Data Enrichment & Validation

The Cost of Poor Postal Data for Your Business and How to Reduce Losses

August 26, 2021
Inkit Team

The cost of poor data quality to the US economy exceeds a trillion per year. The average annual financial losses for an organization reach up to fifteen million. Are you ready to sacrifice that much? If not, it’s time to find the main reasons behind the data quality issues and fix them.

Whereas many businesses keep an eye on customer emails and names, they miss other critical data points. Postal data is one of them. That’s why we suggest paying particular attention to this data category to reduce the high cost of poor data quality.

Postal data includes addresses, ZIP codes, cities, and other details used for delivery or identity verification. If it’s incorrect, you will face misdeliveries, customer claims, higher bounce rates because of poor customer service, identity theft, fraud, and other problems. Each of them undermines your business profitability and results in losses.

So you have bad or insufficient data. What to do now? We know some trusted methods to improve postal data quality. Keep reading to find them out.

What Is Bad Postal Data?

Bad postal data is any address information that has inaccuracies and harms business processes. It may be an incorrect apartment number, wrong street name, bad ZIP code, city, or state. Wrong address formatting is also considered bad postal data. Because of it, automated software at the United States Postal Service may fail to forward mail to the right destination. Incorrect address formatting will prevent you from standardizing and filtering postal data in your database.

Bad postal information is also duplicate data, misspells, and typos. Such slight inaccuracies interfere with your business processes and result in the high cost of poor data quality.

Learn How to Calculate and Reduce Shipping Cost

Why Is the Cost of Poor Data Quality So High?

Everyone knows that data problems harm businesses, and the cost of poor data quality may exceed a million per year. But what are the direct consequences of inaccurate records and bad postal data in particular? Here are the main points to consider:

Incorrect Postal Data Saps Productivity

When you have too many incorrect, unstructured, and unformatted data records, this results in what data scientists call information wrangling. Your employees need to transform raw data into useful records manually. They have to format and verify information, which takes a lot of time and resources because of manual work.

Delivery Mistakes Result in Claims

Even when a buyer is guilty of sharing incorrect postal data, you are the one who manages delivery. Hence, if the order is not delivered, you are likely to face claims. Apart from refunds, claims strain human resources, which additionally increases financial losses for a business.

Bad Postal Data Increases the Risk of Fraud

Address data is used for KYC verification and AVS checks. It’s reliable information that allows you to make sure buyers are who they claim to be. Hence, poor postal data prevents you from verifying customer identity and makes you more likely to become a victim of fraud and lose money.

Customers Leave Because of Late Delivery

17% of consumers claim to have abandoned a brand because of late delivery. Since customer acquisition and retention are pricey, poor data quality and delivery will cost you a lot.

Start verifying addresses today
Don't waste time and money dealing with lost shipments that never made it to your customers.
Start for free

Bad Data: What To Do? 5 Approaches to Make Postal Data More Accurate

Now, when you know the consequences and cost of poor data quality, let’s discuss how to reduce losses. You will need to change how you manage data collection, verification, and storage processes. It’s also necessary to minimize manual postal data handling and take some other steps.

Bad Data: What To Do? - #1. Measure Value

Calculate the financial losses resulting from inaccurate, outdated, or duplicate data. To do this, you need to track the cases when the order was misdelivered, or anything else happened due to poor postal data quality.

Such tracking will also allow you to measure the impact of postal data reforms. You will compare the losses in two different periods to see if anything changes and how.Calculate the financial losses resulting from inaccurate, outdated, or duplicate data. To do this, you need to track the cases when the order was misdelivered, or anything else happened due to poor postal data quality.

Bad Data: What To Do? - #2. Help Customers with Address Autocomplete

It’s a simple step that yields excellent results. When customers fill out a form or share their delivery details at the checkout, you need to autocomplete their addresses.

Address autocomplete software processes the postal data entered by users to offer them suggestions as they type. Machine learning speeds up the onboarding processes and contributes to good quality data. People don’t have to think about formatting, search ZIP codes, or verify other details. Automated address autocomplete gives them a ready address option they can select. Address suggestions are already formatted and verified.

Note that address autocomplete software is usually provided as an API you can integrate with your website or application. Since the integration is easy and doesn’t take much time, you can boost your data quality in a day.

Bad Data: What To Do? - #3. Automatically Clean Your Database

Another thing you should remember about postal data management is that addresses become outdated. When people move to another place, they don’t tell your business about it. Street names and numbers also change. As a result, you keep storing outdated information and face the high cost of poor data quality.

To avoid such issues, you should automatically clean your database at least every three months. We recommend using specialized address verification software like Inkit Verify that can automatically match your existing records with official address databases. Instead of manual address checks, you will enjoy high data quality all the time.

Clean Database

Bad Data: What To Do? - #4. Set the Data Collection Rules

More often than not, people provide inaccurate address information just because they don’t understand what you want. That’s why you should explain to them what postal data formatting is correct.

A small information pop-up can do the trick. When users don’t know what address format is expected, they will hover the information icon near the address field and read your instructions. Such interactive tips can effectively complement address autocomplete and enhance user experience.

Bad Data: What To Do? - #5. Tap Into Data Enrichment

Try automated data enrichment when you already have some data about customers, but other information is missing. Data enrichment solutions use the current information to add more details. For example, they can use street addresses and cities to generate ZIP and state codes. It’s an effective solution to improve the data quality in existing databases.

In addition to the listed methods, you should train your team on big data governance. Data processing and standardization rules are essential for any business that doesn’t want to face the high cost of poor data quality. When teams dealing with postal data are aware of the possible data quality issues, they are ready to troubleshoot and take preventative measures.

How Inkit Verify Helps to Reduce the Cost of Poor Data Quality

Verify is a tool provided in Inkit’s Reach Enablement Platform to automate address verification and autocomplete. It’s an API you can integrate with your website or application to automatically display address suggestions and match postal data with official databases to detect discrepancies. Such features improve the quality of data that exists and verify the accuracy of new records.

Want to test Verify or learn more about how it works? Let’s get in touch.

Start verifying addresses today
Don't waste time and money dealing with lost shipments that never made it to your customers.
Start for free