Customer Targeting & Segmentation
Call it big data, smart data or evidence-based decision-making. It’s not just the latest fad, it’s the future of how business will be guided and grow. Here are a few telling stats that show data is exploding and a new age is upon us: Data is growing faster than ever before, and we’re on track to create about 1.7 megabytes of new information per person every second by 2020. The social universe—which includes every digitally connected person—doubles in size every two years. By 2020, it will reach 44 zettabytes or 44 trillion gigabytes, according to CIO. In 2015, more than 1 billion people used Facebook and sent an average of 31.25 million messages and viewed 2.77 million videos each minute. More than 100 terabytes of data is uploaded daily to the social channel. By 2020, more than 6.1 billion smartphone users will exist globally. And there will be more than 50 billion smart connected devices in the world, all capable of collecting, analyzing and sharing a wealth of data. More than one-third of all data will pass through or exist in the cloud by 2020. The IDC estimates that by 2020, business transactions on the internet—business-to-business and business-to-consumer—will reach 450 billion per day. All of this new data means we’ll be looking at a whole new set of possibilities and a new level of complexity in the years ahead. The data itself is of great value, however, lenders need the right automated decisioning platform to store, collect, quickly process and analyze the volumes of consumer data to gain accurate consumer stories. While lenders don’t necessarily need to factor in decisioning on social media uploads and video views, there is an expectation for immediacy to know if a consumer is approved, denied or conditioned. Online lenders have figured out how to quickly capture and understand big data, and are expected to account for $122 billion in lending by 2020. This places more pressure on banks and credit unions to enhance their technology to cut down on loan approval times and move away from various manual touch points. Critics of automated decisioning solutions used in lending cite compliance issues, complacency by lenders and lack of human involvement. But a robust platform enables lenders to improve and supplement their current decisioning processes because it is: Agile: Experian hosts our client’s solutions and decisioning strategies, so we are able to make and deploy changes quickly as the needs of the market and business change, and deliver real-time instant decisions while a consumer is at the point of sale. A hosted environment also means reduced implementation timelines, as no software or hardware installation is required, allowing lenders to recognize value faster. A data work horse: Internal and external data can be pulled from multiple sources into a lender’s decisioning model. Lenders may also access an unlimited number of scores and attributes—including real-time access to credit bureau data—and integrate third-party data sources into the decisioning engine. Powerful: A robust decision engine is capable of calculating numerous predictive attributes and custom scoring models, and can also test new strategies against current decision models or perform “what if” simulations on historical data. Data collection, storage and analysis are here to stay. As will be the businesses which are savvy enough to use a solution that can find opportunities and learnings in all of that complex data, quickly curate the best possible actions to take for positive outcomes, and allow lenders and marketers to execute on those recommendations with the click of a button. To learn more about Experian’s decisioning solutions, you can additionally explore our PowerCurve and Attribute Toolbox solutions.
Sometimes life throws you a curve ball. The unexpected medical bill. The catastrophic car repair. The busted home appliance. It happens, and the killer is that consumers don’t always have the savings or resources to cover an additional cost. They must make a choice. Which bills do they pay? Which bills go to the pile? Suddenly, a consumer’s steady payment behavior changes, and in some cases they lose control of their ability to fulfill their obligations altogether. These shifts in payment patterns aren’t always reflected in consumer credit scores. At a single point in time, consumers may look identical. However, when analyzing their past payment behaviors, differences emerge. With these insights, lenders can now determine the appropriate risk or marketing decisions. In the example below, we see that based on the trade-level data, Consumer A and Consumer B have the same credit score and balance. But once we see their payment pattern within their trended data, we can clearly see Consumer A is paying well over the minimum payments due and has a demonstrated ability to pay. A closer look at Consumer B, on the other hand, reveals that the payment amount as compared to the minimum payment amount is decreasing over time. In fact, over the last three months only the minimum payment has been made. So while Consumer B may be well within the portfolio risk tolerance, they are trending down. This could indicate payment stress. With this knowledge, the lender could decide to hold off on offering Consumer B any new products until an improvement is seen in their payment pattern. Alternatively, Consumer A may be ripe for a new product offering. In another example, three consumers may appear identical when looking at their credit score and average monthly balance. But when you look at the trend of their historical bankcard balances as compared to their payments, you start to see very different behaviors. Consumer A is carrying their balances and only making the minimum payments. Consumer B is a hybrid of revolving and transacting, and Consumer C is paying off their balances each month. When we look at the total annual payments and their average percent of balance paid, we can see the biggest differences emerge. Having this deeper level of insight can assist lenders with determining which consumer is the best prospect for particular offerings. Consumer A would likely be most interested in a low- interest rate card, whereas Consumer C may be more interested in a rewards card. The combination of the credit score and trended data provides significant insight into predicting consumer credit behavior, ultimately leading to more profitable lending decisions across the customer lifecycle: Response – match the right offer with the right prospect to maximize response rates and improve campaign performance Risk – understand direction and velocity of payment performance to adequately manage risk exposure Retention – anticipate consumer preferences to build long-term loyalty All financial institutions can benefit from the value of trended data, whether you are a financial institution with significant analytical capabilities looking to develop custom models from the trended data or looking for proven pre-built solutions for immediate implementation.
Reactivation campaigns make economic sense. They build on a brand’s previous investments, targeting customers who already are aware of and previously have engaged with your brand. Use these 4 steps to build a successful reactivation framework: 1. Analyze subscriber data to identify reactivation segments to target. 2. Identify subscriber activity to divide customers into at least 3 unique segments. 3. Develop messaging strategies for each segment. 4. Integrate or suppress inactive subscribers based on whether they re-engage. Reactivation campaigns can deliver significant incremental revenue and position inactive subscribers for further engagement in future campaigns. Download report>
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
When you think of criteria for prescreen credit marketing, what comes to mind? Most people will immediately discuss the risk criteria used to ensure consumers receiving the mailing will qualify for the product offered. Others mention targeting criteria to increase response rates and ROI. But if this is all you’re looking at, chances are you’re not seeing the whole picture. When it comes to building campaigns, marketers should consider the entire customer lifecycle, not just response rates. Yes, response rates drive ROI and can usually be measured within a couple months of the campaign drop. But what happens after the accounts get booked? Traditionally, marketers view what happens after origination as the responsibility of other teams. Managing delinquencies, attrition, and loyalty are fringe issues for the marketing manager, not the main focus. But more and more, marketers must expand their role in the organization by taking a comprehensive approach to credit marketing. In fact, truly successful campaigns will target consumers that build lasting relationships with the institution by using the three pillars of comprehensive credit marketing. Pillar #1: Maximize Response Rates At any point in time, most consumers have no interest in your products. You don’t have to look far to prove this out. Many marketing campaigns are lucky to achieve greater than a 1% response rate. As a result, marketers frequently leverage propensity to open models to improve results. These scores are highly effective at identifying consumers who are most likely to be receptive to your offer, while saving those that are not for future efforts. However, many stop with this single dimension. The fact is no propensity tool can pick out 100% of responders. Layering just a couple credit attributes to a propensity score allows you to swap in new consumers. Simultaneously, credit attributes can identify consumers with high propensity scores that are actually unlikely to open a new account. The net effect is even higher response rates than can be achieved by using a propensity score alone. Pillar #2: Risk Expansion Credit criteria are usually set using a risk score with some additional attributes. For example, a lender may target consumers with a credit score greater than 700 and no derogatory or delinquent accounts reported in the past 12 months. But, most of this data is based on a “snapshot” of the credit profile and ignores trends in the consumer’s use of credit. Consider a consumer who currently has a 690 credit score and has spent the past six months paying down debt. During that time, utilization has dropped from 66% to 41%, they’ve paid off and closed two trades, and balances have reduced from $21,000 to $13,000. However, if you only target consumers with a score greater than 700, this consumer would never appear on your prescreen list. Trended data helps spot how consumers use data over time. Using swap set analysis, you can expand your approval criteria without taking on the incremental risk. Being there when a consumer needs you is the first step in building long-term relationships. Pillar #3: Customer profitability and early attrition There’s more to profitability than just originating loans. What happens to your profitability assumptions when a consumer opens a loan and closes it within a few months? According to recent research by Experian, as many as 26% of prime and super-prime consumers, and 38% of near-prime consumers had closed a personal loan trade within nine months of opening. Further, nearly 32% of consumers who closed a loan early opened a new personal loan trade within a few months. Segmentation can help identify consumers who are likely to close a personal loan early, giving account management teams a head start to try and retain them. As it turns out, many consumers use personal loans as a form of revolving debt. These consumers occasionally close existing trades and open new trades to get access to more cash. Anticipating who is likely to close a loan early allows your retention team to focus on understanding their needs. If you don’t, you’re competition will take advantage through their marketing efforts. Building the strategy Building a comprehensive strategy is an iterative process. It’s critical for organizations to understand each campaign is an opportunity to learn and refine the methodology. Consistently leveraging control and test groups and new data assets will allow the process to become more efficient over time. Importantly, marketers should work closely across the organization to understand broader objectives and pain points. Credit data can be used to predict a range of future behaviors. As such, marketing managers should play a greater role as the gatekeepers to the organization’s growth.
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?
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
A recent national survey by Experian revealed opportunities for businesses to build relationships with future homebuyers before they’re ready to obtain a loan. Insights include: 35% of future buyers said they don’t know what steps to take to qualify for a larger loan 75% of future buyers are not preapproved for a home loan 29% of those surveyed would purchase a more expensive home if they had better credit and could qualify for a larger loan A large portion of near-future homebuyers are millennials. Building relationships with this generation now will benefit financial institutions in the future. >> White paper: Building lasting relationships with millennials
All customers are not created equally – at least when it comes to one’s ability to pay. Incomes differ, financial circumstances vary and economic challenges surface. Lost job. Totaled car. Unplanned medical bills. Life happens. Research conducted by a recent Bankrate study revealed just 38 percent of Americans said they could cover an unexpected emergency room visit or a $500 car repair with available cash in a checking or savings account. It’s a scary situation for individuals, and also a source of stress for the lender expecting payment. So what are the natural moments for a lender to assess “ability to pay?” Moment No. 1: When prepping for a prescreen campaign and at origination. Many lenders leverage an income estimation model, designed to give an indication of the customer’s capacity to take on additional debt by providing an estimation of their annual income. Within the model, multiple attributes are used to calculate the income, including: Number of accounts Account balances Utilization Average number of months since trade opened Combined, all of these insights determine a customer’s current obligations, as well as an estimation of their current income, to see if they can realistically take on more credit. The right models and criteria on the front-end – whether used when a consumer applies for new credit or when a lender is executing a prescreen campaign to acquire new customers – minimizes the risk for default. It’s a no-brainer. Moment No. 2: When a customer is already on your books. As the Bankrate study mentioned, sudden life events can send some customers’ lives into a financial tailspin. On the other hand, financial circumstances can change for the better too. Aggressively paying down a HELOC, doubling down on a mortgage, or wiping out a bankcard balance could signal an opportunity to extend more credit, while the reverse could be the first signs of payment stress. Attaching triggers to accounts can give lenders indications on what to do with either scenario, helping to grow a portfolio and protect it. Moment No. 3: When an account goes south. While a lender hates to think any of its accounts will plummet into collections, sometimes, it’s inevitable. Even prime customers fall behind, and suddenly financial institutions are faced with looking at collections strategies. Where should they place their bets? You can’t treat all delinquent customer equally and work the accounts the same way. Collection resources can be wasted on customers who are difficult or impossible to recover, so it’s best to turn to predictive analytics and a collections scoring strategy to prioritize efforts. Again, who has the greatest ability to pay? Then place your manpower on those individuals where you can recover the most dollars. --- Assessing one’s ability to pay is a cornerstone to the financial services business. The quest is to find the sweet spot with a combination of application data, behavioral data and credit risk scoring analytics.
Experian cited in Mobile Fraud Management Solutions report from Forrester as having the most capabilities and one of the highest estimated revenues in total fraud management
False declines are often unwarranted and occur due to lack of customer information Have you ever been shopping online, excited to get your hands on the latest tech gadget, only to be hit with the all-too-common disappointment of a credit card decline? Whom did you blame? The merchant? The issuer? The card associations? The answer is probably all of the above. False declines like the situation described above provoke an onslaught of consumer emotions ranging from shock and dismay to frustration and anger. Of course, consumers aren’t the only ones negatively impacted by false declines. Many times card issuers lose their coveted “top of wallet” position and/or retailers lose revenue when customers abandon the purchase altogether. False declines are unpleasant for everyone, yet consumers struggle with this problem every day — and fraud controls are only getting tighter. How does the industry mutually resolve this growing issue? The first step is to understand why it occurs. Most false declines happen when the merchant or issuer mistakenly declines a legitimate transaction due to perceived high risk. This misperception is usually the result of the merchant or issuer not having enough information to verify the authenticity of the cardholder confidently. For example, the consumer may be a first-time customer or the purchase may be a departure from the card holder’s normal pattern of transaction activity. Research shows that lack of a holistic view and no cross-industry transaction visibility result in approximately $40 billion of e-commerce declines annually. Think about this for a minute — $40 billion in preventable lost revenue due to lack of information. Merchants’ customer information is often limited to their first-hand information and experience with consumers. To solve this growing problem, Experian® developed TrustInsight™, a real-time engine to establish trusted online relationships over time among consumers, merchants and issuers. It works by anonymously leveraging transactional information that merchants and financial institutions already have about consumers to create a crowd-sourced TrustScore™. This score allows first-time online customers to get a VIP experience rather than a brand-damaging decline. Another common challenge for merchants is measuring the scope of the false declines problem. Proactively contacting consumers, directly capturing feedback and quickly verifying transaction details to recoup potential lost sales are best practices, but merchants are often in the dark as to how many good customers are being turned away. The solution — often involving substantial operational expense — is to hold higher-risk orders for manual review rather than outright declining them. With average industry review rates nearing 30 percent of all online orders (according to the latest CyberSource Annual Fraud Benchmark Report: A Balancing Act), this growing level of review is not sustainable. This is where industry collaboration via TrustInsight™ offers such compelling value. TrustInsight can reduce the review population significantly by leveraging consumers’ transactions across the network to establish trust between individuals and their devices to automate more approvals. Thankfully, the industry is taking note. There is a groundswell of focus on the issue of false declines and their impact on good customers. Traditional, operations-heavy approaches are no longer sufficient. A trust-based industry-consortium approach is essential to enhance visibility, recognize consumers and their devices holistically, and ensure that consumers are impacted only when a real threat is present.
Learn More Image
