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The ConsumerView Matching macro enables users to match their customer file to the Experian ConsumerView data. Starting with customer information such as name and address you can leverage the ConsumerView macro in Alteryx to append a variety of information about your customers such as household segmentation, home purchase price, presence of children in a home, estimated education and income levels, length of residence, and many more!
Mosaic BG Dominant and Mosaic BG Household Distribution counts are balanced to Experian’s census estimates. ConsumerView is a marketing file and therefore doesn’t need to be balanced to the census estimates.
Calgary is a list count data retrieval engine designed to perform analyses on large scale databases containing millions of records. One use case for a Calgary database in an App or Macro is to offer users certain predetermined criteria (e.g. geography) to select and then return the corresponding data to them. A back-end process can combine multiple data sources, format and pre-filter as needed, and load them into a Calgary database that can be queried quickly by the app or macro . This way, the bulk of the processing happens outside of the app or macro allowing data to be returned more quickly.
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.
The option to be able to search a field with wildcard characters is often needed. This functionality within the Calgary suite of Tools can be accomplished by having a “Contains” function, where this is accomplished without utilizing a wildcard to search. This also allows you to be able to use the Indexing power that the Calgary products offer, without having to do these searches with downstream functions like Filter or other Formula based tools. Tools used: Here's how 1) Create a base cydb file with this option selected: 2) Once the base file has been created, you now want to modify that cydb file to enable the Fields you want to query with the “Contains” option. In this example dataset, we want to be able to do these type of queries for the “CONAME” and “ADDR” fields: Setup Calgary Input into Calgary Loader, when the following message will appear. Make sure you check “Yes” 3) Select the “Load a single advanced index” option Set the Index Name you would like (in this case, keeping the same name), and choose the field desired from the “Source Field”. Also, set Advanced Index Mode to Full Text 4) Repeat this process with all of the fields you’d like to be able to search. In this sample, placing “mail” in the CONAME and “ln” in the ADDR fields returned this record:
Because most Calgary Databases (files with .cydb extension) contain millions of records with hundreds of fields, a method for allowing a user to quickly determine which records to read is available.
Recently an Alteryx client wanted to select specific records from a Calgary Database containing over 700 million records. The tool the client used to read records was the Input Data tool.
While the configuration of this tool does allow a user to limit the number of records input, it doesn’t provide the ability to select specific records (option 1 in the configuration).
A Filter tool could be attached to the Input Data but this would require all 700 million records be input first. To read 700 million records would require about 10 hours.
The Calgary Input tool enables users to query a Calgary database directly. (For help configuring the Calgary Input tool, see http://help.alteryx.com/current/index.htm#cshid=CalgaryInput.htm).
Since the .cydb file in our example is small (only 15 records), the amount to time it takes to read records using the Input tool isn’t much different than using the Calgary Input tool. However, if this file had contained millions of records, the time savings of using the Calgary Input can be huge.
If you would like see more examples of the Calgary Input tool, sample workflows are available in Alteryx Designer (go to Help --> Sample Workflows --> Data Artistry --> Calgary Tools --> 2. Calgary Input). In addition to Calgary Input, you will find other Calgary related tools.
Attached examples were created using Alteryx Designer 10.1.
Calgary Regression Test Module Business Problem: If you have recurring updates on Calgary files then this module will likely be useful to you. When a Calgary fiIe is updated, it is imperative to be able to check the file for consistency. Depending on the number of columns and indices contained in the file, this can sometimes pose a challenge. Actionable Results:
Compare two versions of a Calgary file to determine if columns and indices remain constant
Complete regression testing through a simple application interface
Easily identify indices where counts have changed over 10%
Overview: Have you ever found yourself in a situation where you want to compare a newly built Calgary file with the previous iteration, in order to see if all the columns are still there, the indices are still there and that they give similar results as the last time? This Module does just that, and gives you a nicely edited document showing the results. For the querying, the app goes through all the indices that are in the Calgary directory, selects a random value from the file to use as the query value, and compares counts from the new and the old dataset, highlighting in red if the counts are more than 10% different. Vertical: Any Required Input: Calgary file with indices (previous and updated versions you wish to test) Application Process: For best results, run as a .yxwz instead of a .yxmd. In the application interface, just navigate to the two files and press Start. Be aware that if you have a lot of indices and a lot of data, it may take a while to run, so if you just want to try it out, start with something small.