Big Data for Sales and Marketing People

Fill the gaps companies need in their big data teams

Who am I

• Sr Leader of Omnichannel and Innovation at Best Buy

• Experience building and scaling big data projects that include data science and data visualization teams – first in the midwest and retail

• Tekne finalist for software innovation• Marketer at heart – Group Alum

What is Big Data

• Ask 5 people, get 5 answers• Often defined by the V’s

– Volume – how much data– Variety – how many kinds of

data– Velocity – how fast data

moves– Viability – how useful is the

data– Value – what value will the

data add

??

Big Data and Your Career

Mckinsey Report on Big Data

Framing Big Data

5

Big Data

Value: Improved Customer Experience

Data Science: Analytics

Technology: What Tools and Why

Data Strategist

- Measurable Results- Multi-Channel Case Studies

- MapReduce, Hadoop- Cassandra, The Cloud- Pig, Hive,- HDFS

- Solve Customer Painpoints- Develop competitive strategy- Alignment with Analytical Infrastructure- Speed to Market- Privacy Considerations

- Data Scientist + Statistician- Where to find talent?- Discovery Analytics- Deep data insights

Big Data: Data becomes your core asset. It realizes its value when you know how to do what.

The Hadoop Vendor Ecosystem

Big Data is beginning to generate some returns

What businesses are saying about big data:Improved Business Decisions: 84%Improved Current Revenue Streams: 43%Also Support of New Revenue Streams: 31%Not Leveraged for Revenue Growth: 27%

However, Businesses are still seeing some gaps:1. Going from Data to Insights2. Taking Insights to Action3. Creating big ideas from Insights.

Source: Avanda Inc. 2012 Big Data Survey

How Sales and Marketers Fit into Big Data

The world of big data is changing. As more companies move to real time, they are starting to realize that a tech driven strategy will not give them the better business performance or customer experience they crave. That’s where sales and marketers come in or the new data strategists.

Data Management Framework• Holistic approach to understand the

information needs of the enterprise & its stakeholders

• Consistency for planning & process development

• 10 major functional areas, including governance

• Aligns data with business strategy (above) and technology (below)

• Takes into account the data lifecycle – creation through destruction

• Internationally recognized through Data Management Association International (DAMA)

Signal Types

Signals have attributes depending on their representation in time or frequency domain can also be categorized into multiple classes

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)

Rate of Change(Slow or Fast)

Quality(Predictive or Descriptive)

Sensitivity(Sensitive or Insensitive)

Frequency(High or Low)

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

Finding Signals in Unstructured DataHigh 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.

For each dimension, develop meta-data, ontology, statistical measures, and

modelsTiming/ Recency

Measure 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

The Data, Insights, Action GapThe 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 Gap

Processes 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”

New Solutions Must Aid Human Insight

Big Data + Personalization + Amplified Human Intelligence

Last Decade

- Structured Data- Conclusive Dashboards- Small scale / sampling

A data architect built a view to reach a specific conclusion

Next 5 Years

- Any data, from anywhere- Intuitive exploration- Making sense of it

at scale

Business users easily find, explore, visualize and navigate insights

Human Motion Graph

19

New Tools Same Solutions

We have new data sets to help us engage customers, the technology can’t solve the customer experience issues. Companies want marketers with an understanding of Tech

Case Study: Rent the Runway

• Rent the Runway rents high end dresses to women, similar to the model of renting tuxedoes to men.

• RTR collects many data points on users experience the same items.

• Hundreds of women rent the same style, site average of 300 orders per dress up to 1000.

• 1/6th of customers have written at least 1 review.• Women are willing to provide information to help others

make decisions, 50% of reviewers share their weight, 60% share their bust size.

• Seeing a photo review increases the likelihood of renting by 200%

• RTR wanted to create a better personalization system for women searching for the right dress.

• How many data points do we need to accurately find other women in our user base like you?

• Start basic: Same size, demographics.• Expand: Similar taste• Evaluate: Clickstream updating

RTR: Calculating Sameness

• Even with only 4 points of comparison (size, age, height, bust) over 100,000 possible combinations.

• Too much detail narrows the results set too far• Slow to compute, large to store.• Simplify, create buckets per characteristic

– Height: Petite, Short, Average, Tall– Bust: Small, med, large– Age: Demographic group– Result: 864 vectors that accurately capture the range

of women shopping the site.

RTR: Future of Fashion Retailing

• The future of fashion retailing is data driven• Crowdsourcing of fit and style matching will

become more widespread.• As confidence in the business model grows, so

will positive experiences with customers.

What is Data ScienceData science is a discipline for making sense of unstructured as well as numerous data sets at scale

Disparate Data- News- Web- Email

- Research- Clickstream

- Various external data

sets

InterpretDeep processing

of data structured and unstructured

ResolveAssemble, organize,

and relate

ReasonUncover

relationships, compare and

correlate

Machine Learning

Distributed Processing (Hadoop)

Alignment with Business Goals

Cross team Customer Experience Improvment

What is Data VisualizationData Visualization is the discipline of telling the story of what the data is saying via visuals

Disparate Data- News- Web- Email

- Research- Clickstream

- Various external data

sets

InterpretAfter data science

finds insights, create the story

ResolveChallenges of story

telling

ReasonExpress large

complex data in easy to

understand visuals

Data visualization tools

Graphic Arts

Light coding

Understand human interaction

What is Data StrategyData strategy is a discipline that managed the customer experience via the understanding of what data says about the customer experience

Disparate Data- News- Web- Email

- Research- Clickstream

- Various external data

sets

InterpretHow the customer

experiences products

ResolvePain points and

business objectives via technology

ReasonUncovers what

motivates customers

Marketing and Sales

High level understanding of technology tools

Understands how to use visualization to sell

Customer’s advocate for a better experience

How to Get Started

• Meetups• Online Classes• Conferences• Read, Read and Read some more.

Meetups

We have several great Meetup groups locally that are free to attend:

• Data Visualization: http://www.meetup.com/Twin-Cities-Visualization-Group/

• Hadoop: http://www.meetup.com/Twin-Cities-Hadoop-User-Group/

• Big Data Developers: http://www.meetup.com/Big-Data-Developers-in-Minneapolis/

Classes

There are free classes available locally and online you can take:

• Big Data University: http://www.bigdatauniversity.com/

• Coursera: https://www.coursera.org/

Conference

There are free classes available locally and online you can take:

• Minneanalytics: http://minneanalytics.org/

• Minnebar: http://minnestar.org/minnebar/

Read

Plenty of free blogs, sites and Linkedin groups to join now:

• The Connected Company, Dave Gray

• The Intention Economy, Doc Searls

Companies Need you

More companies understand the need for the business skills to be added into the big data mix.

Most need help now! 2 years ago hardly anyone was doing this work, now, hardly anyone isn’t.

• Your skills are transferable and needed!