Data & Analytics

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

According to our recent research for the State of Alternative Credit Data, more lenders are using alternative credit data to determine if a consumer is a good or bad credit risk. In fact, when it comes to making decisions: More than 50% of lenders verify income, employment and assets as well as check public records before making a credit decision. 78% of lenders believe factoring in alternative data allows them to extend credit to consumers who otherwise would be declined. 70% of consumers are willing to provide additional financial information to a lender if it increases their chance for approval or improves their interest rate. The alternative financial services space continues to grow with products like payday loans, rent-to-own products, short-term loans and more. By including alternative financial data, all types of lenders can explore both universe expansion and risk mitigation. State of Alternative Credit Data

Alternative credit data. Enhanced digital credit marketing. Faster, integrated decisioning. Fraud and identity protections. The latest in technology innovation. These were the themes Craig Boundy, Experian’s CEO of North America, imparted to an audience of 800-plus Vision guests on Monday morning. “Technology, innovation and new sources of data are fusing to create an unprecedented number of new ways to solve pressing business challenges,” said Boundy. “We’re leveraging the power of data to help people and businesses thrive in the digital economy.” Main stage product demos took the shape of dark web scans, data visualization, and the latest in biometric fraud scanning. Additionally, a diverse group of breakout sessions showcased all-new technology solutions and telling stats about how the economy is faring in 2018, as well as consumer credit trends and preferences. A few interesting storylines of the day … Regulatory Under the Trump administration, everyone is talking about deregulation, but how far will the pendulum swing? Experian Sr. Director of Regulatory Affairs Liz Oesterle told audience members that Congress will likely pass a bill within the next few days, offering relief to small and mid-sized banks and credit unions. Under the new regulations, these smaller players will no longer have to hold as much capital to cover losses on their balance sheets, nor will they be required to have plans in place to be safely dismantled if they fail. That trigger, now set at $50 billion in assets, is expected to rise to $250 billion. Fraud Alex Lintner, Experian’s President of Consumer Information Services, reported there were 16.7 million identity theft victims in 2017, resulting in $16.8 billion in losses. Need more to fear? There is also a reported 323k new malware samples found each day. Multiple sessions touched on evolving best practices in authentication, which are quickly shifting to biometrics-based solutions. Personal identifiable information (PII) must be strengthened. Driver’s licenses, social security numbers, date of birth – these formats are no longer enough. Get ready for eye scans, as well as voice and photo recognition. Emerging Consumers The quest to understand the up-and-coming Millennials continues. Several noteworthy stats: 42% of Millennials said they would conduct more online transactions if there weren’t so many security hurdles to overcome. So, while businesses and lenders are trying to do more to authenticate and strengthen security, it’s a delicate balance for Millennials who still expect an easy and turnkey customer experience. Gen Z, also known as Centennials, are now the largest generation with 28% of the population. While they are just coming onto the credit scene, these digital natives will shape the credit scene for decades to come. More than ever, think mobile-first. And consider this … it's estimated that 25% of shopping malls will be closed within five years. Gen Z isn’t shopping the mall scene. Retail is changing rapidly! Economy Mortgage originations are trending up. Consumer confidence, investor confidence, interest rates and home sales are all positive. Unemployment remains low. Bankcard originations have now surpassed the 2007 peak. Experian’s Vice President of Analytics Michele Raneri had glowing remarks on the U.S. economy, with all signs pointing to a positive 2018 across the board. Small business loan volumes are also up 10% year-to-date versus the same time last year. Keynote presenters speculate there could be three to four rate hikes within the year, but after years of no hikes, it’s time. Data There are 2.5 quintillion pieces of data created daily. And 80% of what we know about a consumer today is the result of data generated within the past year. While there is no denying there is a LOT of data, presenters throughout the day talked about the importance of access and speed. Value comes with more APIs to seamlessly connect, as well as data visualization solutions like Tableau to make the data easier to understand. More Vision news to come. Gain insights and news throughout the day by following #ExperianVision on Twitter.    

The traditional credit score has ruled the financial services space for decades, but it‘s clear the way in which consumers are managing their money and credit has evolved. Today’s consumers are utilizing different types of credit via various channels. Think fintech. Think short-term loans. Think cash-checking services and payday. So, how do lenders gain more visibility to a consumer’s credit worthiness in 2018? Alternative credit data has surfaced to provide a more holistic view of all consumers – those on the traditional file and those who are credit invisibles and emerging. In an all-new report, Experian dives into “The State of Alternative Credit Data,” providing in-depth coverage on how alternative credit data is defined, regulatory implications, consumer personas attached to the alternative financial services industry, and how this data complements traditional credit data files. “Alternative credit data can take the shape of alternative finance data, rental, utility and telecom payments, and various other data sources,” said Paul DeSaulniers, Experian’s senior director of Risk Scoring and Trended/Alternative Data and attributes. “What we’ve seen is that when this data becomes visible to a lender, suddenly a much more comprehensive consumer profile is formed. In some instances, this helps them offer consumers new credit opportunities, and in other cases it might illuminate risk.” In a national Experian survey, 53% of consumers said they believe some of these alternative sources like utility bill payment history, savings and checking account transactions, and mobile phone payments would have a positive effect on their credit score. Of the lenders surveyed, 80% said they rely on a credit report, plus additional information when making a lending decision. They cited assessing a consumer’s ability to pay, underwriting insights and being able to expand their lending universe as the top three benefits to using alternative credit data. The paper goes on to show how layering in alternative finance data could allow lenders to identify the consumers they would like to target, as well as suppress those that are higher risk. “Additional data fields prove to deliver a more complete view of today’s credit consumer,” said DeSaulniers. “For the credit invisible, the data can show lenders should take a chance on them. They may suddenly see a steady payment behavior that indicates they are worthy of expanded credit opportunities.” An “unscoreable” individual is not necessarily a high credit risk — rather they are an unknown credit risk. Many of these individuals pay rent on time and in full each month and could be great candidates for traditional credit. They just don’t have a credit history yet. The in-depth report also explores the future of alternative credit data. With more than 90 percent of the data in the world having been generated in just the past five years, there is no doubt more data sources will emerge in the coming years. Not all will make sense in assessing credit decisions, but there will definitely be new ways to capture consumer-permissioned data to benefit both consumer and lender. Read Full Report

Marketers are keenly aware of how important it is to “Know thy customer.” Yet customer knowledge isn’t restricted to the marketing-savvy. It’s also essential to credit risk managers and model developers. Identifying and separating customers into distinct groups based on various types of behavior is foundational to building effective custom models. This integral part of custom model development is known as segmentation analysis. Segmentation is the process of dividing customers or prospects into groupings based on similar behaviors such as length of time as a customer or payment patterns like credit card revolvers versus transactors. The more similar or homogeneous the customer grouping, the less variation across the customer segments are included in each segment’s custom model development. So how many scorecards are needed to aptly score and mitigate credit risk? There are several general principles we’ve learned over the course of developing hundreds of models that help determine whether multiple scorecards are warranted and, if so, how many. A robust segmentation analysis contains two components. The first is the generation of potential segments, and the second is the evaluation of such segments. Here I’ll discuss the generation of potential segments within a segmentation scheme. A second blog post will continue with a discussion on evaluation of such segments. When generating a customer segmentation scheme, several approaches are worth considering: heuristic, empirical and combined. A heuristic approach considers business learnings obtained through trial and error or experimental design. Portfolio managers will have insight on how segments of their portfolio behave differently that can and often should be included within a segmentation analysis. An empirical approach is data-driven and involves the use of quantitative techniques to evaluate potential customer segmentation splits. During this approach, statistical analysis is performed to identify forms of behavior across the customer population. Different interactive behavior for different segments of the overall population will correspond to different predictive patterns for these predictor variables, signifying that separate segment scorecards will be beneficial. Finally, a combination of heuristic and empirical approaches considers both the business needs and data-driven results. Once the set of potential customer segments has been identified, the next step in a segmentation analysis is the evaluation of those segments. Stay tuned as we look further into this topic. Learn more about how Experian Decision Analytics can help you with your segmentation or custom model development needs.

In the credit game, the space is deep and diverse. From super prime to prime to subprime consumers, there is much to be learned about how different segments are utilizing credit and navigating the financial services arena. With 78 percent of full-time workers saying they live paycheck-to-paycheck and 71 percent of U.S. workers responding that they live in debt, it is not surprising a sudden life event can plunge a solid credit consumer from prime to subprime within months. Think lost job, divorce or unexpected medical bill. This population is not going away, and they are seeking ways to make ends meet and obtain finances for needs big and small. In many instances, alternative credit data can shed a light on new opportunities for traditional lenders, fintech players and those in the alternative financial space when servicing this specific consumer segment. In a new study, Clarity analyzed the trends and financial behavior of subprime loan users by looking at application and loan data in Clarity’s database, as well as overlaying VantageScore® credit score insights from Experian from 2013 to 2017. Clarity conducted this subprime trends report last year, but this is the first time it factored in VantageScore® credit score data, providing a different lens as to where consumers fall within the credit score tiers. Among the study highlights: Storefront single pay loan customers are becoming more comfortable with applying for online loans, with a growing percentage seeking installment products. For the first time in five years, online single pay lending (payday) saw a reduction in total credit utilization per customer. Online installment, on the other hand, saw an increase. While the number of online installment loans increased by 12 percent and the number of borrowers by only 9 percent, the dollar value grew by 30 percent. Online installment lenders had the greatest percentage increase in average loan amount. California and Texas remain the most significant markets for online lenders, ranking first and second for five years in a row due to population size. There has also been growth in the Midwest. The in-depth report additionally delves into demographics, indicators of financial stability among the subprime market and comparisons between storefront and online product use and performance. “Every year, there are more financial lenders and products emerging to serve this population,” said Andy Sheehan, president of Clarity Services. “It’s important to understand the trends and data associated with these individuals and how they are maneuvering throughout the credit spectrum. As we know, it is often not a linear journey.” The inclusion of the VantageScore® credit score showcased additional findings around prime versus subprime financial behaviors and looks at generational trends. Access Full Report

Traditional credit attributes provide immense value for lenders when making decisions, but when used alone, they are limited to capturing credit behavior during a single moment of time. To add a deeper layer of insight, Experian® today unveiled new trended attributes, aimed at giving lenders a wider view into consumer credit behavior and patterns over time. Ultimately, this helps them expand into new risk segments and better tailor credit offers to meet consumer needs. An Experian analysis shows that custom models developed using Trended 3DTM attributes provide up to a 7 percent lift in predictive performance when compared with models developed using traditional attributes only. “While trended data has been shown to provide additional insight into a consumer’s credit behavior, lack of standardization across different providers has made it a challenge to gain those insights,” said Steve Platt, Experian’s Group President of Decision Analytics and Data Quality. “Trended 3D makes it easy for our clients to get value from trended data in a consistent manner, so they can make more informed decisions across the credit life cycle and, more importantly, give consumers better access to lending options.” Experian’s Trended 3D attributes help lenders unlock valuable insights hidden within credit reports. For example, two people may have similar balances, utilization and risk scores, but their paths to that point may be substantially different. The solution synthesizes a 24-month history of five key credit report fields — balance, credit limit or original loan amount, scheduled payment amount, actual payment amount and last payment date. Lenders can gain insight into: Changes in balances over time Migration patterns from one tradeline or multiple tradelines to another Variations in utilization and credit limits Changes in payment activity and collections Balance transfer and debt consolidation behavior Behavior patterns of revolving trades versus transactional trades Additionally, Trended 3D leverages machine learning techniques to evaluate behavioral data and recognize patterns that previously may have gone undetected. To learn more information about Experian’s Trended 3D attributes, click here.

Expert offers insights into turnkey big data access   The data is out there – and there is a lot of it. In the world of credit, there are more than 220 million credit-active consumers. Bolt on insights from the alternative financial services space and that number climbs even higher. So, what can analysts do with this information? With technology and the rise of data scientists, there are certainly opportunities to dig in and explore. To learn more, we chatted with Chris Fricks, data and product expert, responsible for Experian’s Analytical Sandbox™. 1. With the launch of Experian’s all-new Ascend platform, one of the key benefits is full-file access to our Sandbox environment. What exactly can clients access and are there specific tools they need to dig into the data? Clients will have access to monthly snapshots of 12-plus years of the full suite of Experian scores, attributes, and raw credit data covering the full national consumer base. Along with the data access, clients can interact and manipulate the data with the analytic tools they prefer. For example, a client can log into the environment through a standard Citrix portal and land on a Windows desktop. From there, they can access applications like SAS, R, Python, or Tableau to interrogate the data assets and derive the necessary value. 2. How are clients benefiting from this access? What are the top use cases you are seeing? Clients are now able to speed analytic findings to market and iterate through the analytics lifecycle much faster. We are seeing clients are engaging in new model development, reject inferencing, and industry/peer benchmarking. One of the more advanced use cases is related to machine learning – think of artificial intelligence for data analytics. In this instance, we have tools like H2O, a robust source of data for users to draw on, and a platform that is optimized to bring it all together in a cohesive, easy-to-use manner. 3. Our Experian database has details on 220 million credit-active consumers. Is this data anonymized, and how are we ensuring sensitive details are secure? We use the data from our credit database, but we’ve assigned unique consumer-level and trade-level encrypted pins to ensure security.  Once the encrypted PINs are assigned, they remain the same over time. Then all PII is scrubbed and everything is rendered de-identifiable from an individual consumer and lender perspective. Our pinning technique allows users to accurately track individual trades and consumers through time, but also prevents any match back to individual consumers and lenders. 4. I imagine having access to so much data could be overwhelming for clients. Is more necessarily better? You’re right. Access to our full credit file can be a lot to handle. While general users will not “actively” use the full file daily, statisticians and data scientists will see an advantage to having access to the larger universe. For example, if a statistician only has access to 10% of the Sandbox and wants to look at a specific region of the country, they may find their self in a situation with limited data that it is no longer statistically significant. By accessing the full file, they can sample down based on the full population from the region they are concerned with analyzing. 5. Who are the best-suited individuals to dig into the Sandbox environment and assess trends and findings? The environment is designed to serve the front-line analysts responsible for coding and analytics that gets reported out to various levels of leadership. It also enables the socialization of those findings with leadership, helping them to interact and give feedback on what they are seeing. Learn more about Experian’s Analytical Sandbox and request a demo.

Today’s consumer lending environment is more dynamic and competitive than ever, with renewed focus on personal loans, marketplace lending and the ever-challenging credit card market. One of the significant learnings from the economic crisis is how digging deeper into consumer credit data can help provide insights into trending behavior and not just point-in-time credit evaluation. For example, I’ve found consumer trending behavior to be very powerful when evaluating risks of credit card revolvers versus transactors. However, trended data can come with its own challenges when the data isn’t interpreted uniformly across multiple data sources. To address these challenges, Experian® has developed trended attributes, which can provide significant lift in the development of segmentation strategies and custom models. These Trended 3DTM attributes are used effectively across the life cycle to drive balance transfers, mitigate high-risk exposure and fine-tune strategies for customers near score cutoffs. One of the things I look for when exploring new trended data is the ability to further understand payment velocity. These characteristics go far beyond revolver and transactor flags, and into the details of consumer usage and trajectory. As illustrated in the chart, a consumer isn’t easily classified into one borrowing persona (revolver, transactor, etc.) or another — it’s a spectrum of use trends. Experian’s Trended 3D provides details needed to understand payment rates, slope of balance growth and even trends in delinquency. These trends provide strong lift across all decisioning strategies to improve your business performance. In recent engagements with lenders, new segmentation tools and data for the development of custom models is at the forefront of the conversation. Risk managers are looking for help leveraging new modeling techniques such as machine learning, but often have challenges moving from prior practices. In addition, attribute governance has been a key area of focus that is addressed with Trended 3D, as it was developed using machine learning techniques and is delivered with the necessary documentation for regulatory conformance. This provides an impressive foundation, allowing you to integrate the most advanced analytics into your credit decisioning. Alternative data isn’t the only source for new consumer insights. Looking at the traditional credit report can still provide so much insight; we simply need to take advantage of new techniques in analytics development. Trended attributes provide a high-definition lens that opens a world of opportunity.

In the world of marketing, this can happen to all of us. We think we know how much ad spend we should put towards a PPC campaign or what time a TV ad should air. In this digital realm, dealers are given a lot of data when a campaign finishes. Technology has pushed our boundaries so now, someone looking at a video of a specific automobile on a social media platform like YouTube can be targeted to receive video advertisements for your dealership. With that said, how do you know if you are targeting the right people? When it comes to your campaigns, you want to know your money is going towards the right forms of advertisements as well as the right demographics. If a campaign is doing well, extending out that campaign can be a better decision compared to letting it end. On the other side, if a campaign is doing poorly, pausing or evening stopping the campaign can be more beneficial in the future. The big questions you must ask yourself are: Am I targeting the wrong people? There can be a few reasons why you may be targeting the wrong people. First, the type of campaign can be incorrect. Social media is the newest and hottest form of marketing, but that may not work with your audience. Second, your demographics may be off. Without knowing the key demographics that you are targeting, you could be blindly targeting customers not interested in your vehicles or not in the market. The opposite could be said for that as well. You may be leaving out potential customers in key geological areas around your dealership. Because of this, you could be losing sales. Targeting doesn’t just stop with humans. You must think about your location and what you have in your inventory. If you are in a state like Colorado, you will want to allocate more SUVs and vehicles that have all-wheel-drive. By advertising rear-wheel-drive or sports cars to individuals that exclusively drive SUVs or only research SUVs, that can be a loss of advertising dollars. What can I do about it? A revamped marketing campaign is a good start for a strategy, but it may not be enough. Targeting can be a difficult endeavor, but thankfully there are programs and companies that can help. Experian Automotive’s Dealer Positioning System® can target key ZIP Code™ and display which campaigns are working - as well as which should be cut. Thanks to this type of data-driven targeting, market share and sales can increase even while advertising spend decreases. Another great aspect of using a system like Experian DPS is the ability to create campaigns to target vehicles your customers want. Like the example above of what not to do, you can formulate a plan of attack to focus on SUVs and light-duty trucks in a mountainous area. Conquesting intelligently not only means more people coming through your door but more allocations for vehicles your customers will want. Marketing is important. Whatever kind of advertising you do, remember that building your dealership is important. Having a solid game plan to conquest around you relies on smart targeting and the correct forms of advertising. Experian DPS can help boost market share and increase sales core models which is very helpful. To learn more about how Experian Automotive can assist with your targeting, click here.

At their heart, car dealers have always been marketers. It's part of learning the trade and understanding the business to gain natural insight into modern marketing and advertising practices. One could even argue that the experience gained through knowledge passed down, trial and error, and exposure to the automotive game itself can yield better strategies than a marketing degree. With all that said, it's still important to have the right data to guide the decisions as well as the tools necessary to decipher the data. Although we have a vast amount of information at our fingertips, it's very possible to truly build on "actionable data" and allow it to define the parameters for a dealership's marketing strategy. One of the most important things to consider when you're building and enhancing your strategies is that the data allows for decision making on the macro and micro levels. We see trend reports, analytics, and test cases that can influence decisions on both sides of the spectrum. Making decisions on the macro level means wholesale changes or additions. For example, the overall effectiveness of a particular classified advertising website can be broken down to determine whether or not it's making the right type of impact. Dealers have so many options today to advertise both online and offline, so making sure that any particular venue is effective is key to success. On the micro level, decisions can be made about how to position the dealership within the individual venues. You may be a big believer in search pay-per-click advertising, for example, and data can help to guide you or your vendor partners to position the dealership properly on search. Knowing which messages about individual cars are effective can be a guide. Then, understanding what zip codes have the highest opportunity level for the individual model can mold your PPC spend, while demographic data can drive effective messaging and help you optimize campaign creative and landing pages. Having access to the data is only the first step. Looking at the data appropriately is an important second step that many dealers are missing. Putting it all together into a decision-driving model is the step that almost every dealer should embrace to allow them to make the best decisions, macro or micro.

Below is our 5 Results for Dealers and Agencies Using DPS infographic.

  The auto industry has been riding a wave of prosperity for the past seven years, bouncing back nicely from the 2008 market collapse. But, it looks like rising sales of the past 10 years, are, well...a thing of the past. According to Alix Partners, 2016 sales of 17.5 million units might be the high-water sales mark, at least through 2022. Alix Partners says the next five years sales will range between 15.6 million to 16.8 million annually. Suddenly, it will be challenging for dealers to stay in strong growth mode. How can dealers best react to the tightening market? The Experian white paper “Data Tools Evolve to Give Dealers an Edge in a Tight Sales Market” takes a look at how new and improved data and analytic tools can provide deeper insights to help automotive retailers unlock sales. The paper reviews current market sales statistics, historical sales trends and how dealers reacted during similar market conditions in the past. In addition, the paper provides a look at the challenges faced by automotive retailers, in terms of shrinking gross profit, higher advertising expenses and increased competition. Automotive retailers also will find information on the importance of customer conquesting and a look at technology tools to help provide a deeper understanding and actionable intelligence about local markets. Data and analytics are no longer the private purview of large mega-dealers. The Experian white paper outlines today’s data tools that can be implemented quickly and cost effectively by dealers of any size. To learn more about these trends, download the paper here: https://www.experian.com/automotive/dealerwhitepaper.html

You’ve been tasked with developing a new model or enhancing an existing one, but the available data doesn’t include performance across the entire population of prospective customers. Sound familiar? A standard practice is to infer customer performance by using reject inference, but how can you improve your reject inference design? Reject inference is a technique used to classify the performance outcome of prospective customers within the declined or nonbooked population so this population’s performance reflects its performance had it been booked. A common method is to develop a parceling model using credit bureau attributes pulled at the time of application. This type of data, known as pre-diction data, can be used to predict the outcome of the customer prospect based on a data sample containing observations with known performance. Since the objective of a reject inference model is to classify, not necessarily predict, the outcome of the nonbooked population, data pulled at the end of the performance window can be used to develop the model, provided the accounts being classified are excluded from the attributes used to build the model. This type of data is known as post-diction data. Reject inference parceling models built using post-diction data generally have much higher model performance metrics, such as the KS statistic, also known as the Kolmogorov-Smirnov test, or the Gini coefficient, compared with reject inference parceling models built using pre-diction data. Use of post-diction data within a reject inference model design can boost the reliability of the nonbooked population performance classification. The additional lift in performance of the reject inference model can translate into improvements within the final model design. Post-diction credit bureau data can be easily obtained from Experian along with pre-diction data typically used for predictive model development. The Experian Decision Analytics team can help get you started.

Early reports suggest the 2017 holiday season was a good one for retailers. Consumers were in the mood to spend, and as such, Americans’ total credit card debt continued to climb. Americans planned to spend $862 on gifts for the season, a huge jump from the $752 they planned on spending in 2016. And the numbers were significantly higher than their estimate in any November since 2007 -- just before the 2007-2009 recession. 29% of Americans said they planned to spend more than $1,000. What does this mean for card portfolios? Well, business is booming, but they should also prepare for the time of year when consumers are most apt to seek out debt consolidation and transfer options. A recent NerdWallet analysis revealed the average household that’s carrying credit card debt has a balance of roughly $15,654. Dig deeper into retail card specifically and reports indicate Americans are carrying $1,841 in retail debt. “There is seasonality to consumer credit card behavior,” said Denise McKendall, a credit card and trended data specialist for Experian. “As we roll into the late winter months and early spring, consumers often seek ways to transfer card debt to lower interest rate options, consolidate debt from multiple cards and perhaps even pull out personal loans. This makes it an ideal time for card portfolio managers to leverage data to anticipate consumer behaviors and be able to offer the best rates and options to retain cardholders and grow.” Card portfolio managers should consider these questions: What is my portfolio risk? Did some of my consumers overextend themselves? Do I have collections triggers on my accounts to mitigate risk and manage delinquencies? Which consumers in my portfolio will be looking to consolidate debt? Should I reassess credit line limits? Which of my consumers show a high propensity to make a balance transfer? Do I have opportunities to grow my portfolio by offering attractive rates to new customers? Which customers will leave after low introductory rates expire? Can I use this time of year to become the first credit card consumers’ consistently use, rather than the second or third card they pull from wallet? At first glance, it might appear challenging to answer many of these questions, but with the right data and analytics, a card manager can easily establish a game plan to conquest new business, mitigate risk and retain existing, high-value consumers. The robust holiday season was a boom for the economy. Now card companies need to ready themselves for the aftermath.