#MarketingShake - Edward Chenard - Descubrí el poder del Big Data para Transformar la experiencia...
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Transcript of #MarketingShake - Edward Chenard - Descubrí el poder del Big Data para Transformar la experiencia...
Edward Chenard
Big Data and MarketingHow Big Data is Becoming a Marketing Tool
STAV Data
According to Gartner 85% of Fortune 500’s are not doing it.
According to Accenture, of those who are doing it, 75% are failing.
Few can describe it and even fewer know how to do it.
What is Big Data?
1. Big Data Collection (HDFS)
2. Big Data Processing (Hadoop)
3. Data Mining at Scale (Hive)
Breaking down the IT of Big Data
Big Data ToolsWords you May Hear
BlinkDB
CassandraHive
Python
Pig
Stinger
HadoopGiraph
SparkGraphX
MLbase
You don’t need to be an expert in these tools, but knowing how they are used goes a long way
Impala
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Unstructured
Semi Structured
Structured
• Click Streams• Social Streams
• RSS feeds• XML
Documents
• Spreadsheets• Relational
Databases
Data ecosystem, what is it, how to understand it.
Unstructured data is the goldmine, it is growing while structured data is shrinking. But to make big data work for you, you need to structure of the unstructured
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Structured
Unstructured
First understand what kind of data you have to work with.
How to Make Data a Marketing Tool
How we Personalize Big Data and Marketing in Use Combine the strengths of Google and Facebook’s methods with psychograph techniques.
Listen, Adapt, Respond
Services co-created with customers and are interpedently with wider service networks.
Benefits
People will log in more
Higher conversion and AOV
Better emotional bond between company and customer
Psychograph Self
Facebook Self
Google Self
Clash between Today and Future
Aspirational You
Present You
1-1
SentimentExpressed as
positive, neutral, or negative, the
prevailing attitude towards and entity
BehaviorThese signals
identify persistent trends or patterns in behavior over time
Event/AlertA discrete signal generated when certain threshold
conditions are met
ClustersSignals based on an
entity’s cohort characteristics
CorrelationMeasures the correlation of
entities against their prescribed attributes
over time
Rate of Change(Slow or Fast)
Quality(Predictive or Descriptive)
Sensitivity(Sensitive or Insensitive)
Frequency(High or Low)
All signal types have certain qualities that describe how quickly signals can be generated (frequency), how often the signals vary (rate of change), whether they are forward
looking (quality), and how responsive they are to stimulus (sensitivity)
Signals have attributes depending on their representation in time or frequency domain can also be categorized into multiple classes
Signal Types
Timing/ RecencyMeasure the
freshness of the data and of the
insight
SourceMeasure sources’ strength:
originality, importance,
quality, quantity, influence
ContentDerive the
sentiment and meaning from
tracking tools to syntactic and
semantics analysis
ContextCreate symbol
language to describe
environments in which the data
resides
Clickstreams
Social
Articles
Blogs
Tweets
For each dimension, develop meta-data, ontology, statistical measures, and models
High quality signals are necessary to distill the relationship among all the of the Entities across all records (including their time dimension) involving those Entities to turn Big Data into Small Data and capture underlying patterns to create useful inputs to be processed by a machine learning algorithm.
Finding Signals in Unstructured Data
Behavioral Patterns
1 to 1 Marketing
Product/Service Compatibility
Market Trends
Social
How the Data Becomes Customer Experiences
Crowd based user actions
drive recommendatio
ns
Personalized email
marketing
Recommendations based on
products
Use machine learning
algorithms to predict trends
Small world network
communication
Algorithms analyze data
Data Capture Points, Experience Delivery Points, Metrics
Data Capture Ecosystem
The Data, Insights, Action Gap
The Data Insights Gap
Data to insights can often fall short for a number of issues- Difficulties in defining areas of
focus for external data- Only gradual adoption of
exception analytics and automated opportunity seeking
- Example (P&G / Verix Systems)- Opportunity seeking business
alerts- Value share alerts- Out of stock alerts- New Launch alerts
The Insights Action GapProcesses and systems designed prior to big data thinkingExamples:- CRM- Pricing: Buy now in-store pricing- Supply chain and logistics
- Prevalence of operational , internal metrics
- Complex new concepts: “Intents”
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Human Motion Graphs
Human motion graphs help understand movement of customers and helps to predicts timing of marketing activities
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Tracking How People Respond
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Data Discoverers
Data Discoverers are setting the trend in what will be common place in just a few short years.
More people will want to use their data and the consumerization of data and technology will continue.
As this trend goes, only organization that learn to merge the various disciplines of strategy, analytics and IT, will be successful
Data as a Lifestyle
Real-Time Firehose
Services
Apps
Multimedia
Places
Internet of Things
Our Data Sources are Changing
Search On-sites Sensors Re-marketing Customer Feedback
Signals Hub
Social
Personalization Products Customer Service
Digital Marketing In-store
Creating Customer Signal Hubs
Where we are Going
How we organize our data is getting more customized and real-time for real bottom line improvements
Vendors Hadoop Customized Customized Realtime
0%
5%
10%
15%
20%
25%
Big Data Technology Evolution
Personalization Tech-nology Evolution
How to Take Advantage of Data
datavisualization
strategy /review
technologyimplementation
analytics
“The STAV Cycle”
“gaining insight and telling stories with data” © 2014 STAV Datawww.stavdata.com
Phase Typical Issues
Recommended Approach
Strategy / ReviewDefine goals / outcomes / expectations in the form of business benefit / customer benefit; form hypotheses / build business case; Evaluate whether expectations for the current cycle were met; identify opportunities for improvement; set expectations for the next cycle
Ignored / under-emphasized Increase emphasisEstablish formal methodologyBuild capability
Technology ImplementationIdentify the tools needed to accomplish the business goal; define the technical path for accomplishing the business goal; establish development schedule
Over-emphasizedInitiated too earlyInadequate skill set
Decrease emphasisEmploy proof-of-conceptUse external servicesBuild skill set gradually / incrementally
AnalysisAnalyze the data collected by the IT implementation – find the gems; a function for data scientists or traditional BI – not an IT function; data science = 80% data analysis / data cleaning, 20% algorithm creation
Descriptive orientation (business intelligence)Dis-integration of business intelligence / data scienceOrganized in IT function / focus on algorithm creation
Adopt predictive orientation (data science)Integrate business intelligence / data scienceOrganize in business function / focus on data analysis and data cleaning
Data VisualizationTell the story of the patterns in the data; a function for designers – not data scientists; critical to making the analysis useful from a business perspective
Located in IT function / performed by data scientistsFocus on methodology vs. results
Locate in business function – branding or UXAssign to designers
“Making the Impossible Possible”
“Big Data is good for solving impossible problems;it just makes simple problems more complex”
The STAV Cycle will increase the probability of of success for any organization. Implementation of the cycle includes many more details; it needs to be adjusted to each organization and the goals of each project; but the basic framework doesn’t change. If you use this framework, your big data project will be successful.
© 2014 STAV Datawww.stavdata.com
internal /external
(medium investment /medium scope)
internal
(large investment /broad scope)
external
(small investment /narrow scope)
© 2014 STAV Data
www.stavdata.com
Maturity Model / Product Development
Life Cycle
Vision & Goals
Governance
Execution
Clearly articulated vision for marketing and data use, precisely defined goals with how to measure. Defined scope of the product.
Market strategy, customer segmentation, prioritization, org focus, measurement and incentive systems
Production process, flexibility at scale, efficiency, relationship management, benchmarking, metrics, initiatives
How work gets structured
Strategy- Define the goals
SocialDefine how to
engage
ITAssemble the
Technology
AnalyticsMake sense of the Data
Linguistics
Distributed Processing (Hadoop)
Algorithms Development
Cross team Customer Experience Improvement
Data science is a discipline for making sense of unstructured as well as numerous data sets at scale
Develop Your Team
Listen
•Listen to the data streams
Share
•Share the data with the rest of the organization
Engage
•Engage to the data to find the insights
Innovate
•Innovate new ideas from the insights gained from the data
Perform
•Perform insightful actions from the data to create better customer experiences
Always Remember: Data, Insights, Actions
Radio SEO and PPC
Social
Predictive Marketing
Television
You Are Here
Human History of Marketing
Image credit: www.conducthq.com
Using Data for Marketing in the FuturePredictive Marketing
• Extreme machine learning
• Collaborative predictive analytics
• Scale-invariant intelligence
• Neural networks for machine perception
• Real-time interactive big data visualization
• Graph all the things
• Large scale machine learning cookbooks
• Collecting massive data via crowd-sourcing
“Without big data analytics, companies are blind and deaf, wandering out onto the web like deer onto a freeway.”
Big Data: 2014
• Personalization everywhere
• Company and consumer collaboration in service design
• Predictive location based selling
• Digital Concierges
• Real time event networks
• Graph and signal hubs merge for better understanding of ad placement
• Large scale channel disruptions
• Marketing becomes more analytical
Big Data visionaries pose existential threats
Predictive Marketing: 2016
What’s Next: Combining contextual and analytical approaches provide a more complete picture of how customers interact with the firm
Ethnography
• Real people
• Everyday situations
• Narrative Stories
• Patterns / themes
• Experiential relevance
BEHAVIORAL ANALYTICS
• Real behavior
• Observation over time
• Numeric Patterns
• Statistical Significance
• Ability to model and predict
Both approaches privilege observation and understanding what people actually do and
look for opportunities to fix, improve and innovate.
Robin Beers, founder of Business is Human
Location Analysis
Graph Analysis App and Device Analysis
Customer Feedback
Personal Event Networks
Social
Personalization Digital Concierge
Real-time Service
Better Ad Performance True Omni
Signal hubs will become new centers for data, helping to create better customer insights
Predictive Analytics
Creating Customer Signal Hubs of the Future
Although IT can build the systems, it will still be left to analyst and marketers of all types to create the actions needed to engage customers
How Predictive Marketing is Shaping Up
Web
PDS
ECC
Personal Event Network
Appt Scheduler
Add to Calendar
Confirmation Email
Add Confirmation and Appt to
PDS
Using the digital concierge system, we can create easy to use appointment systems, capturing the data and using it for future personalization efforts
Appointment setting with a Digital Concierge
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Engaging millions at a time
Data Monetization
- Keep it- Sell it- Partner with it- Share it
Marketing of a Mass Personalized Scale
Processes are lined, linear chains of cause and effect.
A service is different. Processes are designed to be consistent, personalization services are not consistent but individualized and co-created. The differences are not superficial but fundamental.
Co-created value requires a relationship
Marketing of the Future: Process vs Service
Marketing as a service relies on the ability of an organization to learn from customer’s responses and to listen and adapt to those signals.
Causes of success are never revenue, costs, profits, etc.., those are lagging indicators or effects.
What matters are the activities that generate the profits, activities that create long or short term value. You can measure that via personalization as it is a leading indicator activity if done correctly.
Marketing is about Listening and Learning
An organization’s data is found in its computer systems, but a company’s intelligence is found its biological and social systems --- Valdis Krebs, researcher
Linking things changes things: social networks are good at habit building. As behaviors are repeated, they form stronger associations over time. You form strong bongs with people in your life with whom you spend the most time, the same can be said in a social interactive personalization model, customers will form strong bonds with organizations they interact with the most over a given period of time.
Small world networks: people banding together to achieve a wide variety of shared objectives. These are the most powerful types of social networks and the way to truly engage customers is to beyond just social network sites and to get into the small world networks as a valuable member of the network.
Marketing and Social
Start small, and remember, everyone else is in the same boat
Online Resources
What You can do now
Thank you