Tag: data-driven

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.

Data driven insights about your marketplace are critical to your success. For instance, data can be used to determine if your customers are loyal or if they are likely to defect to another dealership. According to Experian research, there were 54 million consumer vehicle sales transactions in 2017. While that may sound great, not all returning buyers are loyal. In fact, we found that three out of four people are not dealer loyal. Even though only ¼ of a dealer’s customer base regularly return, the remaining ¾ can be conquested. 41 million non-dealer loyal vehicle sales happened in 2017, meaning there were 41 million chances to conquest for dealers across the country. You may be asking yourself “that’s interesting, but how do I win?”.  Start with best in class data. At Experian, we work with our North American Vehicle Database℠, File One℠ Credit Database, and Consumer View℠ Marketing Database. These databases have information including the history of 900 million vehicles in the United States and Canada, 10 billion vehicle history records, to consumer data about credit inquiries and data attributes for consumers and households. Figuring out how to increase customer loyalty and conquesting becomes simple once you consider Experian’s solution: Auto HyperConnect™. Auto HyperConnect is the answer to the question of “how do I use my data to win my market?” Our Auto HyperConnect suite includes two different products. The first is Auto HyperMonitoring™ which improves customer loyalty. The second is Auto HyperTargeting™, which offers four different ways to conquest vehicle owners: through owners/service, expired leases, off-loan, and current vehicle equity. Since there is a lot to talk about regarding conquesting vehicle owners, this will be a basic overview and we will go into detail later. Experian goes beyond providing quality data to our clients- we are your partner in the discovery of critical information to drive your success.  The first step in our Auto HyperTargeting methodology starts with discovery - working with an Experian Automotive representative to create the most effective conquest strategy. After that, quantify and understand what data is available and how similar records have performed historically. Next, execute the strategy by launching campaigns to communicate with prospective customers via direct mail, email, and phone, etc. Finally, measure and track results with quarterly marketing attribution reporting with Experian’s Auto Response Analysis With Auto HyperTargeting, these six product benefits help it to stand apart from the competition: Highly targeted audiences and attributes lists closely fit prospecting profiles. These profiles include geography, vehicle make, vehicle class, and lease maturity data. Append 1,500+ demographic attributes, 650+ psychographics, and 70+ Mosaic segments. Complete, accurate, and actionable data is delivered timely. Data derived from the source with proprietary processes ensure that it’s the highest quality and best coverage. Flexible marketing execution has no firm offer of credit required and customizable messaging for relevancy. Full visibility performance tracking has closed loop ARAs delivered quarterly with performance details. Performance driven audience hyper targeting approach gets dealers the closest to the customer as possible while saving time and money. Focusing on marketing strategy and tactics delivers results and eliminates waste from unproductive volume/cost opportunities. Finally, the competitive advantage takes market share away from the competition by identifying, engaging, and converting the right prospects. Briefly, here are the four different types of conquesting a dealer can do with Auto HyperTargeting: Expired Lease lets a dealer conquest new prospects based on customized input criteria including zip codes, vehicle makes and classes, and lease maturity data with the marketing flexibility necessary to drive engagement and win new customers. There is no firm offer of credit required. Vehicle Owners lets a dealer engage with current owners to enable new relationships and opportunities. These opportunities reach out to service and parts, aftermarket accessories, new/used car, warranty, insurance, and financial services. Vehicle Equity identifies, engages, and acquires new customers with positive vehicle equity status and maximizes sales opportunities. Getting consumers into a new vehicle, into re-finance solutions, into new loans, and get third party offers in front of consumers are all apart of vehicle equity. End of Loan connects dealers with consumers who are reaching the end of their loan term and help them transition into their new vehicle of choice. These include customized offers, getting consumers into a new vehicle, getting consumers into new loans, and getting third party offers in front of consumers. Juggling the requirements to both maintain customer loyalty and conquest for new ones can be difficult, but our Auto HyperConnect suite helps dealers to succeed at both. In our upcoming mini-series on conquesting with Auto HyperTargeting, we will detail it’s four core capabilities in more detail to help dealers to conquest with confidence.