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It can be funny until these customer names show up in YOUR data. And how about Slime Ball, Welfare Leech, Ray Rat, Busy Body, or Dick Jiggles? And right up there with Captain Underpants, the ever popular Poop Pooper. The worst ones are not always the standard curse words, although we certainly do see plenty of those. What happens if you (or your marketing agency) never check your customer database and a mail piece or email reaches your customer using an insulting name? "Dear Slime Ball, We need your vehicle..." How do these even get into a customer database? And even more importantly how do you identify and remove these? How do insulting names enter your database? Believe it or not, a customer or prospect may actually enter that information themselves: An example would be a coupon code or a free download of an e-book or something similar. Since payment is not required, their real name is not necessary. Stressed Employees dealing with a challenging customer or difficult work situation can feel some type of vindication by entering these insulting names. How can you Identify these insulting names? It is definitely a sophisticated process that requires contextual searching. Some names can be insulting in certain circumstances but in other circumstances are actually people's real names. Some examples: Moron Nimrod Dick Ball Humans are crafty creatures and can be very creative when it comes to disguising insulting words from standard searching. " Yankamad*ck" is another real-life example. Searching and flagging utilizing basic functions can remove valid records while leaving the offending data. Contextual searching is required and includes (but is not limited to): checking the total name, identifying if the last name is a probable family name, and searching within the name. Who's hiding in your customer file, and how much is that costing you?

TOP AGENCY TO THE LARGEST DEALER GROUPS​ OVER 1000 DEALERS IN THEIR PORTFOLIO The Challenge: Equity Mining on existing vehicles is a valuable marketing tool when creating new vehicle offers. Both accuracy and completeness of each record is KEY. Our task: Create a customized, automated solutions that processes Sales Data, Service Data, CRM Data to fix incorrect or fill missing data points. Identify no-longer owned vehicles, and Identify and append additional vehicles in the HH Result: Working with their data and marketing teams, we created the many-step, customized, automated process. Currently process an average of 1 million records each month for this client, with more than 79% receiving some type of update or correction.

One of the largest Full Dealer Solutions Companies Thousands of dealers using their DMS and Marketing Platforms The Challenge: First- Party Data and third-party Data Project to drive new service clients for our client’s dealers. Our task: Identify vehicles that are no longer owned, which vehicles were traded at a different dealership and find the new owners with emails addresses Result: Dealer files sent daily, processed over 20 million records in the last 12 months for over 1200 dealers that continue on this program. Our client automated their side to send the email campaigns

How do you treat your duplicates? Stop for a moment and think about this... The natural reaction for most companies is to delete duplicate records without much thought. But we LOVE duplicates! ..so much information to glean. So, there are really 3 main choices to consider. These choices are not mutually exclusive and when used in combination produce favorable results: Kill - One of the top reasons for deleting duplicates is AFTER the combining of the best fields (see #3). Another reason is that the information in each field is exactly the same. If and when you DO choose to outright delete duplicates - be sure you are deleting the CORRECT records based on a PRIORITY of the best fields! Then - and this is key - keep those duplicates and compare them to your final file to ensure you are removing or keeping the correct records. Keep - There are many reasons and methodologies for keeping duplicates. Duplicates can be an indication of a customer visiting multiple store locations, or completing online and offline transactions. There may be times where you need to maintain historical information such as previous addresses and prefer to keep duplicates but move to a separate file. Very often, data is originating from different systems and it is not feasible to dedupe between these systems, instead, code your duplicates so you can identify and handle them correctly. Combine - Extracting the BEST data points from each duplicate and combining them into one record is one ideal solution. A few examples are cases when there is additional contact information such as phones, emails or digital identifiers, also notes, purchases, and dates of contact. Combining duplicates can occur in many different configurations that bring great power and insight into your data. With a complete Data Quality Management Program, there are some thoughts to consider first BEFORE embarking on any deduping. The details are shared in a previous video that we created but as a quick overview, these thoughts include: What are the inputs/sources of the data? What do you consider a duplicate? (Do you even know? ) and, How will you use the data? Not sure what is the best for your data? Email us and unload your issues! pam@xcelerated.com

Data — and data quality — are essential to business success in a data-driven world. You don’t need to be a data analyst or a data quality expert to benefit from a complete data quality management solution. But you do need expert assistance. To manage the data quality of your data-driven business, reach out to us.

Subscription services are everywhere these days, easing our hectic schedules and providing a wide variety of conveniences. Media, personal care products, easy-prep meals, and more can all be delivered to your door via a weekly or monthly subscription. But subscription services rely on quality customer data for updated addresses, accurately documented allergies, and consistent insight into customer preferences. Poor data quality cripples a subscription service, increasing cancellation rates, and complicating reconnecting with previous subscribers. Media services Ignoring data quality is how you end up like Netflix, which facilitated a further drop in both stock price and subscriber numbers by declaring its intention to introduce ads, crack down on password sharing, and cancel your cousin’s favorite show. Data is crucial to understanding your subscribers’ media consumption preferences, what works for them, and what most definitely doesn’t. Streaming services, like Netflix, Hulu, and Disney+, rely on data for pivotal decisions. Subscriber data enables personalized viewing recommendations and targeted promotions specific to certain subscribers. Will your next “bingeworthy” content suggestion be a winner or a flop? It depends on the data. Data from what and how people watch drives everything from content creation to how many ads an audience will endure in return for the next installment of Stranger Things. Food and personal care services If you’ve watched a YouTube video or listened to a podcast in the past few years, you’ve heard of HelloFresh. There are dozens of different food subscription services, each with its own data-driven, attention-grabbing name. Have you heard of Gobble? Freshly? Splendid Spoon? Food is not a one-size-fits-all product, especially when you account for allergies and ingredient intolerance — not to mention customer preferences. A triggered food allergy can turn a fun dinner for two into an overnight visit to the hospital, but high-quality data helps subscription services manage risk, so no one receives food they can’t enjoy. Data quality is also essential for customer preference management and expanding add-on revenue. When one customer is on a diet and another enjoys rich, spicy meals, subscriber preference data reduces delivery mistakes. Data indicates one major appeal of food delivery subscriptions is the customer’s opportunity to expand their palate, perfect opportunities to offer additional products such as a suggested wine. There are also services that provide personal care products. Services range from personalized hair care to shave clubs and feminine hygiene products. Again, customer preferences are critical data points for preventing allergic reactions and tailoring product features and recommendations to specific customers. Unlike streaming subscriptions, food and hygiene services deliver physical products, which creates more risk for something to go wrong. Inaccurate streaming service data can annoy, and sometimes amuse, customers, but with food or hygiene subscriptions, poor data quality can have serious, even fatal, consequences. Data quality management So, let’s say your company is starting a subscription box service to deliver specialty chocolates once a month. How do you plan to measure and maintain the level of data quality necessary to manage risk and satisfy your customers’ wants and needs? Data quality management needs regular attention, especially as chocolate lovers everywhere rush to subscribe to your service — and add to your data assets. Quality data is important to every business, but subscription-based services can be particularly sensitive to data quality challenges — and nothing good comes from bad customer data. For help with data quality management challenges, reach out to pam.lang@xcelerated.com , call (877) 236-9155, or visit xcelerated.com to learn more about custom data quality management solutions for your subscription service business.

Pricing models are a critical DTC business strategy. To quote Patrick Campbell of ProfitWell, “You can’t just take the product to market and hope; the market is out of your control.” Along with your plans for product marketing and distribution, the right pricing strategy is a top priority. How will you know the price is right? You use reliable, high-quality data to build your pricing model. The price is right? Good data is the difference between guesswork and an informed, thoughtful decision. Drew Carey isn’t going to make you gamble your company’s success on a guesstimate, but you still need to know if the price is right. Whether you’re launching a brand-new subscription product or adjusting pricing strategy for an existing DTC line, high-quality data will put your pricing in the Goldilocks Zone: Not too high. Not too low. Just right. Market research data tells you how others in your DTC sector approach pricing, but there’s also plenty of pricing model data within your own business. How much does it cost to make your product and deliver your service? Don’t forget, you pay for materials, labor, shipping, and utilities. These costs are relevant pricing data. If your product is already on the market, you have data about customer behavior, DTC market share, when your subscription pricing strategy is working, and when it needs some work. Got answers? Data determines a lot more than DTC value metrics and subscription market values. When you ask: Do we have what we need in stock? Where is our nearest supplier? What’s the demand for this product ? Who is looking for what we offer? What features do our customers like best? Data has your answers. It may be a lot of information to track, but not keeping track causes big problems. If you don’t track demand, for instance, you end up with too much of a low-performing product and not enough of your most popular offering. And if you don’t track consumer trends, your prices have no staying power. If prices are too high, no one is buying what you’re selling. If they’re too low, you struggle with expenses. To get your DTC subscription pricing model on track, you need the right answers. For those, you need high-quality data. Data-driven pricing Every subscription customer is different, but there are still DTC trends to track. Customer data is good for assessing demand, targeting marketing campaigns, driving repeat business, and upselling subscription status — IF your data is accurate, complete, consistent, current, valid, and unique. Maintaining high-quality customer data makes your DTC business more responsive to what your subscription customers want as individuals and as a demographic. It also helps you create sustainable pricing models. How do you build a data-driven pricing model that works? Start with these steps for data quality management: Collect industry, market, and customer data. Use the data quality dimensions to define management standards. Ensure the quality of your existing data records. Make rules to manage the quality of future customer records. Use your rules for regular data quality checkups and maintenance. Your company’s subscription pricing model must be high enough to make a profit, low enough to keep customers, and consistent enough to keep you competitive. Low-quality data leads to DTC pricing disasters — and sends your most loyal customers running for the hills — like Netflix’s new password sharing ban . High-quality data helps you understand your customers, who they are, what they will pay for your subscription service, and what pricing changes will hurt customer loyalty. For help with data quality management for data-driven pricing, reach out to pam.lang@xcelerated.com , call (877) 236-9155, or visit xcelerated.com to learn more about custom data quality management solutions for your DTC subscription business.

If you’re thinking about getting into the subscription business, you’ve probably heard of the DTC model. It’s increasing popularity runs the gamut of consumer goods — including multiple options for everything from fresh food and entertainment to stationery and personalized shampoo. What is DTC? Why is it suddenly so popular? Is it right for your business? Are you prepared to manage the downpour of data the DTC business model brings? DTC 101 Direct to consumer (DTC) subscription services deliver products directly to consumers from the manufacturer — without the retail go-between. The DTC model is particularly popular for two reasons: It’s convenient for consumers. It’s consistent business for manufacturers. But don’t forget: Direct to consumer delivery also means direct access to consumer data, including critical data points for improving your products, optimizing your pricing model, and building long-term relationships with your subscribers. The DTC difference Transparency and access are the biggest differences between DTC and traditional retail. Consumers have direct access to the manufacturers of the products they prefer, and subscriptions offer companies an efficient mechanism for sales and delivery, but there are a few different approaches to DTC. For services like Dollar Shave Club and Quip, initial sales — of razor handles and electronic toothbrushes respectively — are nice, but the real revenue comes from regularly scheduled refills of replacement cartridges or toothbrush heads. This creates lasting customer relationships, which are an ongoing opportunity for upsells and add-ons. Dollar Shave Club sells shaving lotion and soap as well as razors, and Quip offers toothpaste and dental floss subscriptions alongside quarterly brush and battery replacements. Mystery box services deliver their customers different products on a regular schedule. These subscriptions usually fall within broad consumer product categories, like stationery, games, or science experiments, but the specific contents of each box are a surprise with every delivery. These services rely on consumer interest in their particular specialty, and customers often give this type of subscription as a gift — which means two sets of data to manage. Meal delivery subscriptions fall somewhere in the middle of the refill and mystery models. For example, Hello Fresh offers its subscribers choices from a limited number of options, so consumers can customize their menus but still receive an order that looks a lot like what their fellow subscribers get. Each of these common DTC models has its own pros and cons, but there are some popular features all DTC businesses tend to share. Why DTC? Consumers like DTC for obvious reasons. The most popularly reported reason is the difference in product quality from traditional retail. Prices are lower for a higher-quality product, and DTC customers also report satisfaction with: More and easier access to discounts The convenience of regular deliveries A personalized customer experience All of these benefits are a result of direct, no-hassle access to manufacturers, which consumers consistently find more enjoyable than traditional retail shopping. DTC’s appeal to manufacturers has a lot to do with its popularity with consumers, which results in the world’s most effective form of marketing: word of mouth. Because of the novelty of the product, and the convenience of the service, consumers TALK about their DTC subscription experiences. A successful DTC launch drives investment, which leads to more chatter, which feeds an ongoing cycle of company growth. It’s something of a snowball effect, and the success it brings relies, in part, on your company’s approach to managing the avalanche of DTC data. The DTC data factor With a clear idea of how DTC subscriptions work, it’s easier to understand just how critical high-quality data is to a DTC company. More than half the advantages consumers report as their reasons for doing business via DTC subscription rely on good data. Customers cancel subscriptions for several reasons, including inability to pay, declining interest, or a surplus of the product in question. With high-quality customer data , your DTC company has the insight and opportunity to resolve these issues — which decreases cancellations AND increases your chances of winning back former subscribers. Collecting data is an organic part of the DTC subscription process. In the case of mystery boxes, consumers supply a lot of upfront information about their product and delivery preferences, while refill/replace models acquire more data about consumer habits over time. From a data collection perspective, DTC models are a gold mine of useful consumer information, but sorting, organizing, analyzing, and USING the avalanche of data you acquire is crucial to subscription service success. Create a consistently satisfying subscriber experience by keeping your DTC data accurate, valid, relevant, unique, up-to-date, and complete. If you need help managing the quality of your DTC data, reach out to pam.lang@xcelerated.com , call (877) 236-9155, or visit xcelerated.com to learn more about custom data quality management solutions for your DTC subscription business.

The direct to consumer (DTC) business model has been disrupting traditional retail for years. Yeti, manufacturer of high-end coolers and portable drinkware, pulled its products from Lowes in favor of a DTC model. But new challenges, including social media as the new go-between, are increasing customer acquisition costs, and previously attractive DTC margins are decreasing. How can DTC and subscription-based brands stay engaged with their existing customers and reengage with those who have canceled their subscriptions? DTC in decline? Amazon has emerged as the biggest competition for DTC companies . The online retail giant typically offers faster and cheaper shipping, and most products are available at lower prices on Amazon. It’s also become the default for customers searching for products online. It’s hard for any online retailer to beat Amazon on any of these fronts — and most simply don’t. Even with an established online store, if your products, or a suitable stand-in, can also be found on Amazon, then that’s likely where consumers will make their purchase. Amazon is not the only online marketplace out there, but it’s the one causing the most problems for DTC brands. Along with competition from the world’s biggest retailer, the costs of advertising directly to consumers is rising. Facebook is a primary advertising platform for DTC companies, and a sharp increase in the price of Facebook advertising is hurting DTC brands. Without Facebook, DTC advertising options are severely limited. The loss of cookies is another kicker. Data from third-party cookies enabled targeted ads — a tool critical to the success of DTC businesses. The decline of third-party cookies makes it harder for DTC brands to get their ads in front of potential customers. With supply chain disruptions and a chaotic economy dissuading investors and customers alike, the DTC field is increasingly complicated, but the primary challenges come down to two things: imbalanced competition with retail giants, like Amazon, and barriers to advertising directly to interested consumers. The current landscape Without third-party cookies and easy social media advertising, many DTC companies are struggling to find new customers, retain existing ones, and in the case of subscription services, reengage with canceled customers. Smaller and more niche DTC companies are finding it more difficult to establish brand recognition. Hello Fresh, with its widespread brand awareness, can survive on YouTube sponsorships and word of mouth, but newer and smaller brands are having a hard time gaining a foothold in the public arena. DTC companies, which typically operate and advertise entirely online, aren’t going to have the same opportunities for brand recognition as companies that partner with large retailers. This turns a once navigable landscape into easy pickings for larger, more well-known brands. Hello Fresh, and others with established name recognition, will continue to outperform smaller competitors without a large following or the means to create one. Diversify and let your data drive So, what can DTC brands and subscription services do to overcome these obstacles? Hello Fresh and other big name DTC companies — including Dollar Shave Club, Mint Mobile, Squarespace, and others — have gained recognition with a diversified social media advertising approach. Build a brand presence on Twitter where you can interact directly with existing customers and target markets. Sponsor YouTube programming to reach a broader audience and create relevance with younger consumers. Podcasts are another opportunity for reaching new consumers, and with the enormous range of subject matter they cover, you can choose to sponsor the programs most likely to appeal to your target market. And it can’t hurt to collaborate with other retailers. Quip, for instance, partnered with Target to feature their oral hygiene products in stores. Customers can purchase starter kits at Target, and sign up for a subscription to manage brush and battery replacements. Finally, and most importantly, use your customer data to drive your engagement efforts. DTC and subscription services have mountains of first-party data on current and former customers. Use your email newsletter and direct mail postcards to offer special discounts for returning customers and keep current, former, and potential customers informed about your products. As useful as cookies are, you already have tons of pertinent information about your target market — data that can drive smart marketing decisions. Don’t underestimate the value of the data you have on hand. Use it to get to know your customers, so you can identify where and how to reach the demographics relevant to your business and which partnerships will prove most beneficial to your future success. Quality data is invaluable to DTC and subscription-based businesses. If you need help managing the quality of your DTC data, reach out to pam.lang@xcelerated.com , call (877) 236-9155, or visit xcelerated.com to learn more about custom data quality management solutions for your DTC subscription business.

$11 Billion Dealership Group Over 200 dealers and repair shops The Challenge :   Correct multiple data issues, identify and code duplicates within the strict business rules of their Dealer system consisting of about 5 million records Intense Fear and concern for losing valuable data, and creating inaccuracies in the Dealers’ sales and service data Our Task: Create an automated, end-to-end process that handles the unique issues in their eco-system, updates data points, adheres to privacy, and identifies records to combine based on their restrictions and business rules Result: Worked closely with their teams to analyze thousands of records, create layouts, create automation, review results, implement adjustments and identify and code the duplicates

Comprehensive financial planning Advanced Strategies The Challenge: To validate a unique stock option investment strategy that maintains high returns while reducing risk. Our Task: Analyze over 1,300 spreadsheets, containing 18 million records of stock-option data that spanned 16 years using a variety of complex parameters including: Trading Dates Expiration Dates Strike Prices Daily Closing Prices ATM (At The Money) & OTM (Out of The Money) Options The Result: Boiled down 18 million records into 3 data sets of about 20,000 records each so our client could verify their theory and create better portfolios for their clients.

TOP AGENCY TO THE LARGEST DEALER GROUPS​ OVER 1000 DEALERS IN THEIR PORTFOLIO The Challenge: Agency created an integrated conquest marketing platform for dealerships and required Vehicle Ownership data Intelligence to make targeted marketing decisions.​ Our task: Create a data set that met their requirements using our in-house compiled Vehicle Ownership file of 170 million records with demographics.​ Result: Working with their marketing and data team, we created the needed data set that is updated quarterly. ​