Ready, Aim, Fire! Using Social Media Analytics to Hit Your Target Market

October 11–13, 2012

Social Media Analytics

Seminar AgendaSession Objectives + Moderator and Speaker Introductions

10 mins

Each panelist shares content – 15 mins / panelistNaghmana MajedJanki Vora

30 mins

Moderator/Audience panel Discussion 35 mins

Naghmana Majed

Naghmana Majed is a Certified Sr. IT Architect with Global

Solution Center (GSC) at IBM. Naghmana is one of the

technical leaders at the center; she is currently responsible for

leading high-value, complex solution design and development

for our some of our major clients.

Her current area of focus and expertise is Business Analytics

and Optimization (BAO) including social media analytics and

predictive analytics. She is also BAO lead at the GSC for

Industrial sector.

As a senior technical leader, Naghmana has successfully led many high impact Analytics

initiatives for IBM. Over her more than 16 years of experience, she has extensively

consulted with clients on developing cutting-edge solutions that address tactical as well

as strategic business needs. She is currently leading multiple social media analytics and

predictive analytic solutions for our clients.

Janki Vora is currently pursuing her second masters degree in

Statistics and Predictive Analytics from Northwestern University,

she graduated with bachelors in Computer Science from Nirma

Institute in India and masters in Computer Science from Georgia

State University (GSU), Atlanta, GA.

Professionally, Janki has worked with IBM for over 8 years. She

is currently working as a Senior IT Specialist with Global Solution

Center Dallas at IBM. She has been trusted to handle several

critical projects. Her significant contribution came when she was

deployed in Pune, India to train software engineers and setup a

support center for IBM in India.

Janki, has extensively worked on high impact social media analytics project. Her

experience working on BigData project along with IBM Research where twitter data was

collected for months, analyzed for sentiment of the movies was an eye opener where she

learnt about the challenges of harnessing BigData and the benefits of Social Media

Analytics.

Social business drives an unprecedented need for insight from natural language conversations

5

500 billion impressionsannually made about products and services ** 770 million people

worldwide visited a social networking site *

• Forums and Newsgroups• Wikis, Blogs and Microblogs• Social Networks• Social Media News Aggregators

• Wikis, RSS and Forums• Email and Collaborative Content• Call Center Notes and Recordings• Customer and Employee Surveys• Reports, Minutes and Research

Corporate Social BusinessConversations about strategy, projects, issues, risks, outcomes …

In addition to conversations about quality, experience, price, value, service …

44x information growth by 2020 ***

Sources: * comScore, Social Networking Phenomenon ** Empowered, a book by Josh Bernoff / Ted Schadler*** IDC Digital Universe Study, May 2010

Public Social Media

Conversations about quality, experience, price, value, service …

Social Media Analytics Extends Traditional Segmentation

Age +Income +Geography

Preferred Product Categories

Preferred Channel

Participation in Loyalty Program

Use of In-House Credit Card

Use of Service ProgramsReturn /

Exchange Behavior

Breadth of Categories ShoppedLength of

Time as Customer

Recency + Frequency + Value

Response to Media

Time until Repurchase in Key Categories

Annual Spend Level

Annual Transactions

Econometric: Real-estate & Unemployment

Most segmentation approaches only focus here

Social media analytics can provide an increased aperture of the consumer and the ability to see new patterns and opportunities

© Copyright IBM Corporation 2012

Align – Detect & Capture


Anticipate – Analyze & PredictSegmentation Ability to find hidden clusters

of people Example: Identify those likely

to respond

Association Finding things done in

tandem

Example: Identify products likely purchased together

Classification Identify attributes causing

something

Example: cascading attributes of defection behavior


9

Identify clusters of customers using real time insights

Insights on the Customer

High

Low

High

Frequency

Action Clusters

Online Discounts

Extreme Loyalist

Brand Focused

New Shopper

LowPurchase Size

Offer

Batch

Marketing Model

Traditional

Marketing

Process

Email

Website

Phone Apps

Others…

Marketing Channels

Event-Based

Location

Zip Code

Household

Neighborhood

House Prices

Segmentation

Source: IBM Research© Copyright IBM Corporation 2012

Act – Embed & Engage


Lisa

Marketer's qCrowd UI

4 Lisa’s info is sent to the qCrowd

webpage

6 Marketing Team posts a relevant

message to Lisa

3 qCrowd

Analytics finds

Lisa’s location

Marketing Team

5 Marketing Team analyzes results

and decides to take action

2 qCrowd identifies Lisa

as a prospect

qCrowd Environment

qCrowd

Twitter 1 Lisa

posts a

comment

on Twitter

Understand who your customers are, interact with them, drive them to your business and learn from them

Source: IBM Research© Copyright IBM Corporation 2012

Use Case: Telco - Retail and Social Media

TelcoLocation Analysis

Telco / Retailer Action

Lisa

3 – Intelligent Advisor

platform

processes Lisa’s

activity for

relevant actions using

Telco and

Retailer information

4 Receives a

message with an

offer reminding her

to stop by if she’s

in the area

5 Receives promo

code for offer while

passing by the store

2 – Follows a friend’s

post on FB and clicks

the Like button on a

camera she likes

6 Lisa uses

promo code

to purchase

offer at POS

1 - Registers with Retailer, gives

Permissions to Retailer and

Telco

Tesco Fan Page Customer Profile

TelcoCustomer Profile

Product Catalog

Tesco

Intelligent

Advisor Platform

Source: IBM Academy Study

Why is this important?

Merrill Lynch in 1998 cited estimates that as much as

80% of all potentially usable business information

originates in unstructured form.

Source: Christopher C. Shilakes and Julie Tylman, ‘Enterprise Information

Portals’, Merrill Lynch, 16 November, 1998.

This volume of unstructured content was questioned

and has since been validated. Further, for some

companies the number is closer to 85%.

80%

20%

Electronic documents

ImagesBlogs

Wikis

Video

Audio

Voice

Instant messages

E-mails

Sensors

DatabasesStructured

Unstructured

“…today, 86% of US online adults interact with social media in some way“ – Forrester

May 2012

Entire new market of channels

Old Paradigm New EntrantsAwareness

Intent

Purchase

Source: IBM Academy Study

Use Case: Insurance and Social Media

Source: IBM Fellow, VP, WebSphere CTO – Jerry Cuomo

Use Case: Media and Entertainment

Source: IBM Research

Source: IBM Research

Use Case: Media and Entertainment

Ready, Aim, Fire! Using Social Media Analytics to Hit Your Target Market

Career

Transcript of Ready, Aim, Fire! Using Social Media Analytics to Hit Your Target Market