This study uses content analysis to develop a key performance indicator based on customers’ word-of-mouse, collected randomly from three different social networking and user review sites. Using LIWC text-based analysis software, we conducted a content analysis of over 420 customers’ reviews and comments containing a reference to one of Nokia smartphones models. Our results show that each of Nokia smartphones models has a unique sentiment multi-dimensional profile. Thus, there are differences in customers’ attitudes toward Nokia products. Later, we based on this fact to build consumers’ attitudes index to measure Nokia’s performance from customers’ perspectives over time. Moreover, we go further by using this index to build an early warning system, which could lead Nokia to identify any potential improvements or even to predict potential future developments.

1. Social Media footprint:
Developing a Key performance indicator based on word-of-mouse
A CASE STUDY OF NOKIA
Supervised by: Prof. Dr. Annouk Lievens
Prepared by:
Osama Dukhan

2. Outlines:
- Aim of study
- Research questions and answers
- Limitations
- Suggestions for future researches
- Conclusions

3. Aim of study:
The main goal in this research is finding a tool could help firms to see more
through the eyes of customer, thereby act faster.

4. Aim of study:
“See More
Act Faster”

5. Automated
Sentiment
Analysis
Social Media
Field of
Study
Performance
management
Figure (1) Field of study

6. Research Questions:
 How we can use word-of-mouse to generate an accurate quantitative
indicator?
 Is it possible to measure the performance of the firm based on this KPI?
 To what extent can we use this KPI to drive the Firm’s performance?

7. Research Questions:
 How we can use word-of-mouse to generate an accurate quantitative
indicator?
 Is it possible to measure the performance of the firm based on this KPI?
 To what extent can we use this KPI to drive the Firm’s performance?

8. KPI Developing
Data Cleansing
• Facebook
• Amazon.com
• C|net
Data Collection
Data Testing
Using WOM to generate an accurate quantitative indicator.

9. KPI Developing
Data Cleansing
• Misspelling.
• The quality of
reviews/comments
Data Collection
Data Testing
Using WOM to generate an accurate quantitative indicator.

10. KPI Developing
Data Cleansing
• Consumer’s feelings
and attitudes toward
different products
Data Testing
Data Collection
Data Testing
Using WOM to generate an accurate qualitative indicator.

11. Multi-dimensional profiles
of Nokia Smartphones
Anger

12. KPI Developing
Data Cleansing
• PA Positive attitudes index
• NA Negative attitudes Index
Data Testing
Data Collection
Data Testing
Using WOM to generate an accurate qualitative indicator.

13. KPI Developing
Data Cleansing
• Facebook
• Amazon.com
• C|net
Data Collection
• Misspelling.
• The quality of
reviews/comments
• Consumer’s feelings
and attitudes toward
different products
Data Testing
Moving from Qualitative data to Quantitative data
• Positive attitudes PA
index
• Negative attitudes NA
Index

14. Research Questions:
 How we can use word-of-mouse to generate an accurate quantitative
indicator?
 Is it possible to measure the performance of the firm based on this KPI?
 To what extent can we use this KPI to drive the Firm’s performance?

15. # 1st Step
• Facebook
• Amazon.com
• C|net
Data Collection
Data Cleansing
• Misspelling.
• The quality of
reviews/comments
# 2nd Step
• Consumer’s feelings
and attitudes toward
different products
Data Testing
KPI Developing
• PA Index
• NA Index
• Time variable as
a moderator
Performance
Measurement

16. #
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Nokia - Smartphone
Name of product (Model) Announce date Release date
Lumia 925
May, 2013
June, 2013
Lumia 928
April, 2013
May, 2013
Lumia 720
Feb, 2013
April, 2013
Lumia 520
Feb, 2013
April, 2013
Lumia 505
Dec, 2012
Jan,2013
Lumia 620
Dec, 2012
Jan,2013
Lumia 822
Oct,2012
Nov,2012
Lumia 510
Oct,2012
Nov,2012
Lumia810
Oct,2012
Nov,2012
Lumia 920
Sep,2012
Nov,2012
Lumia 820
Sep,2012
Nov,2012
Lumia 610 NFC
April, 2012
Sep,2012
800C
Mar, 2012
Sep,2012
808 Pure view
Feb, 2012
June, 2012
Lumia 900
Feb, 2012
May,2012
Nokia – Smartphones released over one year

17. #
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Nokia - Smartphone
Name of product (Model) Announce date Release date
Lumia 925
May, 2013
June, 2013
Lumia 928
April, 2013
May, 2013
Lumia 720
Feb, 2013
April, 2013
Lumia 520
Feb, 2013
April, 2013
Lumia 505
Dec, 2012
Jan,2013
Lumia 620
Dec, 2012
Jan,2013
Lumia 822
Oct,2012
Nov,2012
Lumia 510
Oct,2012
Nov,2012
Lumia810
Oct,2012
Nov,2012
Lumia 920
Sep,2012
Nov,2012
Lumia 820
Sep,2012
Nov,2012
Lumia 610 NFC
April, 2012
Sep,2012
800C
Mar, 2012
Sep,2012
808 Pure view
Feb, 2012
June, 2012
Lumia 900
Feb, 2012
May,2012
Nokia – Smartphones released over one year

18. #
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Nokia - Smartphone
Name of product (Model) Announce date Release date
Lumia 925
May, 2013
June, 2013
Lumia 928
April, 2013
May, 2013
Lumia 720
Feb, 2013
April, 2013
Lumia 520
Feb, 2013
April, 2013
Lumia 505
Dec, 2012
Jan,2013
Lumia 620
Dec, 2012
Jan,2013
Lumia 822
Oct,2012
Nov,2012
Lumia 510
Oct,2012
Nov,2012
Lumia810
Oct,2012
Nov,2012
Lumia 920
Sep,2012
Nov,2012
Lumia 820
Sep,2012
Nov,2012
Lumia 610 NFC
April, 2012
Sep,2012
800C
Mar, 2012
Sep,2012
808 Pure view
Feb, 2012
June, 2012
Lumia 900
Feb, 2012
May,2012
Nokia – Smartphones released over one year

19. 6
5
4
3
2
1
0
May-12
Jun-12
Jul-12
Aug-12
Sep-12
Oct-12
Nov-12
Positive Attitudes or PA index
Dec-12
Jan-13
Feb-13
Mar-13
Apr-13
May-13
Jun-13
Negative Attitudes or NA Index
Figure 9: The volatility of PA index and NA index over one year

20. 6.00
5.00
4.00
3.00
2.00
1.00
0.00
May-12
June-12
July-12
August-12 September- October-12 November- December12
12
12
Positive attitudes index
January-13 February-13 March-13
April-13
Negeative Attitudes Index
Figure 7: NOKIA's Customers attitudes over one year
May-13
June-13

21. Research Questions:
 How we can use word-of-mouse to generate an accurate quantitative
indicator?
 Is it possible to measure the performance of the firm based on this KPI?
 To what extent can we use this KPI to drive the Firm’s performance?

22. # 2nd Step
# 3rd Step
Performance Drivers
• Time variable as a
moderator
Performance
Measurement
• Developing early
warning system

23. Warning System:
 We identified many alarm signals during the study period.
 We conducted a simple comparison between Nokia’ stock performance
(NOK), as a one of financial KPI, and consumers’ attitudes index as a nonfinancial KPI.
 We found that it could reflect to what extent both financial and nonfinancial indicators can work together harmoniously.

24. 6.00
5.00
4.00
3.00
2.00
1.00
0.00
May-12
June-12
July-12
August-12 September- October-12 November- December12
12
12
Positive attitudes index
January-13 February-13 March-13
April-13
Negeative Attitudes Index
Figure 7: NOKIA's Customers attitudes over one year
May-13
June-13

25. 6.00
5.00
4.00
3.00
2.00
1.00
0.00
May-12
June-12
July-12
August-12 September- October-12 November- December12
12
12
Positive attitudes index
January-13 February-13 March-13
April-13
Negeative Attitudes Index
Figure 7: NOKIA's Customers attitudes over one year
May-13
June-13

26. NOKIA’s Stock performance
during the period (1st of June, 2012 till 15th of August, 2013)

27. 6.00
5.00
4.00
3.00
2.00
1.00
0.00
Positive attitudes index
Negeative Attitudes Index
Early warning system

28. 6.00
5.00
4.00
3.00
2.00
1.00
0.00
Positive attitudes index
Negeative Attitudes Index
Early warning system

29. Pros and Cons:
 We used word-of-mouse to generate an accurate quantitative indicator.
 We measured the performance of Nokia based on this KPI.
 We used this KPI to build a warming system.

30. Limitations:
We can classify these limitations into Three different categories :
 Limitations related to data.
Limitations related to the selected sample.
Limitations related to the methodology.

31. Limitations:
We can classify these limitations into three different categories :

Limitations related to data.
Limitations related to the selected sample.
Limitations related to the methodology.

32. Limitations related to data:
Example :
“Dropped it from a two feet high and the screen broke, too
sad that I have been using it for only one month :( ”
Reason of ignore:
Based on LIWC2007 dictionaries, this text will be considered as a
negative review, but in fact, the implicit feeling refers to high loyalty and
positive attitudes toward this Smart phone.

33. Limitations:
We can classify these limitations into three different categories :
 Limitations related to data.
 Limitations related to selected sample.
Limitations related to the methodology.

34. Limitations related to the selected sample.
Not all online discussion groups or user review sites have reviews
regard all set of products (models) which were selected in our
sample.
Furthermore, this research was conducted only on a small size of
population. Therefore, to generalized the result, the bigger size of
sample, the better.

35. Limitations:
We can classify these limitations into three different categories :
 Limitations related to data.
Limitations related to selected sample.

Limitations related to the methodology.

36. Limitations related to the methodology.
we used the release date of products as a milestone over the study
period. However, these milestones were not always divided by
equal time lags because launching products to the market was not
conducted based on a regular time base. This point could generate
a bit of confusion, especially when we conducted a comparison
between Nokia’ stock performance (NOK) and consumers’
attitudes index.

37. 6
5
4
3
2
1
0
May-12
Jun-12
Jul-12
Aug-12
Sep-12
Oct-12
Nov-12
Positive Attitudes or PA index
Dec-12
Jan-13
Feb-13
Mar-13
Apr-13
May-13
Jun-13
Negative Attitudes or NA Index
Figure 9: The volatility of PA index and NA index over one year

38. Suggestion for the future researches:
it becomes to invest these techniques to assess customer opinions and to
identify what topics customers are talking about, and that will be useful to
characterize the opinions that they express about those topics.
It is a due time to identify a wide range of opinion expressions, including
motivations, recommendations and speculations.
It is a due time to use word-of-mouse as a tool to driving firms’
performance not just use it as a performance measurement.

39. Conclusion:
• In contrast with many studies, this research mainly focused on the
‘second way’ of social media as a communication channel, by trying to
answer the question; to how extent we can use the data collected
through customers’ review and comments to develop a KPI.
• this indicator could be an easy, timely, and inexpensive source of
information for many stakeholders, like the management, customers,
competitors, shareholders, consumer protection funds and so on.

40. Conclusion:
• Finally, we have to admit that developing KPI through the eyes of
customer could be a more promising investment for:
• performance-focused companies which have Business-to-Customer
(B2C) business model and their products have a plenty amount of
customers’ reviews and comments over various online discussion
groups and user review sites.

41. Thanks for your attention !

42. Social Media footprint:
Developing a Key performance indicator based on word-of-mouse
A CASE STUDY OF NOKIA
For any question; Please don’t Hesitate to contact me
Ody.edu@hotmail.com