When a person reads something, they interpret what they are actually seeing, which can differ from person to person. Fuzzy matching is the technological approach to this natural process. It is not an exact science but takes you one step closer to the human experience of interpretation.
Whether you want to find a Contact, identify duplicates, or build a Single Customer View in your CRM solution, you will have to identify all records that may point to the same entity, this is known as “data matching”.
You can enhance this data matching process by using Fuzzy Matching (aka Approximate String Matching or Probabilistic Matching).
Fuzzy Matching identifies similar but not exact matches of your search term. It is the foundation of many search engines and the main reason you can get relevant search results, even with a typo in your search term or if using a different verbal tense
Many business systems have a search capability that allows users to find data, but most are not based on fuzzy matching. The ability to Search, Find, Lookup, etc, are basic expectations of any enterprise system user. Easily obtaining relevant information is the reason we all have IT systems.
However ubiquitous, this concept of “finding what you need” is far from flawless. The way in which the search engine compares the words you are looking for against the data that exists in the system will determine the results you obtain. In some cases, you will be presented with a 72-page list of irrelevant results; in other cases, the result may be zero.
That is where Fuzzy Matching comes in…
Unbelievably today, the situation is concerningly no different. CRM and other business systems do a poor job of ensuring that the user experience is great when finding and entering data.
As a result, it is not surprising that users add duplicate records, but users are not the only source of this issue.
Let’s look at some of the common myths that exist around Duplicate Data
When you search for a term, the exact matching method looks for strings that exactly matches a pattern. The fuzzy matching methods look for strings that approximately match a pattern by using matching algorithms.
Fuzzy matching identifies the likelihood that two records are a true match based on whether they agree or disagree on the various identifiers.
Fuzzy matching uses several data type-specific algorithms to match data, and an efficient Search solution should include the following match types:
Research reveals that 94% of businesses admit to having duplicate data and fifty-five per cent of business leaders say they lack trust in their data assets, hindering their ability to be fully data-driven. The majority of duplicates are non-exact matches and usually go undetected. Fuzzy matching software helps you make those potential matches automatically using sophisticated matching logic, regardless of spelling errors, unstandardized data, or incomplete information.
But it is not just about deduplication, a fuzzy matching solution is invaluable when creating a Single Customer View (SCV) or Single Version of the Truth for realistic business analytics. The SCV allows companies to understand and properly engage with their customers by knowing who they are and what they need. The importance of having an SCV cannot be understated.
Imagine a scenario where there is no SCV, for example, a customer calls in for information about their account and the agent helping does a search for the customer’s name and finds multiple instances of the same customer. The agent must now review the multiple records to understand the customer’s history (products purchased, notes, billing info etc.) which has a significant impact on the user’s efficiency and creates a poor customer experience considering wait time and inaccurate information relayed to the customer.
Another key area businesses need to consider is data compliance. With the introduction of GDPR and several countries adopting similar legislation, it is imperative businesses consider their marketing efforts. The proper data matching allows for narrowing down search results and ensuring users are engaging with the correct person, which will also prevent duplicates. Since GDPR and similar laws require a person be allowed to be “forgotten,” having the ability to find all instances of a person’s record via fuzzy matching and removing them to ensure compliance is key.
Fuzzy matching also allows for the efficient creation of Marketing lists, where you may need to consolidate members of the same family that are in your database. Imagine mistakenly sending out mailers to multiple members in the same household when you only want to send out one piece of mail to one household. By having a consolidated mailing list, organizations will save on printing and mailing costs.
In CRM systems, most searches are for people and company names, which usually translates into Contacts, Accounts and Leads. Finding a name, however, is much more complicated than you might think. Typos, abbreviations, the way words are segmented or the order in which they are typed, are the most common examples of why users cannot find the record they are looking for in their CRM system. In order to find the Roberts & Bobs of this world or the First Network Communications & 1st Network Comms with one quick search, you need Fuzzy Matching (or a lot of time on your hands to manually go through all the variations of the name you are looking for).
It may sound like a contradiction but actually, Fuzzy Matching makes your search results a lot more accurate. The algorithms that power the Fuzzy Matching engines are based on human logic, and they reflect the normal mistakes we can all make when typing a person’s name, or a company. As a result, this engine can detect not only the records that are a full match to what you are searching for but also those that could be what you are looking for, with real-world situations, rather than relying solely on a mathematical matching ratio. Most Fuzzy Matching solutions will allow for the adjustment of the matching threshold to further refine the results that are returned.
Because Fuzzy Matching allows accurate search results, users will be able to quickly find what they are looking for, eliminating the need to perform additional searches as well as adding the record again, preventing the creation of duplicate records.
Adding Fuzzy Matching to a CRM system can improve many aspects of your system’s reliability, efficiency, and trustworthiness. Here are just some of the main benefits.
Now that you know what fuzzy matching is and its importance in your CRM system, the next step is to learn how it can help you. Find out more about Paribus 365™’s Intelligent Fuzzy Matching and how it can always find what you are looking for, even if you don't spell it correctly, or know the exact name.Find Out More
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