Date post: | 14-Jul-2015 |
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PROPRIETARY AND CONFIDENTIAL
Overview of Machine Learning Opportunities in RetailSushant Shankar | Chief Data Scientist | 01/30/2015Silicon Valley Machine Learning
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PROPRIETARY AND CONFIDENTIAL
Agenda
1. Product Overview
2. ML Algorithms for Personalization
3. ML Algorithms for Planning
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PROPRIETARY AND CONFIDENTIAL 4
A typical visit to an e-commerce site is not straight-forward and not conducive to rules
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t
Google: ‘Converse shoes’
Purchase!
PROPRIETARY AND CONFIDENTIAL
• Segmentation• Campaigns• A/B tests
Current Tools for E-commerce are highly driven by Rules
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Rules are manually specifying conditional probabilities!
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Rules drive:
PROPRIETARY AND CONFIDENTIAL
The Reflektion Platform leverages Machine Learning to learn the ‘optimal policies’
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Implement 1 to 1 experiencesacross devices
Measure performance, identify opportunities and generate insights
Drive lifetime value and incremental traffic
PROPRIETARY AND CONFIDENTIAL
Want to learn the Response of a User interacting with a Context
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Response
User
Context
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Ideally, we would have the users draw us this curve. Realistically, we need to infer this curve.
PROPRIETARY AND CONFIDENTIAL
We can infer this curve through supervised and un-supervised models
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User events Context
Get new experience
New (user, context)
Features (slide 13)
Train models (slide 14,15)
...
...(slide 16)
PROPRIETARY AND CONFIDENTIAL
1. Merchandise2. Brand3. Site4. User demographic5. Core Business Goal
Features need to incorporate domain knowledge
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vs.
User Context
(U, C)
Features
Train
Experience
...
...
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Variety of Machine Learning models can be used
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Source: Data Mining Methods for Recommender Systems (2011)
Features
User Context
(U, C)
Features
Train
Experience
...
...
PROPRIETARY AND CONFIDENTIAL
Prior
Model Selection is itself a multi-level State Space Search
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Internal Model Evaluation (t)
Data
Properties of Data
Best Models ⊂ Models
Optimal Models
User Context
(U, C)
Features
Train
Experience
...
...
Model Evaluation(s)
Model(s)
Experiments
External Model Evaluation (t)
PROPRIETARY AND CONFIDENTIAL
Need to have over-rides that reflect business considerations
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User Context
(U, C)
Features
Train
Experience
...
...
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How did you drive results? What insights can you provide?
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1. Businesses need to understand how results were driven.a. Can expose the Machine-learned weights in a digestible way.
2. Can surface these insights into tools to allow businesses to make decisions about/through:a. Merchandise
i. Assortment Planningii. Inventory Forecasting
b. Marketingi. Channel Managementii. User Segmentationiii. Campaign Management
PROPRIETARY AND CONFIDENTIAL
Auto-segmentation of users and contexts
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(Users, Context)
1. Take interesting Users, Contexts, (users, contexts)
2. Cluster (un)successful behaviors together to:a. ‘Personas’ of consumers based on
what are driving KPIsb. Best contextsc. Sort out interesting business
opportunitiesd. Anomalies from expected behavior
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Predictive models can be used to simulate business decisions
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f 12(price, user location,...)
f13(price, user location,...)
f 34 ...
f35 ...
...
...
...
∆
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We are a growing company and always looking for great [email protected]
Questions?
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Marketing funnel in reality is complex
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Source: http://adamhcohen.com/the-new-marketing-funnel/
PROPRIETARY AND CONFIDENTIAL
At any point in the interaction, there is a (User, Context) state
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