What is Address Matching?
Let us start with address verification and standardization:
- Address verification: Cross-checks addresses against the USPS authoritative database to check for validity. Similarly, it uses files from Canada Post, Royal Mail, and other postal organizations for validation in their countries.
- Address standardization: Convert unformatted addresses into standardized ones using the USPS-prescribed address format.
PostGrid completes these steps simultaneously because you need both to have correct outputs. Standardization and matching facilitate address validation. Thus, all the processes go hand-in-hand.
What Challenges Does Address Matching Face?
Address matching is more than placing two addresses together and comparing them. In reality, it is very technical and complicated. Thus, you might encounter many challenges when using the process, including
User-Made Input Errors
Even a tiny error can cause delivery delays and mail returns. But what happens when users make mistakes when entering their address on your website?
Misspellings, incorrect labels, invalid formats, missing spaces, incorrect abbreviations, synonyms, etc., can make standardization impossible. You may have a database filled with these errors, stopping you from shipping to the correct locations.
Here are some examples of input errors:
- Incorrect label: 100 High Street
- Abbreviation: 100 High Str
- Missing space: 100 HighStreet
- Misspelling: 100 Haigh Street
- *Tokenization: High Street 123
- Capitalization: 100 High Street
These errors are easily recognizable on sight. However, it can make address matching hard for a computer-based system. It takes a lot of time and computational power to complete the process for hundreds and thousands of shipping addresses.
Address Data Linking
You cannot associate or link two databases together when they follow a different address format. Also, the mistakes discussed above are a significant obstacle.
Your databases will have many disparities, like
|Address strings in your database||Matching address records from the USPS files|
|Unstructured texts||Tokenized and structured data|
|Incomplete||Complete, updated records|
|Messy addresses with typos||Error-free information|
|Old to recent addresses, including business locations||Business names are only sometimes available.|
These address-matching disparities add to the challenges of comparing two databases. Hence, it takes weeks, months, or even years to find relevant records in the authoritative database and make necessary adjustments.
The differences complicate address matching, pushing businesses to pair it with other solutions. Combining tokenization, standardization, and validation with a matched record ensures:
- Your sales team can determine where your customers live or work.
- Finance departments have matching address information to send bills.
- Marketers can link mailing databases with their customers and prospects to send marketing mail.
- Insurance firms can notice high-risk locations during natural disasters to decide real estate insurance premiums.
- City planners can send notices to the correct homeowners after zoning changes.
- Healthcare organizations can send patient statements and medical documents in compliance with HIPAA.
Linking datasets helps resolve significant billing, shipping, operational, and marketing workflow issues. It makes address matching effective and efficient. But it requires adequate time and effort.
Massive Computational Power
Your address-matching plans need computational power and time in large amounts. Some find manually identifying address issues simpler than developing systems to manage the task. Since our eyes can spot errors in one glance, matching seems simple.
However, it is entirely different when dealing with large datasets with tens of thousands of address strings.
Every raw address string is different. You require in-depth cleansing and validation to reach your desired match rate.
Also, using scripts to replicate how the human’s visual review works is hard. You must pre-process your data beforehand to compare every character and match them one at a time.
Difficulty in Pre-Processing Data
Cleansing your data before address matching is crucial to get accurate results. It also saves processing time and effort because it simplifies the process. Though tedious and complicated, this setup is worth the effort.
Use Lower Cases for All Addresses
Converting your postal addresses into lowercase letters lets you establish consistency. Here is an example:
Imagine you have this address string- Memphis, Tennessee. But a user enters it as Memphis, Tennessee. Your algorithm cannot match and compare them.
However, converting both strings into lower cases would simplify the process, making address matching possible.
Similarly, you can turn your address record into upper cases. The goal is to get the outcome as “true” when matching both addresses. Otherwise, you would never get a match for your users’ inputs in the authoritative database of your country’s postal organization.
Parse Your Address File
You can get a correct address match by parsing your addresses. It means breaking down your records into separate elements.
You may label every address component for easier identification and comparison.
Here are the two primary parsing methods:
It involves pre-processing the address match string and separating all parts like the house number, street name, city, state, and ZIP or postal code. This step lets you compare all elements individually, facilitating the address-matching process.
It allows for better accuracy because comparing address components (tokens) eliminates the possibility that your system compares different elements.
For example, imagine your customer’s address is 542 Main Road, Los Angeles, California. But your system thinks the street name is Los Angeles because the person entered it first instead of the street address.
Obviously—you won’t find a match!
Let us take another example! Tokenization would help your system avoid matching an address with the term “Seattle Lane” with this address:
17 Park Lane, Seattle, Washington.
Most solutions match these addresses and give inaccurate results- only because two words match. Also, address matching requires tokenization and parsing to resolve the issue because it cannot manage such problems individually.
But tokenization itself is challenging. Splitting up addresses is hard; most people use hyphens, commas, and abbreviations, while some use spaces to separate the components. It can cause your system to misinterpret the data.
It is the opposite of tokenization. Concatenation joins pre-existing tokens to produce a matched record.
It eliminates the need to tokenize arbitrary address files, making address-matching processes easier.
However, concatenation can destroy most of the data, like the labels representing different parts of the mailing locations. Ignoring this information can complicate matching because two addresses might have different syntaxes but similar semantics.
Concatenation works when tokenization is impossible because of unstructured data. Comparing the entire address record can still produce valid results, even though they are not 100% reliable.
Convert Addresses Into a Standard Format
You can have many versions of the same shipping address.
For instance- Avenue vs. Ave or Road vs. Rd can give you two matches for one location. Sticking with any one option and reformatting all addresses using a standard format can simplify address matching for your firm.
4 Problems Address Matching Can Solve
Fuzzy matching can resolve many issues for your business and enable smooth data management.
Here are the five significant problems it can solve:
Spot Phonetic Variations
An advanced fuzzy match solution allows you to identify phonetic differences in two or more addresses. Thus, you can differentiate between 48 Carl Road and 48 Karl Road.
It saves you the trouble of reshipping orders because your address-matching software cannot detect the phonetic variation.
Distinguish Between Non-Standard Data
You may twitch your algorithm to handle standard, minor differences, like lane vs. ln or drive vs. dr. But what happens when dealing with non-standard information?
USPS has abbreviations for nearly 300+ street types. And your system should know them all!
Luckily, PostGrid’s address-matching services can add the correct abbreviations and manage non-standard data without you doing anything in-house.
Combine your databases and deduplicate records via matching. It allows you to spot all similarities, differences, and other details, keeping you ahead of your data management game.
You can also use matching solutions to filter outdated data. It ensures you have the latest, most accurate information always.
Delivery Point Validation (DPV)
Address matching helps you derive the essential metadata from your mailing addresses. You can get Delivery Point Validation (DPV) codes, match rates, and geocode information for every record.
Also, PostGrid lets you access additional authoritative data from SuiteLink, Residential Delivery Indicator (RDI), and more USPS products.
The Downsides of Address Matching You Must Know
An address-matching service does not guarantee 100% accuracy. You might encounter many disadvantages, stretching the process and wasting time.
It Might Miss Some Formatting and Spelling Issues
There is room for mistakes even after you match every address segment. You require an alphanumeric solution to allow you to compare and verify all components simultaneously.
But why do some matching solutions face these issues?
Many vendors refer to private data sources instead of the USPS database. They might have different spellings than the standard ones. Also, they follow formats other than what the postal services need you to use.
Hence, companies relying on private sources or conducting address matching in-house face formatting and spelling issues. They must process their data again using the USPS files to get better outcomes.
Very Little Data
Many solution providers don’t have adequate data to compare your address field records with theirs and find matches.
In these scenarios, they often run your data to get the closest match, which may need corrections.
You compare two address files to know the likelihood of getting similar records during the address-matching process. Unfortunately, the match rate you might get is rarely accurate.
PostGrid encodes location data within its API and software to cross-reference the USPS data for proper matching. We also enable you to deduplicate the records with ample data, ensuring you only have the most updated and correct mailing addresses on your system.
We already discussed how phonetic variations hamper the address-matching algorithm. Many enter their location details based on how the place sounds. It leads to misspelled words and other issues, making it hard for businesses to ship orders or conduct mailing campaigns.
However, you can always avoid making mistakes or mixing up your shipping addresses when using PostGrid. We are CASS-certified, which means we access the USPS official database to match your addresses.
Incorrect Address Matching
You often get invalid results for your addresses. For example, imagine you enter the address- 89 2nd Avenue, Pasadena, California. And you receive this outcome as an address match-
89 2nd Alley, Pasadena, California.
You might send your marketing or transactional mail to the software’s suggested address. What next? You still get mail returns.
After digging into the matter, you determine that people used to call the street 2nd Alley until a few years ago. But it is Avenue now because another place had the same name.
Minor details like this take time to recognize. And many addressing matching solutions still need to catch up.
Different Approaches You Can Take With Address Matching
Businesses can take many address-matching approaches. Below are the four best ones that can add value to your organization.
1. API Integrations or Web Services
Integrating an API into your CRM or website is among the best methods to match addresses. You can easily exchange and validate your customers’ mailing addresses by linking your system with an authoritative database.
For instance, you can use PostGrid’s address validation API to access the USPS database and verify your mailing lists accordingly. Since USPS serves 165 million addresses, you can ensure you don’t miss even a single record. It guarantees you are always up-to-date and have accurate versions of all customer addresses.
APIs are easy to connect and use. Most vendors, like us, offer seamless integrations to allow you to complete the setup hassle-free.
You can integrate PostGrid’s API into Zapier, HubSpot, ActiveCampaign, Salesforce, Quickbooks, Marketo, and other platforms. The ability to connect our address validation and matching features with your favorite tools lets you be flexible and prompt.
You don’t need to worry about uploading files for address matching. All you need to do is complete the API integration and let PostGrid handle the task.
Let us discuss it with a use case:
Imagine you have a list of your least active customers on your HubSpot CRM. You want to re-engage them via a mail marketing campaign. However, the addresses need updates and validation before mailing. So, you use address matching to get valid results and continue planning your mailings.
This solution will allow you to get accurate geocodes for all addresses. Also, it will help you determine whether the locations are deliverable.
The API integration enables you to complete these tasks without switching to another tool or platform. You don’t need to modify your workflows or make an extra effort.
2. Address Matching Software
A software application suits companies wanting a downloadable version of address-matching solutions. You can download it on your computer device and set up your account in a few minutes.
It lets you upload your mailing lists or individual address records for matching and verification.
The results from an API or software are the same. You get the same metadata and speed. Only the deployment method is different.
API integrations are best for on-demand address validation. Also, you can use them when you want to install address matching at the entry point to block invalid data from entering your system.
Using software is suitable when you have bulk address files to process. It lets you save records, upload mailing lists, and perform other functions in one place speedily.
3. Use the Manual Approach or Brute Force
The Brute Force or string-matching algorithm is among the easiest ways to perform address matching for your organization.
It matches the first character of an address pattern with the initial text character. After successfully checking everything, it matches the second, third, and fourth characters, and so on.
If the Brute Force does not get a match, it slides the pattern over one character to restart the process.
This method is among the basic approaches to conducting string matching. It is similar to the manual review approach, wherein a human compares two strings visually to spot discrepancies and mistakes.
You process every character individually, but it needs hefty and time-consuming processing.
Brute Force and the manual approach use the same principles. The downside is that the results might still contain errors.
Top Tools or Methods to Perform Address Matching
You can use many methods to conduct fuzzy address matching and level up your data management procedures. We have mentioned the most accessible ways below for your quick reference. You can use a process that best fits your needs and situations.
The Levensthein Distance Method.
It is one of the best methods to use, irrespective of which coding environment you work on. Some also refer to it as Edit distance. It refers to the number of deletions, substitutions, insertions, and other transformations to transform source strings to 1.
The Levensthein distance method for address matching helps you consider spelling mistakes and formatting issues that lead to failed matches.
You can use the standard formula to calculate the Edit distance for matching addresses.
Choose the distance you need to get your desired accuracy rate while considering multiple mistakes.
It goes a step further than the Levensthein distance for address matching. It enables another function to help you get correct address matches: transposition.
Traspositioning two adjacent characters allows for better flexibility and accuracy in data matching. It helps you account for your users’ input errors and determine the relation between two records more definitely.
You may calculate the Damerau-Levenshtein distance between a and b (mailing addresses). The resulting value using the standard formula for address matching will be the distance between an i-symbol and j-symbol prefixes of strings a and b, respectively.
For instance, you must correct your user’s input from HIHGWAY to HIGHWAY when they make a mistake. You would need the Levenshtein distance of 2 (to replace H with G and vice versa).
But you only need the Damerau-Levenshtein distance of 1 to correct the error, where you can swap H and G. The result is the same. But you can get an address match with a higher likelihood of being correct.
Use PostGrid’s Address Validation Solutions
The Damerau-Levenshtein and Levenshtein distance methods work well for address matching but can be time-consuming. Also, you can only guarantee partially accurate results.
The higher the distance you enable, the broader the room for errors. Thus, you must take many steps to avoid false fuzzy matching positives. You must cleanse and prepare your data for pre-processing.
Also, you must standardize your records to get the exact match. Pre-processing requires you to do everything, from correcting the abbreviations to parsing every address field.
Is it worth the effort?
Your address-matching solutions should simplify the task instead of leaving you with a hundred jobs. It is best to switch to an address validation API, like PostGrid, to complete mapping, matching, standardization, autocomplete, and parsing under one roof.
You don’t need formulas or prepare your address data to match your mailing lists.
Should I Build My Address Matching Software?
Many companies want to build an address-matching solution in-house. It helps them better control everything and get unrestricted flexibility. Also, it allows them to customize how they can input their data and what results they expect.
However, it is easier said than done!
Here are some advantages you might miss out on building your software instead of using legacy solutions:
Eliminate Time-Consuming and Daunting Processes
Developing an in-house software program requires you to assign a dedicated dev team. Also, you must allocate significant resources and time to complete the task. Not to mention, you also need software maintenance from time to time.
Thus, developing the software application for address-matching tasks is not enough. These activities can accumulate one over the other in no time, diverting your attention from your business to technical aspects.
Thus, using an address validation solution is your best bet to avoid resource-intensive tasks and focus on your business’s primary operations.
Creating address-matching software yourself requires a significant upfront cost. You must also pay for development, debugging, testing, updating, and maintenance.
The most crucial thing to remember is that you cannot control the cost. You cannot guarantee how much you will spend on the software’s development.
Thus, most companies opt for readily available solutions where they know the costs beforehand. They can use these services and scale up and down whenever needed.
Organizations that build in-house software might feel pressured to use it more than they need to since they pay high prices!
Using PostGrid’s address validation API or software ensures you are updated and have the most recent data at your fingertips. Does the same happen after building an in-house address-matching software?
You might only sometimes update your software and get the latest address data because your dev team is busy. Furthermore, you also avoid spending on it when you don’t have the necessary usage.
Building software programs can stack up your responsibilities without giving back much.
Companies use address matching to use valid data, ship accurately, and conduct profitable mailing campaigns. But these objectives can only come true when you don’t spend all your savings or profits on software updates.
Broad Range of Advanced Functionalities
Though in-house software development opens more doors for customization, it has disadvantages. An API offers advanced features, like integrations with other platforms, reporting, bulk validation, global addresses, and DPV data. Building these features from scratch is hectic and time-consuming. Furthermore, they might not match the existing solutions’ quality and sophistication.
PostGrid’s Address Validation API: Your Address-Matching Partner
PostGrid’s address validation API helps companies in all industries parse, standardize, and match their customer records without trouble.
Here are some features to help you better understand how we work:
National and International Validation
PostGrid international address validation API can verify and match addresses of 245+ countries and territories. Hence, you don’t need to worry about whether we check Australian, Canadian, or UK addresses.
We have many global postal certifications, like SERP (Software Evaluation and Recognition Program), PAF (Postcode Address File), and AMAS (Address Matching Approval System).
Our address-matching solutions ensure you get premie-level data for an address down the street or the one thousands of miles away!
We offer four pricing packages for our clients:
You can check out their rates and prices by clicking here!
PostGrid offers the most competitive address-matching rates in the US and worldwide. The best part is that you pay much less than creating and maintaining your software.
You don’t need to pay a minimum monthly charge or sign a long-term contract. We have monthly and yearly plans, which you can select from based on your realistic requirements.
Also, please remember that getting updated and correct geospatial data needs many licenses and certifications from many organizations (data providers). We help you get impeccable address validation and matching solutions without you navigating the costs, restrictions, and license paperwork.
Expertise and Experience
We are address validation experts with years of experience. Hence, we can help you get high-quality data and convert outdated lists into updated and valid ones.
Our high-tech features let you perform structured or rule-based parsing, standardization, and address matching without a sweat. You can input an address you want to correct or check, and voila!
The API integration option is even better because you can avail of your features within your existing systems. You don’t need to change how you work or dedicate a team only to manage one business aspect.
County, Census, and Carrier Route (CR) Data
Our clients get massive metadata when validating their addresses with us, like
- DPV codes
- County data
- Census data
- Delivery route information
- RDI data
You can store these details for data mapping and making other business decisions.
National Change of Address (NCOA)
Accessing NCOA data files is essential to your address-matching and validation requirements. It allows you to cross-confirm whether your customers have moved to another location in the past two years.
If yes, our API or software can find a match for the address in the USPS database and make essential changes.
Thus, you can avoid dealing with failed deliveries, mail returns, incorrect forwarding, misplacement, delays, or lost items. Be ready and confident by tracking your customers wherever they go and communicating with them wherever they are!
Our customer assistance team is available 24/7. You can contact us anytime with questions about address matching or other things.
Also, we can help you with setups, integrations, and other processes whenever needed.
Address matching solutions allow you to assign latitudes and longitudes to every customer record and determine their validity.
It helps you send marketing materials to the correct addresses and accurately ship orders. Also, it lets your accounts and finance teams send transactional and compliance mail to the relevant recipients without unnecessary issues.
PostGrid’s address validation solutions combine all advanced features into one for your business. Healthcare, insurance, real estate, retail, eCommerce, financial services, and other industries can use our software or API, irrespective of whether they have small-scale or large-scale needs.
All finance, marketing, HR, customer success, sales, accounts, development, and compliance teams use PostGrid for various reasons. We facilitate their work and allow them to communicate with customers without gaps.
Request a demo for more details on how PostGrid’s address-matching solutions pave the way for successful shipping, marketing, billing, and more!