Tag: trended data

For an industry that has grown accustomed to sustained year-over-year growth, recent trends are concerning. The automotive industry continued to make progress in the fourth quarter of 2016 as total automotive loan balances grew 8.6% over the previous year and exceeded $1 trillion. However, the positive trend is slowing and 2017 may be the first year since 2009 to see a market contraction. With interest rates on the rise and demand peaking, automotive lending will continue to become more competitive. Lenders can be successful in this environment, but must implement data-driven targeting strategies. Credit Unions Triumph Credit unions experienced the largest year-over-year growth in the fourth quarter of 2016, increasing 15% over the previous period. As lending faces increasing headwinds amid rising rates, credit unions can continue to play a greater role by offering members more competitive rates. For many consumers, a casual weekend trip to the auto mall turns into a big new purchase. Unfortunately, many get caught up in researching the vehicle and don’t think to shop for financing options until they’re in the F&I office. With approximately 25% share of total auto loan balances, credit unions have significant potential to recapture loans of existing members. Successful targeting starts with a review of your portfolio for opportunities with current members who have off-book loans that could be refinanced at a lower rate. After developing a strategy, many credit unions find success targeting these members with refinance offers. Helping members reduce monthly payments and interest expense provides an unexpected service that can deepen loyalty and engagement. But what criteria should you use to identify prospects? Target Receptive Consumers As originations continue to slow, marketing response rates will as well, leading to reduced marketing ROI. Maintaining performance is possible, but requires a proactive approach. Propensity models can help identify consumers who are more likely to respond, while estimated interest rates can provide insight on who is likely to benefit from refinance offers. Propensity models identify who is most likely to open a new trade. By focusing on these populations, you can cut a mail list in half or more while still focusing on the most viable prospects. It may be okay in a booming economy to send as many offers as possible, but as things slow down, getting more targeted can maintain campaign performance while saving resources for other projects. When it comes to recapture, consumers refinance to reduce their payment, interest rate, or both. Payments can often be reduced simply by ‘resetting’ the clock on a loan, or taking the remaining balance and resetting the term. Many consumers, however, will be aware of their current interest rate and only consider offers that reduce the rate as well. Estimated interest rates can provide valuable insight into a consumer’s current terms. By targeting those with high rates, you are more likely to make an offer that will be accepted. Successful targeting means getting the right message to the right consumers. Propensity models help identify “who” to target while estimated interest rates determine “what” to offer. Combining these two strategies will maximize results in even the most challenging markets. Lend Deeper with Trended Data Much of the growth in the auto market has been driven by relatively low-risk consumers, with more than 60% of outstanding balances rated prime and above. This means hypercompetition and great rates for the best consumers, while those in lower risk tiers are underserved. Many lenders are reluctant to compete for these consumers and avoid taking on additional risk for the portfolio. But trended data holds the key to finding consumers who are currently in a lower risk tier but carry significantly less risk than their current score suggests. In fact, historical data can provide much deeper insight on a consumer’s past use of credit. As an example, consider two consumers with the same risk score at a point in time. While they may be judged as carrying similar risk, trended data shows one has taken out two new trades in the past 6 months and has increasing utilization, while the other is consolidating and paying down balances. They may have the same risk score today, but what will the impact be on your future profitability? Most risk scores take a snapshot approach to gauging risk. While effective in general, it misses out on the nuance of consumers who are trending up or down based on recent behavior. Trended data attributes tell a deeper story and allow lenders to find underserved consumers who carry less risk than their current score suggests. Making timely offers to underserved consumers is a great way to grow your portfolio while managing risk. Uncertain Future The automotive industry has been a bright spot for the US economy for several years. It’s difficult to say what will happen in 2017, but there will likely be a continued slowing in originations. When markets get more competitive, data-driven targeting becomes even more important. Propensity models, estimated interest rates, and trended data should be part of every prescreen campaign. Those that integrate them now will likely shrug off any downturn and continue growing their portfolio by providing valuable and timely offers to their members.

Sometimes life throws you a curve ball. The unexpected medical bill. The catastrophic car repair. The busted home appliance. It happens, and the killer is that consumers don’t always have the savings or resources to cover an additional cost. They must make a choice. Which bills do they pay? Which bills go to the pile? Suddenly, a consumer’s steady payment behavior changes, and in some cases they lose control of their ability to fulfill their obligations altogether. These shifts in payment patterns aren’t always reflected in consumer credit scores. At a single point in time, consumers may look identical. However, when analyzing their past payment behaviors, differences emerge. With these insights, lenders can now determine the appropriate risk or marketing decisions. In the example below, we see that based on the trade-level data, Consumer A and Consumer B have the same credit score and balance. But once we see their payment pattern within their trended data, we can clearly see Consumer A is paying well over the minimum payments due and has a demonstrated ability to pay. A closer look at Consumer B, on the other hand, reveals that the payment amount as compared to the minimum payment amount is decreasing over time. In fact, over the last three months only the minimum payment has been made. So while Consumer B may be well within the portfolio risk tolerance, they are trending down. This could indicate payment stress. With this knowledge,  the lender could decide to hold off on offering Consumer B any new products until an improvement is seen in their payment pattern. Alternatively, Consumer A may be ripe for a new product offering. In another example, three consumers may appear identical when looking at their credit score and average monthly balance. But when you look at the trend of their historical bankcard balances as compared to their payments, you start to see very different behaviors. Consumer A is carrying their balances and only making the minimum payments. Consumer B is a hybrid of revolving and transacting, and Consumer C is paying off their balances each month. When we look at the total annual payments and their average percent of balance paid, we can see the biggest differences emerge. Having this deeper level of insight can assist lenders with determining which consumer is the best prospect for particular offerings. Consumer A would likely be most interested in a low- interest rate card, whereas Consumer C may be more interested in a rewards card. The combination of the credit score and trended data provides significant insight into predicting consumer credit behavior, ultimately leading to more profitable lending decisions across the customer lifecycle: Response – match the right offer with the right prospect to maximize response rates and improve campaign performance Risk – understand direction and velocity of payment performance to adequately manage risk exposure Retention – anticipate consumer preferences to build long-term loyalty All financial institutions can benefit from the value of trended data, whether you are a financial institution with significant analytical capabilities looking to develop custom models from the trended data or looking for proven pre-built solutions for immediate implementation.

Knowing a consumer’s credit information at a single point in time tells only part of the story. For the whole story, lenders need to assess a consumer’s credit behavior over time. Understanding how a consumer uses credit or pays back debt over several months can better position you to: Offer the right products and terms to increase response rates. Identify profitable customers. Avoid consumers with payment stress. Trended data adds needed color to the consumer’s credit story. And with the right analytics and systems, you can derive valuable insights on consumers. Trended data>

Knowing a consumer’s credit information at a single point in time only tells part of the story. I often hear one of our Experian leaders share the example of two horses, running neck-in-neck, at the races. Who will win? Well, if you had multiple insights into those two horses – and could see the race in segments – you might notice one horse losing steam, and the other making great strides. In the world of credit consumers, the same metaphor can ring true. You might have two consumers with identical credit scores, but Consumer A has been making minimum payments for months and showing some payment stress, while Consumer B has been aggressively making larger pay-offs. Trended data adds that color to the story, and suddenly there is more intel on who to market to for future offers. To understand the whole story, lenders need the ability to assess a consumer’s credit behavior over time. Understanding how a consumer uses credit or pays back debt over time can help lenders: Offer the right products & terms to increase response rates Determine up sell and cross sell opportunities Prevent attrition Identify profitable customers Avoid consumers with payment stress Limit loss exposure The challenge with trended data, however, is finding a way to sort through the payment patterns in the midst of huge datasets. At the singular level, one consumer might have 10 trades. Trended data in turn reveals five historical payment fields and then you multiple all of this by 24 months and you suddenly have 1,200 data points. But let’s be real … a lender is not going to look at just one consumer as they consider their marketing or retention campaigns. They may look at 100,000 consumers. And on that scale you are now looking at sorting through 120M data points. So while a lender may think they need trended data – and there is definitely value in accessing it – they likely also need a solution to help them wade through it all, assessing and decisioning on those 120M data points. Tapping into something like Credit3D, which bundles in propensity scores, profitability models and trended attributes, is the solution that truly unveils the value of trended data insights. By layering in these solutions, lenders can clearly answer questions like: Who is likely to respond to an offer? How does a consumer use credit? How can I identify revolvers, transactors and consolidators? Is there a better way to understand risk or to conduct swap set analysis? How can I acquire profitable consumers? How do I increase wallet share and usage? Trended data sounds like a “no-brainer” and it definitely has the ability to shed light on that consumer credit horse race. Lenders, however, also need to have the appropriate analytics and systems to assess on the huge volume of data points. Need more information on Trended Data and Credit 3D? Contact Us

When you think of criteria for prescreen credit marketing, what comes to mind? Most people will immediately discuss the risk criteria used to ensure consumers receiving the mailing will qualify for the product offered. Others mention targeting criteria to increase response rates and ROI. But if this is all you’re looking at, chances are you’re not seeing the whole picture. When it comes to building campaigns, marketers should consider the entire customer lifecycle, not just response rates. Yes, response rates drive ROI and can usually be measured within a couple months of the campaign drop. But what happens after the accounts get booked? Traditionally, marketers view what happens after origination as the responsibility of other teams. Managing delinquencies, attrition, and loyalty are fringe issues for the marketing manager, not the main focus. But more and more, marketers must expand their role in the organization by taking a comprehensive approach to credit marketing. In fact, truly successful campaigns will target consumers that build lasting relationships with the institution by using the three pillars of comprehensive credit marketing. Pillar #1: Maximize Response Rates At any point in time, most consumers have no interest in your products. You don’t have to look far to prove this out. Many marketing campaigns are lucky to achieve greater than a 1% response rate. As a result, marketers frequently leverage propensity to open models to improve results. These scores are highly effective at identifying consumers who are most likely to be receptive to your offer, while saving those that are not for future efforts. However, many stop with this single dimension. The fact is no propensity tool can pick out 100% of responders. Layering just a couple credit attributes to a propensity score allows you to swap in new consumers. Simultaneously, credit attributes can identify consumers with high propensity scores that are actually unlikely to open a new account. The net effect is even higher response rates than can be achieved by using a propensity score alone. Pillar #2: Risk Expansion Credit criteria are usually set using a risk score with some additional attributes. For example, a lender may target consumers with a credit score greater than 700 and no derogatory or delinquent accounts reported in the past 12 months. But, most of this data is based on a “snapshot” of the credit profile and ignores trends in the consumer’s use of credit. Consider a consumer who currently has a 690 credit score and has spent the past six months paying down debt. During that time, utilization has dropped from 66% to 41%, they’ve paid off and closed two trades, and balances have reduced from $21,000 to $13,000. However, if you only target consumers with a score greater than 700, this consumer would never appear on your prescreen list. Trended data helps spot how consumers use data over time. Using swap set analysis, you can expand your approval criteria without taking on the incremental risk. Being there when a consumer needs you is the first step in building long-term relationships. Pillar #3: Customer profitability and early attrition There’s more to profitability than just originating loans. What happens to your profitability assumptions when a consumer opens a loan and closes it within a few months? According to recent research by Experian, as many as 26% of prime and super-prime consumers, and 38% of near-prime consumers had closed a personal loan trade within nine months of opening. Further, nearly 32% of consumers who closed a loan early opened a new personal loan trade within a few months. Segmentation can help identify consumers who are likely to close a personal loan early, giving account management teams a head start to try and retain them. As it turns out, many consumers use personal loans as a form of revolving debt. These consumers occasionally close existing trades and open new trades to get access to more cash. Anticipating who is likely to close a loan early allows your retention team to focus on understanding their needs. If you don’t, you’re competition will take advantage through their marketing efforts. Building the strategy Building a comprehensive strategy is an iterative process. It’s critical for organizations to understand each campaign is an opportunity to learn and refine the methodology. Consistently leveraging control and test groups and new data assets will allow the process to become more efficient over time. Importantly, marketers should work closely across the organization to understand broader objectives and pain points. Credit data can be used to predict a range of future behaviors. As such, marketing managers should play a greater role as the gatekeepers to the organization’s growth.

Consumers want to pay less. This is true in retail and in lending. No big surprise, right? So in order for lenders to capitalize and identify the right consumers for their respective portfolios, they need insights. Lenders want to better understand what rates consumers have. They want to know how much interest their customers pay. They want to know if consumers within their portfolio are at risk of leaving, and they want visibility into new prospects they can market to in an effort to grow. Luckily, lenders can look to trade level fields to be in the know. These inferred data fields, powered by Trended Data,  allow lenders to offer products and terms that serve two purposes: First, their use in response models and offer alignment strategies drive better performance, ROI and life-time value. As noted earlier, consumers want to pay less, so if they are offered a better rate or money-saving offer, they’re more likely to respond. Second, they ultimately save consumers money in a way that benefits each consumer’s unique financial situation- overall savings on interest paid over the life of the loan, or consolidation of other debt often combined for a lower monthly payment. These trade level fields allow lenders to dig into various trends and insights surrounding consumers. For example, Experian data can identify big spenders and transactors (those who pay off their purchases every month). Research reveals these individuals love to be rewarded for how they use credit, demanding rewards, airline miles or other goodies for the spending they do. They also really like to be rewarded with higher credit lines, whether they use the increased line or not. Fail to serve these transactors in the right way and lenders could be faced with lackluster performance in the form poor response rates, booking rates, activation rates and early attrition. Thus, a little trade level insight can go a long way in helping lenders personalize products, offers and anticipate future financial needs. Knowing the profitability of a customer across all of their accounts is important, and accessing this intelligence in a seamless way is ideal. The data exists. For lenders, it’s just a matter of unlocking it, making those small, but meaningful changes and keeping a pulse on the portfolio. Together, these strategies can help lenders keep their best customers and acquire new ones that stick around longer.

Experian estimates card-to-card consumer balance transfer activity to be between $35 and $40 billion a year, representing a sizeable opportunity for proactive lenders seeking to grow their revolving product line. This opportunity, however, is a threat for reactive lenders that only measure portfolio attrition instead of working to retain current customers. While billions of dollars are transferred every year, this activity represents only a small percentage of the total card population. And given the expense of direct marketing, lenders seeking to capitalize on and protect their portfolio from balance transfer activity must leverage data insights to make more informed decisions. Predicting a consumer’s future propensity to engage in card-to-card balance transfers starts with trended data. A credit score is a snapshot in time, but doesn’t reveal deep insights about a consumer’s past balance transfer activity. Lenders that rely only on current utilization will group large populations of balance revolvers into one bucket – and many of these individuals will have no intention of transferring to another product in the near future. Still, balance transfer activity can be identified and predicted by utilizing trended data. By analyzing the spend and payment data over time to see when one (or multiple) trade’s payment approximately matches another trade’s spend, we have the logic that suggests there has been a card-to-card transfer. What most people don’t realize is that trended data is difficult to work with. With 24 months of history on five fields, a single trade includes 120 data points. That’s 720 data points for a consumer with six trades on file and 72,000,000 for a file with 100,000 records, not to mention the other data fields in the file. It’s easy to see why even the most sophisticated organizations become paralyzed working with trended data. While teams of analysts get buried in the data, projects drag, costs swell, and eventually the world changes as rates climb and fall. By the time the analysis is complete, it must be recalibrated. But there is a solution. Experian has developed powerful predictions tools that combine past balance transfer history, historical transfer amounts, current trades carried and utilized, payments, and spend. Combined, these data fields can help identify consumers who are most likely to transfer a balance in the future. With Experian’s Balance Transfer Index the highest scoring 10 percent of consumers capture nearly 70 percent of total balance transfer dollars. Imagine the impact on ROI of reducing 90 percent of the marketing cost of your next balance transfer campaign and still reaching 70 percent of the balance transfer activity. Balance transfer activity represents a meaningful dollar opportunity for growth, but is concentrated in a small percentage of the population making predictive analytics key to success. Trended data is essential for identifying those opportunities, but financial institutions must assess their capabilities when it comes to managing the massive data attached. The good news is that regardless of financial institution size, solutions now exist to capture the analytics and provide meaningful and actionable insights to lenders of all sizes.

Who will take the coveted Super Bowl title in 2016? Now that we’re down to the final two teams, the commentary will heighten. Sportscasters, analysts, former athletes, co-workers ... even your local barista has an opinion. Will it be Peyton Manning's Denver Broncos or the rising Carolina Panthers? Millions will make predictions in the coming weeks, but a little research can go a long way in delivering meaningful insights. How have the teams been trending over the season? Are there injuries? Who is favored and what’s the spread? Which quarterback is leading in pass completions, passing yards, touchdowns, etc.? Who has been on this stage before, ready to embrace the spotlight and epic media frenzy? The world of sports is filled with stats resulting from historical data. And when you think about it, the world of credit could be treated similarly. Over the past several years, there has been much hype about “credit invisibles” and the need to “score more.” A traditional pull will likely leave many “no-file” and “thin-file” consumers out, so it’s in a lender’s best interest to leverage alternative scoring models to uncover more. But it’s also important to remember a score is just a snapshot, a mere moment in time. How did a consumer arrive to that particular score pulled on any given day? Has their score been trending up or down? Has an individual been paying off debt at a rapid pace or slipping further behind?   Two individuals could have the exact same score, but likely arrived to that place differently. The backstory is good to know – in sports and in the world of credit. Trended data can be attached to balances, credit limits, minimum payment due, actual payment and date of payment. By assessing these areas on a consumer file for 24 months, more insights are delivered and lenders can take note of behavior patterns to assist with risk assessment, marketing and share-of-wallet analysis. For example, looking closer at those consumers with five trades or more, Experian trended data reveals: 27% are revolvers, carrying balances each month 27% are transactors, paying off large portions, or all of their balances 9% are rate surfers, who tend to frequently transfer balances to credit cards with 0% or low introductory rates. Now these consumers can be viewed beyond a score. Suddenly, lenders can look within or outside their portfolio to understand how consumers use credit, what to offer them, and assess overall profitability. In short, trended data provides a more detailed view of a borrower’s historical credit performance, and that richness makes for a more informed decision. Without a doubt, there is power in the score – and being able to score more – but when it comes time to place your bets, the trended data matters, adding a whole new dimension to an individual’s credit score. Place your wagers accordingly. As for who will win Super Bowl 2016? I haven’t a clue. I’m more into the commercials. And I hear Coldplay is on for the half-time show. If you’re betting, best of luck, and do your homework.