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Author Archive

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.

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

Improving Customer Satisfaction Through Data Quality

“Online retailers of all sizes are constantly under attack by sophisticated fraudsters. In fact, credit card fraud costs US online retailers an estimated $3.9 billion each year.” – Geoff Grow, Founder and CEO, Service Objects

At Service Objects, we know that data quality excellence is the key to helping retailers feel confident about improving delivery rates while reducing fraud associated with vacant addresses, PO boxes and commercial mail handlers. This, in turn, helps maintain higher customer satisfaction ratings among your legitimate customers.

This video, featuring Service Objects’ Founder and CEO, Geoff Grow, will show you tools you can use to improve the deliverability of your products and combat fraud. You will learn how to validate addresses against current USPS certified address data to prevent undeliverable and lost shipments, as well as how to validate a customer’s IP address against the billing and shipping information they provide, using data from over many authoritative data sources to stop fraud before it happens.


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


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.


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.

The Talent Gap In Data Analytics

According to a recent blog by Villanova University, the amount of data generated annually has grown tremendously over the last two decades due to increased web connectivity, as well as the ever-growing popularity of internet-enabled mobile devices. Some organizations have found it difficult to take advantage of the data at their disposal due to a shortage of data-analytics experts. Primarily, small-to-medium enterprises (SMBs) who struggle to match the salaries offered by larger businesses are the most affected. This shortage of qualified and experienced professionals is creating a unique opportunity for those looking to break into a data-analysis career.

Below is some more information on this topic.

Data-Analytics Career Outlook

Job openings for computer and research scientists are expected to grow by 11 percent from 2014 to 2024. In comparison, job openings for all occupations are projected to grow by 7 percent over the same period. Besides this, 82 percent of organizations in the US say that they are planning to advertise positions that require data-analytics expertise. This is in addition to 72 percent of organizations that have already hired talent to fill open analytics positions in the last year. However, up to 78 percent of businesses say they have experienced challenges filling open data-analytics positions over the last 12 months.

Data-Analytics Skills

The skills that data scientists require vary depending on the nature of data to be analyzed as well as the scale and scope of analytical work. Nevertheless, analytics experts require a wide range of skills to excel. For starters, data scientists say they spend up to 60 percent of their time cleaning and aggregating data. This is necessary because most of the data that organizations collect is unstructured and comes from diverse sources. Making sense of such data is challenging, because the majority of modern databases and data-analytics tools only support structured data. Besides this, data scientists spend at least 19 percent of their time collecting data sets from different sources.

Common Job Responsibilities

To start with, 69 percent of data scientists perform exploratory data-analytics tasks, which in turn form the basis for more in-depth querying. Moreover, 61 percent perform analytics with the aim of answering specific questions, 58 percent are expected to deliver actionable insights to decision-makers, and 53 percent undertake data cleaning. Additionally, 49 percent are tasked with creating data visualizations, 47 percent leverage data wrangling to identify problems that can be resolved via data-driven processes, and 43 percent perform feature extraction, while 43 percent have the responsibility of developing data-based prototype models.

In-demand Programming-Language Skills

In-depth understanding of SQL is a key requirement cited in 56 percent of job listings for data scientists. Other leading programming-language skills include Hadoop (49 percent of job listings), Python (39 percent), Java (36 percent), and R (32 percent).

The Big-Data Revolution

The big-data revolution witnessed in the last few years has changed the way businesses operate substantially. In fact, 78 percent of corporate organizations believe big data is likely to fundamentally change their operational style over the next three years, while 71 percent of businesses expect the same resource to spawn new revenue opportunities. Only 58 percent of executives believe that their employer has the capability to leverage the power of big data. Nevertheless, 53 percent of companies are planning to roll out data-driven initiatives in the next 12 months.

Recruiting Trends

Companies across all industries are facing a serious shortage of experienced data scientists, which means they risk losing business opportunities to firms that have found the right talent. Common responsibilities among these professionals include developing data visualizations, collecting data, cleaning and aggregating unstructured data, and delivering actionable insights to decision-makers. Leading employers include the financial services, marketing, corporate and technology industries.

View the full infographic created by Villanova University’s Online Master of Science in Analytics degree program.

Reprinted with permission.

What Can We Do? Service Objects Responds to Hurricane Harvey

The Service Objects’ team watched the steady stream of images from Hurricane Harvey and its aftermath and we wanted to know, ‘What can we do to help?’  We realized the best thing we could do is offer our expertise and services free to those who can make the most use of them – the emergency management agencies dedicated to helping those affected by this disaster.

It was quickly realized that as Hurricane Harvey continues to cause record floodwaters and entire neighborhoods are under water, these agencies are finding it nearly impossible to find specific addresses in need of critical assistance. In response to this, we are offering emergency management groups the ability to quickly pinpoint addresses with latitude and longitude coordinates by offering unlimited, no cost access to DOTS Address Geocode ℠ (AG-US). By using Address Geocode, the agencies will not have to rely on potentially incomplete online maps. Instead, using Service Objects’ advanced address mapping services, these agencies will be able to reliably identify specific longitude and latitude coordinates in real-time and service those in need.

“The fallout of the catastrophic floods in Texas is beyond description, and over one million locations in Houston alone have been affected,” said Geoff Grow, CEO and Founder of Service Objects.  “With more than 450,000 people likely to seek federal aid in recovering from this disaster, Service Objects is providing advanced address mapping to help emergency management agencies distribute recovery funds as quickly as possible. We are committed to helping those affected by Hurricane Harvey.”

In addition, as disaster relief efforts are getting underway, Service Objects will provide free access to our address validation products to enable emergency management agencies to quickly distribute recovery funds by address type, geoid, county, census-block and census-track. These data points are required by the federal government to release funding.  This will allow those starting the recovery process from this natural disaster to get next level services as soon as possible.

To get access to Service Objects address solutions or request maps, qualified agencies can contact Service Objects directly by calling 805-963-1700 or by emailing us at

Our team wishes the best for all those affected by Hurricane Harvey.

Image by National Weather Service 

Ensuring Addresses are Accurate and Up-to-Date

“Did you know that nearly 30 million Americans move each year? Did you also know that government agencies like counties, cities, and states are required to keep accurate and up to date records of their private citizens for communication purposes?”

Service Objects is committed to helping businesses reduce waste, and identify and improve operating efficiency through data quality excellence. And according to founder and CEO Geoff Grow, you can do this using databases up-to-the-minute USPS-certified data and more to verify your contact records.

This video will show you how to use simple API and web-based tools that validate and append data to your contact information. You will learn how data quality solutions can:

  • identify change of addresses, making it easier to keep your contact records accurate and up-to-date,
  • validate addresses to maximize delivery rates,
  • geocode addresses to provide highly accurate latitude and longitude information. In addition,
  • and append census, ZIP code and county boundary data.

What Has Changed in Customer Service?

Every week, I’m asked, “What is changing in customer service?” The expected answer is that I’ll talk about all the new ways customer service and support is conducted – and I do. There’s self-service solutions that include robust frequently asked questions and video. There’s social media customer service with multiple channels like Facebook and Twitter. And, AI (Artificial Intelligence) that the experts – myself included – say will potentially change everything.Yes, there is a lot that is changing about how we deliver customer service, so I’m about to make a bold statement. If you look at what customer service is, it is the same as it was fifty years ago. And, it will be the same fifty years from now. Customer service is just a customer needing help, having a question answered or a problem resolved. And, in the end the customer is happy. That’s it. When it comes to the customer’s expectations, they are the same. In other words:

Nothing has changed in customer service!

Okay, maybe it’s better said a different way. When it comes to the outcome of a customer service experience, the customer’s expectations haven’t changed. They just want to be taken care of.

That said, there are different ways to reach the outcome. What has changed is the way we go about delivering service. We’ve figured out how to do it faster – and even better. Back “in the day,” which wasn’t that long ago – maybe just twenty or so years ago – there was typically just two ways that customer service was provided: in person and over the phone. Then technology kicked in and we started making service and support better and more efficient.

For example, for those choosing to focus on the phone for support, there is now a solution that lets customers know how long they have to wait on hold. And sometimes customers are given the option of being called back at a time that is more convenient if they don’t have time to wait. We now have many other channels our customers can connect with us. Beyond the phone, there is email, chat, social media channels and more.

So, as you are thinking about implementing a new customer service solution, adding AI to support your customers and agents, or deciding which tools you want to use, remember this:

The customer’s expectations haven’t changed. They just want to be taken care of, regardless of how you go about it. It starts with someone needing help, dealing with a problem, upset about something or just wanting to have a question answered. It ends with that person walking away knowing they made the right decision to do business with you. How you get from the beginning to the end is not nearly as important as how they feel when they walk away, hang up the phone or turn off their computer.

It’s really the same as it’s always been.

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

Shep Hyken is a customer service expert, keynote speaker and New York Times bestselling business author. For information contact or For information on The Customer Focus™ customer service training programs go to Follow on Twitter: @Hyken

Will Omnichannel Someday Die Out Because of Big Data?

You probably know what omnichannel means, but a quick definition is always helpful. It refers to the various touch points by which a business/organization can reach a customer. The idea — and the ideal — is to get the offer in front of them at the time they’re most likely to be interested. Typically in the modern business ecosystem, omnichannel refers to:

  • Website
  • Brick and mortar locations
  • Social media
  • Other digital efforts
  • How you come across on mobile
  • Face-to-face interactions between customers and employees

There is more you could group under omnichannel, but that’s a good start. Unfortunately, in a few years from now, we may need a different approach entirely.



Consider this: in 2020, it’s possible 1.7 megabytes of new data will be created for every person on the planet every second. If you do the full math on that, the total volume of data globally in 2020 might be around 44 zettabytes. A zettabyte is a trillion gigabytes. This is somewhat because of “The Internet of Things” — connected devices and sensors — which should have an economic value of $3 trillion by 2025. Internet of Things tech alone will be 3-6 zettabytes of that total.

Now we know the rapid scale of Big Data. It’s actually arriving in daily life maybe faster than even mobile did. What are the repercussions?


As noted in this post on Information Age:

Companies hoped “omnichannel experiences” would enable them to anticipate customers’ needs to provide them with a personalised response, which meets or even exceeds their expectations. And this effort is based on the company’s ability to mobilise the necessary data to deliver.

But what happened?

Today, these same companies struggle to draw together all the information required to give them a unified view and appreciation of their customers’ needs. The result is a mixed bag of omnichannel initiatives, many of which result in failures. In the retail sector, for example, only 18% of retailers claim to have an engagement strategy, which covers all channels.

The sheer math looks like this: 44 zettabytes of generated data in 2020 is 10 times — yes, ten times — what we are generating now, three years earlier. Companies are already struggling to manage data properly towards better customer experience. What will happen when 10 times the data is available in 33 months or so?


This is obviously hard to predict. In times of great complexity, though, sometimes sticking to the basics — i.e. The Five Customer Experience Competencies — isn’t a bad idea. A strong base almost always beats an all-over-the-place strategy.

In my mind, this is what needs to happen:

  • Companies need a good handle on what really drives their business now and what could drive it in the future.
  • This involves products/services but also types of customer and platform they use.
  • Once that picture is mostly clear, senior leaders need to be on the same page about the importance of customer-driven growth.
  • “Being on the same page” also involves, ideally, vocabulary and incentive structures.
  • If the customer-driven plan/platforms and senior leadership alignment are there, now you need to make sure the work is prioritized.
  • No one should be running around on low-value tasks when great opportunity is right there.
  • Kill a stupid rule, etc. Basically move as many people as possible to higher-value work, especially if lower-value work can be more easily automated.
  • It’s all been important so far, but let’s bold this: You don’t need to collect all the data. You need data that relates to your priorities and growth. 
  • That data should be analyzed and condensed for executives. You may need “data translators,” yes.
  • Decision-making should come from relevant information and customer interactions.

This flow is hard to arrive at for some companies, but essential.

Phrased another way: trying to be “omnichannel” in five years and looking at an Excel with trillions of touch points/data on it? That will just burn out employees and managers alike. You need a prioritized, aligned plan focused on customer-driven growth and well-articulated goals. That will get you there post-omnichannel.

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

Author’s Bio: Jeanne Bliss, Founder & CEO, CustomerBliss

Jeanne Bliss pioneered the role of the Chief Customer Officer, holding the first-ever CCO role at Lands’ End, Microsoft, Coldwell Banker and Allstate Corporations. Reporting to each company’s CEO, she moved the customer to the strategic agenda, redirecting priorities to create transformational changes to each brands’ customer experience. Her latest book, “Chief Customer Officer 2.0” (Wiley) was published on June 15, 2015.

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