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The winter holiday festivities are underway, and when it comes to the local malls, the holiday spending spirit seems to have already been in place for weeks. The season for swiping (credit cards) has begun. Before many of them set out with holiday gift lists in tow, they may be setting their sights on new lines of credit – by adding to their artillery of plastic. With 477.6 million existing credit card accounts, what do these consumers look like? While we can all agree that the meaning behind winter holiday celebrations is not the act of spending and giving material gifts, the two have come to be synonymous. This year is anticipated to be no different. When asked to describe their anticipated spending for the holidays this year, a recent Mintel survey said 56% of respondents planned to spend the same amount as they did last year. Nearly a quarter of respondents (23%) said they planned to spend more than they did last year. The uptick in spending as the year rounds out is no news flash. It is engrained within the fiscal landscape of each year, arguably its own tradition. According to a recent Experian consumer survey, Americans plan to spend an average of almost $850 on holiday gifts this year. Given what we know of consumers – and ourselves – as increased spending is upon us, credit card openings and usage are also on the rise. In order to capitalize on fulfilling your consumers needs during this bustling time filled with shopping bags and loaded online carts, it’s important to know what consumers look for in a credit card. Want to attract those holiday shoppers? The key to getting your plastic in their wallet is rewards, rewards, rewards. 58% of consumers will select a credit card for its rewards – including cash back, gas rewards, and retail gift cards – according to recent Experian consumer survey research. Is your credit card program stacked with rewards-ready options? Now what? Go where your consumers are – and for many of them that means online. While traditional retailers are still preferred destinations for holiday shopping, online is increasingly becoming a preferred way of shopping. 90% of consumers plan to do holiday shopping online, according to a Mintel study. Online shopping trends and online credit card applications trends seem to go hand in hand, according to Mintel and Experian data. Whether your consumers are looking for deals, that adrenaline rush of waiting until the last minute, or a trip to just get away from it all, credit cards can help them get there. And while the hustle and bustle of the holidays are ramping up, following the holidays quickly comes the new year – another close to 12 months of consumer spending (not just the dollars spent during this festive season). Consumer behavior across the entire year can be the key to enhancing your marketing and account management strategies. By knowing how much your consumers spend on all the plastic in their wallets – think bank cards too – you can offer customized reward programs, create strategies to maximize wallet share and retain profitable customers. Learn more about the first commercially-available spend algorithm built from credit data and tap into your wallet share for each consumer. 1Mintel Comperemedia 2Experian consumer survey research

Ben Franklin was wrong. Death and taxes are not the only two constants in life. For many, debt makes a third. And where there is past-due debt, collections is not far from the conversation, if not included in the same breath. While the turn of the new year may mark some arduous work to be done – losing those holiday pounds, spring cleaning, balance transfers and tax filings – there’s also opportunity for lenders, collectors and consumers alike. Just as the spikes in retail trends are analogous with the holiday months, there’s an evident uptick in collections during tax season year after year. As such, successful lenders, financial institutions and collections agencies know that January, February and March are critical months to engage with past-due customers, specifically as they relate to the tax season. The average tax refund for 2016 and 2017 was $2,860 and $2,769 respectively, according to the IRS. And while some may assume that all consumers look at this money as an opportunity for a “treat yourself” splurge, 35% of consumers expecting a refund said they would use it to pay down debt, according to the National Retail Federation. Additionally, during the 2017 tax season, 45 million consumers paid at least $500 and 10% or more of a tradeline balance(s), according to Experian data. So, if past-due consumers want to pay down debt, and the ultimate goal of collections is to recoup over-due funds, and first quarter collections growth appears to be driven by tax refunds, how do we make the connection? Think of the scene from Jerry Maguire – “Help me, help you!” Help consumers help themselves. Experian’s new Tax Season Payment IndicatorTM examines payment behavior over the past two years to determine whether a consumer has made a large payment to a tradeline balance – or balances – during tax season. “Millions of consumers used their tax refunds to pay down debt and many plan to do it again,” said Denise McKendall, Product Manager. “Collectors that leverage previous tax season payment behavior to identify and strategically engage with this group will benefit the most from the tax refund season.” Engaging this information can be like having a collections crystal ball. Targeting consumers that are likely to use their refund to pay down debt can influence messaging, campaign refinement and the timeliness of your touchpoints, resulting in greater collections ROI. This means as the year closes out and planning begins for 2019, collections prioritization strategy is key. And those conversations should be taking place now. Are you tax season ready? Learn More About Tax Season Payment Indicator

Your model is only as good as your data, right? Actually, there are many considerations in developing a sound model, one of which is data. Yet if your data is bad or dirty or doesn’t represent the full population, can it be used? This is where sampling can help. When done right, sampling can lower your cost to obtain data needed for model development. When done well, sampling can turn a tainted and underrepresented data set into a sound and viable model development sample. First, define the population to which the model will be applied once it’s finalized and implemented. Determine what data is available and what population segments must be represented within the sampled data. The more variability in internal factors — such as changes in marketing campaigns, risk strategies and product launches — and external factors — such as economic conditions or competitor presence in the marketplace — the larger the sample size needed. A model developer often will need to sample over time to incorporate seasonal fluctuations in the development sample. The most robust samples are pulled from data that best represents the full population to which the model will be applied. It’s important to ensure your data sample includes customers or prospects declined by the prior model and strategy, as well as approved but nonactivated accounts. This ensures full representation of the population to which your model will be applied. Also, consider the number of predictors or independent variables that will be evaluated during model development, and increase your sample size accordingly. When it comes to spotting dirty or unacceptable data, the golden rule is know your data and know your target population. Spend time evaluating your intended population and group profiles across several important business metrics. Don’t underestimate the time needed to complete a thorough evaluation. Next, select the data from the population to aptly represent the population within the sampled data. Determine the best sampling methodology that will support the model development and business objectives. Sampling generates a smaller data set for use in model development, allowing the developer to build models more quickly. Reducing the data set’s size decreases the time needed for model computation and saves storage space without losing predictive performance. Once the data is selected, weights are applied so that each record appropriately represents the full population to which the model will be applied. Several traditional techniques can be used to sample data: Simple random sampling — Each record is chosen by chance, and each record in the population has an equal chance of being selected. Random sampling with replacement — Each record chosen by chance is included in the subsequent selection. Random sampling without replacement — Each record chosen by chance is removed from subsequent selections. Cluster sampling — Records from the population are sampled in groups, such as region, over different time periods. Stratified random sampling — This technique allows you to sample different segments of the population at different proportions. In some situations, stratified random sampling is helpful in selecting segments of the population that aren’t as prevalent as other segments but are equally vital within the model development sample. Learn more about how Experian Decision Analytics can help you with your custom model development needs.


