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”

Image Activity Based Thinking

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

Print

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

Email

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