the home depot: driving category-specific business decisions with alteryx
TRANSCRIPT
Proprietary & Confidential to The Home Depot
Driving Category-Specific Business Decisions with Alteryx
Charles ColemanThe Home Depot
Alteryx RoadshowSeptember 15, 2015
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Agenda
About us
What is Assortment Planning?
Initial state – chaos and ugly analytics
The change – advanced data processing
The end result – automated workbooks, profit
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Let’s Get This Out of the Way…
“Sooooo, do you get a discount?”
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Hah, You’re Funny
NO!!!
We work on a ridiculous scale.
2,000 stores + corporate office = 367,000 employees in the US alone.
Put another way, we employ 0.12% of the United States.
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Who We Are, How We Grow
So, we sell tools … The old way: 1979 – 2000’s – Expand!! The new way: optimize sales $/ft2
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What is Assortment Planning?
Assortment: everything the store sells
Assortment Planning: telling every store what to sell
“Where should I sell this hammer (if anywhere)?”
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Some Depot Stats
2000 stores 160,000 SKUs 100,000 SKUs in one store’s assortment 575 categories, each with multiple strategies Transactional data down to the SKU-register-second
level Multiple price points per SKU
… That’s a lot.
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Before Alteryx
Clustering process before: Rudimentary, regional Largely Pro/DIY Little execution, little change
Size changes our granularity? Small categories = sophisticated Tableau workbooks
Large categories = slow, clunky, unusable workbooks
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Clustering Before – Regional
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Clustering Before – Pro/DIY/Both
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The Initial Problem
How to get 20 users able to clean, join, and use their data in Tableau while using business machines?
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At First, It Did Not Go Well…
Too much data
Changing categories
Awful computers and IT wars
Everyone wanted individual workbooks
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My Alteryx Sales Pitch
“So this one process now takes between hours and a few days.”
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We Needed a Quick Win…
Bad data outputs from our systems = 10x records Solution = remove bad data Before: several hours After: ~3 minutes
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Developing a Clustering Data Process
Data sources: Transactional data – TeraData and proprietary tool Product attributes – TeraData Store information
Two different databases One proprietary tool
Clustering data – output from proprietary tool Need to combine all and output a .tde file quickly
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Success?
When we ran data through Alteryx, it: Saved hours to days of processing time
Kept business users out of databases
Required support 16 hrs/day to run ETL
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That’s Nice, But…
What would make this awesome?
Automation.
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How to Automate This?
Only one Designer license – time to learn to code!
Materials: Old, crappy desktop machine Awesome (umm… borrowed) laptop running under a desk Shared drive Management buy-in Team members’ patience
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User’s Process
Put category sales, product files through clustering
Save clustering files onto our shared drive
[Have a Coke and a smile]
Receive complete Tableau Desktop file
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The End Result
A machine under my desk - running 24/7 – that uses scripts to: Pulls users’ files down from a shared drive Uses an Alteryx workflow to clean data, assign a decision tree
to it, and produce a .tde output Change Tableau data source to new extract Use decision tree to modify category charts and graphs Create “fake” PowerPoint slides for presentations Send each user his/her workbook along with an email
All of this in 9 minutes!
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What We Actually Built
In total: mass-customized, highly analytical Tableau workbooks, nearly on-demand
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A Quick, Early View: Alteryx
Data inputs
Clean and join, clean and join Tableau
extractoutputApply decision tree(Now mostly DB
connections)
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A Quick View: Tableau – Change Clusters
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A Quick View: Tableau – Identify, Analyze Outliers
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A Quick View: Tableau - Presentation
“We should sell this hammer here.”
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Results: Metrics and Gains
Time savings per iteration: 98.5% (~80 hours to 1 hour)
[A dramatic increase in] revenue per category
More, better outlier analysis: Sales clustering outliers – how do assortment effects boost
or kill sales? Cannibalization Assortment, line structure, competitor gaps
Demographic outliers – is there more potential
Changed the way we hire and train!
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That was then… What since?
Two extra weeks per user per review gave us time for: More categories; broader, faster impact
More intense localization – by customer
More granularity on substitutable items
Exception reporting by SKU, store, season, market, category, region
Better integration of online data to predict store sales
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Questions?