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Think about it, how many existing customers are you no longer able to contact? Consider this: dealerships invest a considerable sum, roughly $500, to acquire just one new customer. Yet, shockingly, the ability to upkeep the existing customer database is an ongoing obstacle: the struggle to maintain accurate data is real. In the fast-paced world of automobile dealerships, staying ahead of the curve is paramount, especially when it comes to managing customer data. The backbone of their operations lies in their Dealer Management System (DMS), housing crucial information about their customers. However, the data within the DMS swiftly grows outdated. With customers relocating, changing contact details, and human error creeping in during data entry, the accuracy of information dwindles rapidly. While some companies advocate for the adoption of pricey Customer Data Platforms to address this issue, the reality is that dealerships can salvage the situation by prioritizing regular updates within their existing DMS. Failure to keep customer data current not only compromises accuracy but also undermines the very foundation of a dealership's business model. This negligence not only tarnishes customer relations but also jeopardizes sales opportunities, reduces operational efficiency, and creates OEM friction. In an industry where every lead counts, the importance of maintaining updated customer data cannot be overstated. Solutions to enhance DMS capabilities and streamline data updates are available. It's akin to fine-tuning an engine for optimal performance rather than seeking a costly replacement. By exploring and implementing these options, dealerships can breathe new life into their DMS, ensuring that it remains a reliable tool in their arsenal. After all, in an industry where every lead counts, the ability to comprehensively update DMS data is not just an option – it's a necessity for sustained success.

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.

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 101Direct 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.

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.

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.

Do you know the steps to take for dealing with data duplicates? What about the deduping minefields to avoid? Follow these five steps for disaster-free duplicate identification. To learn more about data duplicates, the dangers of deduping, and custom data quality management solutions, reach out to pam.lang@xcelerated.com, or call (877) 236-9155.

In 2021, 75% of companies who made moves to improve the quality of their data exceeded their annual business goals. Data quality is clearly fundamental to the well-being of your business. If your data is wrong, you can’t understand your customers, establish competitive pricing models, or strategize for growth. If you’re ready to improve your company’s data quality, start with a series of clear, measurable goals. Breaking big changes down into smaller steps makes them easier to implement, and data quality management is no exception. If you’re not sure where to start, we’ve got you covered with data quality improvement steps for the next 30 days. Step 1: Identify the problem(s) As with most problem/solution scenarios, the first step to resolving data quality issues is identifying the “right” problem. There are several possible reasons for low-quality data, including ill-managed duplicates, human error, inconsistent or incomplete records, and inefficient data quality management. Once you know what’s wrong, you can begin narrowing your focus to finding the source and strategizing solutions. Step 2: Find the source Once you’ve identified the right problem(s), you need to figure out what’s causing them. This is where the dimensions of data quality come into play. Once you understand these dimensions, you can identify where your problems lie and how they occur. Quality data is: Complete. What percentage of your company’s data is complete? Do you have guidelines in place for qualifying a record as complete? Qualifications for completion typically vary from one business to the next. Unique. What percentage of your business data is unique? Duplicate records can cause problems when they aren’t managed well. Your data should not include unknown or unnecessary redundant records. Timely. When was your data recorded? Old records may be outdated or inaccurate. Company data is typically less accurate and relevant over time. When was the last time your records were updated? Valid. What percentage of your business data is valid? Your data only has value if you can use it to enhance customer interaction and other business activity. Valid data is usable data. Accurate. What percentage of your business data is accurate according to your company’s established rules? Errors contradict the real-world information your data is meant to represent. Consistent. What percentage of your company data is consistent? Are record formats and completion requirements established and regularly met? Is your business data consistent between sets? Review these dimensions of data quality in the context of the problem you’ve identified. A consistency issue can result from a lack of unified understanding regarding what data should be input in what format. Duplicate data may be caused by human error, a bug in your data management system, or both. Whether it’s an issue of policy, mechanics, or simple human error, once you’ve found the source, you can begin to address it. Step 3: Strategize solutions You’ve identified the right problem and verified the source. Now, you must determine the resources necessary to solve it. Do you have the necessary data quality management expertise in house? A small issue with your data collection process (e.g., a repeated field within a record) is simple enough to fix, but a total data quality management overhaul is complex and unique to your organization’s business — and business data — needs. It takes time and technical knowledge, and it’s difficult to conduct business as usual and manage a project of this scope. Some data quality issues can be addressed with policy changes to establish a defined set of rules for recording crucial business data. Automation is also a possibility for verifying data quality and ensuring consistency. Staying on top of potential issues — before they become major problems — also helps avoid a longer, more arduous, or expensive data quality repair process later. Step 4: Expert help Data quality drives your company’s business and future growth, but resolving data quality management issues can be overwhelming when you don’t know where to start. Ignoring problems will only make them bigger and harder to solve down the road. Follow these steps in your next 30 days, and make solving your data quality problems easier with expert assistance. Visit xcelerated.com to learn more about custom total data quality management solutions for your business.

What’s the most dangerous word in data quality management? Deduping. Before you start dumping records, consider what data duplicate records can provide, and whether you’re deleting essential information. To learn more about data duplicates, the dangers of deduping, and custom data quality management solutions, reach out to pam.lang@xcelerated.com, or call (877) 236-9155.

Bad data can have disastrous — even fatal — consequences, but some people still struggle to understand the potential gravity of data quality problems. The longer a business survives without a data quality disaster, the more complacent they tend to become with assessing and managing their data. Check and recheck your math — then check it again Something as simple as a misplaced decimal point can cost you thousands, millions, or even billions of dollars. Just look at the $2.7 billion Spanish S-80 submarine program. One misplaced decimal point led to submarines 70 tons heavier than planned, and engineers feared they would be incapable of resurfacing once submerged. Spain had to spend $14 million to have their weight reduced. Misplaced decimals are bad enough, but what about calculating with the wrong units? In 2013, the Amsterdam city council used software to calculate housing benefits, and accidentally used cents instead of euros. The mistake resulted in the distribution of 188 million euros in benefits rather than the expected 1.8 million euros, and the city council was forced to ask for the money back. While most of it was returned, many already struggling citizens ended up in extreme debt because of the error. NASA isn’t immune to unit errors, either. In 1999, a $125 million Mars orbiter was lost because NASA used the metric system, and the engineering team at Lockheed Martin used the imperial system. Finally, something as simple as a conversion error can be life-threatening. While converting between metric and imperial systems is routine, a small error in the conversion process forced the crash landing of an Air Canada flight in 1983. The math error left the plane with only half the fuel it needed to complete the flight. The plane crash-landed on a racetrack, and its nose was destroyed, but thankfully, no one was killed. Of the 69 people aboard, two suffered minor injuries. Outdated data Making business decisions based on consumer data is a good idea. Relying on outdated consumer data? Not so much. Take, for example, the spectacular disaster that was Crystal Pepsi. At the time of its development, PepsiCo was chasing a health food craze, and artificial colors were increasingly unpopular with people pursuing healthier diets. So, Pepsi created a clear soda. Unfortunately, new data indicated the taste of the drink was too far from that of the original formula, and consumers wouldn’t like it. The company chose to ignore the new information, and Crystal Pepsi flopped. Even game shows have issues with outdated data. Just last year, Jeopardy! relayed inaccurate data about a medical condition known as postural orthostatic tachycardia syndrome (POTS). The prompt used an outdated, offensive nickname for the condition — Grinch syndrome — based on a disproven theory that the hearts of POTS sufferers are smaller than average. The show was forced to make a public apology. Human error Humans are imperfect creatures. That’s why we check, double-check, and have someone else triple-check our work on important projects. Sometimes even that isn’t enough — especially if pride gets in the way. Human error is one of the most common causes of data quality disasters. Some examples include: Challenger. While the dangers of O-ring failure in the space shuttle were known, they weren’t considered a serious issue. Prelaunch safety checks missed that the ring had cracked due to cold temperatures. We don’t need to tell you what happened next. Enron. Enron Corp. was an energy giant until it went out of business in 2001 because of spreadsheet errors. The accidental nature of these “mistakes” remains unclear, but 24% of the company’s spreadsheets contained serious errors. Add untrue claims of profit, and several other ethical issues, and the company didn’t stand a chance. Olympic synchronized swimming. The 2012 London Olympics accidentally sold twice as many tickets as there were seats available for the synchronized swimming event. Why? A typo. Someone entered a “2” instead of a “1,” and the Olympic committee was forced to issue a public apology and convince 10,000 fans to accept tickets to other events. Suffice it to say, human error can lead to serious losses when left unchecked. Data quality is an essential consideration in any field. This is just a small selection of the significant disasters created by data quality issues. From Mars rovers to game shows, bad data leads to loss of revenue, public embarrassment, or even death—proving the necessity for comprehensive data quality management. To learn more about total data quality management solutions for your business, visit xcelerated.com.

Whether your data is right or wrong, it's driving every decision you make. Read that again. Whether your data is right or wrong, it's driving every decision you make. Let's face it. Maintaining quality data is a never-ending, thorny challenge that continually gets postponed or ignored altogether. Why. Is. It. So. Hard?! It can feel like decluttering your garage. Where do you even begin? What do you remove? What do you keep? When do you get help? It's a ginormous project with no definite beginning or end.  You know it needs to get better, but not sure how to rein it in. Here are five common data quality obstacles and what you can do to ease the pain. You don't know where to beginThe most common challenge we hear from our clients is that they don't even know where to start. We've heard this response from multiple clients: "This is a really big project that we're not prepared to take on right now".It's only going to get worse. Putting it off will make it more complicated. The Solution: Identify and prioritize your data quality issues. What are your company's biggest pain points? Here are some examples of challenges we've discovered where data is secretly impacting results Missed deadlines - Is it a personnel challenge or is the team struggling with data issues? Poor marketing results - Bad content or missing/inaccurate/duplicate data? Profit challenges - Blame sales? Or is it incorrect buyer profiles/customer persona?In order to prioritize, you need to identify where you're feeling the most pain. Maybe it's not that insidious, maybe it's as obvious as customers complaining about duplicate messages. Once you've identified the biggest pain point, go below the surface and do the deep dive. Ask questions and analyze the data for specific projects. "Sunlight is the best disinfectant." It's easy to assume the issues lie on the surface where we can see them, but it's critical to peel back the layers and get to the real causes. Using the above scenarios of common challenges, here are some examples of ways to do the "deeper dive". Missed deadlines - Step through and map each process in the data flow from beginning to end and pinpoint: The input data The output data What is combined What are the layouts What steps are taking the most time What steps need to be repeated because they were done incorrectly the first time (and second and third!) Any QC steps that are missingPoor marketing results - Manually sort and examine the customer data that was used for the campaign and look for: Outdated addresses, phones, emails, demographics etc... Missing information (i.e. FIRST NAME field populated with "Unknown") Duplicates Processes that removed "good" records and keep the "bad" records Wrong Information Information in wrong fields (i.e. LAST NAME is in the FIRST NAME column) Data Integrity issues (columns skewed by commas or human error)Profit challenges - Identify and review the data used for customer personas (you are creating customer personas, right?): Pricing - if you're customer data has quality issues, how do you know if your products are priced correctly? Customer acquisition cost - Multiple vendors and processes will increase your acquisition cost leading to decreased profitability Inability to accurately determine the customer lifetime value Your data Is not standardizedThis may sound basic but plagues most customer files. Multiple fields with the same info (Multiple phone fields, multiple emails) Different data sources that need to be combined with fields in a different order Input standards not enforced (may not be able to be enforced) Data in the wrong field Different teams with different standardsThe Solution: Even though the input data for multiple sources will have different layouts, it's important to agree on basic standards. During any processing, data that does not meet standards are updated to comply. Also, to ensure standards are always met, the process must be automated. Any vendor you use should have the ability to fit within your required standards. Your team currently runs multiple, ad-hoc, manual processesDespite having the right tools, teams will implement whatever process they need to get their work done. For example, one week a team may dedupe before running NCOA and the next week, run NCOA first before deduping, producing unexpected and unreliable results.  The Solution: Set up automated processes. Start with one process to avoid being overwhelmed. Your existing tools may allow for automating the needed steps or it may require custom programming. Oftentimes, IT teams have that capability but may not have the time. If that is the case your data quality vendor should be able to help you. You use multiple vendors for various services A reactive approach to data quality consists of disparate data hygiene steps: Choosing different vendors for each process, sending your data to each vendor, receiving back different layouts, having to further manipulate that data, and conducting some additional cleanup in-house. What are your answers to the following questions? What is the minimum cost for each of your data processing vendors? What was the total cost of your most recent list processing? Where do the bottlenecks occur in your data preparation process?Using multiple vendors for various services is tip-toeing through a minefield. The Solution: Use one vendor to handle the data quality end to end. Choose a vendor that takes into account: How the data is collected What type of data it is is it from a sale? Is it from a customer or prospect? Are employees entering information? What is the goal of the data? What are the planned uses? What are the real issues?Instead of using different, laborious processes for solving data problems, these solutions are customized to fix quality issues and ensure ongoing data accuracy and consistency. Custom solutions are based on data sources, industry priorities, and individual company goals, and their interconnectivity allows you to unify information across different teams and divisions of your business to ensure decision-makers at every level are working from the same — accurate and reliable — data. Data quality solutions simplify day-to-day processing and the business operations that incorporate data. You don't have the right tools (and you don't want to invest in them)Excel can do a lot, but it has its limits. Your computer may not be able to keep up. Perhaps your company has in-house legacy tools you're confined to.  The Solution: Focus on investing in your core business and let the right data quality vendor help you overcome your data quality obstacles.

Third-party cookies have long been the go-to resource for consumer data collection, particularly for targeted marketing and advertising campaigns. But privacy and cybersecurity concerns, along with increasing governmental regulation, have major internet and technology companies phasing out third-party cookies. It’s time to adapt, and first-party data is the way of the future. Who’s taking your cookies? For the uninitiated, third-party cookies are data tracking codes created and managed by an outside service to collect data for your company’s benefit. Cookies collect user data across websites, pages, and devices, primarily for the purpose of targeted advertising. Here’s how it works. When you own a business, you want to be sure your ads reach the right people. If your company rents vacation homes, you subscribe to an ad network to target consumers researching travel, vacations, and private getaways. If, on the other hand, you own an obedience training school, you would subscribe to an ad network for consumers researching dog breeds, crate training, and the best dog food for puppies. Using third-party cookies to collect data was, until recently, the smartest choice for gathering information on potential customers. The data is more reliable, and it allows for personalized, targeted ads, which generate more revenue than broad, general advertising campaigns. But as internet users grow increasingly privacy-conscious, it’s more common for people to clear their cookies or use tracking blockers, and many have complained about cookies as an invasion of their privacy. These complaints aren’t unwarranted. Cookies track a user’s internet activity across many sites — no matter how unrelated they may be to the original website. Others worry about data breaches and other possible security risks. As a result of the ongoing outcry, major web browsers are phasing out the use of third-party cookies in favor of less controversial first-party data collection. What is first-party data? First-party data is collected directly by the company that intends to use it. Rather than operating through an outside service, first-party data is collected from websites and other sources owned by the company. This means the data belongs to the company, as opposed to a third-party service. First-party data doesn’t present the same privacy concerns as third-party cookies because it’s collected with the consent of the individual to which it pertains through sources directly connected to your company. This can be data requested via the company website in exchange for discounts or additional content, or it might be gathered via consumer surveys and other methods of market research. Your company collects the data for its own use according to a stated privacy policy. Consumers have less worry that confidential data from one site will be viewed and collected by every other web page they’ve visited since they last cleared their cookies. A world without cookies Ready or not, third-party cookies are going away. As you prepare for a world without cookies, enhance your first-party data collection approach with these essential steps: Provide the compelling products, services, and content consumers want. You can no longer rely on completely customized content because the data you collect will not be as specific and comprehensive. Use the data you can collect to create products, services, and content to appeal to a wider audience. Establish systems to collect data on company channels. This can mean first-party data, email lists, or even Google’s Privacy Sandbox. Research data collection tools and channel options to find those that work best for your company’s circumstances and marketing strategy. Prepare for contextual advertising. Now that specific, customized ads are less of an option, consider setting up advertisements tied to website content that adds value for your target market. Because these ads relate to content the consumer opts in to view, they’re almost guaranteed to reach an interested audience. Camping equipment companies can’t target customers researching sleeping bags anymore, so their future efforts should focus on websites promoting the joys of outdoor living. Set up personalized email newsletters. Reach out to the consumer. Email newsletters aren’t just an efficient way to share company information. They can also be used to collect consumer data. Send out surveys and questionnaires to get consumers interested in sharing their opinions. People value their privacy and the security of their personal information. Third-party cookies are increasingly perceived as a threat to information security. Phase out targeted advertising efforts that rely on third-party data, and turn your attention to first-party data collection and contextual advertising. By preparing in advance, your company can avoid the fallout when cookies are gone for good. For more on first-party data collection and total data quality management solutions for your company, visit xcelerated.com.

From identifying the source(s) of your company’s data to the nitty gritty of data analysis, our team is equipped to provide yours with a complete data quality management solution customized for your unique business needs. Feeling frustrated with your data? Reach out to pam.lang@xcelerated.com.