Social Media Analytics Ahmed Abbasi University of Virginia 1 - - PDF document

social media analytics
SMART_READER_LITE
LIVE PREVIEW

Social Media Analytics Ahmed Abbasi University of Virginia 1 - - PDF document

Social Media Analytics Social Media Analytics Ahmed Abbasi University of Virginia 1 Outline Social Media Overview Social Media for Communication and Collaboration Social Media Analytics Application areas Challenges


slide-1
SLIDE 1

Social Media Analytics 1

Social Media Analytics

Ahmed Abbasi University of Virginia

1

Outline

Social Media Overview Social Media for Communication and

Collaboration

Social Media Analytics

Application areas Challenges

Social Media for Engagement

2

slide-2
SLIDE 2

Social Media Analytics 2

Social Media

Socialnomics video:

3

The Social Ecosystem

4

Collaboration Analytics Engagement

Source: Forrester Research

slide-3
SLIDE 3

Social Media Analytics 3

SOCIAL MEDIA FOR COMMUNICATION AND COLLABORATION

5

Communication and Collaboration: Social Media Usage

Source: McKinsey Quarterly 2012

slide-4
SLIDE 4

Social Media Analytics 4

Communication and Collaboration: Benefits for Internal Use

7 Source: McKinsey Quarterly 2012

Communication and Collaboration: Alternative to Email?

French company Atos

to ban internal email usage.

Statistics:

200 emails per

employee, per day

10% are useful 18% are spam

Exploring other tools,

including social media

8 Source: ABC News, 2011

slide-5
SLIDE 5

Social Media Analytics 5

Communication and Collaboration: Challenges

Social media management policies

Security Usage

75% of employees use social media to stay in

touch with friends

Technology portfolio management

On average, 6 social media tools

Some using 25+ tools!

Unified communication (UC)

9

SOCIAL MEDIA ANALYTICS

10

slide-6
SLIDE 6

Social Media Analytics 6

Social Media Analytics Definition

Technology used to monitor, measure,

and analyze activity by users of the Web 2.0 framework to provide information to make business decisions.

According to a 2011 Bloomberg

Businessweek Survey:

Gartner’s Hype Cycle for Analytics

Source: Gartner 2011

Social Analytics Social Network Analysis Emotion Detection Social Media Monitoring Social Media Metrics

slide-7
SLIDE 7

Social Media Analytics 7

Text Information Categories

13

Low High Identification Complexity Topics Opinions Emotions Events

Multi-class problem Keyword-driven Series of binary or multi-class problems Multi-class problem Overlapping classes Multi-class problem Context dependent Complexities Linguistic features Literary devices (sarcasm, satire, rhetoric, irony, etc.) Spam Context

Social Media Analytics: Opinion Mining

14 Source: Chen 2010

slide-8
SLIDE 8

Social Media Analytics 8

Social Media Analytics: Opinion Mining

15 Source: Chen 2010

ONLINE SENTIMENTS AND FINANCIAL PERFORMANCE

16

slide-9
SLIDE 9

Social Media Analytics 9

Sentiment Indicators and Indexes

Consumer sentiment as an indicator?

Consumer Confidence Index (CCI), Consumer Sentiment Index (CSI), etc.

Web 2.0: Social Media Sentiment?

Online Customer Satisfaction

February 12, 2005 September 25, 2010

2/2008 Shutterfly Gallery 2007-2008 Photo Books Now, free unlimited storage space 4/2009 iPhone App

Sources: Foresee, http://www.foreseeresults.com, CNN Money, http://tech.fortune.cnn.com/2009/10/07/shutterfly-fights-the-photo-recession/ Internet Archives, http://www.archive.org/

slide-10
SLIDE 10

Social Media Analytics 10

Online Customer Satisfaction

19

Increased satisfaction score between 2009 and 2011 also resulted in increased stock price.

Social Media Sentiments as an Indicator?

20

The strong relationship between stock price and sentiment polarity/intensity (sents) for Apple

  • ver a 24-hour

period.

Source: Das 2010

slide-11
SLIDE 11

Social Media Analytics 11

Using Blogs to Predict Movie Sales

Key finding: frequency of positive sentiments is better

indicator than volume of posts alone.

Relationship between movie income per theater

(solid line) for new releases, and frequency of positive blog posts (dashed line).

21

Using Twitter to Predict DJIA Movements

22

86.7% accuracy in predicting closing up and down of DJIA using Twitter tweets

Source: Bollen et al. 2010

slide-12
SLIDE 12

Social Media Analytics 12

Twitter and the Facebook IPO

23

Social Media Analytics: Twitter

Twitter

100M+ active users per month 50% log on every day 55% on mobile 1B Tweets every 3 days

10 billion/month in Oct. 2012 6 billion/month in Sept. 2011 4 billion/month in Mar. 2011 3 billion/month in Jan. 2011 2 billion/month in Apr. 2010 1 billion/month in Jan. 2010 24

slide-13
SLIDE 13

Social Media Analytics 13

Social Media Analytics: Twitter

25

Source: http://www.mediabistro.com/alltwitter/api-billionaires-club_b11424

USING SOCIAL MEDIA FOR DECISION-MAKING

26

slide-14
SLIDE 14

Social Media Analytics 14

Social Media and Product Design: The Case Of The Red Dell

Source: Radian 6, 2010

Social Media and New Logos: Mind the Gap?

2,000+ critical comments on Facebook 5,000+ new critical followers on Twitter 14,000+ parodies of the new logo Gap reverted back to the old logo within days

28 Source: The Guardian 2010

slide-15
SLIDE 15

Social Media Analytics 15

Social Media for M&A Analytics

29

Source: Lau et al. 2012

Social Media for Early Warnings: ADRs

30

slide-16
SLIDE 16

Social Media Analytics 16

Social Media for Early Warnings: ADRs

Current warning mechanisms Some problems:

Might not be enough reported incidents Can take time Differences in time of warning for various

drugs of the same class Social Media may provide early warning…

31

Social Media for Early Warnings: ADRs

32

  • !"

#$ #!" %$& '!"

slide-17
SLIDE 17

Social Media Analytics 17

Social Media for Early Warnings: ADRs

33

Social Media for Early Warnings: ADRs

34

slide-18
SLIDE 18

Social Media Analytics 18

Social Media Analytics: Tiger Case

Why did Nike maintain its relationship with

Tiger Woods?

Why did Accenture part with Tiger Woods? Answer: Social Media Analytics

35

Social Media Analytics: Tiger Case

Sentiment for Tiger Woods before and after

scandal

Combined from Twitter, blogs, forums, social

networking sites, etc.

36 Source: Xenophon Strategies, 2010

slide-19
SLIDE 19

Social Media Analytics 19

Social Media Analytics: Tiger Case

Discussion keywords in Tiger conversations

post scandal

4% - 7% of the postings mention a sponsor

37 Source: Xenophon Strategies, 2010

Social Media Analytics: Tiger Case

  • Far greater reference to Tiger in Accenture conversations than Nike

38 Source: Xenophon Strategies, 2010

slide-20
SLIDE 20

Social Media Analytics 20

Social Media Analytics: Tiger Case

Sentiment for Accenture within Tiger Woods

conversations

39 Source: Xenophon Strategies, 2010

Social Media Analytics: Tiger Case

Sentiment for Accenture within Tiger Woods

conversations after cutting ties with Tiger

40 Source: Xenophon Strategies, 2010

slide-21
SLIDE 21

Social Media Analytics 21

Social Media Analytics: Tiger Case

Sentiment for Nike within Tiger Woods

conversations

41 Source: Xenophon Strategies, 2010

Social Media Analytics: Tiger Case

2010 CMU study on Economic Impact of Nike

sticking with Tiger:

$1.6 million higher revenue in golf ball sales

alone (in 2010) due to sustained relationship

“Tiger’s continued endorsement profitable for

Nike, but perhaps not for non-golf related products”

42

slide-22
SLIDE 22

Social Media Analytics 22

SOCIAL MEDIA ANALYTICS: CHALLENGES

43

Challenges: Spam

Webpages (web spam) – 20%

Our research: 70%-80% of the top 100 Google

search results for “online pharmacy” in 2009- 2011 were spam. Blog spam (splogs) – 12%

User-generated comments to blogs > 50%

Some studies report rates as high as 90%!

Twitter – between 5% and 10%

Our research: varies, depending on topic

44 Sources: Akismet 2012; Websense 2010; Choi et al. 2011

slide-23
SLIDE 23

Social Media Analytics 23

Challenges: Spam – Websites and Blogs

45 Source: Abbasi et al. 2012

Challenges: Spam - Reviews

46 Sources: Ott et al. 2012

slide-24
SLIDE 24

Social Media Analytics 24

Challenges: Spam - Detection

Spam Cues:

Lengthier Higher average word

length

More descriptive and

vivid

47 Sources: Ntoulas et al. 2006; Ott et al. 2011

Challenges: Sentiment Accuracy

Analyzed performance of several SaaS

  • pinion mining options:

Found that many of the tools had overall

accuracies as low as:

42% for sentiment polarity classification 75% for within-one accuracy

In comparison, baseline ML methods:

73% for sentiment polarity classification 98% for within-one accuracy

48

slide-25
SLIDE 25

Social Media Analytics 25

Challenges: Context…the “why”

According to the 2011 Gartner Hype Cycle:

Existing text and social media analytics tools

tend to focus on the semantic dimension of language: what people are saying.

While using such tools organizations have

difficulty understanding discussion context and participants’ actions and underlying intentions.

49 Sources: Gartner 2011

Challenges: Context…the “why”

A Text Analytics Framework for Sense-making

50

slide-26
SLIDE 26

Social Media Analytics 26

Challenges: Context…the “why”

51

Manufacturers can develop new products. Try to develop new products. But I think that manufacturers must invest funds to do academic research. What new product we can try? We can try to develop new flavors of products The research and development cannot be finished in one or two day. Now it must face surplus. New products with health care function We can develop different products according to different consumer groups. Such as tea with health function Currently, milk tea is popular. We can produce more milk tea. Packaging milk tea seems impossible. If developed milk tea poured many times, it might attract more customers. In consider of sales, we can think about seller, promotion mode, brand culture and propaganda. Yeah, manufacturers cannot bear the cost of research and development. Training and recruiting the marketing person , For sales Jumping to a big sale can be effective? Tea bag can be poured repeatly? I think milk tea can be poured repeatly hasn't any practical implication. ? big sale will lose money Making some promotional activities. building the brand concept is pretty important. I think there no famous band in tea bag market. So, we can create our brand though donation. we can find some famous spokesmen. We heard people donate quilt and tents, but didn't hear donating tea bag. Donation CANNOT promote the brand. what kind of spokesperson will contribute to propaganda of tea bag? Spokesperson with positive image and effect is very important. So, they can play good publicity effect ? How to deal with excess productivity How about outsourcing? Big sale is not appropriate. We need to make money and this sales model is failure. Outsourcing the excess productivity. Too much inventory will waste the charges of the stock. Sales might let more consumers to know our brand. Outsourcing is work for others and brings in a little labor cost. It just reuse the excess productivity. Who is outsourcer? And then other people generate bias to our brand. It is CANNOT change such bad impression. Once our brand sold very cheaply, it is hard to rise in price. So, it cannot make such big sales. This pattern will hurt the brand building. Well, yeah, let's think about the others marketing strategy. Discussion: Tea bag manufacturer's dilemma Then the development of new product has certain theory and factual basis. to older man, slimming tea to young woman, and packaging milk tea. Discussion: Tea bag manufacturer's dilemma ☆ ☆ ☆ ☆ Manufacturers can develop new products. Try to develop new products. But I think that manufacturers must invest funds to do academic research. ? What new product we can try? ☆ ☆ ☆ ☆ New products with health care function ☆ ☆ ☆ ☆We can try to develop new flavors of products The research and development cannot be finished in one or two day. Now it must face surplus. yeah, manufacturers cannot bear the cost of research and development. ☆ ☆ ☆ ☆ We can develop different products according to different consumer groups. Such as tea with health function ☆ ☆ ☆ ☆Currently, milk tea is popular. We can produce more milk tea. Packaging milk tea seems impossible. ?Tea bag can be poured repeatedly? ☆ ☆ ☆ ☆If developed milk tea poured many times, it might attract more customers. I think milk tea can be poured repeatedly hasn't any practical implication. ☆ ☆ ☆ ☆ In consider of sales, we can think about sale, promotion mode, brand culture and propaganda. ☆ ☆ ☆ ☆ Training and recruiting the marketing person ☆ ☆ ☆ ☆ , For sales Jumping to a big sale can be effective ? ? Big sale will lose money Big sale is not appropriate. We need to make money and this sales model is failure. ☆ ☆ ☆ ☆Making some promotional activities. Building the brand concept is pretty important. I think there no famous band in tea bag market. ☆ ☆ ☆ ☆ So, we can create our brand though donation. We heard people donate quilt and tents, but didn't heard donating tea bag. ☆ ☆ ☆ ☆ So, we can create our brand though donation ? What kind of spokesperson will contribute to propaganda of tea bag? Spokesperson with positive image and effect is very important. So, they can play good publicity effect ☆ ☆ ☆ ☆ Too much inventory will waste the charges of the stock. Sales might let more consumers to know our brand. And then other people generate bias to our brand. It is CANNOT change such bad impression. Once our brand sold very cheaply, it is hard to rise in price. So, it cannot make such big sales. This pattern will hurt the brand building. Well, yeah, let's think about the others marketing strategy. ? ? How to deal with excess productivity How about outsourcing? ☆ ☆ ☆ ☆ Outsourcing the excess productivity. Outsourcing is work for others and bring in a little labor cost. it just reuse the excess productivity. ? Who is outsourcer? Then the development of new product has certain theory and factual basis. to older man, slimming tea to young woman, and packaging milk tea. Donation CANNOT promote the brand.

SOCIAL MEDIA FOR CUSTOMER ENGAGEMENT

52

slide-27
SLIDE 27

Social Media Analytics 27

Social Media Sources and Control

53 Source: Foresee 2010

Online Social Media Usage

A 2010 study of 99 franchisors’ web presence revealed:

54 Source: One Up Web 2010

slide-28
SLIDE 28

Social Media Analytics 28

Online Social Media Usage

55 Source: One Up Web 2010

The Conversion Funnel: e-Tailer

56

slide-29
SLIDE 29

Social Media Analytics 29

The Conversion Funnel: Social Media

57 Source: Peter Chang, http://webpersonas.blogspot.com/2011/01/deep-dive-into-social-media-conversion.html

SMR Study: How to get Retweeted

25% follow a brand 67% purchase from the brand they follow

58 Source: Malhotra et al. 2012

What Doesn’t Work Asking questions Hashtags Embedding links Contests What Works Leaving room Making it relevant/timely Providing practical information Offering deals Creating anticipation

slide-30
SLIDE 30

Social Media Analytics 30

?

59