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While dealers wait out the chip shortage, many are looking for new revenue streams and creative ways to maintain profit levels. Dealers understand that used vehicles are more valuable than ever and that savvy consumers who are near their lease term can purchase their vehicle with a much higher book value than their negotiated residual and capitalize on that extra equity. At Experian Automotive, we help dealers analyze consumer and market data insights so they can develop marketing strategies that more effectively engage consumers. In today’s market, we recommend dealers focus on targeting consumers who are nearing the term of their lease—because the end of a lease forces consumers to either turn in the vehicle or purchase it—and many consumers realize they can purchase their equity rich vehicle and trade it in anywhere. Dealers desiring to conquest customers would benefit from using data insights to develop marketing strategies to help educate consumers of their options. So, how do dealers make sure their lease customers remain loyal to their dealership? One way is to target those customers that are near term and recommend a trade in. In many cases, the customer can upgrade to a model with more features or a model in a different class—both of which may be compelling, even if the newer vehicle is an older model year than the trade in. This way, the dealer obtains the later model, lower mileage vehicle and the customer gets a model with the extra features they want or an entirely new class of vehicle. Both parties are happy. What about third party lease buy outs? In response to the inventory shortage, many captive lenders are no longer permitting 3rd party lease buy outs. In other words, consumers are required to purchase their leased vehicle at an in-brand dealership. Initially, this would appear to bolster brand loyalty by making it more challenging to switch brands. However, educated consumers know they can simply purchase the vehicle and drive it to another dealership as a trade. We recommend automotive dealers educate consumers on their options through effective marketing strategies that offer options and more freedom in their car purchasing. Dealers looking for every sales opportunity are utilizing data sources such as Experian’s Automotive Intelligence Engine™ (AIE) to identify all consumers with leased vehicles for their brands. Because the OEM will not share lease customer information for neighboring stores, dealers must seek out this information on their own. Experian’s Automotive Intelligence Engine provides off lease consumer information for conquest brands as well as dealer brands. The OEM still expects each store to be successful and using data-driven insights to uncover opportunities in this tight market is one critical way dealers can stay competitive and increase brand loyalty. The chip shortage will eventually recede but until then dealers can use automotive data insights that can help them remain profitable. Learn more about Experian’s Automotive Intelligence Engine.

Hackers are playing the game of data compromise, and they are winning. At this point, companies of all sizes, from all industries, know that consumers have a growing desire to take control of their data and digital privacy. In case you missed the latest webinar and whitepaper release from Javelin Strategy & Research, it makes three things clear about consumers’ current attitudes about fraud and its impact on businesses. 1. Consumers are much more privacy-aware In 2020, consumers turned to social media and telecommunicating platforms to work, stay in touch with friends and family networks and learn. While the broad-scale increase provided a way for global commerce and connections to continue during the worldwide pandemic, it also accelerated cybercrime. The influx of internet traffic created a ready-made environment for fraudsters to profit from consumers in a big way, primarily through scams. Scams were so profitable that they accounted for $43 billion of the $56 billion reported ID fraud losses last year.1 2. Consumers blame Financial Institutions for fraud. It’s the main reason they leave.  When consumers experience fraud, they blame their financial institutions, even if the loss has nothing to do with the institution or its business’s responsibility to the consumer. This attitude shows that consumers hold FIs accountable for their data protection. And when they don’t get it, they take their expectations and their business elsewhere. The data shows the proof. In 2020, 38% of consumers closed a bank account affected by fraud, with 69% saying their primary FIs did not resolve their fraud concerns or losses.1 As the saying goes, perception is reality, and in the case of fraud, consumer thoughts have real consequences for organizations. 3. Consumers leave when breaches happen This point is simple: consumers leave even when personally identifiable information (PII) or other data is not stolen. Be prepared with a playbook or be ready to lose consumer trust To improve the customer experience, build trust and reduce risk, companies need a playbook — a fraud resolution and breach response playbook — a solid plan that falls under their existing business and continuity disaster recovery plan. Why? Because consumers need to know and, more importantly, trust that companies are prepared to react quickly and deliver resolution when a network intrusion occurs.  According to Javelin Strategy & Research data, fraud resolution is the best way to retain customers and members. In addition, consumer perception of cybersecurity plays a significant role in consumer attrition and retention. Again, even if personal information is protected, if your organization is attacked, consumers are more likely to stop doing business with your organization, even if no data was compromised. This means cybersecurity and fraud prevention empowerment is a game-changer, driving 22% of consumers’ satisfaction ratings with online banking.2 When building your playbook, consider two core things:  1. Make sure it’s well-developed A comprehensive fraud resolution and breach response should include a solid approach to collaborate with consumers when fraud occurs. Ensuring your plan includes fraud, cyber, and marketing communications teams will help your company act swiftly and build consumer confidence. 2. Don’t just encrypt data; strengthen perimeter security.  Strong perimeter security will ensure safe interactions with consumers. Even if personal information is protected, consumers will perceive a penetration of the network as a breach and will be more apt to stop doing business with your company. At Experian, preparedness is our business. We know how important fraud resolution and breach response is to your customer’s experience. Developing a solid playbook is key to that experience, building trust and reducing risk. To learn more, read the Giving Consumers Control and Enhancing Fraud Prevention whitepaper, watch the Empowerment and Fraud Prevention are Key webinar and find out how to protect your business with Experian’s Global Data Breach Solutions. 1 Javelin Strategy & Research. March 2021. 2 Javelin Strategy & Research. June 2021.

It is no news that businesses are increasing their focus on advanced analytics and models. Whether looking to increase resources or focus on artificial intelligence (AI) and machine learning (ML), growth is the name of the game. But how do you maximize impact while minimizing risk? And how can you secure expertise and ROI when budgets are strapped?  Does your organization have the knowledge and talent in-house to remain competitive? No matter where you are on the analytics maturity curve, (outlined in detail below), your organization can benefit from making sure your machine learning models solution consists of: Regulatory documentation: Documentation for model and strategy governance is critical, especially as there is more conversation surrounding fair lending and how it relates to machine learning models. How does your organization ensure your models are explainable, well documented and making fair decisions? These are all questions you must be asking of your partners and solutions. Integrated services: For some service providers, “integrated,” is merely a marketing ploy, but it is essential that your solution truly integrates attributes, scores, models and decisions into one another. Not only does this serve as a “checks and balances” system of sorts, but it also is a primary driver for the speed of decisioning, which is crucial in today’s digital-first world. Deep expertise: Models are a major component for your decisioning, but ensuring those models are built and backed by experts is the one-two punch your strategies depend on. Make sure your services are managed by data scientists with extensive experience to take the best approach to solving your business problems. Usability: Does your solution close the loop? To future proof your processes, your solution must analyze the performance of attributes, scores and strategies. On top of that, your solution should make sure the items being built are useable and can be modified when needed. A one-and-done model does not suit the unique needs of your organization, so ensure your solution provides actionable analysis for continual refinement. Does your machine learning model solution check these boxes? Do you want to transform your existing system into a state-of-the-art AI platform? Learn more about how you can take your business challenges head-on by rapidly developing, deploying and monitoring sophisticated models and strategies to more accurately predict risk and achieve better outcomes. Learn more Access infographic   More information: What’s the analytics maturity curve? “Analytics” is the discovery, interpretation and communication of meaningful patterns in data; the connective tissue between data and effective decision-making within an organization. You can be along this journey for different decision points you’re making or product types, said Mark Soffietti, Director of Analytics Consulting at Experian, at our recent AI-driven analytics and strategy optimization webinar. Where you are on this curve often depends on your organization’s use of generic versus custom scores, the systems currently engaged to make those decisions and the sophistication of an organization’s models and/or strategies. Here’s a breakdown of each of the four stages: Descriptive Analytics – Descriptive analytics is the first step of the analytics maturity curve. These analytics answer the question “What is happening?” and typically revolve around some form of reporting. An example would be the information that your organization received 100 applications. Diagnostic Analytics – These analytics move from what happened to, “Why did it happen?” By digging into the 100 applications received, diagnostic analytics answer questions like “Who were we targeting?” and “How did those people come into our online portal/branch?” This information helps organizations be more strategic in their practices. Predictive Analytics – Models come into play at this stage as organizations try to predict what will happen. Based on the data set and an understanding of what the organization is doing, effort is put towards automating information to better solve business problems. Prescriptive Analytics – Optimization is key for prescriptive analytics. At this point in the maturity curve, there are multiple models and/or information that may be competing against one another. Prescriptive analytics will attempt to prescribe what an organization is doing and how it can drive more desired behaviors. For more information and to get personalized recommendations throughout your analytics journey, visit our website.