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Thoughts on Data Quality and Contact Validation

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Posts Tagged ‘Contact Data’

Omnichannel Solutions and Data Quality

Just a few decades ago the concept of a “channel” didn’t exist, other than on your television. If a customer or prospect wanted to contact you, they called you or wrote you a letter. And if you wanted to contact them, you got out your Rolodex – or if you were a large enterprise, perhaps your batch mainframe computer, with disk drives the size of a washing machine.

Today, sales, marketing and customer support take place across multiple touch points that include point of sale, online orders, emails, social media inquiries – and even those same traditional phone calls and letters. Increasingly, this contact data is managed by integrated enterprise systems rather than separate vertical applications. Which also means that all of your sales and support channels often serve as pipelines to a common contact database.

Over the past five years, we have been in the midst of an omnichannel revolution in enterprise solutions. The reason is simple economics – particularly the growth of inexpensive, scalable, cloud-based software-as-a-service (SaaS) applications. Once upon a time, enterprise software seemingly required months of planning and a cast of thousands to implement. Today, even the smallest operation can license applications that integrate ALL of their customer touch points on an inexpensive per-seat basis.

The era of integrated, multi-channel applications also means that the impact of bad contact data is now greatly amplified. Here are some examples:

  • Many customer touch points are notorious for providing incomplete or incorrect contact information. This can range from the person who enters “Mickey Mouse” or a fake address to get free marketing incentives, all the way to customer support tickets with missing contact data.
  • An estimated 25% of marketing contact data is bad – and in an enterprise solutions environment, this bad data propagates across all of your sales and marketing activities, wasting time and resources.
  • Telephone numbers change constantly, and your next telemarketing campaign could find you inadvertently – and illegally – calling consumer cell phones in violation of the Telephone Consumer Protection Act (TCPA), exposing you to potential fines of up to $1500 per violation.
  • Data entry mistakes in order processing can lead to lost shipments, wasted time and human intervention, and customer dissatisfaction.
  • Identity fraud cost businesses over $18 billion in losses in 2014, and much of it could be avoided by matching IP address locations to customer orders – so, for example, your system can red-flag a big-ticket domestic order originating from an overseas computer.

The solution to issues like these is to build data quality right in to your enterprise contact data, with a little help from Service Objects. Our tools can validate, append and update addresses using continually verified data from the USPS or Canada Post. We can geocode and analyze your order data for fraud verification, tax compliance and more. We can do real-time phone number verification to help you maintain TCPA compliance. More strategically, we can do lead scoring and enhancement to turn your contact data into a revenue-generating engine. Using API and batch processing interfaces, these tools and more provide a seamless way to put your contact data quality on autopilot.

The omnichannel era is here to stay – and in the process, contact data has become a strategic asset for companies of any size. We can help you leverage the power of this asset, by making sure this data is genuine, accurate, and up-to-date. And with the right partner, you can let data quality drive a tangible difference in revenue across all of your channels.

The 2018 European Data Protection Regulation – Is Your Organization Prepared?

The General Data Protection Regulation (GDPR) is a regulation intended to strengthen and unify data protection for all individuals within the European Union (EU). It also addresses the export of personal data outside the EU. The primary objectives of the GDPR are to give citizens and residents back control of their personal data and to simplify the regulatory environment for international business by unifying the regulation within the EU.

According to research firm Gartner, Inc., this regulation will have a global impact when it goes into effect on May 25, 2018.  Gartner predicts that by the end of 2018, more than 50 percent of companies affected by the GDPR will not be in full compliance with its requirements.

To avoid being part of the 50 percent that may not be in compliance one year from now, organizations should start planning today. Gartner recommends organizations focus on five high-priority changes to help organizations to get up to speed:

    1. Determine Your Role Under the GDPR
      Any organization that decides on why and how personal data is processed is essentially a “data controller.” The GDPR applies therefore to not only businesses in the European Union, but also to all organizations outside the EU processing personal data for the offering of goods and services to the EU, or monitoring the behavior of data subjects within the EU.
    2. Appoint a Data Protection Officer
      Many organizations are required to appoint a data protection officer (DPO). This is especially important when the organization is a public body, is processing operations requiring regular and systematic monitoring, or has large-scale processing activities.
    3. Demonstrate Accountability in All Processing Activities
      Very few organizations have identified every single process where personal data is involved. Going forward, purpose limitation, data quality and data relevance should be decided on when starting a new processing activity as this will help to maintain compliance in future personal data processing activities. Organizations must demonstrate an accountable ground posture and transparency in all decisions regarding personal data processing activities. It is important to note that accountability under the GDPR requires proper data subject consent acquisition and registration. Prechecked boxes and implied consent will be largely in the past.
    4. Check Cross-Border Data Flows
      As of today, data transfers to any of the 28 EU member states, as well as 11 other countries, are still allowed, although the consequences of Brexit are still unknown. Outside of the EU, organizations processing personal data on EU residents should select the appropriate mechanism to ensure compliance with the GDPR.
    5. Prepare for Data Subjects Exercising Their Rights Data subjects have extended rights under the GDPR, including the right to be forgotten, to data portability and to be informed (e.g., in case of a data breach).

Having poor quality data has several impacts on an organization and could hinder your efforts to being in compliance. Visit Service Objects’ website to see how our global data quality solutions can help you ensure your contact data is as genuine, accurate and up-to-date as possible.

Big Data – Applied to Day to Day Life

With so much data being constantly collected, it’s easy to get lost in how all of it is applied in our real lives. Let’s take a quick look at a few examples starting with one that most of us encounter daily.

Online Forms
One of the most common and fairly simple to understand instances we come across on a daily basis is completing online forms. When we complete an online form, our contact record data points, like; name, email, phone and address, are being individually verified and corrected in real time to ensure each piece of data is genuine, accurate and up to date. Not only does this verification process help mitigate fraud for the companies but it also ensures that the submitted data is correct. The confidence in data accuracy allows for streamlined online purchases and efficient deliveries to us, the customers. Having our accurate information in the company’s data base also helps streamline customer service should there be a discrepancy with the purchase or we have follow up questions about the product. The company can easily pull up our information with any of the data points initially provided (name, email, phone, address and more) to start resolving the issue faster than ever (at least where companies are dedicated to good customer service).

For the most part we are all familiar with business scenarios like the one described above. Let’s shift to India & New Orleans for a couple new examples of how cities are applying data to improve the day-to-day lives of citizens.

Addressing the Unaddressed in India
According to the U.S. Census Bureau, India is the second most populated country in the world with 1,281,935,911 people. With such a large population there is a shortage of affordable housing in many developed cities, leading to about 37 million households residing in unofficial housing areas referred to as slums. Being “unofficial” housing areas means they are not mapped nor addressed leaving residents with very little in terms of identification. However, the Community Foundation of Ireland (a Dublin based non-profit organization) and the Hope Foundation recently began working together to provide each home for Kolkata’s Chetla slum their very first form of address consisting of a nine-digit unique ID. Beside overcoming obvious challenges like giving someone directions to their home and being able to finally receive mail, the implementation of addresses has given residents the ability to open bank accounts and access social benefits. Having addresses has also helped officials identify the needs in a slum, including healthcare and education.

Smoke Detectors in New Orleans
A recent article, The Rise of the Smart City, from The Wall Street Journal highlights how cities closer to home have started using data to bring about city wide enhancements. New Orleans, in particular, is ensuring that high risk properties are provided smoke detectors. Although the fire department has been distributing smoke detectors for years, residents were required to request them. To change this, the city’s Office of Performance and Accountability, used Census Bureau surveys and other data along with advanced machine-learning techniques to create a map for the fire department that better targets areas more susceptible to deaths caused by fire. With the application of big data, more homes are being supplied with smoke detectors increasing safety for entire neighbors and the city as a whole.

FIRE RISK | By combining census with additional data points, New Orleans mapped the combined risk of missing smoke alarms and fire deaths, helping officials target distribution of smoke detectors. PHOTO: CITY OF NEW ORLEANS/OPA

While these are merely a few examples of how data is applied to our day to day lives around the world, I hope they helped make “Big Data” a bit more relatable. Let us know if we can answer any questions about how data solutions can be applied to help your company as well.

From Hello Operator to Hey Siri – Accurate Contact Data Has Always Been Crucial

Fueled by our desire to communicate with one another, no matter distance, the telephone has undergone extraordinary technological enhancements since the first test call on March 10, 1876. Today, the average wireless phone even functions as a portable computer offering a multitude of ways to communicate. Although phone technology dramatically changed over the last 141 years and continues to change, one aspect of placing a call remains vitally important: accurate contact data.

Originally, the telephone was sold in pairs of two with a single connection to each other. Since these early telephones were directly connected to each other, phone numbers were not yet required. However, with the invention of the switchboard in 1878, callers could connect with many other subscribers leading to the establishment of phone numbers consisting of a few digits. By 1910 the U.S. population grew to 92,228,496, over seven million of whom were phone subscribers. To accommodate so many users the length of the phone number increased.  For the majority of the 1900s, whether using a candlestick, rotary or push button phone, the telephone operator manually connected callers by switchboard and without accurate contact information to start with callers could not be properly connected. As the pool of subscribers grew further, alphanumeric numbers were introduced and used through the 1960s. This format consisted of two letters representative of location (name of the village, town or city) of the central office that the phone was connected to, followed by numbers.  Although fewer miscommunication between callers and operators occurred with the use of alphanumeric numbers, having accurate information to begin with was still imperative.

Jumping forward to today, various devices ranging from wireless phones, computers, tablets, and even televisions can be used to place calls. Somewhat reminiscent of telephone operators, virtual assistants like Apple’s Siri and Amazon’s Alexa can even be used to connect to someone by dictation which is how a four year old boy recently contacted emergency services to save his mother’s life. Although a phone number is still required for most devices, platforms such as Skype and FaceTime also use email address as unique identifiers to connect callers. While new types of contact information like email are being used more commonly, once the information is entered into the calling device you don’t need to remember it again. With just a few taps on a screen or a simple phrase, “hey Siri, call mom,” the call is initiated.

Whether placing a call now or 141 years ago starting with genuine, correct and up to date contact data is essential for reaching each other by phone. As forms of contact data continue to evolve with technology, our validation tools will as well to ensure your business communications are as fast and easy as possible.

Peer-to-Peer: The Next Frontier

Peer-to-Peer, Service ObjectsHow do you get millennials interested in a cause?

For starters, you don’t use traditional direct marketing techniques. Millennials won’t even answer a call from an unknown phone number more than 95 percent of the time. Email is something their grandparents used to use, with conversion rates hovering around 1 percent. And many of them don’t sit in front of a television every night passively watching advertising – they live within a broad web of individual human connections, fueled by smartphones and social media.

These are the kinds of numbers that motivated Bay Area startup Hustle (hustle.life) to create a new paradigm: large-scale peer-to-peer communications via text messaging.

The Hustle platform is an enabling technology that allows text messages to be sent rapidly to people’s phones, using automated templates that can be personalized for each message. While still requiring human intervention to send messages, it dramatically increases the productivity of organizations trying to reach large amounts of people for an event, cause or campaign – and these people can text back and get responses from a real human being. The result is often a response rate in the 30-40% range.

As a result, Hustle has now attracted substantial venture funding, and its product was used to reach nearly 4 million people during the latest election season. More important, the concept of mass communication between individuals is now attracting a great deal of attention.

Of course, peer-to-peer communications are much more than a marketing technique. They are quickly becoming a revolution. You can see it in action when you use Uber to get a ride from a private car owner, or AirBnB to rent someone’s house for a week. Uber owns no vehicles, and AirBnB owns no real estate, but both companies connect people to other people on a massive scale. And in the future, respected prognosticators like Daniel Burrus and Donald Tapscott predict the same paradigm will transform banking, voting, education and many other industries that fuel our daily life.

So how can you prepare for the peer-to-peer revolution? By having better access to these peers. When you are blasting text messages to thousands of people, these numbers need to be correct. Otherwise, you face unintended consequences ranging from intrusive spamming to wasted human effort. Moreover, as you move from organizing to marketing, any one-to-one contact model needs verification tools to assess the legitimacy of your contacts and prevent fraud and waste.

Thankfully effective tools existing for verifying phone contact information. These tools include reverse lookup capabilities that can verify wireless or other numbers against US and Canadian databases, including geocoded carrier information and phone type. You can also detect numbers such as VoIP or prepaid phones for use in lead validation or fraud prevention. Taking things a step further, qualified phone numbers can have other contact information appended to them, and entered phone numbers can be contacted via phone or text for active verification by the customer.

The world is increasingly moving away from centralized market models to a distributed peer-to-peer marketplace. This means that now, more than ever, the data quality of both your contact database and your inbound contacts are emerging as key business drivers for the future. With a small incremental investment in maintaining this quality, you can be prepared to grow in an increasingly interconnected world.

People, Process, and Technology: The Three Pillars of Data Quality

Shot of two colleagues using a laptop in a meeting at the office

For many people, managing data quality seems like a daunting task. They may realize that it is an important issue with financial consequences for their organization, but they don’t know how to proceed in managing it. With the right strategy, however, any organization can reap the benefits of consistent data quality, by focusing on three core principles: People, Process, and Technology.

Taken together, these three areas serve as the cornerstones of a structured approach to data quality that you can implement and manage. And more important, a framework that lets you track the ROI of successful data quality. Let’s look at each of these in detail:

People

This is frankly where most organizations fail at the data quality game: not allocating dedicated gatekeepers for the health of their data. It is a very easy mistake to make when budgets are tight, resources are focused on revenue-generating functions like sales or product development, and the business case for data quality gets lost amidst a host of competing priorities.

The single biggest thing an organization can do for data quality is to devote dedicated resources to it. This becomes an easier sell once you look at the real costs of bad data: for example, research shows that 25% of all contact records contain bad data, a third of marketing leads use fake names, and half of all phone numbers provided won’t connect. Run these numbers across the direct costs of customer acquisition, add in missed sales opportunities, increased customer care costs, and even potential compliance fines, and you often have the financial justification for a data quality gatekeeper.

Process

How much control do you have over data entry points, data accuracy, and verification? For too many organizations, the answer is none – with resulting costs due to factors such as duplicate data entry, human error, or lack of verification. And who is responsible for maintaining the integrity of your business data? Too often, the answer is “no one,” in a world where data rarely ages well. An average of 70% of contact data goes bad in some form each year, which ushers in yet another level of direct and indirect costs.

One of the more important roles of a data gatekeeper is to have processes in place to manage the touch points for your data, engineer data quality in on the front end of customer and lead acquisition, and maintain this data over the course of its life cycle. Having the right policies and procedures in place gives you control over your data, and can make the mechanics of data quality frictionless and cost-effective. Or as your teachers used to put it, an ounce of prevention is worth a pound of cure.

Technology

Data quality solutions range from simply scanning spreadsheets for duplicates and mistakes, all the way to automated tools for tasks such as address validation, lead validation, and verification of email or phone contact information. And far too often, the solution of choice for an organization is to do nothing at all.

Ironically, using the best available automated tools for data quality is often a surprisingly cost-effective strategy, and can yield your best ROI. Automated tools can be as simple as verifying an address, or as sophisticated as creating a statistical ranking value for the quality of a lead or contact record. Used properly, these tools can put much of the hard work of data quality on autopilot for you and your organization.

Ensuring your organization’s data quality can seem like an overwhelming task. But broken into its component parts – your people, your process, and your technology – this task can turn into logical steps that pay themselves back very quickly. It is a simple and profitable three-step strategy for any organization that runs on data.

How Much Is Bad Contact Data Costing Your Organization?

Businessman trying to figure some numbers out leaning on the table

Collecting visitor and customer data through a variety of channels allows you to quickly grow your contact list. When contact information comes in, your company is provided many new opportunities to expand your business. However, receiving high-quality data isn’t necessarily a given. After all, some visitors will enter bad contact data, such as bogus phone numbers, in an attempt to limit telemarketing calls; others may think they’re being funny by entering fake names such as Donald Duck or Homer Simpson; others might accidentally misspell a street name. Even autocorrect may change a legitimate entry, and some visitors may intentionally enter a bad address in an attempt to commit fraud.

Managing the effects of bad contact data is a surprisingly large cost for many organizations, involving a great deal of human intervention as well as diluted contact effectiveness. This is one area where an ounce of prevention is worth a pound of cure – particularly in today’s era of automated contact verification tools. Whatever your touch points are with customers or prospects – including customer data, contest entry forms, phone surveys, lead generation and point of sale interactions – some data will be inaccurate, incomplete, or fraudulent.

Why Data Quality Matters

Do you really need to worry about a few Donald Ducks or Homer Simpsons in your contact database? Yes, you do! Poor data quality translates into wasted time, resources, and money. For example, if you have a mailing list of 10,000 addresses, and 10 percent of those are inaccurate, you will waste 1,000 pieces of mail — plus the cost of product and postage – not to mention the human resources involved in processing incorrect outgoing and returned mail.

Data quality from a customer service perspective is another big concern. Suppose that a customer orders an expensive product from your website, and accidentally enters their address as “2134 Main Street” instead of the correct address of “1234 Main Street”? First of all, you would ship the package to the wrong address. Not your fault, but it doesn’t matter: delivery will be delayed, the customer will have a poor experience, and you will incur re-shipping costs. You may even not get the original shipment back. It’s a lose-lose situation.

The problem is compounded when it comes to getting these customers in the first place: an estimated 25% percent of marketing contact data is bad. And according to the Data Warehouse Institute, this ocean of bad data costs businesses over US $600 billion per year. At the level of the individual company, this means that over a quarter of your sales and marketing resources are lost to bad prospects, for reasons that range from intentional fake contact data to the natural contact record aging process. In fact, Salesforce.com reports that after just one year, nearly 70% of contact data goes bad in some form as people change jobs, phone numbers and email addresses.

So how can your company avoid the challenges associated with bad data? Start by assessing what areas of your company need better data quality control. Whether you identify a single area or several areas that could benefit from improved data quality, realigning your data quality goals to hit the data trifecta can improve your bottom line, optimize your human capital and even help the environment.

To read more about this topic, download our white paper Hitting the Data Trifecta – Three Secrets of Achieving Data Quality Excellence.

Name Deduplication Techniques

Name Tag, Hello My name is ....

Identifying Duplicate Records

The bane of any Database Administrator is maintaining duplicate records. They take up unnecessary space and generally do not provide any added value to contact records. A more challenging task for Database Administrators is how to identify and merge records which might be duplicates, and in particular, duplicate names.

There may be variants for a given name which might not be easily identified in a query, but they are invariantly linked. A common example might be Joe Smith vs Joseph Smith. Both could be referring to the same person depending on how the user may have entered their name.

Name Variants, Finding the Common Name

A particularly useful feature of the Name Validation 2 service is the Related Names output field. This field provides a comma separated list of first name variants for a provided name. For example using the given name; Joe, related names returned include Joel, Joeseph, Joey, Josef, Joseph, and José.

With this information, it becomes easier to identify names which are related but in a different form. There may be cases, however, where names cannot be identified as related but can be linked from similarity. Some examples include names that are misspelled or alternate names which are not related but similar. These names can still be identified through the Similar Names output fields of the Name Validation 2 service.

Similar Sounding Names

DOTS Name Validation 2 employs sophisticated similar name matching algorithms to match names drawing from a database of international names with up to 1.4 million first names and 2.75 million last names. First and last name similar results are returned in a comma separated list which can be used to compare against names that already exist in the database.

An example similar name result for the given name; Robert Smith, would return similar first names Rhobert,Róbert,Robertt,Roebert,Roibert,Rubert,Robbert, and similar last names Smyth,Smithe,Smiith,Smiyth. Of the similar names that are found, names are returned in order of most common to least common.

Merge and Promote the Winning Record

Using these results, a query can potentially link similar or related names and identify records which are duplicates. Once duplicate records are identified, the question becomes which should be promoted as the winning record? This decision can depend on factors based on business logic, perhaps a record which contains other vital contact points such as address or phone number or perhaps entry date is chosen as the winning record. Once a winning record is chosen, a merge process is incorporated to merge contact fields from identified duplicates to build a complete record.

Conclusion

Ridding your database of duplicate contact records can be an arduous task, but with the help of Name Validation 2, it doesn’t have to be. Leveraging the vast quantity of names that Name Validation 2 draws upon yields a top quality solution to identifying duplicates through related and similar names.

For more information about Name Validation 2 service, or to receive a free trial key, click here.

For developers, our Name Validation 2 documentation can be found here.

Make Customer Data the Foundation of Your Marketing Campaigns

do you know your customer on touch screen

Gaining Insight on Customers and Prospects

No question: customers are the backbone of your business and in order to make a connection, and ultimately generate sales, you need to maintain good insight into not only who and where they are, but what they are interested in. Making that connection between a customer’s needs and your product is one of the key components in driving your company to success!

Thus, by gaining insight into your customer’s geographic and demographic details, you will be in a better position to geo-target your marketing and understand the socio-economic levels of your customers. Ultimately these insights will help you to craft more accurate and targeted messages and products to drive sales.

Unlocking Customer Value

With data being the foundation of your marketing campaigns, it’s imperative that you get a handle on the information within your contact records before your marketing efforts, budgets, and customer satisfaction is affected.

So what can happen when customer insight is not on point? Let’s look at a specific scenario:

During a recession year, your company has made the decision to capitalize on existing customers, versus putting exhaustive efforts into acquiring new accounts. Your goal is to increase profitability and customer retention by 80% in the next year.

In order to carry this out effectively, you need to better understand not only who your customers are but where they live and what specific interests are driving them to market, or better yet, to your product/services.

After extensive research, your team realizes that there are many missing data points within your customer contact records, precluding you from creating a better customer value management solution. Without solid customer insight, you are not able to understand, target, reach, and interact with their customers in the most effective way possible. 

To avoid this type of scenario in your organization, you need to have a solution that enables you to tap into the hidden insights that are waiting to be discovered within your contact records. After all, the better you can communicate with your prospects and customers, the more likely the chance that you will increase new sales opportunities and upsell your products and solutions to existing customers.

Service Objects proprietary algorithms enable you to append over 130 data points to a contact record, such as household income, age distribution, ethnic distribution, education and much more, enabling you to make more informed business decisions, and clearly determine new revenue opportunities and gain a competitive advantage.

Integrating Service Objects with Salesforce

Salesforce is a great CRM that allows businesses to easily put customers at the center of their attention. But even a tool like Salesforce can be halted by poor quality customer data. Well, luckily for businesses and their data, Salesforce allows its users to call outside APIs and web services like our data validation services. If this is something that you haven’t done before, it can be a bit tricky; but don’t worry! The integration specialists here at Service Objects have you covered and we can show you how to get up and running in Salesforce in no time!

Remote Site Settings

One of the first things you will want to do to get your data validated is to add the Service Objects domain as an allowed site to access from within Salesforce. To do this, log into your Salesforce account and enter “Remote” in the Quick Find search bar as shown below.

Salesforce, Service Objects

Select “Remote Site Settings” and you will be taken to a page that lists all of the external sites that you can access from within Salesforce. Select “New Remote Site” and enter the information as shown below.

Salesforce, Service Objects

Once you click “Save” you will be ready to validate your data through a Service Objects web service. You should also add the site wsbackup.serviceobjects.com to the list of remote site settings as this will allow the ability to integrate proper fail over configuration into your application.

SOAP vs. REST

If you are using REST to access a Service Objects web service, then you are good to start validating data. You simply have to make the http call to one of our web services and then decide how you want to implement your newly validated data.

If you happen to be using SOAP to connect to our services, then you will have to alter the WSDL you use to connect to our services. A WSDL is a machine-readable document that tells a platform and coding language how to connect and interact with a web service. In order to do this, a bit of the WSDL will need to be altered. Lucky for you, we have already done this! We have a flight of updated WSDLs, Apex code examples, and handy guides for each of our services that will help you get up and running in Salesforce in no time.

To upload the edited WSDL, select the “Develop” link under the “Build” heading on the left-hand side of the main screen. Then select “Manage Apex Classes”.

Salesforce, Service ObjectsSalesforce, Service ObjectsOn this screen, you can select the “Generate from WSDL” button and then choose the updated WSDL to upload to Salesforce. If the WSDL has been properly edited then all the necessary classes will be successfully created and you can begin accessing Service Objects web services through Salesforce.

Your data is now ready to be validated! Once you have ensured the integrity of your customer data, you can get back to using Salesforce to guarantee the best interactions possible with your customers!

Also, be sure to check out our Free Salesforce Chrome Extension for a quick and easy validation tool to use in your browser!

Service Objects is the industry leader in real-time contact validation services.

Service Objects has verified over 2.5 billion contact records for clients from various industries including retail, technology, government, communications, leisure, utilities, and finance. Since 2001, thousands of businesses and developers have used our APIs to validate transactions to reduce fraud, increase conversions, and enhance incoming leads, Web orders, and customer lists. READ MORE