Fuzzy Joins for Grouping Values Into Buckets

At my company we often work with panel data and need to be able to easily group individual panelists into buckets or profiles.  On a recent project I needed to identify which panelists were Light, Medium, Heavy or Extreme buyers within specific product categories based on how many times they purchased that category each year.

The criteria for what constitutes a certain buyer group was different for each category so I couldn’t just use a simple mathematical formula to figure out which group each buyer falls into.  Instead I needed to compare my panelist data with a criteria file to identify which group a user falls into.  Since this kind of task is commonly encountered and cannot be solved via a simple join, I thought I’d write a quick post explaining how I solved this issue with Alteryx.

Below is a screenshot of my panelist data showing the transaction count for each user ID/Category combination.

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Calculating Correlations and R Squared Values with Alteryx

I’ve been busy on a bunch of big projects lately as you can probably tell by my neglected blog.  Today I tried a new tool and thought I’d share how it works and how it can be improved on using a batch macro.  My goal was to compute a bunch of correlation factors using Alteryx.  Below is a sample of my data:

As you can see, each product has five weeks of data.  I wanted to correlate each of my five factor columns to the Dollar Share column to see which factor was most predictive of the dollar share.  The goal was to return the R Squared value of each correlation just as you could easily do in Excel using the RSQR() function.  The R Squared function in Excel is built on the Pearson Product Moment Correlation function.   I found the Pearson Correlation tool in the Data Investigation tab in Alteryx.  Once that was dropped into the workflow, I selected which variables I wanted to compute a correlation.

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Dynamic Field Renaming in Alteryx

In my work at TABS Analytics, I often find myself building re-usable alteryx workflows for processing sales data coming from syndicated sources such as Nielsen or IRI.  The goal is to build tools that can be applied to data coming from multiple different clients.  Though the basic content of the data is the same, the field names and format are often different depending on the client.  A basic measure such as sales dollars, could come with many different titles such as (Dollars, Sales Dollars, DOLLARS, Value Sales, etc.)  In this example I’m going to walk though how I use the dynamic rename tool to “iron out” these variations up front to avoid errors downstream.

Imagine that we have two different data sources with slightly different field names as shown below.

The goal is to build a workflow that can load both data sources, properly identify the fields, and process without errors.  In this case, the first thing we’ll need to do is rename the fields so that they are consistently identified throughout the rest of the workflow.  The standard Select tool won’t work since it will throw errors if you try to map two different raw field names to the same output name (i.e. mapping Dollars and Sales Dollars to both be renamed as just Dollars).

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Alteryx 10.0 Review

I just upgraded to Alteryx 10.0 today and wanted to share my findings.  Overall the upgrade was quite seamless though they do require an uninstall of the prior version effectively preventing true side-by-side comparisons on the same box.  Below are some key new features that jumped out at me.

Browse Anywhere

Probably the most popular feature everyone was looking forward to was the new “Browse Anywhere” option which is indeed very handy.  You can now view a sample of data flowing through any tool by simply selecting the tool and viewing the Results panel (formerly called output).  Note how the results screen now has several icons on the left that represent different viewing tabs.

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Spreading Time Periods with Alteryx

One challenge most data integrators eventually face is the need to spread data down to different time periods.  This could mean spreading weekly data down to days, or  multi-week events down to individual weeks.  This can be challenging with traditional databases since spreading typically involves generating an unspecified number of new rows of data which SQL is not really designed to do.

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Hacking Alteryx – Editing XML Directly

Ok, ok, maybe this isn’t technically hacking, but it sure feels like it!  While at the Alteryx Inspire conference this year I learned that you can actually access the XML behind any workflow tool effectively bypassing the GUI entirely.  Granted, you won’t need to do this very often, since they do have a good GUI, but there are cases where it sure comes in handy.  In one of my recent posts, I showed how this can be a big help with the select tool, and today I’ll show how I just used it on the formula tool.

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