When developing a risk model, validation is an essential step in evaluating and verifying a model’s predictive performance. There are two types of data samples that can be used to validate a model. In-time validation or holdout sample: Random partitioning of the development sample is used to separate the data into a sample set for development and another set aside for validation. Out-of-time validation sample: Data from an entirely different period or customer campaign is used to determine the model’s performance. We live in a complicated world. Models can help reduce that complexity. Understanding a model’s predictive ability prior to implementation is critical to reducing risk and growing your bottom line. Learn more

As I mentioned in my previous blog, model validation is an essential step in evaluating a recently developed predictive model’s performance before finalizing and proceeding with implementation. An in-time validation sample is created to set aside a portion of the total model development sample so the predictive accuracy can be measured on a data sample not used to develop the model. However, if few records in the target performance group are available, splitting the total model development sample into the development and in-time validation samples will leave too few records in the target group for use during model development. An alternative approach to generating a validation sample is to use a resampling technique. There are many different types and variations of resampling methods. This blog will address a few common techniques. Jackknife technique — An iterative process whereby an observation is removed from each subsequent sample generation. So if there are N number of observations in the data, jackknifing calculates the model estimates on N - 1 different samples, with each sample having N - 1 observations. The model then is applied to each sample, and an average of the model predictions across all samples is derived to generate an overall measure of model performance and prediction accuracy. The jackknife technique can be broadened to a group of observations removed from each subsequent sample generation while giving equal opportunity for inclusion and exclusion to each observation in the data set. K-fold cross-validation — Generates multiple validation data sets from the holdout sample created for the model validation exercise, i.e., the holdout data is split into K subsets. The model then is applied to the K validation subsets, with each subset held out during the iterative process as the validation set while the model scores the remaining K-1 subsets. Again, an average of the predictions across the multiple validation samples is used to create an overall measure of model performance and prediction accuracy. Bootstrap technique — Generates subsets from the full model development data sample, with replacement, producing multiple samples generally of equal size. Thus, with a total sample size of N, this technique generates N random samples such that a single observation can be present in multiple subsets while another observation may not be present in any of the generated subsets. The generated samples are combined into a simulated larger data sample that then can be split into a development and an in-time, or holdout, validation sample. Before selecting a resampling technique, it’s important to check and verify data assumptions for each technique against the data sample selected for your model development, as some resampling techniques are more sensitive than others to violations of data assumptions. Learn more about how Experian Decision Analytics can help you with your custom model development.

There’s no question today’s consumers have high expectations. As financial services companies wrestle with the laws and consumer demands, here are a few points to consider: While digital delivery channels may be new, the underlying credit product remains the same. With digital delivery, adhere to credit regulations, but build in enhanced policies and technological protocols. Consult your legal, risk and compliance teams regularly. Embrace the multitude of delivery methods, including email, text, digital display and beyond. When using the latest technology, you need to work with the right partners. They can help you respect the data and consumer privacy laws, which is the foundation on which strategies should be built. Learn more

There are many factors attributing to the success of dealerships. When it comes to dealers, empirical guidance is a great way to study effective advertising. Experian brought Auto, Targeting, and the Dealer Positioning System capabilities together in a nationwide study to answer the ultimate question: what drives sales? The answers can be found in Experian’s 2018 Attribution Study. This is a wide-ranging, dealer-focused sales-driven attribution study that analyzed a few key variables. We deployed 187,701 tracking pixels to devices in 41,012 distinct households, focused on 15 digital metrics to learn about shopper behavior, and tied that digital shopping data to 2,436 vehicle sales. An industry first, Experian’s ability to combine automotive registration data, sales data, and website analytics and online behavior data puts us in a position to do something that very few companies can do. We use the household identifiers to not only see who bought a car and who bought specifically from a participating dealer, but also how they shopped the dealer’s site. Our ability to accurately identify a household’s digital behavior is based on the fact that we are a source compiler of the data and have it sitting under one roof.  Others that attempt to provide this type of insight need to contract out for registrations, sales data imports from the dealership, website analytics, household identifiers, or all the above, which generally adds time to the insights. Using our sales-based approach, we can deliver unbiased attribution. Sales-based attribution is attributing credit to different advertising sources/campaigns based on actual vehicle sales – including those targeted consumers that may have purchased outside of the dealership. This is the Holy Grail of attribution for car dealers since it ties an offline activity such as buying a car back to the online advertising that’s taking up most their budgets every month. Because of that offline-online disconnect, sales-based attribution is difficult. Other automotive attribution models are typically focused on website conversions or website behavior – “what advertising can I attribute website leads to” (conversions) or “what advertising is driving users who follow the behavior that I think shows they’re likely to buy from my dealership” (website behavior.) What are the takeaways?   We found three takeaways from our study. First off, we look at shopper behavior instead of isolating KPIs. Later we will discuss how traditional website metrics do not tie-in to sales. Second, we look at optimizing your paid advertising. Finally, we look at third-party investments. Although third parties drive sales, they may not be your sales. Looking at shopper behavior, not isolated KPI’s Traditional website metrics don’t tell the sales story for dealers. Traditional conversion stats are equal for buyers vs. all traffic such as VDPs or page views What this means is on average, buyers converted at a lower rate than overall website traffic. Looking solely at form submissions, hours and directions pageviews, and mobile clicks-to-call, don’t give the best view of what advertising is driving sales. With that, 98% of buyer traffic never submitted a form or went to the hours and directions page. This is a typical website conversion that dealers, vendors, and advertising agencies focus on.  Since traditional web metrics don’t tell the story, there is another way. These are called High-Value Users, or HVU. They purchase at a 34% higher rate than overall traffic although they make up 11% of all traffic.   High-Value Users are an Experian derived KPI. What makes someone an HVU are four different measurements. They must visit a website at least three times Spend at least six minutes on the site in total View at least eight pages in total View at least one VDP High-Value Users correlates to sales better than Vehicle Detail Page or VDP metrics. In this study, the correlation for VDP was measured at .595 which is rated a medium correlation. Meanwhile, HVU scored a .698 which is rated a high correlation. Looking at many different behavioral KPIs, like we do with our High-Value User (HVU) metric, correlates better to sales than just looking at how many VDPs you had. Driving more VDPs won’t necessarily help sales. But driving more HVUs is more likely to correlate with more sales. This also gets back to the attribution discussion above: Experian sales-based attribution is the best, and Experian’s HVUs are a good method for web-based attribution. From this attribution study, High-Value Users are a vital group for dealers to utilize. In our next post, we will go over the second and third takeaways from the attribution study: optimizing paid advertising and evaluating third-party investments.

Data can be a powerful tool. But the key to data isn’t just accessing it. It’s interpreting it — and using it to make better decisions that benefit your business and your customers. Here are four key areas where business leaders can use data in more meaningful ways to impact decisions: Grow your business — Reveal patterns, trends and associations to better evaluate business opportunities and respond to market fluctuations. Improve efficiency — Optimize operations and improve use of time to acquire more customers for less. Manage fraud and credit risk — The better you know your customers, the less risk you’ll have. Validate manually entered information — Determine the best actions to deliver the most effective outcomes for both existing and future customers. According to Forbes, by the year 2020 about 1.7 megabytes of new information will be created every second for every human being.1 Get the most out of our data-driven economy to remain competitive. Learn more> 1Bernard Marr, “Your enterprise competes to win. Does your digital infrastructure?,” Forbes, September 2015.

The early stages of establishing a startup are some of the most difficult. In fact, it is said 90 percent of startups fail. Challenges include forming the right team, raising capital, and constructing a business model. But no one will deny that one of the most important parts of a startup’s business strategy is the data and technology that underpin its solution. On the one hand, new startups don’t benefit from a wealth of historic data on their clients, prospects, and partners like their more established competitors. While this isn’t the end of the world, it does emphasize the importance of finding a trusted data partner to build those data insights into the design for their application or platform. By using a trusted third-party data provider, companies can ensure they receive reliable and accurate data to utilize in their products and services. On the other hand, startups have the luxury of not being bogged down and burdened by legacy systems and older tech. While building a solution from the ground up is never an easy feat, startups can generally move faster. They can benefit from the latest technology to build new apps and products, making them nimbler than the incumbents in the space. Cloud technology enables organizations to quickly get their business up and running. In addition, companies are exposing many of their data assets and services through application programming interfaces (APIs), allowing others to more easily create their own solutions. Rather than reinventing the wheel, companies can leverage existing services to build more complex solutions and launch faster. “We’ve talked to countless startups and businesses and know they want easy, fast, and secure access to our data assets and services,” said Alpa Jain, vice president of Experian’s API Center of Excellence. “That’s why we’ve launched our API Developer Portal.” The list of APIs available through Experian’s Developer Portal includes solutions like consumer credit data, commercial credit data, commercial public record information, data quality, vehicle history information, and more. Companies can browse the list of available APIs, create an account, and start utilizing the APIs for building out a product within minutes. “Our goal is to help companies unlock untapped market opportunities and grow,” said Jain. “Success with APIs requires a successful developer program and portal to accelerate developer productivity – we believe we’ve created both with our new portal experience."

When it comes to vehicle history reporting, there are many offerings on the table. Some are better known than others, but only one comes from the global leader in data-driven solutions. AutoCheck® vehicle history reports are backed by Experian Information Solutions and have many key features that competitors don’t have. You and your customers can make more confident decisions knowing that the vehicle’s history is backed by data from Experian. Selling is made easier by providing greater transparency which strengthens consumer confidence in your inventory and brand to sell more cars. Below, we will help you better understand the value of AutoCheck throughout your dealership and take you through the five best practices for using AutoCheck. AutoCheck Best Practice #1: Integrate AutoCheck in all your dealership’s applications and websites. The good thing about AutoCheck is the ease of integration within a dealership’s applications and websites. AutoCheck works with hundreds of software providers, meaning it is highly flexible with whatever your dealership is using. It doesn’t matter if the user is a buyer, manager, technician, or any other role at a dealership. There are no additional costs for multiple users since there is an unlimited number of users for a dealership. If a dealership works with someone that AutoCheck doesn’t already work with, Experian will still set the dealership up and work with them to make sure they have a seamless integration. AutoCheck Best Practice #2: Run an AutoCheck on every vehicle acquisition. Since AutoCheck is a vehicle history reporting software, it can uncover unknown history that could pass off to a dealer or a consumer. AutoCheck checks for multiple owners, title brands, open recalls, previous auction announcements, prior vehicle uses, odometer fraud, accidents and so much more! The reason why this is so important comes down to the number of vehicles in operation. Per NADA Data, there were over 264 million cars and light-duty trucks in operation in the United States in 20161. If approximately 20% of the cars and light-duty trucks on the road have been in an accident, that is over 50 million vehicles currently on the road that have been in an accident².  The average diminished value of a vehicle in an accident is $3,0193. Finding only one accident per month you did not know about justifies and pays for the cost of an AutoCheck subscription. AutoCheck Best Practice #3: Promote your inventory with AutoCheck. AutoCheck can also be used to directly promote a dealer’s inventory. All a dealer does is integrate AutoCheck with their dealership’s website. An AutoCheck link is automatically added to every vehicle. There is no additional charge which provides savings to both the dealer and the consumer. The most current data is provided with every click to give feedback to dealers. AutoCheck is the only vehicle history provider on all the top online shopping sites. Consumers can look for AutoCheck on Autotrader℠, Cars.com™, CarGurus®, ebay™ Motors, Edmunds®, and Kelly Blue Book®. AutoCheck Best Practice #4: Build confidence in every sale with AutoCheck.   The patented AutoCheck Score is a numerical rating summarizing the events about the vehicle. This helps dealers and consumers to compare vehicles of similar class and age based on a scale of 1 to 100. It also predicts the likelihood the car will be on the road in 5 years. The Score helps to understand a vehicle’s reliability as it pertains to the vehicle’s age, number of owners and accidents. When comparing two vehicles, it is also important to look at the Similar Vehicles Score. Even though a vehicle may have a score of 89 compared to a similar vehicle which scored an 85, the first vehicle may have a score range of 91-96. This would mean the vehicle that scored an 89 is lower than the average. The AutoCheck Score is based on many variables including age, vehicle class, mileage, number of owners, and vehicle use and event. Along with the AutoCheck Score, the BuyBack Protection program from AutoCheck will help build confidence. Experian will buy back a vehicle if the AutoCheck report fails to list certain brands available to Experian at the time the report was issued. This program is up to 110% of NADA Guides retail value, plus up to $500 in aftermarket accessories. Registered and qualified vehicles have this protection available at no cost and will have a badge on their report. AutoCheck Best Practice #5: Promote your service department by providing service data. The final aspect and best practice focuses on the service department and service data. Dealers can display services they have performed within AutoCheck. With AutoCheck, dealers and consumers can see that a vehicle has been well maintained with reported service data. Reporting service data provides an easy to understand format for customers and builds confidence for shoppers. All-in-all, AutoCheck can be used in every department successfully. To recap, these are the five best practices for AutoCheck. Integrate AutoCheck in all your dealership’s applications and websites. Run an AutoCheck on every vehicle. Promote your inventory with AutoCheck. Build consumer confidence in every sale with AutoCheck. Promote your service department and display service records on AutoCheck.         ¹Source: NADA DATA, Annual General Overview 2016, page 3. https://www.nada.org/2016NADAdataHighlights/   ²Source: Experian Analysis, more than 18 % of cars and light duty trucks in operation have been in an accident. 3Source: Mitchell Industry Trends Report, Q1 2017, page 32 http://www.mitchell.com/Portals/0/Assets/industry-trends/itr-vol-17-no-1-winter-2017-apd.pdf

Although it’s hard to imagine, some synthetic identities are being used for purposes other than fraud. Here are 3 types of common synthetic identities and why they’re created: Bad — To circumvent lag times and delays in establishing a legitimate identity and data footprint. Worse — To “repair” credit, hoping to start again with a higher credit rating under a new, assumed identity. Worst — To commit fraud by opening various accounts with no intention of paying those debts or service fees. While all these synthetic identity types are detrimental to the ecosystem shared by consumers, institutions and service providers, they should be separated by type — guiding appropriate treatment. Learn more in our new white paper produced with Whitepages Pro, Fighting synthetic identity theft: getting beyond Social Security numbers. Download now>

An introduction to the different types of validation samples Model validation is an essential step in evaluating and verifying a model’s performance during development before finalizing the design and proceeding with implementation. More specifically, during a predictive model’s development, the objective of a model validation is to measure the model’s accuracy in predicting the expected outcome. For a credit risk model, this may be predicting the likelihood of good or bad payment behavior, depending on the predefined outcome. Two general types of data samples can be used to complete a model validation. The first is known as the in-time, or holdout, validation sample and the second is known as the out-of-time validation sample. So, what’s the difference between an in-time and an out-of-time validation sample? An in-time validation sample sets aside part of the total sample made available for the model development. Random partitioning of the total sample is completed upfront, generally separating the data into a portion used for development and the remaining portion used for validation. For instance, the data may be randomly split, with 70 percent used for development and the other 30 percent used for validation. Other common data subset schemes include an 80/20, a 60/40 or even a 50/50 partitioning of the data, depending on the quantity of records available within each segment of your performance definition. Before selecting a data subset scheme to be used for model development, you should evaluate the number of records available in your target performance group, such as number of bad accounts. If you have too few records in your target performance group, a 50/50 split can leave you with insufficient performance data for use during model development. A separate blog post will present a few common options for creating alternative validation samples through a technique known as resampling. Once the data has been partitioned, the model is created using the development sample. The model is then applied to the holdout validation sample to determine the model’s predictive accuracy on data that wasn’t used to develop the model. The model’s predictive strength and accuracy can be measured in various ways by comparing the known and predefined performance outcome to the model’s predicted performance outcome. The out-of-time validation sample contains data from an entirely different time period or customer campaign than what was used for model development. Validating model performance on a different time period is beneficial to further evaluate the model’s robustness. Selecting a data sample from a more recent time period having a fully mature set of performance data allows the modeler to evaluate model performance on a data set that may more closely align with the current environment in which the model will be used. In this case, a more recent time period can be used to establish expectations and set baseline parameters for model performance, such as population stability indices and performance monitoring. Learn more about how Experian Decision Analytics can help you with your custom model development needs.

The business case for identity verification and risk assessment tools is most compelling when it includes a broad range of both direct and indirect factors. Here are 3 indirect measures we suggest you consider: Customer experience improvement — With 72% of businesses focused on service, according to Forrester Research,* the value of reduced friction can’t be overstated Reputation and brand protection — The monetary cost of fraud losses can be high, but the impact on customer relationships and brand integrity can be even higher. Compliance — Noncompliance costs an average of 2.65 times more than investing in a technology-based compliance solution. Justifying investment in fraud prevention technology can be challenging. A business case built on the right data can pave the way to upgrading your identity verification and risk assessment technology. Learn more in our buyer's guide>

Data is a part of a lot of conversations in both my professional and personal life. Everything around us is creating data – whether it’s usable or not is a business case for opportunity. Think about how many times a day you access the television, your phone, iPad or computer. Have a smart fridge? More data. Drive a car? More data. It’s all around us and can help us make more informed decisions. What is exciting to me are the new techniques and technologies, like machine learning, artificial intelligence and SaaS-based applications, that are becoming more accessible to lenders for use in managing their relationships with customers. This means lenders – whether a multi-national bank, online lender, regional bank or credit union – can make better use of the data they have about their customers. Let’s look at two groups – Gen-X and Millennials – who tend to be more transient than past generations. They rent not buy. They are brand loyal but will flip quickly if the experience or their expectations aren’t met. They live out their lives on social media yet know the value of their information. We’re just now starting to get to know the next generation, Gen Z. Can you imagine making individual customer decisions at a large scale on a population with so many characteristics to consider? With machine learning and new technologies available, alternative data – such as social media, visual and video data – can become an important input to knowing when, where and what financial product you offer. And make the offer quickly! This is a stark change from the days when decisions were based on binary inputs, or rather, simple yes/no answers. And it took 1-3 days (or sometimes weeks) to make an offer. More and more consumers are considering nontraditional banks because they offer the personalization and speed at which consumers have become accustomed.  We can thank the Amazons of the world for setting the bar high. The reality is - lenders must evolve their systems and processes to better utilize big data and the insights that machine learning and artificial intelligence can offer at the speed of cloud-based applications. Digitization threatens to lower profits in the finance industry unless traditional banks undertake innovation initiatives centered on better servicing the customer. In plain speak – banks need to innovate like a FinTech – simplify the products and create superior customer experiences. Machine learning and artificial intelligence can be a way to use data for making more informed decisions faster that deliver better experiences and distinguish your business from the next. Prior to Experian, I spent some time at a start-up before it was acquired by one of the large multi-national payment processors. Energizing is a word that comes to mind when I think back to those days. And it’s a feeling I have today at Experian. We’re taking innovation to heart – investing a lot in revolutionary technology and visionary people. The energy is buzzing and it’s an exciting place to be. As a former customer of 20 years turned employee, I’ve started to think Experian will transform the way we think about cool tech companies!

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.

Consumers and businesses alike have been hyper-focused on all things data over the past several months. From the headlines surrounding social media privacy, to the flurry of spring emails we’ve all received from numerous brands due to the recent General Data Protection Regulation (GDPR) going into effect in Europe, many are trying to assess the data “sweet spot.” In the financial services space, lenders and businesses are increasingly seeking to leverage enhanced digital marketing channels and methods to deliver offers and invitations to apply. But again, many want to know, what are the data rules and how can they ensure they are playing it safe in such a highly regulated environment. In an Experian-hosted webinar, Credit Marketing in the Digital Age, the company recently featured a team of attorneys from Venable LLP’s award-winning privacy and advertising practice. There’s no question today’s consumers expect hyper-targeted messages and user experiences, but with the number of data breaches on the rise, there is also the concern around data access. Who has my data? Is it safe? Are companies using it in the appropriate way? As financial services companies wrestle with the laws and consumer expectations, the Venable legal team provided a few insights to consider. While the digital delivery channels may be new, the underlying credit product remains the same. A prescreened offer is a prescreened offer, and an application for credit is still an application for credit. The marketing of these and other credit products is governed by an array of pre-existing laws, regulations, and self-regulatory principles that combine to form a unique compliance framework for each of the marketing channels. Adhere to credit regulations, but build in enhanced policies and technological protocols with digital delivery. With digital delivery of the offer, lenders should be thinking about the additional compliance aspects attached to those varying formats. For example, in the case of digital display advertising, you should pay close attention to ensuring delivery of the ad to the correct consumer, with suitable protections in place for sharing data with vendors. Lenders and service providers also should think about using authentication measures to match the correct consumer with a landing page containing the firm offer along with the appropriate disclosures and opt-outs. Strong compliance policies are important for all participants in this process. Working with a trusted vendor that has a commitment to data security, compliance by design, and one that maintains an integrated system of decisioning and delivery, with the ability to scrub for FCRA opt-outs, is essential. Consult your legal, risk and compliance teams. The digital channels raise questions that can and must be addressed by these expert audiences. It is so important to partner with service providers that have thought this through and can demonstrate a compliance framework. Embrace the multitude of delivery methods. Yes, there are additional considerations to think about to ensure compliance, but businesses should seek opportunities to reach their consumers via email, text, digital display and beyond. Also, digital credit offers need not replace mail and phone and traditional channels. Rather, emerging digital channels can supplement a campaign to drive the response rates higher. In Mary Meeker’s annual tech industry report, she touched on a phenomenon called the “privacy paradox” in which companies must balance the need to personalize their products and services, but at the same time remain in good favor with consumers, watchdog groups and regulators. So, while financial services players have much to consider in the regulatory space, the expectation is they embrace the latest technology advancements to interact with their consumers. It can be done and the delivery methods exist today. Just ensure you are working with the right partners to respect the data and consumer privacy laws.  

The economy remains steady, maintaining a positive outlook even though the GDP growth slowed in the first quarter. Real estate is holding ground even as rates rise. We’ve reached a 7-year high in 30-year fixed-rate mortgages, which could have a longer-term effect on this market. Bankcard may be reaching its limit — outstanding balances hit $764 billion and delinquency rates continue to rise. While auto originations were flat in Q1, performance is improving as focus moves away from subprime lending. The economy remains steady as we transition from 2017. Keep an eye on inflation and interest rates in regard to their possible short-term economic impact. Learn more about these and other economic trends with the on-demand recording of the webinar. Watch now

Who is the ideal dealership customer? Wouldn’t they be one that buys or leases a car and becomes a repeat customer? Loyal customers are ideal because they prefer to go to your dealership to purchase a vehicle, get their vehicle serviced, and even have their family and friends purchase from you. This brings up an important question: what is customer loyalty worth to you? According to the White House Office of Consumer Affairs, on average, loyal customers are worth up to 10 times as much as their first purchase. They also found that it is six to seven times more expensive to acquire a new customer than it is to keep a current one. Marketing Metrics found the probability of selling to a new prospect is only between 5-20%. But if you are selling to an existing customer, the probability rises to 60-70%. So, knowing this, what holds dealers back from actively conquesting loyal customers? Time, money, resources, expertise, priority, process and systems, and data are the key factors that keep them from pursuing these ideal customers. Even though you may stare across the street at them every day, you must remember that your competition is much bigger than the dealerships next door to you. According to recent Experian® research, Whether it is a new, certified used, or non-certified used vehicle, auto manufacturers will have the highest level of loyalty by owned vehicle acquisition. Next to that, you have the Make of a vehicle followed the Model.  Dealerships rank last in loyalty against these major factors. This leads to asking a few “what-ifs”. What if you have the unique opportunity to improve customer loyalty, make more money, and prevent defection to the competition? What if you had actionable insights to know your customer’s buying and loyalty propensities with a high degree of accuracy? How about if you had knowledge of timing on when to engage with your customers to appropriately deliver the right message and offers with the highest potential conversion rate? Finally, what if you had an easy, cost-effective, yet powerful way to unify big data relating to consumer, vehicle, and market and your customer data to make better marketing decisions? Thanks to Experian® and Auto HyperConnect™, you don’t have to ask those questions anymore. Auto HyperConnect leverages the most robust combination of data assets under one roof.  Our loyalty component is called Auto HyperMonitoring™ and takes loyalty to the next level. Auto HyperMonitoring is an event-based customer loyalty measurement solution that gives you the ability to more effectively manage and strengthen your customer retention efforts.  With insights derived from the monitoring of both macro- and micro-environments relating to the vehicle, consumer events, and the overall automotive landscape, clients can quickly gain a deep understanding of consumer loyalty propensities and can create and execute initiatives that maximize their customer loyalty opportunities. Starting with a client’s customer file, Auto HyperMonitoring provides data hygiene that verifies the VIN matches the customer household and will only monitor the VINS that have a match. Next, there is monitoring for vehicle events such as accidents or airbags going off.  Consumer events equate to having a baby or moving.  Market events involve incentives, OEM loyalty, and warranty expiration. Data events are phone numbers, email address, or VIN verification through the hygiene process.. These events feed into the creation of analysis & insights to identify your customers’ behavioral patterns attributed to loyalty, purchasing, and other factors.  When key opportunities are identified, there is client notification. This is used to manage the customer relationship and loyalty through a dealer’s CRM system and comes in an email. How you would use Auto HyperMonitoring? It can be used to bring customers back into the showroom or service lanes in a few different ways. Initially, Dealers can call consumers to open the lines of communication. Next, sending consumers emails and direct mail with special offers are both effective. Finally, Auto HyperMonitoring can also be used to activate digital media targeting campaigns to better reach them where they’re spending their time. Finally, we have the product benefits of Auto HyperMonitoring. First off, it enhances customer engagement & loyalty. By proactively engaging with clients at the right moment based on important and relevant vehicle, customer, and market-related event triggers, loyalty can be systematically strengthened. Second, it improves marketing efficiency. Knowing when to engage with your customer base to minimizes the risk of over and under marketing exposure; improve conversion and reduce cost. Third, complete, accurate, & actionable data is delivered in a timely manner. Auto HyperMonitoring leverages both a client’s customer file and Experian’s rich data assets to enable a complete view of customer opportunities. Finally, Auto HyperMonitoring compliments and supports OEM/dealer loyalty programs. Maximizing revenue opportunities by achieving/surpassing OEM/Dealer loyalty program goals is possible with Auto HyperMonitoring. Customer loyalty is important and will directly impact dealership sales in both your showroom and your service lanes – including the benefit of referral customers. The challenges of competing with manufacturers and other dealerships are mitigated with Experian’s Auto HyperConnect suite and Auto HyperMonitoring. With these, you will have greater success when targeting customer loyalty and using data to keep the relationship between the dealership and the customer alive.