Industries

Traditional credit data has long been the end-all-be-all ruling the financial services space. Like the staple black suit or that little black dress in your closet, it’s been the quintessential go-to for decades. Sure, the financial industry has some seasonality, but traditional credit data has reigned supreme as the reliable pillar. It’s dependable. And for a long time, it’s all there was to the equation. But as with finance, fashion and all things – evolution has occurred. Specifically, how consumers are managing their money has evolved, which calls for deeper insights that are still defensible and disputable. Alternative credit data is the new black. It's increasingly integrated in credit talks for lenders across the country. Much like that LBD, it's become a lending staple – that closet (or portfolio) must-have – to leverage for better decisioning when determining creditworthiness. What is alternative data? In our data-driven industry, “alternative” data as a whole may best be summed up as FCRA-compliant credit data that isn't typically included in traditional credit reports. For traditional data, think loan and inquiry data on bankcards, auto, mortgage and personal loans; typically trades with a term of 12 months or greater. Some examples of alternative credit data include alternative financial services data, rental data, full-file public records and account aggregation. These insights can ultimately improve credit access and decisioning for millions of consumers who may otherwise be overlooked. Alternative or not, every bit of information counts FCRA-compliant, user permissioned data allows lenders to easily verify assets and income electronically, thereby giving lenders more confidence in their decision and allowing consumers to gain access to lower-cost financing. From a risk management perspective, alternative credit data can also help identify riskier consumers by identifying information like the number of payday loans acquired within a year or number of first-payment defaults. Alternative credit data can give supplemental insight into a consumer’s stability, ability and willingness to repay that is not available on a traditional credit report that can help lenders avoid risk or price accordingly. From closet finds that refresh your look to that LBD, alternative credit data gives lenders more transparency into their consumers, and gives consumers seeking credit a greater foundation to help their case for creditworthiness. It really is this season’s – and every season’s – must-have. Learn more

Machine learning (ML), the newest buzzword, has swept into the lexicon and captured the interest of us all. Its recent, widespread popularity has stemmed mainly from the consumer perspective. Whether it’s virtual assistants, self-driving cars or romantic matchmaking, ML has rapidly positioned itself into the mainstream. Though ML may appear to be a new technology, its use in commercial applications has been around for some time. In fact, many of the data scientists and statisticians at Experian are considered pioneers in the field of ML, going back decades. Our team has developed numerous products and processes leveraging ML, from our world-class consumer fraud and ID protection to producing credit data products like our Trended 3DTM attributes. In fact, we were just highlighted in the Wall Street Journal for how we’re using machine learning to improve our internal IT performance. ML’s ability to consume vast amounts of data to uncover patterns and deliver results that are not humanly possible otherwise is what makes it unique and applicable to so many fields. This predictive power has now sparked interest in the credit risk industry. Unlike fraud detection, where ML is well-established and used extensively, credit risk modeling has until recently taken a cautionary approach to adopting newer ML algorithms. Because of regulatory scrutiny and perceived lack of transparency, ML hasn’t experienced the broad acceptance as some of credit risk modeling’s more utilized applications. When it comes to credit risk models, delivering the most predictive score is not the only consideration for a model’s viability. Modelers must be able to explain and detail the model’s logic, or its “thought process,” for calculating the final score. This means taking steps to ensure the model’s compliance with the Equal Credit Opportunity Act, which forbids discriminatory lending practices. Federal laws also require adverse action responses to be sent by the lender if a consumer’s credit application has been declined. This requires the model must be able to highlight the top reasons for a less than optimal score. And so, while ML may be able to deliver the best predictive accuracy, its ability to explain how the results are generated has always been a concern. ML has been stigmatized as a “black box,” where data mysteriously gets transformed into the final predictions without a clear explanation of how. However, this is changing. Depending on the ML algorithm applied to credit risk modeling, we’ve found risk models can offer the same transparency as more traditional methods such as logistic regression. For example, gradient boosting machines (GBMs) are designed as a predictive model built from a sequence of several decision tree submodels. The very nature of GBMs’ decision tree design allows statisticians to explain the logic behind the model’s predictive behavior. We believe model governance teams and regulators in the United States may become comfortable with this approach more quickly than with deep learning or neural network algorithms. Since GBMs are represented as sets of decision trees that can be explained, while neural networks are represented as long sets of cryptic numbers that are much harder to document, manage and understand. In future blog posts, we’ll discuss the GBM algorithm in more detail and how we’re using its predictability and transparency to maximize credit risk decisioning for our clients.

The August 2018 LinkedIn Workforce Report states some interesting facts about data science and the current workforce in the United States. Demand for data scientists is off the charts, but there is a data science skills shortage in almost every U.S. city — particularly in the New York, San Francisco and Los Angeles areas. Nationally, there is a shortage of more than 150,000 people with data science skills. One way companies in financial services and other industries have coped with the skills gap in analytics is by using outside vendors. A 2017 Dun & Bradstreet and Forbes survey reported that 27 percent of respondents cited a skills gap as a major obstacle to their data and analytics efforts. Outsourcing data science work makes it easier to scale up and scale down as needs arise. But surprisingly, more than half of respondents said the third-party work was superior to their in-house analytics. At Experian, we have participated in quite a few outsourced analytics projects. Here are a few of the lessons we’ve learned along the way: Manage expectations: Everyone has their own management style, but to be successful, you must be proactively involved in managing the partnership with your provider. Doing so will keep them aligned with your objectives and prevent quality degradation or cost increases as you become more tied to them. Communication: Creating open and honest communication between executive management and your resource partner is key. You need to be able to discuss what is working well and what isn’t. This will help to ensure your partner has a thorough understanding of your goals and objectives and will properly manage any bumps in the road. Help external resources feel like a part of the team: When you’re working with external resources, either offshore or onshore, they are typically in an alternate location. This can make them feel like they aren’t a part of the team and therefore not directly tied to the business goals of the project. To help bridge the gap, performing regular status meetings via video conference can help everyone feel like a part of the team. Within these meetings, providing information on the goals and objectives of the project is key. This way, they can hear the message directly from you, which will make them feel more involved and provide a clear understanding of what they need to do to be successful. Being able to put faces to names, as well as having direct communication with you, will help external employees feel included. Drive engagement through recognition programs: Research has shown that employees are more engaged in their work when they receive recognition for their efforts. While you may not be able to provide a monetary award, recognition is still a big driver for engagement. It can be as simple as recognizing a job well done during your video conference meetings, providing certificates of excellence or sending a simple thank-you card to those who are performing well. Either way, taking the extra time to make your external workforce feel appreciated will produce engaged resources that will help drive your business goals forward. Industry training: Your external resources may have the necessary skills needed to perform the job successfully, but they may not have specific industry knowledge geared towards your business. Work with your partner to determine where they have expertise and where you can work together to providing training. Ensure your external workforce will have a solid understanding of the business line they will be supporting. If you’ve decided to augment your staff for your next big project, Experian® can help. Our Analytics on DemandTM service provides senior-level analysts, either onshore or offshore, who can help with analytical data science and modeling work for your organization.

Federal legislation makes verifying an individual’s identity by scanning identity documents during onboarding legal in all 50 states Originally posted on Mitek blog The Making Online Banking Initiation Legal and Easy (MOBILE) Act officially became law on May 24, 2018, authorizing a national standard for banks to scan and retain information from driver’s licenses and identity cards as part of a customer online onboarding process, via smartphone or website. This bill, which was proposed in 2017 with bipartisan support, allows financial institutions to fully deploy mobile technology that can make digital account openings across all states seamless and cost efficient. The MOBILE Act also stipulates that the digital image would be destroyed after account opening to further ensure customer data security. As an additional security measure, section 213 of the act mandates an update to the system to confirm matches of names to social security numbers. “The additional security this process could add for online account origination was a key selling point with the Equifax data breach fresh on everyone’s minds,” Scott Sargent, of counsel in the law firm Baker Donelson’s financial service practice, recently commented on AmericanBanker.com. Read the full article here. Though digital banking and an online onboarding process has already been a best practice for financial institutions in recent years, the MOBILE Act officially overrules any potential state legislation that, up to this point, has not recognized digital images of identity documents as valid. The MOBILE Act states: “This bill authorizes a financial institution to record personal information from a scan, copy, or image of an individual’s driver’s license or personal identification card and store the information electronically when an individual initiates an online request to open an account or obtain a financial product. The financial institution may use the information for the purpose of verifying the authenticity of the driver’s license or identification card, verifying the identity of the individual, or complying with legal requirements.” Why adopt online banking? The recently passed MOBILE Act is a boon for both financial institutions and end users. The legislation: Enables and encourages financial institutions to meet their digital transformation goals Makes the process safe with digital ID verification capabilities and other security measures Reduces time, manual Know Your Customer (KYC) duties and costs to financial institutions for onboarding new customers Provides the convenient, on-demand experience that customers want and expect The facts: 61% of people use their mobile phone to carry out banking activity.1 77% of Americans have smartphones.2 50 million consumers who are unbanked or underbanked use smartphones.3 The MOBILE Act doesn’t require any regulatory implementation. Banks can access this real-time electronic process directly or through vendors. Read all you need to know about the MOBILE Act here. Find out more about a better way to manage fraud and identity services.   References 1Mobile Ecosystem Forum, MEF Mobile Money Report (https://mobileecosystemforum.com/mobile-money-report/), Feb. 5, 2018. 2Pew Research Center, Mobile Fact Sheet (http://www.pewinternet.org/fact-sheet/mobile/), Jan. 30, 2017. 3The Federal Reserve System, Consumers and Mobile Financial Services 2015 (https://www.federalreserve.gov/econresdata/consumers-and-mobile-financial-services-report-201503.pdf), March 2015.

Millennials have been accused of “killing” a lot of things. From napkins and fabric softener to cable and golf, the generation which makes up the largest population of the United States (aka Gen Y) is cutting a lot of cords. Despite homeowning being listed as one of the notorious generational group’s casualties, it’s one area that millennials want to keep alive, according to recent statistics. In fact, a new Experian study revealed 86% of millennials believe that buying a house is a good financial investment. However, only 15% have a mortgage today. One explanation for this gap may be that they appear too risky. Younger millennials (age 22-28) have an average near prime score of 652 and older millennials (age 29-35) have a prime score of 665. Both subsets fall below the average VantageScore® credit score* of U.S. consumers – 677. Yes, with the majority of millennials having near prime or worse credit scores, we can agree that they will need need to improve their financial hygiene to improve their overall credit rankings. But their dreams of homeownership have not yet been dashed. Seemingly high aspirations (of homeownership), disrupted by a reality of limited assets, low scores, and thin credit files, create a disconnect that suggests a lack of resources to get into their first homes – rather than a lack of interest. Or, maybe not. Maybe, after surviving a few first-time credit benders that followed soon after opening the floodgates to credit, millennials feel like the combination of low scores and the inability to get any credit is only salt in their wounds from their lending growing pains. Or maybe it’s all the student loans. Or maybe it’s the fact that so many of them are underemployed. But maybe there’s still more to the story. This emerging generation is known for having high expectations for change and better frictionless experiences in all areas of their life. It turns out, their borrowing behavior is no different. Recent research by Experian reveals consumers who use alternative financial services (AFS) are 11 years younger on average than those that do not. What’s the attraction? Financial technology companies have contributed to the explosive growth of AFS lenders and millennials are attracted to those online interactions. The problem is many of these trades are alternative finance products and are not reported to traditional credit bureaus. This means they do nothing to build credit experience in the eyes of traditional lenders and millennials with good credit history find it difficult to get access to credit well into their 20s. Alternative credit data provides a deeper dive into consumers, revealing their transactions and ability to pay as evidenced by alternative finance data, rental, utility and telecom payments. Alt data may make some millennials more favorable to lenders by revealing that their three-digit credit score (or lack there of) may not be indicative of their financial stability. By incorporating alternative financial services data (think convenient, tech-forward lenders that check all the boxes for bank removed millennials, not just payday loan recipients), credit-challenged millennials have a chance at earning recognition for their experience with alternative financial services that may help them get their first mortgage. Society may have preconceived notions about millennials, but lenders may want to consider giving them a second look when it comes to determining creditworthiness. In a national Experian survey, 53% of consumers said they believe some of these alternative sources would have a positive effect on their credit score. We all grow up sometime and as our needs change, there may come a day when millennials need more traditional financial services. Lenders who take a traditional view of risk may miss out on opportunities that alternative credit data brings to light. As lending continues to evolve, combining both traditional credit scores and alternative credit data appears to offer a potentially sweet (or rather, home sweet home) solution for you and your customers.   *Calculated on the VantageScore® credit score model. Your VantageScore® credit score from Experian indicates your credit risk level and is not used by all lenders, so don't be surprised if your lender uses a score that's different from your VantageScore® credit score.

Consumer confidence is nearing an 18-year high. Unemployment figures are at record lows. Retail spend is healthy, and expected to stay that way through the back-to-school and holiday shopping booms. Translation for credit card issuers? The swiping and spending continue. In fact, credit card openings were up 4% in the first quarter of 2018 compared to the same time last year, and card utilization is hovering around 20.5%. Even with the Fed’s gradual 2018 rate hikes, consumers are shopping. In a new Mintel report, outstanding credit card debt is now $1.03 trillion (as of the end of Q1, 2018), and the number of consumers with credit cards is growing fastest among people aged 18 to 34. In the retail card arena specifically, boomers and Gen X’ers are leading the charge, opening 45% and 27% of new retails cards, respectively. “A stronger economy always bodes well for credit cards,” said Kelley Motley, director of analytics for Experian. “Now is the time for card issuers to zero in on their most loyal consumers and ensure they are treating them with the right offers, rewards and premium benefits.” Consumer data reveals the top incentives when selecting a rewards-based card includes cash back, gas rewards and retail cards (including travel rewards and airfare). In fact, for younger consumers, offering rewards has proven to be the most effective way to get them to switch from debit to credit cards. Cash back was the most preferred reward for consumers aged 18 to 44 when asked about their motivation to apply for a new card. For individuals 45 and older, 0% interest was the top motivator. Of course, beyond credit card opens, the ideal is to engage with the consumers who are utilizing the card the most. From a segmentation standpoint, the loyal retail cardholder has an average VantageScore® credit score of 671 with an average total balance of $1,633. They use the card regularly and consistently make payments. Finding more loyalists is the goal and can be achieved with informed segmentation insights and targeted prescreen campaigns. On the flip side, insights can inform card issuers with data, helping them to avoid wasting marketing dollars on consumers who merely want to game a quick credit card offer and then close an account. A batch and blast marketing approach no longer works in the credit card marketing game. “Consumers expect you to know them and their financial needs,” said Paul DeSaulniers, senior director of Experian’s segmentation solutions. “The data exists and tells you exactly who to target and how to structure the offer – you just need to execute.”

Customer Identification Program (CIP) solution through CrossCore® Every day, I work closely with clients to reduce the negative side effects of fraud prevention. I hear the need for lower false-positive rates; maximum fraud detection in populations; and simple, streamlined verification processes. Lately, more conversations have turned toward ID verification needs for Customer Information Program (CIP) administration. As it turns out, barriers to growth, high customer friction and high costs dominate the CIP landscape. While the marketplace struggles to manage the impact of fraud prevention, CIP routinely disrupts more than 10 percent of new customer acquisitions. Internally at Experian, we talk about this as the biggest ID problem our customers aren’t solving. Think about this: The fight for business in the CIP space quickly turned to price, and price was defined by unit cost. But what’s the real cost? One of the dominant CIP solutions uses a series of hyperlinks to connect identity data. Every click is a new charge. Their website invites users to dig into the data — manually. Users keep digging, and they keep paying. And the challenges don’t stop there. Consider the data sources used for these solutions. The winners of the price fight built CIP solutions around credit bureau header data. What does that do for growth? If the identity wasn’t sufficiently verified when a credit report was pulled, does it make sense to go back to the same data source? Keep digging. Cha-ching, cha-ching. Right about now, you might be feeling like there’s some sleight of hand going on. The true cost of CIP administration is much more than a single unit price. It’s many units, manual effort, recycled data and frustrated customers — and it impacts far more clients than fraud prevention. CIP needs have moved far beyond the demand for a low-cost solution. We’re thrilled to be leading the move toward more robust data and decision capabilities to CIP through CrossCore®. With its open architecture and flexible decision structure, our CrossCore platform enables access to a diverse and robust set of data sources to meet these needs. CrossCore unites Experian data, client data and a growing list of available partner data to deliver an intelligent and cost-conscious approach to managing fraud and identity challenges. The next step will unify CIP administration, fraud analytics and a range of verification treatment options together on the CrossCore platform as well. Spoiler alert. We’ve already taken that step.

Trivia question: Millennials don’t purchase new vehicles. True or False?

When it comes to driving the automotive industry forward, Experian Automotive has a motto: “The right cars. The right customers. The right data to know the difference.” We deliver an integrated perspective based on the highest quality automotive information and market intelligence. Data is important to us, and we have the most robust combination of data assets under one roof. The North American Vehicle Database℠, File One℠ Credit Database, Consumer View℠ Marketing Database, Online Activity Databases, and National Fraud Database℠ all fed into Experian’s Auto HyperConnect™.   Auto HyperConnect houses two different solutions: Auto HyperTargeting™ and Auto HyperMonitoring™. There are four different components to Auto HyperTargeting, and the one we will focus on today is Expired Lease. Dealers have found success marketing to their own customers when vehicle leases come due. The challenge has been conquesting off-lease consumers who didn’t buy from them. While off-lease consumer marketing lists are available, they only provide the estimated lease end date and monthly lease payment.  When restricted to this data, it’s hard to tell if a $550 a month payment is for a Ford F150 or a BMW 3-series. And yet this is exactly the level of detail needed to successfully target off-lease marketing. Experian’s new Auto HyperTargeting- Expired Lease addresses this challenge by enabling dealers to identify and engage off-lease audiences through a highly targeted approach that has never been possible! Now, finding customers is much easier and cost-effective. You can use the best mix of selection criteria to maximize your off-lease conquesting strategy. Some of the ways you can identify your audience are: Lease Maturity Date Vehicle Make Vehicle Model Zip Code Model Performance Tier Distance from Dealer Ship Vehicle Status (New or Used) Signing up for Auto HyperTargeting- Expired Lease comes with five perks. The first is a monthly file. This includes name and address, lease maturity date, make/model, monthly payment bands, and phone and email address when available. The second is the ability to make sign up for a subscription monthly payments. Third is marketing flexibility with direct mail, email, and phone. Four there is no firm offer of credit required for this. Finally, a quarterly sales report. ARA reports provide sales metrics industry wide for your targets. Other competitive features also available in Auto HyperTargeting- Expired Lease is the customers full contact information with name, address, email address and phone number. And for online and offline channels, you can select a specific audience that is customizable. Auto HyperTargeting has some amazing features to successfully conquest customers, and Expired Lease is one of the four ways to do this. Stay tuned for Auto HyperTargeting- Vehicle Owners coming up next!

As more financial institutions express interest and leverage alternative credit data sources to decision and assess consumers, lenders want to be assured of how they can best utilize this data source and maintain compliance. Experian recently interviewed Philip Bohi, Vice President for Compliance Education for the American Financial Services Association (AFSA), to learn more about his perspective on this topic, as well as to gain insights on what lenders should consider as they dive into the world of alternative credit data. Alternative data continues to be a hot topic in the financial services space. How have you seen it evolve over the past few years? It’s hard to pinpoint where it began, but it has been interesting to observe how technology firms and people have changed our perceptions of the value and use of data in recent years. Earlier, a company’s data was just the information needed to conduct business. It seems like people are waking up to the realization that their business data can be useful internally, as well as to others.  And we have come to understand how previously disregarded data can be profoundly valuable. These insights provide a lot of new opportunities, but also new questions.  I would also say that the scope of alternative credit data use has changed.  A few years ago, alternative credit data was a tool to largely address the thin- and no-file consumer. More recently, we’ve seen it can provide a lift across the credit spectrum. We recently conducted a survey with lenders and 23% of respondents cited “complying with laws and regulations” as the top barrier to utilizing alternative data. Why do you think this is the case? What are the top concerns you hear from lenders as it relates to compliance on this topic? The consumer finance industry is very focused on compliance, because failure to maintain compliance can kill a business, either directly through fines and expenses, or through reputation damage. Concerns about alternative data come from a lack of familiarity. There is uncertainty about acquiring the data, using the data, safeguarding the data, selling the data, etc. Companies want to feel confident that they know where the limits are in creating, acquiring, using, storing and selling data. Alternative data is a broad term. When it comes to utilizing it for making a credit decision, what types of alternative data can actually be used?  Currently the scope is somewhat limited. I would describe the alternative data elements as being analogous to traditional credit data. Alternative data includes rent payments, utility payments, cell phone payments, bank deposits, and similar records. These provide important insights into whether a given consumer is keeping up with financial obligations. And most importantly, we are seeing that the particular types of obligations reflected in alternative data reflect the spending habits of people whose traditional credit files are thin or non-existent.  This is a good thing, as alternative data captures consumers who are paying their bills consistently earlier than traditional data does. Serving those customers is a great opportunity. If a lender wants to begin utilizing alternative credit data, what must they know from a compliance standpoint? I would begin with considering what the lender’s goal is and letting that guide how it will explore using alternative data. For some companies, accessing credit scores that include some degree of alternative data along with traditional data elements is enough. Just doing that provides a good business benefit without introducing a lot of additional risk as compared to using traditional credit score information. If the company wants to start leveraging its own customer data for its own purposes, or making it available to third parties, that becomes complex very quickly.  A company can find itself subject to all the regulatory burdens of a credit-reporting agency very quickly. In any case, the entire lifecycle of the data has to be considered, along with how the data will be protected when the data is “at rest,” “in use,” or “in transit.” Alternative data used for credit assessment should additionally be FCRA-compliant. How do you see alternative credit data evolving in the future? I cannot predict where it will go, but the unfettered potential is dizzying. Think about how DNA-based genealogy has taken off, telling folks they have family members they did not know and providing information to solve old crimes. I think we need to carefully balance personal privacy and prudent uses of customer data. There is also another issue with wide-ranging uses of new data. I contend it takes time to discern whether an element of data is accurately predictive.  Consider for a moment a person’s utility bills. If electricity usage in a household goes down when the bills in the neighborhood are going up, what does that tell us? Does it mean the family is under some financial strain and using the air conditioning less? Or does it tell us they had solar panels installed? Or they’ve been on vacation?  Figuring out what a particular piece of data means about someone’s circumstances can be difficult. About Philip Bohi Philip joined  AFSA in 2017 as Vice President, Compliance Education. He is responsible for providing strategic direction and leadership for the Association’s compliance activities, including AFSA University, and is the staff liaison to the Operations and Regulatory Compliance Committee and Technology Task Forces. He brings significant consumer finance legal and compliance experience to AFSA, having served as in-house counsel at Toyota Motor Credit Corporation and Fannie Mae. At those companies, Philip worked closely with compliance staff supporting technology projects, legislative tracking, and vendor management. His private practice included work on manufactured housing, residential mortgage compliance, and consumer finance matters at McGlinchey Stafford, PLLC and Lotstein Buckman, LLP. He is a member of the Virginia State Bar and the District of Columbia Bar. Learn more about the array of alternative credit data sources available to financial institutions.

In our previous post, we talked about Experian’s 2018 Attribution Study. This study is a wide-ranging, dealer-focused sales-driven attribution study to answer the question of what drives sales. Three takeaways were formed and the first was looking at shopper behavior instead of isolated KPIs. Now, we look at the second and third takeaways. Optimize your paid advertising Paid search is an important part of dealership marketing and precise targeting will help with both traffic and purchasing rate. Branded search drives quality traffic to websites. 7% of all traffic and 12% of HVU traffic comes from branded search. Along with traffic, the purchase rate was found to be 54% higher than average. In terms of value, New Make and Model has impressive results. 2% of all traffic and 12% of HVU traffic comes from New Make and Model. The purchase rate is 137% higher than average. Of note, we found that in order to optimize paid search to drive sales, you must buy your own name. 100% of dealers studied had buyers via branded search. With that, optimized “New Make Model” campaigns are crucial. 82% of dealers had buyers come through Make Model keywords. Traditionally, New Make Model would just create a higher funnel but since we are in the digital age, this does not happen anymore. Some participating dealers spent <20% of search budget on New Make Model campaigns. A key point is that no dealer had buyers come through via competitors’ keywords. As for a dealer’s website, display leasing offers and content for buyers since many search queries contained lease keywords. Although paid search is important, there are other channels to optimize and segmentation is key. This study found that conquesting with bulk email-blasts is not effective. You can achieve better email marketing results by targeting shoppers more intelligently and using quality lists. CRM-driven loyalty campaigns performed well in this segment. Display campaigns revealed that social media is no longer just for posting content but paid social advertising campaigns were effective. In fact, this study found Facebook display to be more effective than Google and other display networks. The reason is it is easier to segment and target a specific audience through Facebook. Google is more difficult to segment and has too many set-it-and-forget-it campaigns. Evaluate your third-party investments This brings us to third-parties, such as Automotive Shopping Portals and other lead providers. Performance with third-parties can vary and dealers should evaluate third-party performance as it relates to your sales. The third-party buy rate was under-indexed for dealers versus all sales. The business model for third-parties is not focused on driving traffic to dealer sites. Since there are so many vehicles listed on third-party sites from franchises, independent dealers, and private parties, there is a lot of “noise” for customers to sift through. For dealers, finding out what drives sales has led to Experian's 2018 Attribution. Our wide-ranging, dealer focused sales-driven attribution study focused on key variables and delivers three recommendations: Focus on website shopper behavioral metrics to drive sales. optimize paid search, specifically branded search and new make model campaigns. and evaluate third-party performance as it relates to your sales. With these, a dealer can be more confidant with their marketing and their data.

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 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.

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."