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The Fuzzy Match Tool provides some pretty amazing flexibility for string joins with inexact values – usually in the case of names, addresses, phone numbers, or zip codes because many of the pre-configured match styles are designed around the formats of those types of string structures. However, taking advantage of the custom match style and carefully configuring the tool specific to human entered keyword strings in your data can also allow you to use the loose string matching feature of the tool to match those values to cleaner dictionary keyword strings. If done properly, it can help you take otherwise unusable strings and, matching by each individual word, recombine your human entered data to a standardized format that can be used in more advanced analyses.
In life, there are few things black and white. There are gray areas everywhere and the lines that separate can be a little fuzzy. The same holds true for data – especially when it’s human entered. That’s why we have the Fuzzy Match Tool – if your data isn’t clear as day, you can still get value out of your records by matching them to something a little more standardized.
The Fuzzy Match Tool has the ability to match first names against a set of Nicknames to help return better matches. The Nickname table (which can be found at C:\Program Files\Alteryx\bin\RuntimeData\FuzzyMatch\Nicknames) is used as a lookup within the Fuzzy Match tool when you select it as an option. Selecting “Name w/ Nickname” as your Match Style automatically selects the Common Nicknames table, but often users would like to add to this list or even create their own custom table. This article will walk you through how to edit this list, and provide you with some tips and tricks when matching with nicknames.
Creating a Nickname table
The nickname table is installed by default in C:\Program Files\Alteryx\bin\RuntimeData\FuzzyMatch\Nicknames and is saved as an Alteryx database file (.yxmd). We can easily pull this into Alteryx to add additional names, or we can even generate our own table. The .yxdb file contains 2 fields:
Full Name goes here
Nickname goes here
Adding Additional Names: Creating your own file: Once the file is created, place the .yxmd in the directory above. You should now be able to see multiple tables available from the dropdown within the tool.:
See the attached v10.6 workflow for an example of the above!
Tips and Tricks when working with Nicknames
Set Generate Keys to “None” when using the Names w/ Nicknames match style IF you have the First Name in a single field.
If your name is contained in a single field (John Smith or Smith, John), you will want to select a method to Generate Keys and check the box “Generate Keys for Each Word”.
The “Soundex” method of generating keys is generally preferred when working with names.
Household Level Analytics Module
Business Problem: Businesses investing in new customer acquisition will be more successful in reaching prospects if they know which consumer profiles best describe their current customers. Compiling customer databases through marketing or loyalty card programs allows businesses to know who their customers are, as well as where they are located. When correctly leveraged, this type of information enables strategic and focused spending of marketing funds. Actionable Results :
Understand the demographic attributes of your customer base
Target new customers that fit the profile of your current customers
Ensure that your advertising and marketing funds are spent in the most effective way possible
Overview: Would you like to identify key demographic traits of your target customers? By appending household-level characteristics to a customer file, you can achieve the most accurate Consumer Profiling of both existing and prospective cstomers. This analysis allows business owners to target households that are not in their customer database, but are in their trade area and match the demographics of current customers. Customer acquisition using targeted households is a more efficient way to direct spending on advertising and marketing programs. Vertical: Retail Data Utilized: Customer file containing the following fields:
Customer Address containing street number, street name, city, state
Customer ZIP Code
Alteryx Data: Experian Household File Application Process:
The selected customer file is run through the Calgary Join tool using Experian household data to isolate the Experian records that match the customer records.
Fuzzy Matching is then performed to eliminate all duplicate records.
Finally, the wizard outputs the customer file with appended household-level data.