overview of machine learning opportunities in retail @ svml 2015.01.30
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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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Agenda
1. Product Overview
2. ML Algorithms for Personalization
3. ML Algorithms for Planning
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Product Overview
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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!
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• 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:
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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
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ML Algorithms for Personalization
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Want to learn the Response of a User interacting with a Context
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Response
User
Context
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Typical optimization is maximizing the average responses
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, w
A better approach is to maximize each user’s responses
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Ideally, we would have the users draw us this curve. Realistically, we need to infer this curve.
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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)
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...(slide 16)
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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
...
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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
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Model Evaluation(s)
Model(s)
Experiments
External Model Evaluation (t)
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Need to have over-rides that reflect business considerations
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User Context
(U, C)
Features
Train
Experience
...
...
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ML Algorithms for Planning
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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
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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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Backup Slides
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Marketing funnel in reality is complex
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Source: http://adamhcohen.com/the-new-marketing-funnel/
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At any point in the interaction, there is a (User, Context) state
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(Users, Context) Metric
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Understanding consumer behavior needs to understand user, context, and response attributes