leading the product 2017 - wendy glasgow
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Leading The Product 2017Speaker Slides
Melbourne and Sydney, Australia
Wendy GlasgowGoogle
For more information go towww.leadingtheproduct.com
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Confidential + ProprietaryConfidential + Proprietary
Data: Considering more than the [email protected]
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Data to define what we
build
Data generated from our product
Data powering
our product
2 31
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Data to define what
we build
1
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Metrics to define success - moving away from the gutGet them right - we can truly build a great product that grows our business
Get them wrong - we can look successful on paper but completely miss the mark
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Google Confidential and Proprietary
weight target quantity target
Russian nail factory workersEarly 20th century
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Macro: What is my business trying to do? What is my team trying to do?
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Build a strong profitable business
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Executives that adhere to metrics that tie directly tobusiness objectives
3x more likely to hit their goals
Source: New Study Reveals Why Integrated Marketing Analytics are Critical to Success, Think with Google, Forrester, March 2016
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Understand the macro before diving into the micro
Product Strategy
Market Penetration | Brand Positioning | Profit Targets
Product Plan
Metrics
Marketing Str… Sales
Cost / Service External DriversEngagement
Product Teams
Your Product strategy
enables focus
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Jack Welch, Former CEO of GE
“There are only two sources of competitive
advantage:
The ability to learn more about our customers
faster than the competition,
and the ability to turn that learning into action faster
than the competition.”
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Customers are NOT created equal
Focus: Who is your customer?
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Know your ‘best’ customers
$ $$$$$$$$$$
$
Value Spend less Cost to Acquire
Ideal Customer
Base
Cost toService, Support, Retain
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Widen the scope of considered data
Marketing
Ad Logs Search
DM EDMSMS
CompetitionsNewsletters
Product, Websites
Stores
Engagement Analytics
Transactions
Customer Services &
Support
Call centreCustomer
interactions
Finance
TransactionBusiness
Costs
Operations & Logistics
Operational costs
Delivery
Sales
CRMPOS
Customer Value
People & Culture
People costs Skills matrix
Attrition
Tech
POSLogs
Analytics
Relevant Data
Data Strategy
Data Governance
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Data generated from the product
2
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● Deal with data early
● Ensure you have a data strategy
● Add a section at the definition stage
● Make it mandatory
● Provide a process, make it consistent
● Over capture and
LABEL!
You’re focused on getting your product live
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We’re capturing data - what is important to consider?
● Data storage is CHEAP - $0.02 per TB per month in BigQuery!
● Capture everything possible - but make it readable
● Consider the data points you’re capturing
● Make the data meaningful - LABEL
● Link your data - IDs, Labels
● Timestamps are critical
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Tools
BigQueryAttribution
Data StudioData Vis
Tag ManagerTag Mgt
OptimizeTesting/Personalisation
Google AnalyticsAnalytics
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Make friends with statistics
- OR -with someone who already iscorrelation
causation
Get intimate with your data
relevance
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Data as an Asset
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There is no competitive advantage within an organisation!
Share your Data
There is no competitive advantage within an organisation!
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Data powering our
product
3
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Machine Learning is the new ground for gaining competitive edge & creating business value
*Source: MIT Survey 2017; n=375Bain Consulting Study
Competitive advantage ranked as top goal of machine-learning projects for 46% of IT
leaders & 50% of adopters can quantify ROI
2X more data-driven decisions
5X faster decisions
than others3X faster execution
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Machine Learning Allows You to Solve a Problem Without Codifying the Solution
✓ Recognizes patterns in data✓ Predictive analytics at scale✓ Builds ML models seamlessly✓ Fully managed service✓Deep Learning capabilities
Google Cloud AI
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First Step in This Journey Begins with Data
“Every Company will be a Data Company”
*Source: Wired, Bloomberg, Fortune, McKinsey
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Machine Learning Lifecycle at a Glance
How do I collect, store and make data available to the right systems?
How do I understand what data is required to solve my business problem?
User
Data Objective
TrainServe
How do I get to a working model within the period of time where my objective is still relevant?
How do I scale prediction into production systems?
How do I keep my model relevant with continuously updated data?
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Flow to build a custom ML model
Identify business problem
Develop hypothesis
Acquire + explore data
Build amodel
Train the model
Apply andscale
1 2 3 4 5 6
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Structured Data● Spreadsheets, Logs, Databases● Text that includes structure● Data needs to be separated● Typical data generated from products
Unstructured Data● Natural Language, Images ● More complex but sometimes these are
better understood● Number of existing ML APIs -
Supervised Learning● Need labels on the data● Build examples to train the system
Unsupervised Learning● Data is grouped / clustered ● Drawing inferences from data sets
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A feature in ML is very different from a feature in Product
In ML, a feature is an individual measurable property or characteristic of a phenomenon being observed.
Choosing informative, discriminating and independent features is a crucial step for effective algorithms in pattern recognition, classification and regression.
Feature Engineering
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A feature is a data point, so what is good?
Represent raw data in a form conducive for ML
1. Should be related to the objective
2. Should be known at production-time
3. Has to be numeric with meaningful magnitude
4. Has enough examples (absolute minimum of 5)
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What can I do today to plan for ML1. Find your Data Strategy and Governance owners –
get familiar with it or create it!
2. Identify the decisions your product makes today.
3. Consider suitability for automation with ML.
4. What data do you have today and what do you need to capture?
5. Capture data in line with your strategy and governance guidelines – update them if necessary.
6. Capture LOTS of data, but LABEL it well and consistently!
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Takeaway 1 Takeaway 2
Value is in use of data
Think inside, outside
& future
It’s what we do with the data that mattersBUT… early consideration can increase value
How does you relate to your surroundingsRelevance, correlation and causation
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● Predictive maintenance or condition monitoring
● Warranty reserve estimation● Propensity to buy● Demand forecasting● Process optimization● Telematics
Manufacturing
● Predictive inventory planning● Recommendation engines● Upsell and cross-channel
marketing● Market segmentation and
targeting● Customer ROI and lifetime value
Retail
● Alerts and diagnostics from real-time patient data
● Disease identification and risk satisfaction
● Patient triage optimization● Proactive health management● Healthcare provider sentiment
analysis
Healthcare and Life Sciences
● Aircraft scheduling● Dynamic pricing● Social media – consumer
feedback and interaction analysis
● Customer complaint resolution● Traffic patterns and
congestion management
Travel and Hospitality
● Risk analytics and regulation● Customer Segmentation● Cross-selling and up-selling● Sales and marketing
campaign management● Credit worthiness evaluation
Financial Services
● Power usage analytics● Seismic data processing● Carbon emissions and trading● Customer-specific pricing● Smart grid management● Energy demand and supply
optimization
Energy, Feedstock and Utilities
Cloud Machine Learning Use Cases