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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.

Consumers are hungry for more personalized marketing, and I’m an actual example. As a new stepmom to two young kids, who has a full-time job, I rarely have any down time. No revelation there. I no longer have time to surf the web to buy clothes. And shepherding everyone to an actual store to shop? #forgetaboutit I’m not alone. Of the 57 percent of women in the U.S. workforce, 70 percent have a child under the age of 18. We don’t always have the time to shop for clothes, financial products, and nearly anything else, but it doesn’t mean we don’t need or want to. I would give the right bank or retailer my data in exchange for personalized marketing offers in my inbox, social feeds and mailbox. And many others would, too. Sixty-three percent of Millennial consumers and 58 percent of Gen Xers are willing to share data with companies in exchange for personalized offers, discounts and rewards. This indicates consumers are craving more customized marketing. Providing their personal data to get that is acceptable to them. In the financial services space, Mintel research shows that just 61 percent of male consumers, 49 percent of consumers aged 18-44, and 44 percent of Hispanic Millennials have a general-purpose credit card, either with or without rewards (Mintel’s Marketing Financial Services Report for June 2017). This indicates a significant market opportunity for cards that offer segmented or boosted rewards based on specific sectors and categories. Here are some other interesting trends specific to financial services: Relying on Experts Although chatbots and robo-advisors allow easy access to many financial services, 81 percent of consumers prefer in-person meetings when it comes to personalized financial advice. According to Mintel, men aged 18-44 are most interested in a free consultation with a financial advisor, and 19 percent of consumers are open to a free consultation. This interest surpasses attending free classes about finance and receiving email and mobile alerts from a financial institution. Quick, Efficient Delivery While consumers are calling for increased personalization, they also want it delivered quickly and efficiently. These expectations create unique challenges for financial institutions of all sizes. Some banks have embraced “card finder” apps, which allow consumers the convenience of inputting personal information to generate customized offers. There is a huge opportunity for financial institutions to leverage available consumer data to understand their target audience, and then deliver relevant products via multiple channels where they are consuming media now. Those who do will be positioned to provide personalized financial recommendations that were impossible just a few years ago.

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.

The U.S. Senate Banking Committee passed a financial regulatory relief bill (S. 2155) in December 2017 aimed at reducing regulatory burdens on community banks, credit unions and smaller regional banks.  Committee Chairman Senator Mike Crapo (R-ID), sponsored the bill, which has strong bipartisan support, with 23 cosponsors (11 Republicans and 12 Democrats and an independent). The package is likely to be considered by the full Senate in early 2018. The legislation includes two provisions related to consumer credit reporting.  Both were adopted, in part, in reaction to the Equifax data breach. As the bill moves through the legislative process during 2018, it will be important for all participants in the consumer credit ecosystem to be aware of the potential changes in law. One provision deals with fraud alerts and credit freezes for consumers and the other deals with how medical debt is processed for veterans who seek medical treatment outside the VA system. Credit Freezes The bill amends the Fair Credit Reporting Act to provide consumers with the ability to freeze/unfreeze credit files maintained by nationwide credit reporting agencies at no cost, and would extend the time period for initial fraud alerts from 90 days to one year. The credit freeze provisions would also establish a process for parents and guardians to place a freeze on the file of a minor at no cost. The bill would require the nationwide credit reporting agencies to create webpages with information on credit freezes, fraud alerts, active duty alerts and pre-screen opt-outs and these pages would be linked to the FTC’s existing website, www.IdentityTheft.gov.  The credit freeze and minor freeze provisions would preempt State laws and create a national standard. Protections for Veterans The bill also incorporates a provision that would prohibit credit bureaus from including debt for health-care related services that the veteran received through the Department of Veterans Affairs’ Choice Program. The provision would cover debt that the veteran incurred in the previous year, as well as any delinquent debt that was fully paid or settled. The legislation would require a consumer reporting agency to delete medical debt if it receives information from either the veteran or the VA that the debt was incurred through the Veteran’s Choice Program. What’s next The bill now awaits consideration before the full Senate. Senate Majority Leader Mitch McConnell has said that the bill is a “candidate for early consideration” in 2018, but the exact timing of floor debate has yet to be scheduled. Once the package passes the Senate, it will need to be reconciled with the regulatory relief package that was passed by the House last spring.

The nation’s economic recovery is continuing in a positive upward trend with consumer credit scores coming exceptionally close to pre-recession numbers—the healthiest in a decade. Experian’s 8th annual State of Credit report reveals the nation’s average credit score is up two points year-over-year to 675, and is just four points shy of the 2007 average of 679. “The trend line we are seeing is quite promising,” said Michele Raneri, Experian vice president of analytics and new business development. “With employment and consumer confidence on the rise, the data is indicating that we have made great progress as a country since the recession. The economy is expected to expand at a healthy pace this year and we believe that credit will continue to rebound. All of the factors point towards a good year for credit in 2018.” The study also revealed that year-over-year: Personal loan and auto loan originations increased 11 percent and 6 percent, respectively. The average number of retail cards remains at 2.5 per consumer, while the average retail debt increased $73 to $1,841 per consumer. The average number of bankcards increased slightly from 3.03 to 3.06, with the average card balance also increasing by $166 to $6,354. Instances of late payments (includes bankcard and retail) remained about the same at under 1 percent. And importantly, consumers have a positive outlook with consumer confidence up 25 percent. Top of the credit charts As part of the annual study, Experian analyzed consumer credit habits in U.S. cities. As in previous years, Minnesota continues to stand out with three of its cities — Minneapolis, Rochester and Mankato—leading the way with credit scores of 709, 708 and 708, respectively. Wausau, Wis. (706), Green Bay, Wis. (705) round out the top five. Again, Greenwood, Miss., and Albany, Ga., ranked the lowest with scores of 624 and 626. While still at the bottom of the list, Greenwood and Abany residents did improve their scores by two points. Riverside, Calif.,—fifth on the list—improved its score by four points—the greatest increase of any city in the bottom 10. Generational divide Taking the research further, Experian analyzed consumer credit information by generation, and found: Generation Z (born 1996 and later) is building credit through different methods than the generations before them, with heavier student loan debts and fewer credit cards and department store cards. And they are keeping debts low and managing them well. Generation Y/Millennials (born 1977-1995) have seen their scores climb four points over the past year. They’ve also decreased their overall average debt by nearly eight percent, but have added six percent in mortgage debt. Generation X (born 1965-1976) has a credit score of 658, the highest mortgage debt of all generations, and a high instance of late payments compared to the national average. Their scores have improved, so they are managing their debts better than in the past,. Baby Boomers (born 1946-1964) continue to carry quite a bit of mortgage debt, and have the lowest late payment instances of all the generations. The Silent Generation (born 1945 and before) has quite a bit of mortgage debt, but are keeping other debts low and making payments on time. At 729, they have the best credit score of all generations and the fewest late payments of any generation. To review findings from Experian’s 2017 State of Credit report, join WiseBread’s chat Jan. 18. To register, go to www.wisebread.com and follow #wbchat. To chat with Experian live, and learn more about credit, join #CreditChat hosted by @Experian_US on Twitter every Wednesday at Noon PT/3 p.m. ET.

As soon as the holiday decorations are packed away and Americans reign in the New Year, the advertisements shift to two of our favorite themes – weight loss and taxes. No wonder the “blues” kick in during February. While taxes aren’t due until April 17, the months of January, February and March have consumers prepping to file. Coincidentally, it is also a big season for lenders to collect after the high-spending months of October through December. “Knowing which of your customers may receive a refund is critical,” said Colleen Rose, an Experian product manager specializing in the collections industry. “This information can help lenders create a strategy to capitalize on this important segment during the compressed collections window.” The industry has become more familiar with trended data and its ability to predict how consumers are faring on the credit score slider, but many don’t know that it has also proven popular in identifying people who may get a tax refund, and who is likely to use a refund to pay down delinquent balances. The past two tax seasons are evaluated to provide a complete picture of a customer’s behavior during tax refund season. Balance, credit limit and other historical fields are incorporated with tradeline-level data to determine who paid down their delinquent balances during this time. According to the IRS, in fiscal year 2016, the average individual income tax refund was about $3,050, so it’s a prime time for consumers to come into some unexpected cash to either pay down debt or spend. It’s estimated that 35% of consumers who get a refund will pay down debt. “Using Experian’s trended data attributes, we’ve identified past-due customers who paid down a delinquent tradeline balance by at least 10% and made a large payment during tax season,” said Rose. “With these specific attributes, we can help clients target a very desirable population during the critical collections months, helping them to refine their campaigns and create offers geared toward this population.” Anticipating who is likely to receive a refund and use it to pay down debt can influence how collections departments develop their messaging, call outreach and mailings. And for consumers who owe multiple debts, these well-timed touchpoints and messages could influence who they pay back first. The collectors with the best data, once again win, with trended data providing the secret sauce for predictions. ‘Tis the season for taxes.

Student loan debt is weighing down Americans of all generations, but a college education is still prized as the ticket to opportunity. So will the debt continue to climb? Where will students turn for funding? We interviewed Vince Passione, founder and CEO of LendKey, a lending-as-a-service platform specializing in student lending, to gain his perspective on the state of student lending and how the space is evolving for both consumers and lenders. We’ve all seen the headlines about U.S. student loan debt now accounting for $1.4 trillion. The majority of these loans are government-funded, but do you see this shifting? There are many factors at play here. Tuition is rising rapidly and will soon outpace the current level of governmental support available to students searching for loans. Meanwhile, today’s geopolitical climate signals that the current levels of federal funding will also decrease. With these two confounding trends, the need for competitively priced private financing and refinancing options will increase. The student loan industry will shift toward private lenders such as credit unions and banks in order for students to continue to obtain the funds they need for tuition and other college expenses. The key to helping this transition happen is for banks and credit unions to adopt the user-friendly technology platforms that appeal to these prospective student borrowers. Your end-to-end cloud-based technology platform enables lenders to get into the student loan space. How does this work and what must lenders consider as they underwrite and manage a student loan portfolio? Our turnkey platform is unique, in that it lets lenders control underwriting and pricing, unlike the “disruptive” model utilized by many other technology companies in the industry. Most community banks and credit unions lack the in-house resources to develop, implement and maintain an online lending platform. At the same time, millennials and young borrowers continue to prefer the online interface rather than engaging with a brick-and-mortar establishment. We’re committed to partnering with banks and credit unions to allow them to offer private consumer loans, such as student loans, and support them with our technology (loan application and decisioning) and people (customer service agents and loan processors). A strong grasp on the technology and support aspects of online lending platforms alone is merely the foundation for a successful program. As the student lending asset classes are highly regulated, and the regulations are constantly changing, lenders must look to partner with a firm that has a concrete understanding of the regulation, risk and customer service to translate the information to prospective borrowers. I’ve heard you use the phrase “HENRY.” Can you explain what this is and why these individuals are so lucrative for lenders? HENRY stands for “High Earners, Not Rich Yet” and is a term that can be applied to many millennials and young people in today’s economy. This demographic is typically college graduates with well-paying jobs, but have not yet established themselves financially or accrued enough wealth to subsidize larger purchases like cars, homes, renovations and advanced degrees. This is also why they are so lucrative for lenders. HENRYs have just entered their prime borrowing years and are consumers who will easily be able to pay back loans for cars, homes and renovations. But for most of this demographic, their first experience with a financial service product will be a student loan. It is important to get in on the ground floor with these borrowers through student lending to establish a trusted relationship that will result in repeat loans and referrals. You’ve done a great deal of research on millennials and how they are managing student loans. Can you share some of your key learnings? Do you believe Generation Z will behave and manage student debt similarly? It’s no secret that millennials are more apprehensive of student loans than previous generations. As Gen Z begins to enter college, many are plagued with stigmas set forward by the poor experiences millennials experienced with student loans, making them wary of debt. According to a study, 63 percent of the students said they would “possibly” take on student debt, down from 71 percent in 2016. Gen Z is better prepared by seeing the preceding generation grapple with loan issues. Many are making smarter decisions on schools and programs, and are attentive when it comes to monitoring for updates in regulation. As this generation continues to go through the typical collegiate years, the geopolitical climate, as well as rising tuition costs, will increase the need for competitively priced private financing options for Gen Zers. Finally, what trends or predictions do you see occurring in the student lending space over the next five years? The need for student loans continues to exist and shows no sign of slowing down anytime soon, but lenders are only recently opening their eyes to the opportunity that this massive market presents. With the impending drop in federal funding, more FinTech companies will continue to pop up to address this need. This spike in disruption also poses a threat to banks and credit unions, however. With more FinTechs available to help shoulder the burden of student lending, banking and credit union executives must be more judicious when vetting technology partners to ensure they’re working with a partner that meets their regulatory standards, supports their current and prospective clients, and lets them retain the control they wish to keep in-house.

  The journey to a mortgage is complex and expensive, so of course the transaction will require more than a few swipes on a smartphone. The U.S. existing home median sales price in October was $274,000 – not cheap. Still, with advancements in digital verification, lenders can dramatically accelerate the process, providing benefits to both their own operations and the consumer mortgage experience. Underwriting a sizeable loan can take weeks with the task of collecting income and asset documents to analyze and verify. In fact, one source from the Mortgage Bankers Association says the average mortgage application has ballooned to 500 pages. The consumer is typically asked to find, print and scan papers revealing insights around employment status and wages, bank and retirement accounts, debts and beyond. The good news is that this process can be handled digitally, and I’m not talking about simply scanning and emailing. Verification solutions exist to enable consumers to grant limited and secure access to digitally verify assets and income. As lenders evaluate verification solutions, one of the key differentiators to seek is Fannie Mae Day 1 Certainty, which claims to slash the average cycle time for income validation by 8.1 days, employment validation by 11.9 days, and asset validation by 6.1 days. * Fannie Mae features a list of approved vendors who provide Fannie Mae-approved verification reports. This group of authorized suppliers receive freedom from representations and warranties for more efficient risk management, and additionally receive the benefit of a more streamlined process through Fannie Mae’s Desktop Underwriter® (DU®). DU’s latest enhancement leverages a verification of asset report derived from aggregated bank account data, something Finicity (an Experian partner) is approved to utilize. Building on Day 1 Certainty, Finicity is participating in a new single source pilot with Fannie Mae to validate income, assets and employment. While it will take time for lenders to embrace this new technology – and consumers will need to feel comfortable granting the digital access and understanding how the process works – the thought is the mortgage journey will become faster and offer an optimized borrower experience. Like so many other aspects in our lives, mortgage is bound to go digital. *Average days saved reflects data captured between January 2017 and June 2017.

Alternative Data Shedding New Light on Consumers Why Investors Want Alternative Data Banks and Tech Firms Battle Over Something Akin to Gold: Your Data Alternative data for credit has created national headlines in the past year and a lasting buzz in the financial services world. But what exactly qualifies as alternative data in credit? How can it benefit lenders? Consumers? Ask two people these questions and you may get very different answers. Experian defines alternative data as FCRA-compliant data points that are not typically considered when evaluating a potential customer’s creditworthiness. These data points may include rent payments; utility payments, including gas, electric; telecommunications payments, such as mobile telephones; insurance payments; and any other recurring financial obligations. Taking these alternative data points into account can benefit consumers and lenders in multiple ways. Consider that roughly 45 million Americans have either no credit history, or a credit history that is too scarce or outdated to manufacture a credit score. This group of consumers includes not only minority consumers or those from low income neighborhoods, but also the shared economy workforce and millennials without traditional credit histories. Some estimate 121 million U.S. adults are credit-challenged with thin-to-no credit file and subprime credit scores below 600. “People with little or no credit history, or who lack a credit score, have fewer opportunities to borrow money to build a future, and any credit that is available usually costs more,” said Richard Cordray, while he was director of the Consumer Financial Protection Bureau. Indeed, these consumers are in a catch-22; many lenders will not lend to consumers with credit scores of under 620. In turn, these consumers have trouble building credit, and they are blocked from achieving goals like buying a car, owning a home or starting a business. By combining credit reports with alternative data, a more complete picture of subprime, near-prime and thin-file consumers can develop. And analysis of this data can help lenders evaluate a consumer’s ability to pay. When alternative data like rent payments and an individual’s short term lending history are trended appropriately, it can be an accurate predictor of an individual’s financial behavior, and can be an important step toward promoting greater financial inclusion for more consumers. In addition to using alternative data in underwriting, lenders can leverage the data to help with: Expanding the prospecting universe. Data can be used to enrich batch prospecting decisioning criteria to identify better qualified prospects, suppress high-risk consumers, and offer a more complete borrowing history Account review. Alternative data can help signal a consumer’s financial distress earlier, better manage credit lines and grow relationships with existing consumers. Collections. Identify consumers who are rebuilding credit with specialty finance trades, or who are exhibiting high-risk behaviors in the alternative financial services space. More info on Alternative Credit Data More Info on Alternative Financial Services

The credit card marketplace is a crowded and complex landscape. Recent research by Experian shows the average U.S. consumer has 3.1 credit cards and 2.5 retail cards, with an average balance of $6,354 and $1,841, respectively. So how can you build upon your existing customer relationships and offer the right products to the right people at the right time? By understanding consumer behavior. Pretty simple concept. But targeting viable consumers and making enticing offers takes some detective work. Gone are the days of demographic-based approach to audience segmentation for credit marketing campaigns. Consumers are now engaged on their smartphones, laptops, tablets, fitness bands, across countless apps, browsers, emails and more. Simply knowing a person’s gender and age doesn’t provide any information about how he spends the day, his consumer behaviors, personal interests, unique wants or needs. Developing rich consumer personas based on transaction credit data can be a powerful tool to understanding consumers so lenders can design more relevant and personalized credit offers, experiences and products to a very targeted audience. Experian DataLabs can help by analyzing transaction data to understand the consumers in your portfolio. For example, looking at your portfolio of 40-year-olds in the U.S. provides basic demographic information. A closer look at transaction data could reveal unique details within the age group to help you group and target, such as: Frequent travelers: These road warriors log serious miles. If they’re not traveling for work, they’re cashing in miles for vacation. This unique group leads your portfolio in airfare, cruise line, car rental, hotel and travel agency spend. With so much time spent away from home, this group is rarely found in grocery stores. Local business owners: Advertising, computer equipment, and software are typical expenses of this segmented group. There may be an opportunity to capture spend outside their business activity or to ensure they have the right card to fit their business needs. Constant commuters: These consumers use their card for local travel and transportation. And they are less likely to use their card for expenses related to other types of travel or maintaining a vehicle. After a long day, they like to grab a drink while waiting for the train. Online Shoppers: Consumers in this group use their card with various online merchants, including Amazon, Etsy, iTunes, and PayPal. Online shoppers are also above average spenders in elementary education, child care services, and family clothing. Social hipsters: They can be found meeting with friends for coffee and drinks, and are more likely to rely on local transportation and tend to eat out instead of cooking in. Effective audience segmentation ensures that your marketing dollars are invested in real people who are most likely to respond on certain media, have already expressed an interest in your product, and are geographically accessible to a specific retail location. Every campaign should be as dynamic and unique as its consumers. The powerful combination of consumer and transaction data allows you to customize audience segments to maximize customer engagement and drive campaign success.

As we enter the holiday season, headlines abound around the shifts and trends in retail. How are consumers shopping? What are they buying online versus in-store? How can retailers maintain share and thrive? To gain some fresh perspective on the retail space, we interviewed John Squire, CEO and co-founder of DynamicAction, a business featuring advanced analytics solutions designed specifically for eCommerce, store and omnichannel retail teams. Squire has had a tenured career in the retail and technology sectors serving in key executive roles for IBM Smarter Commerce and Coremetrics. He has spent the past decade guiding nearly every retail brand to a better understanding of their customers and utilization of their data to make profitable decisions.  Business headlines claim we are in the midst of a retail apocalypse. Is this statement a reality?  The reality is that retail is in a renaissance – a revolution driven by the most empowered, connected consumer in history, a burgeoning technology infrastructure and retail tech innovators who have disrupted the status quo. The most agile of retailers and brands are leaning forward to serve their customer with remarkable experiences in the store, online and anywhere the customer decides to interact with the brand. And for those retailers, the days ahead are filled with newfound opportunity. However, the retailers and brands who don’t have a strong core purpose beyond being filler between anchor stores may no longer have a place in this new world of retail. The strongest retailers and brands will tap into their wealth of customer data to better understand, and therefore better serve their customers, creating long-term relationships. They should only continue gain in strength as consumers concentrate more and more of their time (and wallets) with businesses that passionately focus on their unique needs and buying patterns. It seems like shoppers are increasingly turning online to make their purchases. Is this the case, and do we see seasonal spikes with this trend? The key for successful retailers is to understand that customers aren’t just searching, browsing, buying and returning online OR in-store. They are shopping online AND in-store … and even online while in-store. Shoppers simply do not see channels, and the sooner that retailers reorganize their mindset, their organizations and their data understanding around this reality, the more successful they will become. Shoppers are indeed moving online with increasing frequency and larger amounts of their overall spending. Connecting data across the enterprise, across their partners and across social channels is critical in enabling their retailing teams to make decisions on how best to simultaneously serve their customers and their company’s shareholders. If retailers have a store credit card to offer to consumers, how can they encourage use and get them to maximize spend? Are there particular strategies they should employ? As with any loyalty program or service item, consumers are looking for tools and offers they value. Therein lies the opportunity and the challenge. Value can come in many forms, depending on the individual. Does the credit card offer travel or retail points, or dollars that they can accumulate? Does the credit card save them time? Provide them with additional purchasing power? Reduce their friction of making large purchases? Increase the security of the initial purchase and long-time use of the product or service? The competition for just a consumer’s current and future wallet is being upended by retailing offers that are serving up entertainment, services, convenience and broader product selections. Understanding the high-value activities correlated to their VIP consumers generating the highest amount of profit for the business is the essential to building strategies for encouraging card use. Beyond online shopping, are there other retail trends you see emerging in the coming year? What excites you about the space? Online shopping is not a trend; it is retail’s greatest disruption of the last 100 years. Digitization of shopping in both the online and store setting is what thrills me. One to watch is Wal-mart. The company is taking a highly energized track to build a business of next-gen brands and using their supply chain acumen to battle Amazon, while simultaneously gaining huge amounts of market share from other less sophisticated and strong retailers. In addition, seeing how next-gen brands like Warby Parker, Everlane, Untuckit, Bonobos, Indochino and Rent the Runway are rapidly building out a store experience, albeit radically different than the stores of the past, is exciting to watch. Seeing the growth in Drone deliveries outside the US for retail and commercial applications is surely the next big jump for ‘Next Hour’ in-home delivery. Made-to-order with a very short lead-time is also a big trend to keep an eye on. However, what excites me most in the industry is the universal mind shift that is becoming undeniable in retail: that data understanding and action will be the very basis for customer centricity and companies’ growth. Retailers have had access to these data pools for ages, but the ability to sync the data sets across channels, make sense of the findings and take action at the speed the consumer expects is truly the next leap forward for great retailers. To learn more about the state of retail credit cards, access our latest report.

If someone asked you for stats on your retail card portfolio, would you respond with the number of accounts? Average spend per month? Or maybe you know the average revolving balance and profitability. Notice something about that list? Too many lenders think of their portfolio and customers as numbers when in reality these are individuals expressing themselves through their transactions. In an age where consumers increasingly expect customized experiences, marketing to account #5496115149251 is likely to fall on deaf ears. Credit card transaction data including bankcard, retail, and debit cards holds a wealth of information about your consumers' tastes and preferences. Think about all the purchases you made using a credit card this past month. Did you shop at high-end retail stores or discount stores? Expensive restaurants or fast food? Did you buy new clothes for your kids? Maybe you went to the movies, or met friends at a bar. How you use your card paints a picture of who you are. The trick is turning all those numbers into insights. You may have been swept up in all the excitement around Apple’s announcement of the iPhone X in August. However, you may have overlooked the incorporation of Neural Embedding, or machine learning, as one of the most powerful features of the new phone. Experian DataLabs has developed an innovative approach to analyzing transaction data using similar techniques. Unstructured machine learning is applied and patterns begin to emerge around customer spending. The patterns are highly intuitive and give personality to what was previously an indecipherable stream of data. For example, one group may be more likely to spend on children’s clothing, child care services, and theme parks while another spends on expensive restaurants, airlines, and golf courses. If these two consumers happened to spend approximately the same each month on your card, you’d probably treat them as category. But understanding one is a young family and their other is jet setter allows you to tailor messaging, offers, and terms to their needs and use of your products. Further, you can ensure they have the best product based on their lifestyle to minimize silent attrition as their needs evolve. But it’s not just about marketing. When your latest attrition dashboard is updated, what period are you measuring? Do you analyze account closures from the previous month? Maybe a few months back? Understanding churn is important, but it’s inherently reactive and backward looking. You wouldn’t drive a car looking in the rearview mirror, would you? Experian enables clients to actively monitor the portfolio for attrition risk by analyzing usage patterns and predicting future spend. Transactions are then monitored up to daily and, when spend doesn’t occur as expected, an alert is sent so you can proactively attempt to save the account before it closes. These algorithms are finely tuned to reduce false positives that can come from seasonality or predictable gaps in spend such as only using a card at certain times during the week. Most importantly, it gives you an opportunity to manage each account and address changing customer needs instead of waiting for customers to call to cancel. So how well do you know your customers? If you’re still looking at them as numbers, it may be time to explore new capabilities that allow you to act small, no matter how large your portfolio. Transaction Data Insights brings cutting-edge machine learning capabilities to lenders of all sizes. By digging into behavioral segments and having tools to monitor and send alerts when a consumer is showing signs of attrition risk, card portfolios can suddenly treat customers like people, providing the customized experience they increasingly expect.  

In 2017, 81 percent of U.S. Americans have a social media profile, representing a five percent growth compared to the previous year. Pick your poison. Facebook. Instagram. Twitter. Snapchat. LinkedIn. The list goes on, and it is clear social media is used by all. Grandma and grandpa are hooked, and tweens are begging for accounts. Factor in the amount of data being generated by our social media obsession – one report claims Americans are using 2,675,700 GB of Internet data per minute – and it makes some lenders wonder if social media insights can be used to assess credit risk. Can banks, credit unions and online lenders look at social media profiles when making a loan decision and garner intel to help them make a credit decision? After all, in some circles, people believe a person’s character is just as important as their income and assets when making a lending decision. Certainly, some businesses are seeing value in collecting social media insights for marketing purposes. An individual’s interests, likes and click-throughs reveal a lot about their lifestyle and potential brand linkages. But credit decisions are different. In fact, there are two key concerns when considering social media data as it pertains to financial decisions. There is that little rule called the Equal Credit Opportunity Act, which states credit must be extended to all creditworthy applicants regardless of race, religion, gender, marital status, age and other personal characteristics. A quick scan of any Facebook profile can reveal these things, and more. Credit applications do not ask for these specific details for this very reason. Social media data can also be manipulated. One can “like” financial articles, participate in educational quizzes and represent themselves as if they are financially responsible. Social media can be gamed. On the flip side, a consumer can’t manipulate their payment history. There is no question that data is essential for all aspects of the financial services industry, but when it comes to making credit decisions on a consumer, FCRA data trumps everything. In the consumer’s best interest, it is essential that credit data be both displayable and disputable. The right data must be used. For lenders, their primary goal is to assess a consumer’s stability, ability and willingness to pay. Today, social media can’t address those needs. It’s not to say that social media data can’t be used in the future, but financial institutions are still grappling with how it can be predictive of credit behavior over time. In the meantime, other sources of data are being evaluated. Everything from including on-time utility and rental payments, insights on smaller dollar loans and various credit attributes can help to provide a more holistic view of today’s credit consumer. There is no question social media data will continue to grow exponentially. But in the world of credit decisioning, the “like” button cannot be given quite yet.

Everyone loves a story.  Correction, everyone loves a GOOD story. A customer journey map is a fantastic tool to help you understand your customer’s story from their perspective. Perspective being the operative word. This is not your perspective on what YOU think your customer wants. This is your CUSTOMER’S perspective based on actual customer feedback – and you need to understand where they are from those initial prospecting and acquisition phases all the way through collections (if needed). Communication channels have expanded from letters and phone calls to landlines, SMS, chat, chat bots, voicemail drops, email, social media and virtual negotiation. When you create a customer journey map, you will understand what channel makes sense for your customer, what messages will resonate, and when your customer expects to hear from you. While it may sound daunting, journey mapping is not a complicated process. The first step is to simply look at each opportunity where the customer interacts with your organization. A best practice is to include every department that interacts with the customer in some way, shape or form. When looking at those touchpoints, it is important to drill down into behavior history (why is the customer interacting), sociodemographic data (what do you know about this customer), and customer contact patterns (Is the customer calling in? Emailing? Tweeting?). Then, look at your customer’s experience with each interaction. Again, from the customer’s viewpoint: Was it easy to get in touch with you? Was the issue resolved or must the customer call back? Was the customer able to direct the communication channel or did you impose the method? Did you offer self-serve options to the right population? Did you deliver an email to someone who wanted an email? Do you know who prefers to self-serve and who prefers conversation with an agent, not an IVR? Once these two points are defined: when the customer interacts and the customer experience with each interaction, the next step is simply refining your process. Once you have established your baseline (right channel, right message, right time for each customer), you need to continually reassess your decisions.  Having a system in place that allows you to track and measure the success of your communication campaigns and refine the method based on real-time feedback is essential. A system that imports attribute – both risk and demographic – and tracks communication preferences and campaign success will make for a seamless deployment of an omnichannel strategy. Once deployed, your customer’s experience with your company will be transformed and they will move from a satisfied customer to one that is a fan and an advocate of your brand.

Direct mail is dead. It’s so 90s. Digital is the way to reach consumers. Marketers have heard this time and again, and many have shifted their campaign focus to the digital space. But as our lives become more and more consumed by digital media, consumers are giving less time and attention to the digital messages they receive. The average lifespan of an email is now just two seconds and brand recall directly after seeing a digital ad is just 44%, compared to direct mail which has a brand recall of 75%. Further research shows direct mail marketing is one of the most effective tools for customer acquisition and loan growth. The current Data & Marketing Association (DMA) response rate report reveals the direct mail response rates for 2016 were at the highest levels since 2003. Additionally, while mailing volume has trended down since October 2016, response rates have trended up, and reached 0.68% in March 2017, up from 0.56% in October 2016. Using data and insights to tailor a direct mail campaign can yield big results. Here are some attention-grabbing tips: Identify Your Target Market: Before developing your offer and messaging strategy, begin with the customer profile you are trying to attract. Propensity models and estimated interest rates are great tools for identifying consumers who are more likely to respond to an offer. Adding them as an additional filter to a credit-qualified population can help increase response rates. Verify your Mailing List: Experian’s address verification software validates the accuracy and completeness of a physical address, flags inaccuracies, and corrects errors before they can negatively impact your campaign. Personalize the Offer: Consumers are more likely to open offers that are personalized, and appeal to their life stage, organizational affiliations or interests. Experian’s Mosiac profile report is a simple, inexpensive way to gather data-based insight into the lifestyle and demographics of your audience. Time the Offer: Timing your campaign with peak market demand is key. For example, personal loan demand is highest in the first quarter after the holidays, while student loans demand peaks in the Spring. Direct mail can help overcome digital fatigue that many consumers are experiencing, and when done right, it’s the printed piece that helps marketers boost response rates.