If you work with email lists, you know that keeping your email addresses current is a bit of a nightmare. This is especially true if you purchase email lists.
Beyond keeping emails current, you need to avoid spam traps, invalid and catch-all email addresses, and abuse emails. Otherwise you could run into email deliverability issues later on.
The workload of keeping your email lists current and keeping them clean is too much to do without some sort of automation.
There was a time when checking emails in bulk was nearly impossible.
Validating an email required sending a test message to every email address. This meant that anyone verifying a lot of emails was bound to run into deliverability issues of their own. So some test messages wouldn’t get delivered. This compromises email verification results.
Fortunately, it’s easier to check lists in bulk now.
Here’s why this is good news.
Why you should use batch deliverability validation on your email lists
There are uses for bulk email verification that go beyond protecting your email deliverability.
First, it’s the most efficient way to weed out bad emails.
It’s also the most affordable way to do it. Manual email verification can be time intensive and expensive.
With bulk email verification, you simply upload your email list. The returned file will identify which emails are unsafe. You can then exclude those from your email sends.
But it can indirectly save you time and money as well.
Use a bulk email deliverability check to evaluate the quality of your list suppliers
While it is the responsibility of the email list provider to quality control their product, emails are slippery things.
They can be changed and created easily. People make mistakes when they enter email addresses all the time. So even a well-checked email list can quickly become ridden with bad emails. Especially if the list undergoes any manual data entry.
Additionally, some list providers do a syntax check on their email lists just to make sure that all the email addresses have a valid format. But that doesn’t mean that there’s actually an inbox associated with every email address.
So before using any new list, it’s best to check the actual status of all the email addresses. This will give you an idea of how well the list provider is quality controlling their email lists.
If a list provider repeatedly gives you lists that are mostly invalid emails, it may be time to change suppliers.
Also, a bulk email validator is your best bet for quality assurance if you’re a list provider.
Use a bulk email validator to expedite your email sending
Email deliverability is important. However, your sending domain reputation can tolerate sending messages to a few invalid email addresses.
Additionally, things that are considered soft bounces don’t hurt your deliverability. Inboxes that are temporarily full or receiving server errors don’t count as a hit on your sender reputation.
And sending messages to invalid emails won’t immediately tank your sending reputation. But invalid email addresses do eat up your sending limits.
It’s the spam traps and emails on the global suppression list (GSL) that you really want to avoid.
So there are tolerances for the number of bad email on an email list.
Say you process a list of 100 emails, and 90 of them come back as valid and safe to email. The other 10 are simply invalid.
Depending on your operational tempo, it may not be worth the time and effort to remove those 10 emails.
Obviously, your tolerances for invalid email addresses will vary. If you regularly process lists of 5000 emails, ten percent is actually a lot of invalid emails.
You can use bulk email validation to rate your lists out quickly by assessing the number of quality emails they have and determining what your threshold for bad emails is.
Then simply sort your email lists according to your go/no go criteria and send the low quality lists for cleaning.
There are tools that perform an overall list assessment like this. Typically they return results in a grade fashion. An email list will be qualified as safe to use so long as a certain amount of the email addresses are valid.
But these tools often don’t report the status of the email addresses or give information as to why the invalid email addresses can’t receive messages.
A batch email validator that returns the specifics of why each bad email is non-functioning gives you much more utility. And if you use the right one, it won’t cost you any more than an overall list evaluation tool.
Knowing why an email address can’t receive messages gives you more granular control over your list cleaning.
You may want to keep temporarily unavailable email addresses so you can try them again later. Email addresses that simply have an invalid syntax might be easily correctable and retried.
james#searchbug.com is an obvious typo that can be corrected.
But you can salvage some of your emails with more specific batch email validation reporting.
Again, if you’re running very high email volumes, this could amount to a significant number of emails.
Users enter email addresses incorrectly. Servers go down periodically. And people using legacy email services sometimes have full inboxes.
For businesses that run through a lot of email lists, the batch email verification process can be automated with an API so that incoming lists can be processed and routed based on their overall quality.
Automated batch email validation protects your databases.
While it’s best to screen emails before they even get added to any list, that doesn’t always happen. Even then, small typos sometimes aren’t detected because the email address still has a valid syntax. But it doesn’t route to any inbox.
So batch email validation provides an additional filter to help keep your databases clean, which reduces stress on your entire organization and saves you time and money on data management.
To wrap up, if you use a third party list provider, batch email validation is a must. You just can’t ensure that only sendable email addresses are included on every list.
Without a bulk email validator, the worst case scenario is that you hit spam traps or GSL emails, which could disrupt your entire email program. At best, you end up sending a number of useless emails.
If you’re developing email lists yourself, you can use an API to automate the process of validating email addresses at the point of entry to save yourself a lot of headache in quality controlling your lists.
So how do you use bulk email validation? Are you doing list overviews or pulling each list apart to exclude any non-functioning email address?
Check out our real-time email deliverability check, and leave a comment to let us know how you use it and how well it worked for you!
Your data affects all aspects of your business, from marketing to product management. And bad data makes business rough.
The day-to-day decision making and management of your organization suffers if you have incorrect, misleading, or poorly formatted data in your databases,
This means inefficient operation. And it negatively impacts your bottom line.
But, bad data isn’t the source of all your problems, right?
It’s true. So, you must determine whether or not your business has a data problem. Otherwise you might end up chasing a red herring.
Fortunately, bad data has symptoms.
Here’s how bad data shows itself in each aspect of your business.
If you’ve got a data problem, your Agents, telemarketers, and operations staff can suffer from these problems:
- Trouble tracking owners for wholesale properties.
- Missing owner contact data such as a recent cell phone.
- No clue as to who owners are or where to find them.
- Buyers Leads lists get stagnant or outdated.
- Telemarketers and agents calling the wrong people and waste time.
- Little or no standard data management procedures.
If you’ve got a data problem, your marketing operation will suffer from these problems:
- Trouble tracking customer preferences and managing customer privacy.
- Missing data.
- No clue as to how much or what data is missing.
- Compliance problems.
- No way to perform data matching with internal or external files.
- Marketing cannot identify ideal customers.
- No standard data management procedures.
Here’s how bad data affects your sales teams:
- Data correction occupies a lot of time for sales teams.
- Sales representatives must manually enter a lot of data during sales calls, which is increasing call times.
- Sales teams must dig through multiple applications to find the data they need.
- Incorrect or outdated CRM data.
Check for these issues in your business intelligence:
- There are a lot of different data sets and spreadsheets in the department.
- Data mismatches cause people to distrust the data.
- Too many data sources.
- No data cleansing tools.
- The same information is stored in multiple formats.
- Variable definitions are inconsistent.
Look at how your human resources department is running:
- Correcting data is a regular part of the work process.
- Your human resources department has a high turnover rate.
- The hiring process is too long and often hires people that are a poor fit.
Evaluate the efficiency and effectiveness of your supply chain:
- Correcting data is a standard part of your procurement process.
- Orders are sent to the wrong suppliers because supplier lists are unmanageable.
- Data is not consistent across all systems.
- Inability to get spending analytics because there is no centralized material information.
- Supplies are purchased, but never used.
Research and Development
How much work is research and development doing:
- Product improvement efforts are duplicated or redundant because product data is inaccurate or outdated.
- Customers don’t know about product improvements and changes.
- Research and development teams have inconsistent product specifications and standards, or no specifications and standards at all.
- Development and deployment cycles are excessively long.
- Marketing is out of sync with research and development efforts.
Find out what sort of errors and issues finance is having:
- Billing information inaccuracies cause payment delays and strain customer service resources.
- Customers get billed for cancelled services because CRM and financial systems have inconsistent data.
- Account profiles have incorrect payment and contact information.
- Customers receive products or services they didn’t pay for. Other customers don’t receive the goods they did pay for.
Information Tech (IT)
IT is responsible for data continuity:
- It’s impossible to keep data consistent across all systems.
Obviously, none of these issues alone will sink your ship.
However, each issue chips away at your efficiency. Enough of these problems can add up to a lot of dollars lost.
Of course, the snowballing effect also works in reverse. Improving your data will actually solve quite a few of these problems in one fell swoop.
So, how do you improve your data for big gains on your bottom line?
Verify Data at the Point of Entry
This is a big one because it largely involves your customer data. However, you can verify internal data at the point of entry as well.
People often forget to fill in all form fields or fail to enter complete data. This is the biggest issue with end-user data entry.
There’s nothing malicious about this. But it has widespread effects that can take a lot out of your business.
This is especially true for data that customers enter in form fields. Customers are notoriously bad at entering data. And customer data is one of the most important kinds.
Verifying data at the point of entry is the best way to protect your databases and save a lot of time on the back end.
The most efficient way to do this is to use an API to validate information in real time.
For the form fields that customers typically fill out, you can get a prebuilt API from a data verification company.
Then just plug and play the API on your customer facing form fields.
For internal data validation, you can use the same API in some cases.
For things like human resources, where you’re collecting mostly standard personal information, the same API that you use for customer form fields will work.
However, you may need to do a little bit of development to create a custom API for things like product specifications and other proprietary information validation.
Either way, checking the data as it comes into your databases will save you a lot of time and money in the long run.
The system will stop and prompt the user to correct errors or fill in incomplete fields. The data validation happens in the background and is entirely transparent to the user.
This will keep the bad data bugs out of your system.
Cleanse Your Databases
If you notice symptoms of bad data, you’ve got some bad information running around in your system already.
Validating incoming data will help. But you still need to remove the bad data that’s already made its way in.
In this case, you’ll need to do some data cleansing.
Cleansing the data itself is fairly straight forward. Simply compile and aggregate your data into a usable file—.txt, .csv, and Excel files are best—and hand it off to a data cleansing service for processing.
This can often be done entirely online.
Where things get tricky depends on how you use your data.
In some cases, invalid entries can be simply tossed out. A list of email addresses that you’ve collected using a lead magnet or trip wire? Just get rid of any invalid email addresses.
But say you’re a hospital that’s cleansing patient data. You obviously can’t just delete any patient entry with incorrect information.
In cases like this, you’ll most likely need to separate out the entries with errors. Then you’ll manually contact patients to get correct information.
The process of correcting the data can be arduous. But data cleansing makes it very easy to identify the bad data and create a hit list of entries that need to be corrected. So it still saves a lot of time.
Between validating your data at the point of entry and cleansing your existing lists, you should be in pretty good shape, datawise.
There’s just one last piece of the clean data puzzle.
Setup Data Management Procedures
Data management is all about how you treat the data in your databases.
Poorly organized data can behave a lot like bad data, even if all the information is correct.
This is especially true if you use a lot of bulk data processing or artificial intelligence for big data analytics.
The key is to create standardization for the important aspects of your databases.
Here are the big things to address in your data management policies:
- Data formatting. Uniform formatting ensures that both people and computers can find the right data when they need it.
- Adding data fields. This goes along with formatting. Always add new variables methodically.
- Automate processes. Automating data management processes reduces the margin for error. There’s a chance that errors will be introduced whenever people manually transfer data. So it’s best to let computers do the copy and pasting whenever you can.
- Continually cleanse and filter your data. In most businesses, it’s impossible to always handle data in a way that disallows the introduction of errors. So you’ll need a good system for consistently quality controlling your information.
- Backup your data. This one goes without saying. Always backup your data. One is the most dangerous number in business and in life.
All this will help keep your data healthy. But keep an eye on things once you’ve got all the bad data bugs worked out.
Bad data gets introduced into your databases as you expand your operations and adopt new software and systems. It’s almost inevitable.
So, periodically audit your organization. Check your various departments. Find out if your data has been significantly compromised.
If you find bad data issues, it may be time to do a little data spring cleaning and refresh your quality control measures.
What’s the most common problem you experience with your data?
Leave a comment and let us know what it is and how you solve it!
In the digital age, data is almost as good as currency.
This trend is largely powered by the advancement of AI and automation. Machine learning requires massive amounts of data. Not only that, but if you’re going to create more efficient processes, you need more material to work with.
Having a stockpile of accurate data helps on the analog side, too. This includes good, old fashioned snail mail sales letters.
Better data makes customer outreach more reliable and personalized, which empowers sales teams and marketing departments.
However, more efficient data usage also amplifies the effect of bad data.
Thousands of emails might be sent or hundreds of prospects called before errors are identified and corrected.
Entire batches of data analytics results can be ruined by bad data.
That’s why good data management policies are critical.
A vital part of your data management ecosystem is data cleansing.
What is data cleansing?
Data cleansing is the process of combing through data, and correcting errors or completing missing information.
No matter how thorough your data collection systems are, it’s impossible to prevent errors from getting into your databases.
People make mistakes on data entry forms. People intentionally omit or enter invalid information. There’s just no way to account for every possibility.
Moreover, there are massive benefits to having huge stores of prospect and client data. New data collection methods are being created all the time to help exploit these benefits.
Then, the expansion of digital resources and ways that customers can engage with companies means that error-free data collection is probably impossible. If it is possible, it won’t happen for a long time.
That means data cleansing is one of the most important mechanisms in data management.
Obviously, you should still be vigilant in catching errors during data collection, but regularly cleansing your data is really the only way to prevent errors from causing problems in many parts of your organization.
The best way to keep your data error free is to employ efficient data cleansing tools.
How to cleanse your data
Even with data cleansing tools, cleaning your data is still a process.
Since you need to regularly clean your data, creating an easily repeatable data cleansing process will make cleansing new data and refreshing old data far more efficient and saves money in the long run.
Identify key data fields
The most important pieces of information will vary from organization to organization. It depends on your customers, product, marketing strategy, and even your employees.
In any case, the first step in creating a solid data cleansing process is identifying what information is most valuable to your company.
This step also helps you create data validation guidelines, which improve your data collection process. With your key data identified, you can minimize the amount of unusable data entries that appear in your databases.
This step alone improves the quality of your data.
Analyze your data
With your key data in hand, you can go to your data stockpile and identify the gaps.
The analysis phase of cleaning your data is also an opportunity to organize it and remove any data fields that you don’t really need.
If you’re using Excel or Google Sheets, you can create scripts and workflows to streamline and automate a surprising amount of this process.
Here are a few key things to do as you’re analyzing and organizing your data:
Remove duplicate rows.
Duplicate rows can cause problems if you need to import your data. So cleaning these out can save you headache later on.
Remove spaces and nonprinting characters.
Extra spaces (unicode character set values 32 and 160) and weird characters (unicode character values 0 to 31, 127, 129, 141, 143, 144, and 157) can cause issues for sorting, filtering, and searching.
Getting rid of these makes your data management life much easier.
Merge and split columns.
If you’ve imported data, make sure that it’s divided up the way you need it to be. For example, you may want to split a single name column into a first and last name column.
This will help you identify missing information later on.
Having your data properly organized will also increase the internal efficiency of your company, since people will be able to more easily search and find what they need in your databases.
Append missing data
This is where the first two steps really start to pay off.
If you’ve got your data organized and you know which information is most important to you, you can pretty easily process your data in batches.
You have a couple options here.
- You can use a batch data appending service. Simply upload a CSV, TXT, or Excel file. After that, the service will append the missing data that you need to your list.
- You can use an API to create an integrated data append process so that your data processing is more internal to your organization.
The cool thing about using an API, is that you can also embed the data completion capabilities into the customer facing side of your website or app. With some creativity, this offers opportunities to streamline and automate your data validation at the point of collection.
Using the same data append services that you used to complete your data, you can automate continued maintenance and cleaning of your databases.
This is especially easy if you use an API to integrate data appending into your internal tools. Simply create a script that runs the data append function at regular intervals.
One mistake companies make is running through the first three steps every once in a while, like a spring cleaning for their data.
The trouble with this method is that these companies end up working with bad data for a month or two or more until they go through their databases again.
Creating automation that will continually refresh your data will keep you working with accurate information all the time.
Even with a good system for cleaning your data in place, you still need to consistently manage your databases to maintain data sets that your automated tools can work with, while remaining relevant to your needs as your business evolves.
Here’s what you need to do to keep your data in good shape:
This one goes without saying. Backup your data, or you could find yourself having to recreate or buy entire silos of data all over again.
Input validation is your first line of defense against bad data. As we mentioned before, perfect input validation probably isn’t possible.
But crafting your opt in processes, creating scripts that disallow incorrectly formatted data to be entered, and establishing procedures for manual data entry can help minimize the amount of data cleansing you’ll need in the long run.
This is actually kind of a subset of input validation.
Data validation pairs with input validation to create a sort of two-step data entry process. Essentially, what you want is a mechanism that checks newly inputted data before it actually gets put into use.
A great way to do this is to build a script that simply removes incomplete or invalid data from a database and creates a separate container of data that will eventually become the file that gets run through the data cleanser.
Yep. Data cleansing is a key element of good data management. Refer to steps one through four in this article for more details.
Data aggregation can be achieved using built-in functions in your data management software like Excel or Google Sheets.
Aggregating your data essentially prepares it for use based on what elements of the data set are important for your company and objectives.
Another benefit of aggregating your data is that it provides one more layer of protection against bad data, since missing or invalid data can cause errors in the aggregation process.
So, aggregating your data can tell you if you’ve got any more tidying up to do before you send the data over to the teams who are going to use it.
This part will be pretty easy for data sets that you’ve constructed with your own collection methods, since you probably only collect the data that you need.
However, if you’re using imported data that you acquired from a secondary source, there’s a good chance that there’s a bunch of stuff in there that’s not particularly useful to you.
In this case you’ll need to filter the data and toss anything that’s not useful. This makes it much easier for your teams to work with the data and get what they need from it.
If you frequently work with imported data, it’s a good idea to create a way to automate this process or at least have a workflow to streamline it.
This one is pretty straightforward:
If you can, you should merge multiples of the same type of data into a single container, so that it’s easier to search and process.
We covered data append when we talked about cleansing. Append any missing data to your lists to create complete data sets.
Deduping is just a super technical sounding term for removing duplicates of any data you have. Unless you have a specific need for duplicate data, it’s best not to have more than one of each (with the exception of your backups).
Data transformation is converting data from one structure or format into another. Typically, this is most useful if you use imported data. Sometimes you’ll get data in a format that your systems can’t work with. So you’ll need to transform it to make it workable for you.
Often, the program you use to work with your data, like Excel, won’t have good data transformation capabilities.
Fortunately, there’s a wide range of data transformation tools for getting this done.
Standardization makes this process simple. Avoid working with multiple data formats whenever possible. It just complicates things.
So that’s data cleansing (and data management).
While data cleansing alone isn’t enough to keep your data in shape, it will prevent headaches and can save your business a lot of money.
Feel free to check out our data cleansing services. Then leave a comment and let us know how you keep your data in order!
For over a decade, the United States has been on high alert to terrorist attacks, just as other parts of the world have too. There are so many world conflicts taking place that a country must take the proper precautions to protect its citizens.
In an effort to keep our country safe, the United States Government has put together several government databases listing known criminals, terrorists, products, and companies that may not do import and export business with the U.S. This also means that individuals or entities in the U.S. are forbidden to do business with any person or business that have been blocked, denied, and debarred and appear in these databases.
Where Do I Find the Government Databases?
There are websites that one can access easily to view the government databases. There are over 80 known lists to cross check whether a person or entity has been banned from doing business with the United States. When a U.S. company is conducting business with a foreign entity, it is the company’s responsibility to do their due diligence to ensure the safety of its country.
The U.S. Department of Homeland Security’s site will provide several links to lists providing information on blocked and denied entities, as well as a debarred persons lists.
The agencies the site refers to are the Bureau of Industry and Security which will provide a Denied Persons List and Entity List
The Office of Foreign Assets Control which provides a Special Designated Nationals and Blocked Person List
The Office of Defense Trade Controls, which gives a Debarred Parties List
The U.S. Government Printing Office, which will provide the Federal Register.
- Each of these agencies is responsible for keeping its lists up-to-date. The purpose of the lists is to provide exporters with companies, entities, and persons sanctioned by the U.S. Government and are forbidden to export goods from the U.S. It is the job of the exporter to be sure they are conducting transactions using the proper procedures and that exports are authorized properly.
- The U.S. Department of State is another site that can provide useful information to ensure the safety of the country. The site provides links to the following lists: The Foreign Terrorist Organizations List (FTO), which provides a list of names of individuals or organizations that are or have been known to conduct business with terrorist groups or themselves are part of a terrorist group. Once these targets are listed, they will not be allowed to travel in the U.S. and any financial accounts with U.S. institutions are frozen.The State Sponsors of Terrorism is a list of countries that have been directly involved with support of terrorist groups. Currently there are four countries on this list, which are Cuba, Iran, Syria, and Sudan. Executive Order 13224, which was signed by President Bush in 2001, designates that the U.S. Government can impede terrorist funding. They have the power to stop financial support networks and block assets of foreigners or business that have given support to terrorists or pose a threat of doing so. The last link this site provides is to the Terrorism Designations Press Releases page, which lists the announcements from the Office of the Spokesperson as new terrorists or organizations are identified.
- The U.S. Food and Drug Administration (FDA) provides a list of companies and individuals that are debarred under sections 306(a), (b)(1) and (b)(2) of the Federal Food, Drug, and Cosmetic Act, which is published in the Federal Register. Not only does this site provide the names of the offenders, but it gives the effective date they were added to the list, how long they are barred, and in most cases, a document about the case is provided from the Federal Register.
All of the sites referenced not only have links to the various lists, but also provide detailed information on how the lists are determined and used. Ultimately, it is the responsibility of individuals and businesses to use reasonable care and due diligence when conducting business affairs with foreign countries.