![]() When a regular patron calls to place an order, the restaurant could suggest a new dish based on his/her order history and the recipes that he/she has surfed. The restaurant could leverage the SCV information as follows: This often requires fuzzy string matching (in our scenario Tables 1 and 2 have to be joined using the Customer Name attribute). Fig1.Ī large organization is bound to have a multitude of such tables which they could join to obtain a single customer view. Table1 contains information regarding your customers’ order history and Table2 contains information about their surfing patterns on the restaurant’s website. You might have the following data sources/tables. Let’s look at some real-world examples of using Fuzzy Matching.ġ) Creating a Single Customer View (SCV):A single customer view (SCV) refers to gathering all the data about customers and merging it into a single record.į or example: Assume that you manage a restaurant. There are many situations where Fuzzy Matching techniques can come in handy. How does Fuzzy Matching help in real-world scenarios? With the help of Fuzzy Matching that identifies two pieces of text that are approximately similar, we’re able to match the same hotel listing from each of those sites though their descriptions aren’t exactly the same. ![]() We can see that Expedia and Priceline describe the same listing in slightly different ways. Fuzzy Matching (also called Approximate String Matching) is a technique that helps identify two elements of text, strings, or entries that are approximately similar but are not exactly the same.įor example, let’s take the case of hotels listing in New York as shown by Expedia and Priceline in the graphic below. ![]()
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