Compared to just several years ago, the number of channels and devices companies are using to communicate with their customers has significantly increased. As a result, the audience gets more and more fragmented. This is making it increasingly difficult to send the right message, at the right moment to the right person. In the same time, data is coming from many different sources. As a result, businesses often store data in separate silos. Moreover, each store is usually using its different format. In this article we will explain how to use fuzzy matching software to increase customer satisfaction.

To appeal to your customers and increase their satisfaction it is essential to building comprehensive overviews that include both online (website visits, mobile apps, social media posts) and offline channels (in-store transactions, loyalty cards). One of the approaches that businesses are using to build a comprehensive overview of their customers and improve satisfaction is data onboarding.

Data onboarding carries offline data to online, matching offline records with online data in the process. Its scope is to build a holistic view of your customers and their behavior.

But before performing this process, there’s an important aspect you need to deal with: data matching. Most probably you collect data from many different channels. Each channel has its own validation rules and its own particularities (an address might be collected as free text in a web form, but a mobile app could use several fixed text fields). Therefore, you will not get the best results the by using the classical method of deterministic matching.

Related Reading: Indepth Fuzzy Matching Guide For 2021

Fuzzy Matching

Probabilistic or fuzzy matching relies on the application of a mathematical process.

The algorithm determines the similarities between different sets of data using. To do this, the algorithm uses weighting techniques which assigns a score. You can use this score  to classify the likelihoods of matching into categories like Unmatched, Possible Match, Unmatched.

Here are a few examples of how fuzzy matching can help:

  • fix misheard data due to noisy environments, phonetic errors or difficulties with accents
  • correct manually entered data that can be transcribed with errors if the original handwriting is illegible
  • repair erred data collected using web forms
  • adjust data collected using different validation rules, that otherwise are almost impossible to deal with.

By applying fuzzy matching on your combined online and offline data, you can get a full understanding of your target audience. For example, if you own both a physical and an online store, fuzzy matching will allow you to accurately collect customer data regarding all the customer touch points:

– Online behavior: including landing page, ads the customer clicked, etc

– Online store purchases: past purchases, wishlists, etc

– Physical store behavior: whether your customer previously used coupons or not

– Physical store purchases: see if your customer has made purchases in the past.


Customer satisfaction relies on the creation and distribution of relevant, valuable and consistent content. To do this, you have to get all of your online and offline data in one place, cleansed and de-duplicated. WinPure Clean & Match, combines an intelligent fuzzy matching algorithm with an inbuilt knowledge base library system, and it is designed to find the most true matches with least false matches. Not only does it presents duplicate matches with individual scores but also provides many options like merge/purge, auto select master records with golden record/survivorship, delete, update and export results.

Download the 30 days free trial and see for yourself how our fuzzy matching software can help you.

By Andrei Popescu | March 10th, 2018 | Posted in Fuzzy Matching

About Andrei Popescu

Andrei is a detail-oriented writer and enjoys the process of researching and learning new things all data quality.

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