and deployment cycles for new or recalibrated modelsUnlocking the possibilities within large datasetsKeeping up with competing lenders Some of the use cases for machine learning solutions have a direct impact on the bottom line — improving credit risk assessment can decrease charge-offs. Others are less direct but still meaningful. For instance, machine learning models might increase efficiency and allow further automation. This takes the pressure off your underwriting team, even when application volume is extremely high, and results in faster decisions for applicants, which can improve your customer experience. CASE STUDY:  Atlas Credit, a small-dollar lender, used a machine learning-powered model and automation to nearly double its loan approval rates and decrease credit losses by up to 20 percent. Incorporating large data sets into their decisions also allows lenders to expand their lending universe without taking on additional risk. For example, they may now be able to offer risk-appropriate credit lines to consumers that traditional scoring models can't score. And machine learning solutions can increasecustomer lifetime value when they're incorporated throughout the customer lifecycle by stopping fraud, improving retention, increasing up- or cross-selling and streamlining collections. Hurdles to adoption of machine learning in lending There are clear benefits and interest in machine learning and analytics, but adoption can be difficult, especially within credit underwriting. In August 2021, Forrester Consulting conducted a study commissioned by Experian and found the main barriers to adopting machine learning were:4 Explainability of machine learning models (35 percent)Model deployment into decisioning strategy management systems (34 percent)Model deployment into live operational runtime environment (31 percent)Lack of access to in-house transactional data (30 percent)Lack of access to a wide range of traditional and non-traditional data assets (30 percent) Explainability comes down to transparency and trust. Financial institutions have to trust that machine learning models will continue to outperform traditional models to make them a worthwhile investment. The models also have to be transparent and explainable for financial institutions to meet regulatory fair lending requirements.1 WATCH:  Explainable Artificial Intelligence: The Case of Fair Lending A lack of resources and expertise could hinder model development and deployment. It can take around nine months to build and deploy a custom model, and there's a lot of overhead to cover during the process.5 Large lenders might have in-house credit modeling teams that can take on the workload, but they also face barriers when integrating new models into legacy systems. Small- and mid-sized institutions may be more nimble, but they rarely have the in-house expertise to build or deploy models on their own. The models also have to be trained on appropriate data sets. Similar to model building and deployment, organizations might not have the human or financial resources to clean and organize internal data. And although vendors offer access to a lot of external data, sometimes sorting through and using the data requires a large commitment. How Experian is shaping the future of AI in lending Lenders are finding new ways to use AI throughout the customer lifecycle and with varying types of financial products. However, while the cost to create custom machine learning models is dropping, the complexities and unknowns are still too great for some lenders to manage. But that's changing.5 Experian built the Ascend Intelligence Services™ to help smaller and mid-market lenders access the most advanced analytics tools. The managed service platform won a Fintech Breakthrough Award in 2021, and it can significantly reduce the cost and deployment time for lenders who want to incorporate AI-driven strategies and machine learning models into their lending process. The end-to-end managed analytics service gives lenders access to Experian's vast data sets and can incorporate internal data to build and seamlessly deploy custom machine learning models. The platform can also continually monitor and retrain models to increase lift, and there's no “black box" to obscure how the model works. Everything is fully explainable, and the platform bakes regulatory constraints into the data curation and model development to ensure lenders stay compliant.5 Learn more about our machine learning solutions. * When we refer to “Alternative Credit Data," this refers to the use of alternative data and its appropriate use in consumer credit lending decisions as regulated by the Fair Credit Reporting Act (FCRA). Hence, the term “Expanded FCRA Data" may also apply in this instance and both can be used interchangeably. 1FinRegLab (2021). The Use of Machine Learning for Credit Underwriting: Market & Data Science Context2Experian (2021). Global Insights Report September/October 2021 3Experian (2020). Machine Learning Decisions in Milliseconds 4Experian (2022). Explainability: ML and AI in credit decisioning 5Experian (2021). Podcast: Advanced Analytics, Artificial Intelligence and Machine Learning in Lending