Deterministic and Probabilistic Matching: Concept, Considerations, and Differences
Marketers rely heavily on address verification to ensure that their campaigns reach the intended audience. It improves the effectiveness of targeting and segmentation strategies. Identity matching techniques like deterministic and probabilistic matching empower address verification. They are helping marketers create comprehensive customer profiles and facilitate personalized marketing.
However, they have their limitations. A better alternative to get complete, accurate, and up-to-date addresses can be an address verification tool with address-matching capabilities.
This blog will discuss deterministic and probabilistic matching, their advantages, disadvantages, and key considerations. You will also learn their differences, how they help with address verification, and why you should implement a hybrid approach for better results.
Key Takeaways
- Deterministic matching relies on exact data points such as names, and email addresses to ensure high accuracy. It can be used to create precise customer profiles. However, due to its reliance on static data, it struggles with scalability.
- Probabilistic matching identifies matches based on likelihood instead of exact criteria. It can manage variation and generate a broad range of matches. This makes it ideal for understanding customer behavior.
- Deterministic matching is best for scenarios requiring high accuracy like financial institutions. Probabilistic matching is best suited for broad marketing efforts where variation is acceptable.
- Instead of choosing one, organizations can use both methods as a hybrid approach to achieve higher accuracy, reduce false negatives, and enhance overall data quality.
- PostGrid has address-matching capability in its address verification tool. It will match all the elements of an address to ensure accuracy and consistency.
What is Deterministic Matching?
This method uses exact, static data to evaluate and match the customer’s records. The data can range from name, email address, phone number, and birth date. They work on the principle of certainty and only rely on the data that tends to remain constant. The dependence upon unique identifiers helps in creating accurate, micro-level data consolidation. Businesses can use this to create customer profiles with a high degree of confidence.
The reliance on unique identifiers makes it a competent method for address matching. By matching exact address components like street name, and ZIP code, it enhances accuracy in identifying addresses that correspond to the same location. Deterministic matching works in the following ways
- Single-field matching: It only considers one unique identifier to establish a match. This can lead to inaccurate data.
- Composite field matching: It considers multiple fields (two or more) before establishing whether or not there is a match.
- Cascading deterministic heuristic matching: It functions the same way as composite field matching but with an if/then rule.
If you need address matching capabilities within your CRM, you can integrate PostGrid’s address verification API. It allows you to deduplicate and standardize your data, giving you a consolidated view of your records and simplifying data management.
Advantages
Accuracy
Deterministic matching offers a high level of accuracy. Since it uses personally identifiable factors like email addresses and mobile numbers, there is no scope for ambiguity. It can be used to create a reliable customer database to implement targeted marketing campaigns.
Personalization
You can control the matching rules based on your marketing goal. It allows you to set granular criteria like gender, and race. You can use this information to create personalized marketing materials. This will enhance the brand’s ability to connect with its customers on a deeper level.
Enhanced Customer Experience
When used for communication and deliveries, deterministic matching ensures address accuracy. This improves customer satisfaction and trust.
Disadvantages
Lack of Scalability
As mentioned above, it relies on static data. However, not every customer is willing to share their details when browsing the site. Moreover, customer’s information gets updated with time. Since it requires exact matches, any variation might lead to incorrect classifications. This limits its effectiveness in adapting to the changes in the real world, which is needed for marketing strategies to work.
False Negatives
When a system fails to identify a match or condition that exists, it is referred to as a false negative. In context with address matching, two addresses should be considered a match but are classified as non-matching. Deterministic matching requires exact matching of the specific field. If any field component does not match may be due to abbreviations or differences in formatting, it might classify it as non-matching. This is why address standardization is important as it enhances the accuracy of address matching.
What is Probabilistic Matching?
It leverages statistical methods to conclude that two records belong to the same entity. It is based on the principle of probability. The technique compares the different fields and assigns a similarity score to each field. It uses various information points such as behavior patterns, demographic data, and other relevant data to establish a connection between dissimilar data sets.
While not as accurate as deterministic matching, probabilistic tools are now using deterministic data sets to boost accuracy. Moreover, they are also incorporating natural language processing to automatically identify and extract crucial information from records.
The probabilistic technique also leverages machine learning and data analysis algorithms to generate matches even when there are no explicit identifiers. This helps businesses gain a comprehensive view of customer interactions across different touch points.
Since it works on the premise of likelihood, a business can get more matches from the probabilistic model when compared to the deterministic one. Due to this, probabilistic matching can be used for broad audience coverage and to create holistic customer personas. Probabilistic matching works in the following ways.
- Fuzzy string matching: It finds a match by allowing for some difference between data sets. Search engines that suggest, “Did you mean this” when you write a misspelled word use this technique.
- Advanced machine learning matching: It involves using Artificial Intelligence to understand the relationship between words and concepts and determine a match. It also uses Neural matching to evaluate the relationship between questions and the web pages, instead of just relying on keywords.
- Phonetic matching: It uses a lookup table or machine learning algorithm to determine if the two same-sounding words have the same spelling, for example, Catherine or Katherine.
PostGrid enables you to incorporate fuzzy address logic into your system. It checks every delivery address for incorrect street abbreviations, misspelled words, typos, capitalization errors, misplaced spaces or alphabets, directional errors, etc. Thus, it gets easier and faster for your system to conduct probabilistic matching.
Advantages
Manage Spelling Variants
Since they do not follow pre-defined rules like deterministic matching, they can handle spelling variants and offer data that closely resembles the search.
By accounting for variation, it ensures relevant information is found even when there are minor discrepancies such as misspellings, spelling differences, or typos.
High Volume of Matches
It does not need to collect personal information like email addresses to identify devices and create matches. Hence it generates more results compared to deterministic matching.
It does not have to search for exact matches between data entry and search terms, hence it can quickly scan through vast amounts of data and identify relevant results. This flexibility makes it suitable for industries dealing with massive datasets that can not be standardized such as eCommerce.
Enhance Content Marketing Efforts
Since it allows building comprehensive customer profiles, over target messaging, it can significantly improve top-of-the-funnel content. At this early stage, the goal is to attract and engage potential consumers. Keeping variations into consideration, probabilistic matching ensures that a comprehensive customer profile is identified. This model will recognize similar points and group the customers into segments, even with incomplete or inconsistent data. This can prove instrumental in designing and implementing broad ad marketing strategies.
Disadvantages
Low Precision
Since they guess the possibilities through analyzing behavior and patterns, you can not rely on the model’s accuracy. Moreover, consumer behavior may change over time due, which can render the data inaccurate.
Privacy Regulations
Federal Trade Commission has classified certain pieces of data as personally identifiable information including IP address and device IDs. Businesses might have to stop collecting these data from customers or they will need consent. This makes it challenging to collect third-party data that probabilistic matching needs.
How Can Deterministic and Probabilistic Matching Help With Address Verification?
Deterministic Matching Will Ensure Accuracy
It can compare addresses with a reliable database like that of the United States Postal Service and verify it down to its ZIP code on being paired with an address verification tool, like PostGrid. Moreover, it can also check the format of the addresses like the placement of commas, abbreviations, and spacing to ensure uniformity across the database.
Probabilistic Matching Will Handle Variations
It can be used in situations where the address format may vary. For example, 678 Main Street and 678 Main St. may not exactly match, however, a probabilistic model will recognize them as the same, based on learned patterns. Moreover, if the address is missing certain components, it can suggest likely completions based on the historical patterns. This will speed up address verification.
Things to Consider When Deciding Between Deterministic and Probabilistic Matching
Matching Exactness
Determine your accuracy requirements. In situations where exact matches are crucial, for example, legal correspondence, you should prefer deterministic matching. In situations where approximate matches are acceptable, you can use probabilistic matching. Take, for instance, consumer behavior on an eCommerce might vary between platforms. You can use probabilistic matching to match a user’s behavior on a website when it appears similar to the pattern on a mobile app.
Use Cases
Understand the use case of each matching before making a decision. For example, when you want to send personalized campaigns, deterministic data would be helpful. With accurate information like user ID, purchase history, and preferences, you can send targeted messages to the customers.
You can also use deterministic data in cross-device user tracking. You can show personalized product recommendations to customers across their desktop and mobile devices, even if they log in to the website using their email.
Probabilistic Matching vs. Deterministic Matching
Deterministic Matching
- It looks for an exact match between addresses.
- The data can be used to create device relationships with personally identifiable information like name or phone number.
- It heavily relies on high-quality data which can be achieved through cleansing and standardization.
Probabilistic Matching
- It leverages statistics to evaluate whether or not the records represent the same individual.
- It uses weights to calculate the matching score and determine whether the records are an exact match, a possible match, or a non-match.
- While evaluation, it also takes the frequency of the occurrence of the data into account. Take, for instance, William when matched with another William will lead to a low score, because it is considered a common name.
Confused Between Probabilistic and Deterministic Matching? Why not Choose Both?
The probabilistic vs deterministic matching section above might make you wonder which one to choose. It would be unfair to select either one of these two because they have their strengths and weaknesses. You can leverage a combination of both. Moreover, adding address autocomplete, validation, and standardization capabilities to your system can help you enhance your identity matching efforts. Let us take an example of ecommerce to understand it.
Deterministic Matching for Data Correctness
Deterministic matching can be used to identify unique eCommerce customers based on matches of identifiers like name, email address, or phone number. It will ensure that customer data is accurate and there are no duplicates.
Take, for example, Jenny is a regular customer of an ecommerce store. She has created her account by adding details like her email address and phone number. Deterministic matching will use the unique identifiers to ensure the preference, save items and purchase history are accurately linked to the account. It can also be used to send personalized marketing campaigns like a targeted email to increase the likelihood of engagement.
Probabilistic Matching For Scalability
Probabilistic matching can be used to recommend products to users based on their browsing behavior and purchase history. It will analyze patterns and similarities to suggest relevant products, even if certain attributes don’t match.
For example, Jenny frequently visits and looks for specific items like a red dress, or blue denim jacket. The probabilistic matching will collect information such as what people looking for a red dress or blue denim jacket also look at and present it to Jenny as recommendations. This will broaden the scale at which the products reach users.
By using a hybrid approach, you can enhance accuracy and coverage and create a comprehensive customer profile. Deterministic matching will help you ensure high-quality, personalized engagement while probabilistic matching will extend your campaign reach.
PostGrid plays a significant role here as it helps bring your mailing addresses in one place as a refined, standardized, and updated list. It enables you to ensure your deterministic and probabilistic matching activities succeed.
Also Read: Conclusion
Deterministic and probabilistic matching are two competent techniques for identity verification. Pairing them with PostGrid can do wonders for your business.
PostGrid’s address verification API offers address matching as a part of its comprehensive solution. With features like International address validation API, and access to NCOA, you will get accurate and up-to-date address results. Whether you want to deduplicate your address records or reformat them, PostGrid’s address-matching ability ensures you always have access to ready-to-use data. Want to know more? Schedule a demo to experience PostGrid’s address verification solutions firsthand!
Frequently Asked Questions
How is Fuzzy Matching Different From Deterministic and Probabilistic Matching?
Fuzzy matching is a data-matching technique that evaluates the similarity between two text strings. It produces a similarity score considering factors like character overlap, phonetic similarity, and edit distance. Deterministic matching uses a unique identifier to find a match between different records. Probabilistic matching leverages statistical methods to find commonalities between the data sets.
Are Identity Resolution and Matching the Same?
No, identity resolution and identity matching are different concepts. Identity resolution links data from multiple sources to a single representation. It aims at identifying the person. Identity matching, on the other hand, tracks users’ behavior across multiple devices like desktops, and smartphones. It works towards which data points refer to the same entity.
Can You Use Deterministic Matching and Probabilistic Matching Together?
Yes, many organizations take a hybrid approach. Deterministic matching produces accurate results and probabilistic matching helps widen the reach. Take for instance, when you want to target actual buyers, you should go with the deterministic approach. However, when you want to target more buyers, probabilistic matching will help you.
