Tag: in the market models

Driving growth in a down mortgage market can be tricky. It’s a mad scramble to obtain quality mortgage leads that convert into profitable loans. At Experian Mortgage, we have a front row seat into the efficacy of different lead generation strategies, and what we know for certain, is that data matters in both the audience creation and outreach approach. I’ve compiled several best practices for identifying qualified prospects early in the homebuying journey and using analytics to focus your outreach on those most likely to convert. Best practice #1: credit-based triggers First, let’s focus on borrower-behavior triggers, as they’re key for getting ahead of the competition. I occasionally hear skepticism about tried-and-true credit-based prospect triggers, but many find them indispensable. Credit triggers alert you when borrowers apply for credit and when other indicators meet your specific lending criteria, including credit scores, score trends, credit limits, utilization and much more.  They’re effective – and not just for big lenders. Our clients leverage credit-based triggers to quickly pursue “hot leads,” and have reported higher response rates, lower acquisition costs and revenue growth. Best practice #2: property listing triggers Another borrower behavior to watch is listing a property for sale, which can be done using property listing triggers. You can use listing triggers to monitor current customers – and with Experian, you can prospect for new customers outside your portfolio. One of our clients instituted property listing triggers and immediately identified 40,000 homeowners in their footprint who had recently listed a property for sale. Experian research shows that a homeowner lists their property for sale, on average, 35 days before applying for a new mortgage. This means this lender had over a month to reach those consumers with a tailored message. Now that’s getting a jump on the competition! But what about those homeowners who list a property for sale but don’t move? We hear anecdotally about more homeowners putting their homes on the market to see what offers they can get. According to recent data, a higher percentage of listings fail to sell today than last year. While property listing remains one of the most predictive behaviors for purchase, there’s room to optimize. Whether your prospect came to you via a property or credit trigger, there’s an opportunity to improve your ROI by identifying trigger leads most likely to convert. Best practice #3: in-the-market models A key best practice in audience segmentation is to incorporate in-the-market models (ITMM). A good model is based on sophisticated analytics across hundreds of data elements and millions of loan applications. Additionally, a good model is tailored to your product. A consumer in the market to buy their first house will “look” very different than a consumer in the market for a Home Equity Line of Credit (HELOC). Experian clients are doing two impactful things with ITMM. First, they create their audience list by bundling ITMM with credit, income, and property data to identify qualified consumers likely to be in the market soon. Second, they optimize an existing marketing list. However, when it comes to a mortgage lead generation program, you can only optimize what you measure. Experian has been helping clients by analyzing their lost leads and lost loans. Several clients recently asked us to analyze their efficacy with marketing lists originating from digital mortgage lead aggregators (i.e., lists of consumers who sought information online about mortgages). I’ll focus here on the leads who did NOT originate a mortgage with our clients, but DID open a tradeline with someone else. My first observation is that prospects who opened a tradeline were significantly more likely to open a credit card than a mortgage. My second observation is when the prospect opened a mortgage loan with a different institution, 80% of the time that lender was a non-bank. This is higher than the current non-bank share of the market, which indicates non-banks are aggressive with their leads and poised to grow their share. Here’s where ITMM comes into play. By incorporating an ITMM specifically for your product – HELOC, purchase, refinance – you can focus attention on borrowers most likely to open a mortgage. In summary, instituting credit and property triggers is a critical best practice and will open the door to a plethora of prospects. If you want to level up your marketing strategy, incorporating an ITMM is key and will help you segment the trigger leads and home in on those that are most likely to convert. Be sure to check out the final blog post in this series, Lead Conversion Through Tailored Messaging and a Multichannel Mortgage Marketing Strategy. To learn about Experian Mortgage solution offerings, click here. Learn more

As industry experts are still unsure when the economy will fully recover, re-entry into marketing preapproved credit offers seems like a far-off proposal. However, several of the top credit card issuers are already mailing prescreen offers, with many other lenders following suit. When the time comes for organizations to resume, or even expand this type of targeting, odds are that the marketing budget will be tighter than in the past. To make the most of the limited available marketing spend, lenders will need to be more prescriptive with their selection process to increase response rates on fewer delivered offers. Choosing the best candidates to receive these offers, from a credit risk perspective, will be critical. With delinquencies being suppressed due to CARES Act reporting guidelines, identifying consumers with the ability to repay will require additional assessment of recent credit behavior metrics, such as actual payment amounts and balance migration. Along with the presence of explicit indicators of accommodated trades (trades affected by natural disaster, trades with a balance but no scheduled payment amount) on a prospect’s credit file, their recent trends in payments and balance shifts can be integral in determining whether a prospect has been adversely impacted by today’s economic environment. Once risk criteria have been developed using a mix of bureau scores (like the VantageScore® credit score), traditional credit attributes and trended attributes measuring recent activity, additional targeting will be critical for selecting a population that’s most likely to open the relevant trade type. For credit cards and personal installment loans, response performance can be greatly improved by aligning product offers with prospects based on their propensity to revolve, pay in full each month or consolidate balances. Additionally, the process to select final prospects should integrate a propensity to open/respond assessment for the specific offering. While many lenders have custom models developed on previous internal response performance, off-the-shelf propensity to open models are also available to provide an assessment of a prospect’s likelihood to open a particular type of trade in the coming months. These models can act as a fast-start for lenders that intend to develop internal custom models, but don’t have the response performance within a particular product/geography/risk profile. They are also commonly used as a long-term solution for lenders without an internal model development team or budget for an outsourced model. Prescreen selection best practices Identify geography and traditional credit risk assessment of the prospect universe. Overlay attributes measuring accommodated trades and recent payment/balance trends to identify prospects with indications of ability to pay. Segment the prospect universe by recent credit usage to determine products that would resonate. Make final selections using propensity to open model scores to increase response rates by only making offers to consumers who are likely looking for new credit offers. While the best practices listed above don’t represent a risk-free approach in these uncertain times, they do provide a framework for identifying prospects with mitigated repayment risk and insights into the appropriate credit offer to make and when to make it. Learn about in the market models Learn about trended attributes VantageScore® is a registered trademark of VantageScore Solutions, LLC.

Changing consumer behaviors caused by the COVID-19 pandemic have made it difficult for businesses to make good lending decisions. Maintaining a consistent lending portfolio and differentiating good customers who are facing financial struggles from bad actors with criminal intent is getting more difficult, highlighting the need for effective decisioning tools. As part of our ongoing Q&A perspective series, Jim Bander, Experian’s Market Lead, Analytics and Optimization, discusses the importance of automated decisions in today’s uncertain lending environment. Check out what he had to say: Q: What trends and challenges have emerged in the decisioning space since March? JB: In the age of COVID-19, many businesses are facing several challenges simultaneously. First, customers have moved online, and there is a critical need to provide a seamless digital-first experience. Second, there are operational challenges as employees have moved to work from home; IT departments in particular have to place increase priority on agility, security, and cost-control. Note that all of these priorities are well-served by a cloud-first approach to decisioning. Third, the pandemic has led to changes in customer behavior and credit reporting practices. Q: Are automated decisioning tools still effective, given the changes in consumer behaviors and spending? JB: Many businesses are finding automated decisioning tools more important than ever. For example, there are up-sell and cross-sell opportunities when an at-home bank employee speaks with a customer over the phone that simply were not happening in the branch environment. Automated prequalification and instant credit decisions empower these employees to meet consumer needs. Some financial institutions are ready to attract new customers but they have tight marketing budgets. They can make the most of their budget by combining predictive models with automated prescreen decisioning to provide the right customers with the right offers. And, of course, decisioning is a key part of a debt management strategy. As consumers show signs of distress and become delinquent on some of their accounts, lenders need data-driven decisioning systems to treat those customers fairly and effectively. Q: How does automated decisioning differentiate customers who may have missed a payment due to COVID-19 from those with a history of missed payments? JB: Using a variety of credit attributes in an automated decision is the key to understanding a consumer’s financial situation. We have been helping businesses understand that during a downturn, it is important for a decisioning system to look at a consumer through several different lenses to identify financially stressed consumers with early-warning indicators, respond quickly to change, predict future customer behavior, and deliver the best treatment at the right time based on customer specific situations or behaviors.  In addition to traditional credit attributes that reflect a consumer’s credit behavior at a single point in time, trended attributes can highlight changes in a consumer’s behavior. Furthermore, Experian was the first lender to release new attributes specifically created to address new challenges that have arisen since the onset of COVID. These attributes help lenders gain a broader view of each consumer in the current environment to better support them. For example, lenders can use decisioning to proactively identify consumers who may need assistance. Q: What should financial institutions do next? JB: Financial institutions have rarely faced so much uncertainty, but they are generally rising to the occasion. Some had already adopted the CECL accounting standard, and all financial institutions were planning for it. That regulation has encouraged them to set aside loss reserves so they will be in better financial shape during and after the COVID-19 Recession than they were during the Great Recession. The best lenders are making smart investments now—in cloud technology, automated decisioning, and even Ethical and Explainable Artificial Intelligence—that will allow them to survive the COVID Recession and to be even more competitive during an eventual recovery. Financial institutions should also look for tools like Experian’s In the Market Model and Trended 3D Attributes to maximize efficiency and decisioning tactics – helping good customers remain that way while protecting the bottom line. In the Market Models Trended 3D Attributes  About our Expert: [avatar user="jim.bander" /] Jim Bander, PhD, Market Lead, Analytics and Optimization, Experian Decision Analytics Jim joined Experian in April 2018 and is responsible for solutions and value propositions applying analytics for financial institutions and other Experian business-to-business clients throughout North America. He has over 20 years of analytics, software, engineering and risk management experience across a variety of industries and disciplines. Jim has applied decision science to many industries, including banking, transportation and the public sector.

The lending market has seen a significant shift from traditional financial institutions to fintech companies providing alternative business lending. Fintech companies are changing the brick-and-mortar landscape of lending by utilizing data and technology. Here are four ways fintech has changed the lending process and how traditional financial institutions and lenders can keep up: 1. They introduced alternative lending models In a traditional lending model, lenders accept deposits from customers to extend loan offers to other customers. One way that fintech companies disrupted the lending process is by introducing peer-to-peer lending. With peer-to-peer lending, there is no need to take a deposit at all. Instead, individuals can earn interest by lending to others. Banks who collaborate with peer-to-peer lenders can improve their credit appraisal models, enhance their online lending strategy, and offer new products at a lower cost to their customers. 2. They offer fast approvals and funding In certain situations, it can take banks and credit card providers weeks to months to process and approve a loan. Conversely, fintech lenders typically approve and fund loans in less than 24 hours. According to Mintel, only 30% of consumers find various banking features easy-to-use. Financial experts at Toptal suggest that banks consider speeding up the loan application and funding process within their online lending platforms to keep up with high-tech companies, such as Amazon, that offer customers an overall faster lending process from applications to approval, to payments.   3. They're making use of data Typically, fintech lenders pull data from several different alternative sources to quickly determine how likely a borrow is to pay back the loan. The data is collected and analyzed within seconds to create a snapshot of the consumer's creditworthiness and risk. The information can include utility, rent. auto payments, among other sources. To keep up, financial institutions have begun to implement alternative credit data to get a more comprehensive picture of a consumer, instead of relying solely upon the traditional credit score. 4. They offer perks and savings By enacting smoother automated processes, fintech lenders can save money on overhead costs, such as personnel, rent, and administrative expenses. These savings can then be passed onto the customer in the form of competitive interest rates. While traditional financial institutions generally have low overall interest rates, the current high demand for loans could help push their rates even lower. Additionally, financial institutions have started to offer more customer perks. For example, Goldman Sachs recently created an online lending platform, called Marcus, that offers unsecured consumer loans with no fees. Financial institutions may feel stuck in legacy systems and unable to accomplish the agile environments and instant-gratification that today's consumers expect. However, by leveraging new data sets and innovation, financial institutions may be able to improve their product offerings and service more customers. Looking to take the next step? We can help. Learn More About Banks Learn More About Fintechs

Are You #TeamTrended or #TeamAlternative? There’s no such thing as too much data, but when put head to head, differences between the data sets are apparent. Which team are you on? Here’s what we know: With the entry and incorporation of alternative credit data into the data arena, traditional credit data is no longer the sole determinant for credit worthiness, granting more people credit access. Built for the factors influencing financial health today, alternative credit data essentially fills the gaps of the traditional credit file, including alternative financial services data, rental payments, asset ownership, utility payments, full file public records, and consumer-permissioned data – all FCRA-regulated data. Watch this video to see more:    Trended data, on the other hand shows actual, historical credit data. It provides key balance and payment data for the previous 24 months to allow lenders to leverage behavior trends to determine how individuals are utilizing their credit. Different splices of that information reveal particular behavior patterns, empowering lenders to then act on that behavior. Insights include a consumer’s spend on all general purpose credit and charge cards and predictive metrics that identify consumers who will be in the market for a specific type of credit product. In the head-to-head between alternative credit data and trended data, both have clear advantages. You need both on your roster to supplement traditional credit data and elevate your game to the next level when it comes to your data universe. Compared to the traditional credit file, alternative credit data can reveal information differentiating two consumers. In the examples below, both consumers have moderate limits and have making timely credit card payments according to their traditional credit reports. However, alternative data gives insight into their alternative financial services information. In Example 1, Robert Smith is currently past due on his personal loan, whereas Michelle Lee in Example 2 is current on her personal loan, indicating she may be the consumer with stronger creditworthiness.   Similarly, trended data reveals that all credit scores are not created equal. Here is an example of how trended data can differentiate two consumers with the same score. Different historical trends can show completely different trajectories between seemingly similar consumers.   While the traditional credit score is a reliable indication of a consumer’s creditworthiness, it does not offer the full picture. What insights are you missing out on? Go to Infographic Get Started Today

Many institutions take a “leap of faith” when it comes to developing prospecting strategies as it pertains to credit marketing. But effective strategies are developed from deep, analytical analysis with clearly identified objectives. They are constantly evolving – no setting and forgetting. So what are the basics to optimizing your prospecting efforts? Establish goals Unfortunately, far too many discussions begin with establishing targeting criteria before program goals are set. But this leads to confusion. Developing targeting criteria is kind of like squeezing a balloon; when you restrict one end, the other tends to expand. Imagine the effect of maximizing response rates when soliciting new loans. If no other criteria are considered, you could end up targeting high-risk individuals who cannot get approved elsewhere. Obviously, we’re not interested in increasing originations at all cost; risk must be understood as well. But this is where things get complicated. Lower-risk consumers tend to be the most coveted, get the best offers, and therefore have lower response rates and margins. Simplicity is best              The US Navy developed the KISS acronym (keep it simple, stupid) in the 1960s on the philosophy that complexity increases the probability of error. This is largely true in targeting methodologies, but don’t mistake limiting complexity for simplicity. Perhaps the most simplistic approach to prescreen credit marketing is using only risk criteria to set an eligible population. Breaking a problem down to this single dimension generally results in low response rates and wasted budget. Propensity models and estimated interest rates are great tools for identifying consumers that are more likely to respond. Adding them as an additional filter to a credit-qualified population can help increase response rates. But what about ability to pay? So far we’ve considered propensity to open and risk (the latter being based on current financial obligations). Imagine a consumer with on-time payment behavior and a solid credit score who takes a loan only to be unable to meet their obligations. You certainly don’t want to extend debt that will cause a consumer to be overextended. Instead of going through costly income verification, income estimation models can assist with identifying the ability to repay the loan you are marketing. Simplicity is great, but not to the point of being one-dimensional. Take off the blindfold Even in the days of smartphones and GPS navigation, most people develop a plan before setting off on a road trip. In the case of credit marketing, this means running an account review or archive analysis. Remember that last prescreen campaign you ran? What could have happened with a more sophisticated targeting strategy? Having archive data appended to a past marketing campaign allows for “what if” retrospective analysis. What could response rates have been with a propensity tool? Could declines due to insufficient income have been reduced with estimated income? Archive data gives 20/20 hindsight to what could have been. Just like consulting a map to determine the shortest distance to a destination or the most scenic route, retrospective analysis on past campaigns allows for proactive planning for future efforts. Practice makes perfect Even with a plan, you probably still want to have the GPS running. Traffic could block your planned route or an unforeseen detour could divert you to a new path. Targeting strategies must continually be refined and monitored for changes in customer behavior. Test and control groups are essential to continued improvement of your targeting strategies. Every campaign should be analyzed against the goals and KPIs established at the start of the process. New hypotheses can be evaluated through test populations or small groups designed to identify new opportunities. Let’s say you typically target consumers in a risk range of 650-720, but an analyst spots an opportunity where consumers with a range of 625-649 with no delinquencies in the past 12 months performs nearly at the rate of the current population. A small test group could be included in the next campaign and studied to see if it should be expanded in future campaigns. Never “place bets” Assumptions are only valid when they are put to the test. Never dive into a strategy without testing your hypothesis. The final step in implementing a targeting strategy should be the easiest. If goals are clearly understood and prioritized, past campaigns are analyzed, and hypotheses are laid out with test and control groups, the targeting criteria should be obvious to everyone. Unfortunately, the conversation usually starts at this phase, which is akin to placing bets at the track. Ever notice that score breaks are discussed in round numbers? Consider the example of the 650-720 range. Why 650 and not 649 or 651? Without a test and learn methodology, targeting criteria ends up based on conventional wisdom – or worse, a guess. As you approach strategic planning season, make sure you run down these steps (in this order) to ensure success next year. Establish program goals and KPIs Balance simplicity with effectiveness Have a plan before you start Begin with an archive Learn and optimize In God we trust, all others bring data

It’s more than mercury that will be up this summer. As temperatures climb, so do automotive sales, which often reach annual highs during the warmest months of the year. Fueled by pent-up demand coming out of the recession, historically low interest rates, and increased competition among both manufacturers and lenders, auto sales are continuing to be a bright spot in the U.S. economy. Summer sales spike According to recent research by Experian Automotive, 2015 sales of new non-luxury vehicles began rising in May and peaked in August at nearly 20 percent above the monthly average for the year. It is not surprising, given the number of notable manufacturer marketing campaigns that often air through the summer months, beginning with Memorial Day and running all the way through Labor Day weekend. The projection is that this trend will continue in 2016. Financing moves metal Financing continues to play an important role in facilitating new car sales. Experian research shows a consistent increase in the percentage of new vehicles sold with financing with the trend reaching a period high of 85.9 percent in Q4 2015, a 2.3 percent increase over the previous year. The increased financing, is due in part, to continued post-recession liquidity. As the economy has rebounded, lenders have re-emerged with attractive financing rates for buyers. In addition, captive lenders are continuing to support manufacturers with 0 percent subvention offers to increase sales. Total loan value is on the rise as well, reaching $29,551 in Q4 2015, a 4.1 percent increase over the previous year. Average MSRP is trending up too, but at a slower year-over-year rate of 3.6 percent. The slower growth in MSRP relative to total loan value is leading to increased loan-to-value ratios which reached 109.4 percent in Q4 2015. The increases in loan value and MSRP are putting pressure on monthly payment with average new vehicle payments reaching $493 per month on new loans in the fourth quarter. Seeking relief, consumers are turning to longer loan terms and leasing to maintain lower payments. As a result, average new vehicle loan terms ticked slightly higher to 67 months while lease penetration on new vehicles reached 28.9 percent, a 19 percent increase over the previous year. Leveraging the trends Timing is everything when it comes to auto lending. Direct mail remains an effective communication tool for lenders, but mass mailers without regard to response rates yield poor ROIs and put future campaigns in jeopardy. Targeting consumers who are most likely to be in the market at a point in time can increase response rates and improve overall campaign performance. Experian’s In the Market Model – Auto leverages the power of trended credit data to identify consumers that will be most receptive to an offer. By focusing on high-propensity consumers, lenders can conduct more marketing campaigns during the year with the same budget and achieve supercharged results. Context-based marketing allows lenders to tailor offers by leveraging insights on a consumer’s existing loans. Product offers can additionally be customized based on estimated interest rates, months remaining, or current loan balance on open auto loans. Targeted refinance offers can also be delivered to consumers with high interest rates or focus new-loan offers on consumers with minimal months or balance remaining on existing loans. Understanding current auto loans allows lenders to target offers that are relevant to their prospects and gain an advantage over the competition. Increases in loan-to-value (LTV) ratios at origination and longer loan terms are putting many consumers in deep negative equity positions. As a result, many consumers will not qualify for refinance offers without significant down payments leading to low underwriting conversion rates and poor customer experience. Lenders seeking to improve on these metrics should leverage Experian’s Auto Equity Model, which provides an estimate of the amount of equity a consumer has in their existing auto trades. Focusing refinance offers on consumers with negative equity, while suppressing those with deep negative positions, can help improve response rates while minimizing declines due to LTV requirements. Takeaways Lenders should be gearing up for the summer auto sales spike. Proactive strategies will allow savvy marketers to deploy capital and grow their portfolio by taking advantage of customer insight. Timing and context matter, and as auto sales trends reveal, now is the opportune time to optimize marketing efforts and capitalize on the season.