NoLimitID - Case Study from Social Media Monitoring & Analytic Tools

SOCIAL MEDIA MONITORING & ANALYTIC TOOLS

Case Study

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SOCIAL MEDIA

SOCIAL MEDIA

03

SOCIAL MEDIA

04

HISTORY

Follower Engagement Response ?

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HISTORY

Social Media

Data Metric Pattern Knowledge

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SOCIAL MEDIA SOLUTION

Tools Expert Solution

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HOW IT WORKS

Brainstorming

Objective Identification

Research & Assessment

Strategy Building

Execution

Measurement & Evaluation

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OBJECTIVE

• Performance Assessment

• Customer Service

• Competition Landscape

• Customer Understanding

• Campaign Assisting

• Industry Trend

• Channel Optimization

• Reputation Management

• Product Decision Journey

• Content Marketing Optimization

STUDY CASE

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INDEX

• Banking

• Pharmaceutical and Healthcare

• Entertainment

BANKING

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BANKING

Type

Pre-Campaign Assisting

Background

Bank A is one of the biggest Bank Indonesia. In order to develop a product that suits people need, Bank A need to understand the people’s behavior

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BANKING

Challenge

Indonesia demography currently is dominated by young people who become the focus in the product development

Objective

Understanding and comparing people’s financial behavior in terms of spending and saving

Time frame: 13-20 March 2014

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BANKING

Word Analysis

• Defining Twitter keywords that suit with saving and spending behavior

Buzz Monitoring

• Crawling all tweets which contain defined keywords

Peak Time Analysis

• Looking into incoming mention number enhancement in some period of time

Method

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KEYWORD ANALYS

SAVING

Deposito DebitReksaDana

Tabungan Investasi

SPENDING

Dugem Nongkrong Belanja

Liburan Nonton

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SAVING VS SPENDING BUZZ

30393

16240

9179

3495

875

130

798406

167622

148025

61978

8740

0 100000 200000 300000 400000 500000 600000 700000 800000 900000

Nonton

Liburan

Belanja

Nongkrong

Dugem

Investasi

Asuransi

Tabungan

Reksa Dana

Debit

Deposito

Spending Saving

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SPENDING TRENDS

• Play

• Go to vacation to Bali and EuropeLiburan

• Movies

• TelevisionNonton

• Clothes

• FoodBelanja

• Drinking Coffee

• Surfing InternetNongkrong

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SPENDING TRENDS

12.00-16.00 16.00-20.00 20.00-24.00

Sunday Belanja Liburan

Monday Nonton Nonton

Tuesday Nongkrong

Thursday Liburan

Liburan

Nongkrong

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BANKING

Result

• People more interested on spending than on saving, proved by total conversation and tweet share of spending much higher than saving. Conversation about saving is only 5% of spending conversation.

• People like to talk about spending, mostly about shopping, going holiday, watching movies and hanging out, after 12 p.m. on some days.

LESSON LEARNED

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Determining the right keyword is vital as it will greatly affect the monitoring result

• The result of wrong keyword will not related to the research’ topic. Thus, It has no value and useless

PHARMACEUTICAL & HEALTHCARE

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PHARMACEUTICAL AND HEALTHCARE

Type

Customer Understanding

Background

A pharmacy company Y has a facebook fanpage Y Health Page and Twitter @yealthwhich are used to build a community focus on healthcare. Y didn’t use the social neither to promote nor selling their product

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Challenge

Increase customer loyalty by giving information which suit to community’s interest

Objective

Understanding the behavior of the active member of X Health community

Time frame: 15-26 September 2014

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Method

Media Monitoring

• Measuring every social media metric of Y Health

Media Comparison

• Comparing community performance of each Y Health social media which is Facebook and Twitter

Social Media Ethnography

• Analyzing community members characteristic by implementing ethnography method on social media

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MEDIA MONITORING AND COMPARISON

The Facebook account drives more conversation than Twitter account

3.38%

0.50%

0.00%

0.50%

1.00%

1.50%

2.00%

2.50%

3.00%

3.50%

4.00%

Engagement Rate

Facebook (106.232) Twitter (2.936)

ETNOGRAPHY

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Ethnography (n) : sociological method

that explores how people live and

make sense of their lives with one

another in particular places

(Columbia University)

ETHNOGRAPHY FRAMEWORK

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Space Objects Acts

Actors Time Activities

Feelings Events Goals

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CASE STUDY: TIME, GOALS, FEELING

Morning(6-10)

Noon(10-14)

Afternoon(14-18)

Night(18-22)

Participating in engagement activities

50% 0% 100%

Sharing Experience -20% 55.6% 14%

Asking Further Question -66.7% -100% -100%

Praising and Saying Thank You

100% 100% 100%

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Result

• Building community engagement on Facebook is giving better result than

on Twitter.

• The best time of publishing post to get better sentiment is at night,

meanwhile the bad time is at noon. The best sentiment score is gotten by

praising activities. On the other hand the worst sentiment score is gotten

by asking further question activities.

LESSON LEARNED

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• Social Media could be used as a tool to determine the ethnography of a particular group

• The advantage of using Social Media compared to field monitoring is data trackback ability that could speed up the monitoring process

ENTERTAINMENT

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ENTERTAINMENT

Type

Channel Optimization

Background

Singing competition X gives audience the feeling of being judge by giving them a mobile application that can be used to give vote which account for 70% of the contestant final score. The mobile app is integrated with Twitter and will automatically tweet and mention @AjangX when a vote is given.

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ENTERTAINMENT

Challenge

Activation of Audience who mostly are young people with smartphone and a social media avid user

Objective

Identify the effect of mobile app usage towards the social media account of @ajangX

Time frame: 19 Oktober-8 November 2014

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ENTERTAINMENT

Metode yang digunakan

Buzz Monitoring

• Getting all mentions to @ajangX

Peak Time Analysis

• Looking into incoming mention number enhancement in some period of time

Mention Categorization

• Categorizing incoming mention into organic mention and mention from official app

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PEAK TIME

0

5000

10000

15000

20000

25000

30000

35000

40000

Mention

The number of mentioner increase rapidly (40-60x) on Friday

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PARTICIPANT MENTION CATEGORIZATION

• App : Tweet that comes from Ajang X’s official app. Marked by “I Voted YES/NO” phrase on the tweet

• Organic: General tweet from people using other type of application

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ADOPTION RATE

33% of mentioner is using AjangX official app to vote for their idol

33%

67%

App User Non-User

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MENTION DISTRIBUTION

The tweet from app account for 25% of all mentions to @ajangX during

the performance day

7737 8002 7351

27644 23729 28481

0

5000

10000

15000

20000

25000

30000

35000

40000

1st week 2nd week 3rd week

App Organic

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VOTING PERFORMANCE

Most people use the app to support their idol instead of bringing down

the competitors

73457633

8322392

365

357

0

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000


YES NO

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VOTING PEAK TIME

Most people use their vote between 22.00-23.00. The number of votes is very dependent on the contestant that was performing

0

500

1000

1500

2000

2500

3000

3500

4000

4500

21.00-22.00 22.00-23.00 23.00-24.00 24.00-01.00


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ENTERTAINMENT

Hasil

• Enhancement of total mention to @ajangX account is 60 times bigger on the show day than usual and 50% of it are from mobile application used for voting

LESSON LEARNED

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Co-creation can be implemented by integrating mobile app with social media.

• Social media give reason for people to download and use the app because they like to show off their experience

• People also have a chance to appear on national television when they give their vote

• Mobile app is one of ways in conducting co-creation. Besides, AjangX also conducting social media campaign and meet & greet with idol to build the co-creation ecosystem

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In the end, social media monitoring is just a tool.

Sometimes we don’t really need a sophisticated

and complicated metric. What we really need

though, is a great methodology

There is NoLimit in the future

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