Data & Analytics
When it comes to buying a vehicle, we found that consumers who owned a Certified Pre-Owned (CPO) used vehicle are most loyal to the original vehicle manufacturer — to the tune of 75% — when purchasing another CPO used vehicle. Consumer buying patterns show that the loyalty rate to the manufacturer is also high when: Moving from a new vehicle to another new vehicle (60.9%). Switching from a CPO used vehicle to a new vehicle (54.1%). By understanding loyalty rates and other key market trends, manufacturers, dealers and resellers can make smarter decisions that create more opportunities for themselves and in-market consumers. More insights>
Big changes for the new year 2017 is expected to bring some big changes. But what do those changes mean for the financial services space? Here are 3 trends and twists Experian expects to occur over the next 12 months: Trump and the Republican-controlled Congress will move forward with a deregulatory agenda. Recognizing and scoring more previously invisible consumers through alternative data sources will be emphasized. Personalized credit offers delivered via multiple digital channels in a sequenced, trackable manner. What are your predictions for 2017? Only time will tell, but we’re certain that regulations and advancements in digital will be huuuge. >>More 2017 trends
Experian integrated Cloudera Enterprise onto its cloud environment so clients can make innovative decisions in milliseconds with data as the core technology.
Using digital technology like a big bank How was your holiday? Are the chargebacks rolling in yet? It’s no secret - digital technology like mobile device usage has increased significantly over the years, making it a breeding ground for fraudsters. As credit unions continue to grow their membership, their fraud security treatments need to grow as well. Bigger banks are constantly updating their fraud tools and strategies to fight against cybercrime and, therefore, fraudsters are setting their eyes on credit unions. Even as I write this, fraudsters are searching and targeting credit unions that don’t have their mobile channel secured. They attempt to capitalize on any weakness or opportunity: Registering stolen cards to mobile wallets Taking over an account via mobile banking apps Using a retailers’ mobile app to make fraudulent payments Disabling the SIM card in the victim’s phone and diverting the one-time password sent through text message to their own phones These are clever ways to commit fraud. But credit unions are becoming wise to these new threats and are serious about protecting their members. They are incorporating device intelligence with a solid identity authentication service. This multi-layered approach is essential to securing mobile channels, and protecting your Credit Union from chargebacks. To learn more about our fraud solutions, click here.
Experian shares five trends and twists coming over the next 12 months, that could push new boundaries and in many cases improve the customer experience as it pertains to the world of credit and finance.
Looking to score more consumers, but worried about increased risk? A recent VantageScore LLC study found that consumers rendered “unscoreable” by commonly used credit scoring models are nearly identical in their financial and credit behavior to scoreable consumers. To get a more detailed financial portrait of the “expanded” population, credit files were supplemented with demographic and economic data. The study found: Consumers who scored above 620 using the VantageScore 3.0 model exhibited profiles of sufficient quality to justify mortgage loans on par with those of conventionally scoreable consumers. 3 to 2.5 million – a majority of the 3.4 million consumers categorized as potentially eligible for mortgages – demonstrated sufficient income to support a mortgage in their geographic areas. The findings demonstrate that VantageScore is a scalable solution to expanding mortgage credit without relaxing credit standards should the FHFA and GSEs accept VantageScore 3.0. Want to know more?
Technology sharing can unlock a more effective strategy in fighting fraud. Experian’s multi-layered and risk-based approach to fraud management is discussed as many businesses are learning that combining data and technology to strengthen their fraud risk strategies can help reduce losses. Evolving fraud schemes, changes in regulatory requirements and the advent of new digital initiatives make it difficult for businesses to manage all of the tools needed to keep up with the relentless pace of change.
2017 data breach landscape Experian Data Breach Resolution releases its fourth annual Data Breach Industry Forecast report with five key predictions What will the 2017 data breach landscape look like? While many companies have data breach preparedness on their radar, it takes constant vigilance to stay ahead of emerging threats and increasingly sophisticated cybercriminals. To learn more about what risks may lie ahead, Experian Data Breach Resolution released its fourth annual Data Breach Industry Forecast white paper. The industry predictions in the report are rooted in Experian\'s history helping companies navigate more than 17,000 breaches over the last decade and almost 4,000 breaches in 2016 alone. The anticipated issues include nation-state cyberattacks possibly moving from espionage to full-scale cyber conflicts and new attacks targeting the healthcare industry. \"Preparing for a data breach has become much more complex over the last few years,\" said Michael Bruemmer, vice president at Experian Data Breach Resolution. \"Organizations must keep an eye on the many new and constantly evolving threats and address these threats in their incident response plans. Our report sheds a light on a few areas that could be troublesome in 2017 and beyond.\" \"Experian\'s annual Data Breach Forecast has proven to be great insight for cyber and risk management professionals, particularly in the healthcare sector as the industry adopts emerging technology at a record pace, creating an ever wider cyber-attack surface, adds Ann Patterson, senior vice president, Medical Identity Fraud Alliance (MIFA). \"The consequences of a medical data breach are wide-ranging, with devastating effects across the board - from the breached entity to consumers who may experience medical ID fraud to the healthcare industry as a whole. There is no silver bullet for cybersecurity, however, making good use of trends and analysis to keep evolving our cyber protections along with forecasted threats is vital.\" \"The 72 hour notice requirement to EU authorities under the GDPR is going to put U.S.-based organizations in a difficult situation, said Dominic Paluzzi, co-chair of the Data Privacy & Cybersecurity Practice at McDonald Hopkins. \"The upcoming EU law may just have the effect of expediting breach notification globally, although 72 hour notice from discovery will be extremely difficult to comply with in many breaches. Organizations\' incident response plans should certainly be updated to account for these new laws set to go in effect in 2017.\" Omer Tene, Vice President of Research and Education for International Association of Privacy Professionals, added \"Clearly, the biggest challenge for businesses in 2017 will be preparing for the entry into force of the GDPR, a massive regulatory framework with implications for budget and staff, carrying stiff fines and penalties in an unprecedented amount. Against a backdrop of escalating cyber events, such as the recent attack on Internet backbone orchestrated through IoT devices, companies will need to train, educate and certify their staff to mitigate personal data risks.\" Download Whitepaper: Fourth Annual 2017 Data Breach Industry Forecast Learn more about the five industry predictions, and issues such as ransomware and international breach notice laws in our the complimentary white paper. Click here to learn more about our fraud products, find additional data breach resources, including webinars, white papers and videos.
It’s that time of year — for turkey. During Thanksgiving 2015, 736 million pounds of turkey were consumed in the United States. Hungry for more turkey data? The average weight of turkeys purchased for Thanksgiving is 16 pounds. An estimated 46 million turkeys were eaten on Thanksgiving, 22 million on Christmas and 19 million on Easter last year. More than 212 million turkeys were consumed in the United States in 2015. From all of us at Experian, we wish you a very happy Thanksgiving! Courtesy of the National Turkey Federation
The best way to increase email open rates? Include a subscriber’s name in the subject line. A recent Experian study found that in addition to higher open rates, personalized subject lines have a27% higher unique click rate, an 11% higher click-to-open rate and more than double the transaction rates of other promotional mailings from the same brands. Other proven personalization tactics include: Customizing subject lines based on browsing behavior Dynamically populating product choices based on the past purchases of the subscriber Triggering emails based on Instagram or Pinterest selections, connecting social media choices to email opportunities In addition to personalization, companies should coordinate social media programs with email and mobile campaigns in order to optimize engagement across all channels. >> Consumer credit trends
Businesses believe that 23% of their customer or prospect data is inaccurate. Since 84% of companies have a loyalty or customer engagement program in place, poor data is a costly issue. The unfortunate reality is that 74% of companies have encountered problems with these programs — and 12% of revenue is believed to be wasted as a result. Is your loyalty program suffering from poor data? There is a cure. Think of data quality as preventative medicine for a costly and entirely avoidable illness. >>Learn more
Prescriptive solutions: Get the Rx for your right course of action By now, everyone is familiar with the phrase “big data” and what it means. As more and more data is generated, businesses need solutions to help analyze data, determine what it means and then assist in decisioning. In the past, solutions were limited to simply describing data by creating attributes for use in decisioning. Building on that, predictive analytics experts developed models to predict behavior, whether that was a risk model for repayment, a propensity model for opening a new account or a model for other purposes. The next evolution is prescriptive solutions, which go beyond describing or predicting behaviors. Prescriptive solutions can synthesize big data, analytics, business rules and strategies into an environment that provides businesses with an optimized workflow of suggested options to reach a final decision. Be prepared — developing prescriptive solutions is not simple. In order to fully harness the value of a prescriptive solution, you must include a series of minimum capabilities: Flexibility — The solution must provide users the ability to make quick changes to strategies to adjust to market forces, allowing an organization to pivot at will to grow the business. A system that lacks agility (for instance, one that relies heavily on IT resources) will not be able to realize the full value, as its recommendations will fall behind current market needs. Expertise — Deep knowledge and a detailed understanding of complex business objectives are necessary to link overall business goals to tactical strategies and decisions made about customers. Analytics — Both descriptive and predictive analytics will play a role here. For instance, the use of a layered score approach in decisioning — what we call dimensional decisioning — can provide significant insight into a target market or customer segment. Data — It is assumed that most businesses have more data than they know what to do with. While largely true, many organizations do not have the ability to access and manage that data for use in decision-making. Data quality is only important if you can actually make full use of it. Let’s elaborate on this last point. Although not intuitive, the data you use in the decision-making process should be the limiting factor for your decisions. By that, I mean that if you get the systems, analytics and strategy components of the equation right, your limitation in making decisions should be data-driven, and not a result of another part of the decision process. If your prescriptive environment is limited by gaps in flexibility, expertise or analytic capabilities, you are not going to be able to extract maximum value from your data. With greater ability to leverage your data — what I call “prescriptive capacity” — you will have the ability to take full advantage of the data you do have. Taking big data from its source through to the execution of a decision is where prescriptive solutions are most valuable. Ultimately, for a business to lead the market and gain a competitive advantage over its competitors — those that have not been able to translate data into meaningful decisions for their business — it takes a combination of the right capabilities and a deep understanding of how to optimize the ecosystem of big data, analytics, business rules and strategies to achieve success.
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 defines how businesses should approach Identity Relationship Management for identity and devices to enable better fraud protection through our latest perspective paper, The 3 Pillars of Identity Relationship Management: How organizations can reduce risk and increase engagement.
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.
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