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Presentation to retail executives in Brazil around implementing agile analytics in retail organizations.
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The Perfect Retail Experience?
Apple Stores have more than 2x Sales/Square Foot than their nearest
competitor.
(source RetailSails: http://www.retailsails.com.php53-12.dfw1-1.websitetestlink.com/site-content/live/3/
rs200_rankings.pdf)
What Makes Me Likely to Buy?
Provide me products that improve my life…
…found in a place convenient to me
…in a store where the products are easy to find
…where they are provided by friendly people
…who are able to anticipate my needs
…at a time convenient for me
…for a price I am willing to pay*.
* Note, this isn’t necessarily the lowest price
What Makes Me Likely to Buy?
Provide me products that improve my life…
…found in a place convenient to me
…in a store where the products are easy to find
…where they are provided by friendly people
…who are able to anticipate my needs
…at a time convenient for me
…for a price I am willing to pay*.
Given our product set, which products are customers demonstrating the most interest in? Which ones are
they likely to be interested in next season?
Historical Product Sales
Customer Demographi
cs
Provide me products I want…
Customer Research
Social Media
Targeted Upsell in Store
Analytics Informed Merchandising
Targeted Offers Online
Targeted Social Media Advertising
“…..he was able to identify about 25 products that, when
analyzed together, allowed him to assign each shopper a
“pregnancy prediction” score. More important, he could also
estimate her due date to within a small window, so Target could send coupons
timed to very specific stages of her pregnancy.”
What Makes Me Likely to Buy?
Provide me products that improve my life…
…found in a place convenient to me
…in a store where the products are easy to find
…where they are provided by friendly people
…who are able to anticipate my needs
…at a time convenient for me
…for a price I am willing to pay*.
Given our customer buying patterns, demographics, and migration patterns, what are the best locations for our retail locations? Should we offer different types of retail locations oriented at different types of buyers?
Purchasing Patterns
Migratory Patterns
… at a place convenient to me …
Offline/online
purchasing trends
Social Media
Store Differentiation (i.e. Walgreens)
Retail Location Optimization
Optimization of Product Mix per Retail Location
Targeted Physical Print Advertising
Mobile Advertising
What Makes Me Likely to Buy?
Provide me products that improve my life…
…found in a place convenient to me
…in a store where the products are easy to find
…where they are provided by friendly people
…who are able to anticipate my needs
…at a time convenient for me
…for a price I am willing to pay*.
How can I arrange layout of products that customers want? How can I do so in a way that maximizes the
likelihood that customers will purchase higher margin products?
Video capture of in-store shopping behavior
… where products easy to find…
Offline/online purchasing
trends
Further insight into customer
preferences around product
Heat map of which square meters have highest rev/margin
Insight into how to position products in
specific stores
Insight into what to offer people online after an offline visit
Audio analysis of what people
say about products in
store
What Makes Me Likely to Buy?
Provide me products that improve my life…
…found in a place convenient to me
…in a store where the products are easy to find
…where they are provided by friendly people
…who are able to anticipate my needs
…at a time convenient for me
…for a price I am willing to pay*.
Are customers having negative experiences in stores? Can we analyze comments in reviews of selected
locations to know whether our customers are getting the service they expect?
What Makes Me Likely to Buy?
Provide me products that improve my life…
…found in a place convenient to me
…in a store where the products are easy to find
…where they are provided by friendly people
…who are able to anticipate my needs
…at a time convenient for me
…for a price I am willing to pay*.
Can we better predict what customers want in the store based on what they have browsed for online?
How about offering them things online that people like them have looked at or purchased in the store?
Video capture of facial
expressions/emotion of staff
… friendly people who anticipate my needs…
Social media analysis of good/bad
experiences
Insight into what communication modes sell what
products
Greater understanding
salespeople’s non-verbal
communication skills
What Makes Me Likely to Buy?
Provide me products that improve my life…
…found in a place convenient to me
…in a store where the products are easy to find
…where they are provided by friendly people
…who are able to anticipate my needs
…at a time convenient for me
…for a price I am willing to pay*.
Can we adjust hours and sales associate schedules based on predicted traffic flow? Based on level of activity our in-store cameras manage to pick up?
Sales by hour trends over
time
… at a time convenient to me …
Online purchases (planned) v
offline (impulse)
Insight into what people tend to plan as purchases versus
impulse purchase
Further insight into what business hours for which locations
What Makes Me Likely to Buy?
Provide me products that improve my life…
…found in a place convenient to me
…in a store where the products are easy to find
…where they are provided by friendly people
…who are able to anticipate my needs
…at a time convenient for me
…for a price I am willing to pay*.
Can we quickly adjust pricing based on convenience, scarcity/abundance, or demographics in order to
optimize margin? Can we predict what a given type of customer will pay in a more sophisticated way?
Real time inventory levels
per store
… for a price I am willing to pay.
Buyer’s ability to pay
Supply chain adjustments
Further per store pricing optimization
What is Agile Analytics?
What is Agile Analytics?
Agile Analytics is the application of data science…
…to pressing business questions
…which are predictive in nature
…where solutions are usually not obvious
…involving data that is often diverse, messy, and high volume
…where feedback lends itself to continuous improvement
…for which answers have significant business impact.
What is Agile Analytics Not?
Data Warehouses
Consolidate data, get “one true version” of the truth.
Business Intelligence
Drive reports from data. Allow users to explore data and drive their own reports and needs. Good at describing the past, but inadequate for predicting the future.
Analytics
Using advanced maths, statistics, machine learning, monte-carlo simulation, and other advanced techniques to drive insight from data.
What is a Data Scientist
Like many popular buzzwords, “data scientist” is already becoming diluted. When ThoughtWorks uses the label Data Scientist, we are describing someone with at least three of these qualities:
The depth and expertise in mathematics to apply the appropriate statistical techniques to solve a problem
A strong blend of mathematical and development skills to enable them to implement analytical models
Expertise in machine learning techniques and technologies
Expertise in a the use of analytical techniques in a specific domain
To ensure that our people meet these qualifications, we’ve hired individuals with advanced degrees, specifically PhD’s in Physics or Mathematics with research experience in applying statistical methods
What Makes Agile Analytics Different
Traditional Analytics
Often depends on data being in a perfect state. Delayed for years while waiting for long running Enterprise Data Warehouse projects to finish.
Focus on building a perfect predictive model before trying it out. Not designed for iterative learning.
Often focused on the software tool, not the data science that goes into a solution. Software involved are often packages that cost into the millions of USD.
Much higher up-front costs – not just for software licenses, but for implementation.
Much higher risk due to the costs – and more importantly – time spent on the solution before you see results.
Agile Analytics
Data as it is, not how we wish it to be. Understand that there will never be a perfect data warehouse. Data growth is fast outstripping the ability of a data warehouse group to make it perfect.
Focus on time to market. Get a model out there, get feedback, improve it, repeat. Perfect is the enemy of the good!
Think like a startup. Use Open Source Software. FlightCaster’s founders did not seek big enterprise software vendors – yet they are far superior to large airlines at predicting flight delays.
Minimize the “cost-to-experiment”. Ramp up investment based on results, not speculation or hubris.
Putting the Science in Data Science
The Scientifi
c Method
Define Question
Gather Informati
on
Form Hypothesi
s
Test Hypothesi
s
Analyze Results
Draw Conclusio
ns
Publish Results
Retest
Define Question
Gather Informati
on
Form Hypothesi
s
Test Hypothesi
s
Analyze Results
Draw Conclusio
ns
Publish Results
RetestThe Scientifi
c Method:5/8ths of the steps in the
scientific method are
about testing our hypothesis
and doing something with
it.
Idea
BuildTest
Analyze
Define Question
Gather Informati
on
Form Hypothesi
s
Test Hypothesi
s
Analyze Results
Draw Conclusio
ns
Publish Results
RetestAgile Analytic
s:Application of the scientific method, lean
principles, and agile practices
to analytics.
Lean Startup
“The creation of rapid prototypes designed to test market assumptions, and uses customer feedback to evolve them much faster than via more traditional product development practices.”… applies to agile analytics efforts as
much as it does to startups in general.
Getting Started
Start Small – establish a few smaller areas of focus, seek to get some results and momentum as fast as possible. Take a humble approach to this as your organization learns how to apply these techniques. Once you understand how this works for you, then scale up.
Embrace Failure – seek to validation – or invalidate - your first hypothesis as soon as you can. Build out a “minimum viable model”. Don’t be afraid to try something small and fail. Focus on building a capability to measure what works, so you can more effectively iterate over the model and make it great.
People over Tools – agile analytics is much more about intellectual capital than tools, processes, or even data. A small team of data scientists can be much more effective than millions of dollars in hardware and software.
Diversity over Size – data is important, but the hype around the bigness of data obscures the importance of taking advantage of the diversity of data. Remember you will often get insights from smaller sources of data that happen to have the inputs that help drive a great predictive model.