Posts Tagged ‘Business Solutions’

Recognizing the vital role contact data quality plays in GDPR compliance, Service Objects is offering affected businesses a free data quality assessment.

Free Data Quality Assessment Helps Businesses Gauge GDPR Compliance Ahead of May Deadline

As the May 25, 2018, deadline looms, Service Objects, the leading provider of real-time global contact validation solutions, is offering a GDPR Data Quality Assessment to help companies evaluate their if they are prepared for the new set of privacy rules and regulations.

“Our goal is to help you get a better understanding of the role your client data plays in GDPR compliance,” says Geoff Grow, CEO and Founder, Service Objects. “With our free GDPR Data Quality Assessment, companies will receive an honest, third-party analysis of the accuracy of their contact records and customer database.”

Under the GDPR, personal data includes any information related to a natural person or ‘Data Subject’ that can be used to identify the person directly or indirectly. It can be anything from a name, a photo, an email address, bank details, posts on social networking websites, medical information, or a computer IP address.

Even if an organization is not based in the EU, it may still need to observe the rules and regulations of GDPR. That’s because the GDPR not only applies to businesses located in the EU but to any companies offering goods or services within the European Union. In addition, if a business monitors the behavior of any EU data subjects, including the processing and holding of personal data, the GDPR applies.

Recognizing the vital role contact data quality plays in GDPR compliance, Service Objects decided to offer a free data quality assessment to help those industries affected by the regulation measure the accuracy of their contact records and prepare for the May 2018 deadline.

The evaluation will include an analysis of up to 500 records, testing for accuracy across a set of inputs including name, phone, address, email, IP, and country. After the assessment is complete, a composite score will be provided, giving businesses an understanding of the how close they are to being compliant with GDPR’s Article 5.

Article 5 of the GDPR requires organizations collecting and processing personal information of individuals within the European Union (EU) to ensuring all current and future customer information is accurate and up-to-date. Not adhering to the rules and regulations of the GDPR can result in a fine of up to 4% of annual global turnover or €20 Million (whichever is greater).

“To avoid the significant fines and penalties associated with the GDPR, businesses are required to make every effort to keep their contact data is accurate and up-to-date,” Grow added. “Service Objects’ data quality solutions enable global businesses to fulfill the regulatory requirements of Article 5 and establish a basis for data quality best practices as part of a broader operational strategy.”

 

For more information on how to get started with your free GDPR Data Quality Assessment, please visit our website today.

When that data is incomplete, poorly defined, or wrong, there are immediate consequences: angry customers, wasted time, and difficult execution of strategy. Employing data quality best practices presents a terrific opportunity to improve business performance.

The Unmeasured Costs of Bad Customer and Prospect Data

Perhaps Thomas Redman’s most important recent article is “Seizing Opportunity in Data Quality.”  Sloan Management Review published it in November 2017, and it appears below.  Here he expands on the “unmeasured” and “unmeasurable” costs of bad data, particularly in the context of customer data, and why companies need to initiate data quality strategies.

Here is the article, reprinted in its entirety with permission from Sloan Management Review.

The cost of bad data is an astonishing 15% to 25% of revenue for most companies.

Getting in front on data quality presents a terrific opportunity to improve business performance. Better data means fewer mistakes, lower costs, better decisions, and better products. Further, I predict that many companies that don’t give data quality its due will struggle to survive in the business environment of the future.

Bad data is the norm. Every day, businesses send packages to customers, managers decide which candidate to hire, and executives make long-term plans based on data provided by others. When that data is incomplete, poorly defined, or wrong, there are immediate consequences: angry customers, wasted time, and added difficulties in the execution of strategy. You know the sound bites — “decisions are no better than the data on which they’re based” and “garbage in, garbage out.” But do you know the price tag to your organization?

Based on recent research by Experian plc, as well as by consultants James Price of Experience Matters and Martin Spratt of Clear Strategic IT Partners Pty. Ltd., we estimate the cost of bad data to be 15% to 25% of revenue for most companies (more on this research later). These costs come as people accommodate bad data by correcting errors, seeking confirmation from other sources, and dealing with the inevitable mistakes that follow.

Fewer errors mean lower costs, and the key to fewer errors lies in finding and eliminating their root causes. Fortunately, this is not too difficult in most cases. All told, we estimate that two-thirds of these costs can be identified and eliminated — permanently.

In the past, I could understand a company’s lack of attention to data quality because the business case seemed complex, disjointed, and incomplete. But recent work fills important gaps.

The case builds on four interrelated components: the current state of data quality, the immediate consequences of bad data, the associated costs, and the benefits of getting in front on data quality. Let’s consider each in turn.

Four Reasons to Pay Attention to Data Quality Now

The Current Level of Data Quality Is Extremely Low

A new study that I recently completed with Tadhg Nagle and Dave Sammon (both of Cork University Business School) looked at data quality levels in actual practice and shows just how terrible the situation is.

A new study that I recently completed with Tadhg Nagle and Dave Sammon (both of Cork University Business School) looked at data quality levels in actual practice and shows just how terrible the situation is.

We had 75 executives identify the last 100 units of work their departments had done — essentially 100 data records — and then review that work’s quality. Only 3% of the collections fell within the “acceptable” range of error. Nearly 50% of newly created data records had critical errors.

Said differently, the vast majority of data is simply unacceptable, and much of it is atrocious. Unless you have hard evidence to the contrary, you must assume that your data is in similar shape.

Bad Data Has Immediate Consequences

Virtually everyone, at every level, agrees that high-quality data is critical to their work. Many people go to great lengths to check data, seeking confirmation from secondary sources and making corrections. These efforts constitute what I call “hidden data factories” and reflect a reactive approach to data quality. Accommodating bad data this way wastes time, is expensive, and doesn’t work well. Even worse, the underlying problems that created the bad data never go away.

One consequence is that knowledge workers waste up to 50% of their time dealing with mundane data quality issues. For data scientists, this number may go as high as 80%.

A second consequence is mistakes, errors in operations, bad decisions, bad analytics, and bad algorithms. Indeed, “big garbage in, big garbage out” is the new “garbage in, garbage out.”

Finally, bad data erodes trust. In fact, only 16% of managers fully trust the data they use to make important decisions.

Frankly, given the quality levels noted above, it is a wonder that anyone trusts any data.

When Totaled, the Business Costs Are Enormous

Obviously, the errors, wasted time, and lack of trust that are bred by bad data come at high costs.

Companies throw away 20% of their revenue dealing with data quality issues. This figure synthesizes estimates provided by Experian (worldwide, bad data cost companies 23% of revenue), Price of Experience Matters ($20,000/employee cost to bad data), and Spratt of Clear Strategic IT Partners (16% to 32% wasted effort dealing with data). The total cost to the U.S. economy: an estimated $3.1 trillion per year, according to IBM.

The costs to businesses of angry customers and bad decisions resulting from bad data are immeasurable — but enormous.

Finally, it is much more difficult to become data-driven when a company can’t depend on its data. In the data space, everything begins and ends with quality. You can’t expect to make much of a business selling or licensing bad data. You should not trust analytics if you don’t trust the data. And you can’t expect people to use data they don’t trust when making decisions.

Two-Thirds of These Costs Can Be Eliminated by Getting in Front on Data Quality

“Getting in front on data quality” stands in contrast to the reactive approach most companies take today. It involves attacking data quality proactively by searching out and eliminating the root causes of errors. To be clear, this is about management, not technology — data quality is a business problem, not an IT problem.

Companies that have invested in fixing the sources of poor data — including AT&T, Royal Dutch Shell, Chevron, and Morningstar — have found great success. They lead us to conclude that the root causes of 80% or more of errors can be eliminated; that up to two-thirds of the measurable costs can be permanently eliminated; and that trust improves as the data does.

Which Companies Should Be Addressing Data Quality?

While attacking data quality is important for all, it carries a special urgency for four kinds of companies and government agencies:

Those that must keep an eye on costs. Examples include retailers, especially those competing with Amazon.com Inc.; oil and gas companies, which have seen prices cut in half in the past four years; government agencies, tasked with doing more with less; and companies in health care, which simply must do a better job containing costs. Paring costs by purging the waste and hidden data factories created by bad data makes far more sense than indiscriminate layoffs — and strengthens a company in the process.

Those seeking to put their data to work. Companies include those that sell or license data, those seeking to monetize data, those deploying analytics more broadly, those experimenting with artificial intelligence, and those that want to digitize operations. Organizations can, of course, pursue such objectives using data loaded with errors, and many companies do. But the chances of success increase as the data improves.

Those unsure where primary responsibility for data should reside. Most businesspeople readily admit that data quality is a problem, but claim it is the province of IT. IT people also readily admit that data quality is an issue, but they claim it is the province of the business — and a sort of uneasy stasis results. It is time to put an end to this folly. Senior management must assign primary responsibility for data to the business.

Those who are simply sick and tired of making decisions using data they don’t trust. Better data means better decisions with less stress. Better data also frees up time to focus on the really important and complex decisions.

Next Steps for Senior Executives

In my experience, many executives find reasons to discount or even dismiss the bad news about bad data. Common refrains include, “The numbers seem too big, they can’t be right,” and “I’ve been in this business 20 years, and trust me, our data is as good as it can be,” and “It’s my job to make the best possible call even in the face of bad data.”

But I encourage each executive to think deeply about the implications of these statistics for his or her own company, department, or agency, and then develop a business case for tackling the problem. Senior executives must explore the implications of data quality given their own unique markets, capabilities, and challenges.

The first step is to connect the organization or department’s most important business objectives to data. Which decisions and activities and goals depend on what kinds of data?

The second step is to establish a data quality baseline. I find that many executives make this step overly complex. A simple process is to select one of the activities identified in the first step — such as setting up a customer account or delivering a product — and then do a quick quality review of the last 100 times the organization did that activity. I call this the Friday Afternoon Measurement because it can be done with a small team in an hour or two.

The third step is to estimate the consequences and their costs for bad data. Again, keep the focus narrow — managers who need to keep an eye on costs should concentrate on hidden data factories; those focusing on AI can concentrate on wasted time and the increased risk of failure; and so forth.

Finally, for the fourth step, estimate the benefits — cost savings, lower risk, better decisions — that your organization will reap if you can eliminate 80% of the most common errors. These form your targets going forward.

Chances are that after your organization sees the improvements generated by only the first few projects, it will find far more opportunity in data quality than it had thought possible. And if you move quickly, while bad data is still the norm, you may also find an unexpected opportunity to put some distance between yourself and your competitors.

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Service Objects spoke with the author, Tom Redman, and he gave us an update on the Sloan Management article reprinted above, particularly as it relates to the subject of the costs associated with bad customer data.

Please focus first on the measurable costs of bad customer data.  Included are items such as the cost of the work Sales does to fix up bad prospect data it receives from Marketing, the costs of making good for a customer when Operations sends him or her the wrong stuff, and the cost of work needed to get the various systems which house customer data to “talk.”  These costs are enormous.  For all data, it amounts to roughly twenty percent of revenue.

But how about these costs:

  • The revenue lost when a prospect doesn’t get your flyer because you mailed it to the wrong address.
  • The revenue lost when a customer quits buying from you because fixing a billing problem was such a chore.
  • The additional revenue lost when he/she tells a friend about his or her experiences.

This list could go on and on.

Most items involve lost revenue and, unfortunately, we don’t know how to estimate “sales you would have made.”  But they do call to mind similar unmeasurable costs associated with poor manufacturing in the 1970s and 80s.  While expert opinion varied, a good first estimate was that the unmeasured costs roughly equaled the measured costs.

If the added costs in the Seizing Opportunity article above doesn’t scare into action, add in a similar estimate for lost revenue.

The only recourse is to professionally manage the quality of prospect and customer data.  It is not hyperbole to note that such data are among a company’s most important assets and demand no less.

©2018, Data Quality Solutions

 

Study after study has shown that investing in employee experience impacts the customer experience and can generate a high ROI for the company.

The Un-Ignorable Link Between Employee Experience And Customer Experience

Engaged employees lead to happy customers.

There is an undeniable link between employee experience and customer experience. Companies that lead in customer experience have 60% more engaged employees, and study after study has shown that investing in employee experience impacts the customer experience and can generate a high ROI for the company. Here are 10 companies that have seen the benefit of engaging their employees to build customer experience.

“Take care of associates and they’ll take care of your customers.” -J.W.Marriott

Marriott International founder J.W. Marriott said, “Take care of associates and they’ll take care of your customers.” It still holds true at the company—employees are valued, which makes them want to share that experience with guests. Marriott publicly rewards employees for a job well done, celebrates diversity and inclusion, values loyalty, and offers a wide variety of training programs. It has been regularly rated a top place to work and a top company for customer experience.

Chick-Fil-A Encourages Employees To Build Relationships With Customers

With its chicken and waffle fries, Chick-fil-A generates more revenue per restaurant than any other chain in the country. But it’s not just the food that sets the restaurant apart—it’s the employees. Franchise owners are given thorough training but also have bandwidth to explore creative ideas. Employees are encouraged to build relationships with customers because they have strong relationships with each other and with the company.

The Zappos Contact Center Calls Its Team Customer Loyalty Team Members

E-commerce site Zappos is known for connecting with its customers and for responding to issues quickly. That’s likely because the company also has a great reputation for connecting with its employees. Every employee plays a role in the company’s customer-first culture—even call center employees are referred to as customer loyalty team members. When employees feel connected to and valued by the brand, they want to bring customers into the circle.

Nordstrom Only Asks Employees To Use Their Best Judgement

Employees at Nordstrom are given just one rule in their employee handbook: “Use best judgment in all situations. There will be no additional rules.” Instead of being bogged down with corporate guidance, empowered employees know they are trusted and valued. That translates to their interactions with customers and is a large reason why the “Nordstrom Way” of doing customer service is well respected.

Taco Bell Provides An Easy Way For Employees To Ask For Help

Fast food giant Taco Bell puts employees first by always providing them a way to contact management. The company has a network of 1-800 numbers to field complaints, answer questions, and alert management of potential red flags for its 175,000-plus employees. It also holds regular employee roundtable meetings and company-wide surveys to gage employee satisfaction. With their needs met and questions answered, employees can focus on helping customers.

Jet Blue Employees Are Allowed To Go The Extra Mile For Customers

Jet Blue is consistently rated one of the best airlines, and a large part of that is the great customer experience. Jet Blue’s employees are given the freedom to go the extra mile to help customers. Instead of being constrained by red tape and bureaucracy, employees have power to solve problems themselves, which means they often consider customers problems to be their own. Jet Blue also fosters a spirit of collaboration and teamwork with employees that extends to customers.

Starbucks Provides Extensive Training On How To Interact With Customers

Starbucks knows that happy employees lead to happy customers. The company is consistently at the top of every customer experience “best” list, and this recognition comes from taking care of its employees. Starbucks provides employees competitive wages, health benefits, and stock options. Each employee is trained not only on how to make the drinks but also how to interact with customers. The welcoming atmosphere of a Starbucks coffee shop is echoed in the company, where every employee knows they are welcomed and included.

Airbnb Helps Employees Focus On Personal Growth

Airbnb’s mission statement of “Belong Anywhere” extends beyond customers to also include employees. Airbnb is invested in every aspect of its employees’ lives, not just what they do at the office. The company works to create a culture that sets employees up for success in their personal and professional lives, from having a flexible, open office space to being transparent with the goals of the company. Employees can focus on their personal growth and the mission of the company, which allows them to create better customer experiences.

Adobe Ties Employee Compensation To Customer Experience

Instead of viewing customers and employees as separate entities, Adobe brings them together to drive positive, connected experiences. Employees are trained on customer experience metrics and how each person’s role impacts the overall customer experience. It also encourages employees to be advocates for customers’ needs and jump in when they see a problem instead of waiting for something to run its course. At Adobe, employee compensation is tied to customer experience. When employees are connected with customers and see the role they can each play individually, they want to create a better experience (disclosure: Adobe is a client).

GE Uses Root Cause Analysis To Improve Customer Satisfaction

It takes an innovative HR department to drive employee experience at General Electric. Employees are involved in the process to make sure they have the physical space and technological tools to do their best work and that training programs keep employees moving forward. When a division of GE saw it had low customer satisfaction scores, it worked to find the root cause and streamline internal processes. Cutting red tape keeps employees happier and allows them to be more productive, which helped the customer satisfaction score jump more than 40% in two years.

Your employees are your often your most untapped resource when it comes to building powerful customer experiences. I hope you are just as inspired by the companies highlighted here as I was.

The article above was first published on Forbes.com and reprinted with permission. View original post here.

About the author: Blake Morgan is a customer experience futurist, author of More Is More, and keynote speaker. You can read more of Blake’s articles by visiting her website.

A Daisy Chain of Hidden Customer Data Factories

I published the provocatively-titled article, Bad Data Costs the United States $3 Trillion per Year in September, 2016 at Harvard Business Review. It is of special importance to those who need prospect/customer/contact data in the course of their work.

First read the article.

Consider this figure: $136 billion per year. That’s the research firm IDC’s estimate of the size of the big data market, worldwide, in 2016. This figure should surprise no one with an interest in big data.

But here’s another number: $3.1 trillion, IBM’s estimate of the yearly cost of poor quality data, in the US alone, in 2016. While most people who deal in data every day know that bad data is costly, this figure stuns.

While the numbers are not really comparable, and there is considerable variation around each, one can only conclude that right now, improving data quality represents the far larger data opportunity. Leaders are well-advised to develop a deeper appreciation for the opportunities improving data quality present and take fuller advantage than they do today.

The reason bad data costs so much is that decision makers, managers, knowledge workers, data scientists, and others must accommodate it in their everyday work. And doing so is both time-consuming and expensive. The data they need has plenty of errors, and in the face of a critical deadline, many individuals simply make corrections themselves to complete the task at hand. They don’t think to reach out to the data creator, explain their requirements, and help eliminate root causes.

Quite quickly, this business of checking the data and making corrections becomes just another fact of work life.  Take a look at the figure below. Department B, in addition to doing its own work, must add steps to accommodate errors created by Department A. It corrects most errors, though some leak through to customers. Thus Department B must also deal with the consequences of those errors that leak through, which may include such issues as angry customers (and bosses!), packages sent to the wrong address, and requests for lower invoices.

The Hidden Data Factory

Visualizing the extra steps required to correct the costly and time consuming data errors.

I call the added steps the “hidden data factory.” Companies, government agencies, and other organizations are rife with hidden data factories. Salespeople waste time dealing with erred prospect data; service delivery people waste time correcting flawed customer orders received from sales. Data scientists spend an inordinate amount of time cleaning data; IT expends enormous effort lining up systems that “don’t talk.” Senior executives hedge their plans because they don’t trust the numbers from finance.

Such hidden data factories are expensive. They form the basis for IBM’s $3.1 trillion per year figure. But quite naturally, managers should be more interested in the costs to their own organizations than to the economy as a whole. So consider:

There is no mystery in reducing the costs of bad data — you have to shine a harsh light on those hidden data factories and reduce them as much as possible. The aforementioned Friday Afternoon Measurement and the rule of ten help shine that harsh light. So too does the realization that hidden data factories represent non-value-added work.

To see this, look once more at the process above. If Department A does its work well, then Department B would not need to handle the added steps of finding, correcting, and dealing with the consequences of errors, obviating the need for the hidden factory. No reasonably well-informed external customer would pay more for these steps. Thus, the hidden data factory creates no value. By taking steps to remove these inefficiencies, you can spend more time on the more valuable work they will pay for.

Note that very near term, you probably have to continue to do this work. It is simply irresponsible to use bad data or pass it onto a customer. At the same time, all good managers know that, they must minimize such work.

It is clear enough that the way to reduce the size of the hidden data factories is to quit making so many errors. In the two-step process above, this means that Department B must reach out to Department A, explain its requirements, cite some example errors, and share measurements. Department A, for its part, must acknowledge that it is the source of added cost to Department B and work diligently to find and eliminate the root causes of error. Those that follow this regimen almost always reduce the costs associated with hidden data factories by two thirds and often by 90% or more.

I don’t want to make this sound simpler than it really is. It requires a new way of thinking. Sorting out your requirements as a customer can take some effort, it is not always clear where the data originate, and there is the occasional root cause that is tough to resolve. Still, the vast majority of data quality issues yield.

Importantly, the benefits of improving data quality go far beyond reduced costs. It is hard to imagine any sort of future in data when so much is so bad. Thus, improving data quality is a gift that keeps giving — it enables you to take out costs permanently and to more easily pursue other data strategies. For all but a few, there is no better opportunity in data.

The article above was originally written for Harvard Business Review and is reprinted with permission.
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In January 2018, Service Objects spoke with the author, Tom Redman, and he gave us an update on the article above, particularly as it relates to the subject of data quality.

According to Tom, the original article anticipated people asking, “What’s going on?  Don’t people care about data quality?”

The answer is, “Of course they care.  A lot.  So much that they implement ‘hidden data factories’ to accommodate bad data so they can do their work.”  And the article explored such factories in a generic “two-department” scenario.

Of course, hidden data factories take a lot of time and cost a lot of money, both contributing to the $3T/year figure.  They also don’t work very well, allowing lots of errors to creep through, leading to another hidden data factory.  And another and another, forming a sort of “daisy chain” of hidden data factories.  Thus, when one extends the figure above and narrows the focus to customer data, one gets something like this:

I hope readers see the essential truth this picture conveys and are appalled.  Companies must get in front on data quality and make these hidden data factories go away!

©2018, Data Quality Solutions

Is Your Data Quality Strategy Gold Medal Worthy?

A lot of you – like many of us here are Service Objects – are enjoying watching the 2018 Winter Olympics in Pyeongchang, Korea this month. Every Olympics is a spectacle where people perform incredible feats of athleticism on the world stage.

Watching these athletes reminds us of how much hard work, preparation, and teamwork go into their success. Most of these athletes spend years behind the scenes perfecting their craft, with the aid of elite coaches, equipment, and sponsors. And the seemingly effortless performances you see are increasingly becoming data-driven as well.

Don’t worry, we aren’t going to put ourselves on the same pedestal as Olympic medalists. But many of the same traits behind successful athletes do also drive reliable real-time API providers for your business. Here are just a few of the qualities you should look for:

The right partners. You probably don’t have access to up-to-the-minute address and contact databases from sources around the world. Or a database of over 400 million phone numbers that is constantly kept current. We do have all of this, and much more – so you can leverage our infrastructure to assure your contact data quality.

The right experience. The average Olympic skater has invested at least three hours a day in training for over a decade by the time you see them twirling triple axels on TV, according to Forbes. Likewise, Service Objects has validated nearly three billion transactions since we were founded in 2001, with a server uptime reliability of 99.999 percent.

The right strategy. In sports where success is often measured in fractions of a second, gold medals are never earned by accident: athletes always work against strategic objectives. We follow a strategy as well. Our tools are purpose-built for the needs of over 2500 customers, ranging from marketing to customer service, with capabilities such as precise geolocation of tax data, composite lead quality scores based on over 130 criteria, or fraud detection based on IP address matching. And we never stop learning and growing.

The right tools. Olympic athletes need the very best equipment to be competitive, from ski boots to bobsleds. In much the same way our customers’ success is based around providing the best infrastructure, including enterprise-grade API interfaces, cloud connectors and web hooks for popular CRM, eCommerce and marketing automation platforms, and convenient batch list processing.

The right support. No one reaches Olympic success by themselves – every athlete is backed by a team of coaches, trainers, sponsors and many others. We back our customers with an industry-leading support team as well, including a 24×7 Quick Response Team for urgent mission-critical issues.

The common denominator between elite athletes and industry-leading data providers is that both work hard to be the best at what they do and aren’t afraid to make big investments to get there. And while we can’t offer you a gold, silver, or bronze medal, we can give you a free white paper on how to make your data quality hit the perfect trifecta of being genuine, accurate and up-to-date. Meanwhile, enjoy the Olympics!

How to Convince Your Boss Your Business Needs a Data Quality Solution

Many developers come to Service Objects because they recognize their company has a need for a data quality solution. Maybe you were tasked by a manager to help find a solution, perhaps someone in Marketing mentioned one of their pain points, or maybe you just naturally saw opportunities for improvement. Whatever the reason was that got you looking for a data quality solution, you most likely need to get someone in management to sign off on your solution. Often, especially in larger organizations, this can be a challenge. So how are others accomplishing this?

Service Objects has been in business for over 16 years and in that time frame, we have helped potential customers just like you get sign off for our services. In addition to being armed with information from your Account Executive on how you can achieve ROI for the service you have chosen, the following recommendations are the ones we have found to be the most useful.

Test Out Our Services

If you haven’t already, get a free trial key for the service you are interested in.  We allow you to try out as many of our 23 data quality solutions as you would like.  My blog, Taking Service Objects for a Test Drive, can help you figure out which method of testing our services is best for you.

One of the values in testing our services is that it reinforces your own understanding of our products. Testing allows you to quickly visualize and show others in your organization the benefits of utilizing a data quality solution.  Showing your boss an actual test integration you did with our API is more powerful than just explaining how it works.  Or, if you decide to run a test batch with us, you will see first hand how we can improve your contact data.

You can also leverage our quick look up page and set up on a screen share with a larger team and run live examples for them.  Let your team choose some data to run live and see the results first hand.

Data Summaries

Data summaries are a great visual aid to take a look at how our services help.  Take a subset of any of your data sets and send them over for a batch test.  Each batch test contains a comprehensive summary, providing you with how we validated your data and what the results mean.  Your Account Executive can review this and guide you through the results. Having a detailed report that clearly shows the current state of your data is a great tool to have when you are ready to go forward with any type of presentation to your team. A recent blog shows what one of our reports includes.  All data has a story, and with our batch summaries we try to tell that story.

Integration Plan

Having an integration plan is an asset when getting sign off for our services.  You are bound to get some questions after showing your plan, such as “who?” “how?” and “how long?”.  It is a good idea to be prepared to answer these questions.

Integrating with us is not a complicated task and we have several resources to guide you.  Our sample code can quickly get you up and running and we can even customize it for your specific use case. We also have best practices available to help you with your integration. For instance, you may need to check if there are any network or firewall issues your IT team needs to complete. If you are switching from another solution or vendor, you may need to have your integration timeline in sync with turning off an old service and turning our service on.  And never forget about testing, which is one of the most important parts of any integration.  Finally, you may need to account for any training you want to provide to your team about how to work with our data or your new system.  Having answers to these questions can help arm you with everything you need to keep the ball rolling.

Customer Service and Support

Support.  It is just one word but it is key.  Sometimes even the most important word.  You can purchase a top-notch product or service, but if you can’t get adequate customer support when you need it, your purchase loses most of its value to you.  With Service Objects, this is never the case.  In fact, customer service is one of our core values. We care about your success and truly want you to be succeed.  If you want to get buy in from your team, then it is very important to discuss our customer service expertise. Having a dedicated customer support resource, comprised of engineers, will ease your transition to our offerings and in the end, will save your organization significant time and money.

Value Proposition

At Service Objects, our value proposition is very clear. Our data quality solutions ensure your data is as genuine, accurate and up-to-date as it can possibly be. A large percentage of our customers have been with us for many years, and this is due to the effort we put into the quality, accuracy, speed, reliability and expertise we deliver.

Getting Buy-In from Colleagues

It is always helpful to get people on your side when you want to present a new service to your company. Talking it over with colleagues and managers is great and many of the things written in this blog can help get them on your side to support your idea.  It also goes a long way in educating your team on the data quality issues you are facing and how a Service Objects’ solution can alleviate many of those problems.

I hope some of these ideas or tips can help you along the way in presenting our services to your team or manager.  The last thing you want to do is have a conversation with your boss without adequate preparation.  Your ideas are good, so take your time and plan the right action in getting them implemented.  In the end, the results our services will provide for your company will be impactful and worth your time.

Visit our product page to Get started testing any of our services.

To Be Customer-Centric, You Have To Be Data-Centric

In today’s fast-paced world, customers have become more demanding than ever before. Customer-centric organizations need to build their models after critically analyzing their customers, and this requires them to be data-centric.

Today, customers expect companies to be available 24/7 to solve their queries. They expect companies to provide them with seamless information about their products and services. Not getting such features can have a serious impact on their buying decision. Customer-centric organizations need to adopt a data-centric approach not just for targeted customer interactions, but also to survive competition from peers who are data-centric.

Customer-centric organizations need to go data-centric

Why?

Today, customers enquire a lot before making a decision. And social media enquiries are the most widely used by customers. A satisfactory experience is not limited to prompt answers to customer queries alone. Customers need a good experience before making the purchase, during the installment of product or deployment of service, and even after making the purchase. Thus, to retain a customer’s loyalty and to drive in regular profits, companies need to be customer-centric. And that can only happen if companies adopt a data-centric approach. Big data comes handy in developing a model that gives optimum customer experience. It helps build a profound understanding of the customer, such as what they like and value the most and the customer lifetime value to the company. Besides, every department in a data-centric organization can have the same view about its customer, which guarantees efficient customer interactions. Companies like Amazon and Zappos are the best examples of customer-centric organizations that heavily depend on data to provide a unique experience to their customers. This has clearly helped them come a long way.

How?

Companies can collect a lot of information that can help them become customer-centric. Here are some ways in which they can do so:

  • Keep a close eye on any kind of new data that could help them stay competitive, such as their product prices and the money they invest in logistics and in-product promotion. They need to constantly monitor the data that tells them about the drivers of these income sources.
  • Reach out to customers from different fields with varying skill sets to derive as many insights as possible so as to help make a better product.
  • Develop a full-scale data project that will help them collect data and apply it to make a good data strategy and develop a successful business case.

Today, there is no escaping customer expectations. Companies need to satisfy customers for repeat business. Customer satisfaction is the backbone of selling products and services, maintaining a steady flow of revenue, and for the certainty of business. And for all of that to happen, companies need to gather and democratize as much information about their customers as possible.

Reprinted with permission from the author. View original post here.

Author: Naveen Joshi, Founder and CEO of Allerin

The Difference Between Customer Experience And User Experience

There are a lot of buzzwords thrown around in the customer sphere, but two of the big ones relate to experiences—customer and user. Although CX and UX are different and unique, they must work together for a company to have success.

User experience deals with customers’ interaction with a product, website, or app. It is measured in things like abandonment rate, error rate, and clicks to completion. Essentially, if a product or technology is difficult to use or navigate, it has a poor user experience.

Customer experience on the other hand focuses on the general experience a customer has with a company. It tends to exist higher in the clouds and can involve a number of interactions. It is measured by net promoter score, customer loyalty, and customer satisfaction.

Both customer experience and user experience are incredibly important and can’t truly exist and thrive without each other. If a website or mobile app has a bad layout and is cumbersome to navigate, it will be difficult for customers to find what they need and can lead to frustration. If customers can’t easily open the mobile app from an email on their phone, they likely won’t purchase your product. Likewise, if the product layout is clunky, customers likely won’t recommend it to a friend no matter how innovative it is. User experience is a huge part of customer experience and needs to play a major role when thinking like a customer.

Although UX and CX are different, they need to work closely together to truly be successful. Customer experience representatives should be working alongside product engineers to make sure everything works together. By taking themselves through the entire customer journey, they can see how each role plays into a customer’s overall satisfaction with the product and the company. The ultimate goal is a website or product that beautifully meshes the required elements of navigation and ease with the extra features that will help the brand stand out with customers.

When thinking about customer experience, user experience definitely shouldn’t be left behind. Make both unique features an essential part of your customer plan to build a brand that customers love all around.

Reprinted with permission from the author. View original post here.

Author Bio: Blake Morgan is a customer experience futurist, author of More Is More, and keynote speaker.

Go farther and create knock your socks-off customer experiences in your organization by enrolling in her new Customer Experience School.

Leverage Service Objects’ Industry Expertise to Reach Your Data Quality Goals

At Service Objects, we are fully committed to our customers’ success, which is a main factor in why over 90% of our business is from repeat customers. And with over 16 years of experience in contact validation, we have accumulated a broad base of industry expertise, created numerous best practices and are considered thought leaders in global data validation.

It is because of this knowledge that some of our customers turn to us when they lack the internal resources to carry out their data quality project. Whether it is assistance in implementing a data quality initiative, asking for customization to our products to meet specific business needs or help integrating our solutions into Marketing or Sales Automation platforms, Service Objects’ Professional Services can assist your business in achieving optimal results on your project in a quick and efficient manner.

Here are just three of the ways we can help:

Integration Programming and Code Support

If your team is overwhelmed or lacks the technical resources to integrate data quality solutions into your existing systems, Service Objects can step in and quickly get your project moving. We provide your team with the technical knowledge, support, and best practices needed to implement your chosen solution in a timely fashion and within your budget.

CRM or Marketing Automation Platform Integration

We have created cloud connectors for the leading sales and marketing platforms and have developed extensive knowledge on how these systems work with our data quality solutions. We enable your organization to implement best practices, allowing your business to verify, correct and append contact data at the point of entry. The result is your contact database contains records that are as genuine, accurate and up-to-date as they can possibly be.

Custom Services

Our engineers have years of experience creating, implementing and supporting data quality services in many different programming languages. As a result, we can customize our existing services to solve a challenge that is specific to your business. Our proactive support services team will work with your technical team to refine, test and implement the custom service to work for your business’ specifications.

These are just some of the ways we can help. For more information about how you leverage our industry expertise and technical knowledge, contact us.