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NVALUE
WORDS, TWEETS &
PERSONAS
NVALUE: THE PROCESS OF ADDING VALUE
2
ADDED
VALUE
NVALUE: THE PROCESS OF ADDING VALUE
3
Rough diamond Polished diamond
Data Insights
Some examples of how we
can your data
4
Cross-sell well Cross-sell poorly
CROSS SELLING ANALYSIS
5
IDENTIFY CROSS-SELLING PATTERNS AND OPTIMIZE THE STRATEGY (BUNDLING,
CO-MARKETING, RECOMMANDATION, ETC.)
Sales data
SAP, Webshop
eCommerce, etc.
Business Reports
& Dashboards
Operational focus
Cross-sell
Matrix
Strategic focus
CAMPAIGN PERFORMANCE ANALYSIS
6
IDENTIFY WHICH PARAMETERS PREDICT BETTER CONVERSION RATE IN ORDER TO
OPTIMIZE YOUR CAMPAIGNS MID-RUN
Campaign data
CRM, Marketing
automation, etc.
Business Reports
& Dashboards
Operational focus
Conversion
Analysis
Strategic focus
7
GROUPING OF FOLLOWERS BASED ON WORDS
ANALYSIS
SEGMENT YOUR TWITTER FOLLOWERS TO IDENTIFY PERSONAS TO TARGET
WITH YOUR MARKETING MESSAGE
Social media data
Twitter, Facebook,
LinkedIn, etc.
Social Media
Dashboards
Operational focus
Semantic
clustering
Strategic focus
AND MUCH MUCH MORE
 Basket analysis
 Clustering of customer based on buying patterns
 Competitors data analysis
 Trade-fairs/events analysis
 
All aiming to your data
8
NVALUE
WORDS, TWEETS &
PERSONAS
KEY CONCEPT:
The words we choose when
communicating paint a picture of
ourselves, even if we dont realise it
10
11
Like all the best families, we
have our share of eccentricities,
of impetuous and wayward
youngsters and of family
disagreements
YO wen u gettin da ps4 tings in
moss side? Aint waitin no more.
Plus da asian guy whu works
dere got bare attitude
#wasteman
I'm not even thinking of
weddings, cause obviously I'm
not divorced from the 'tranny' yet
WHO SAID WHAT?
12
Like all the best families, we
have our share of eccentricities,
of impetuous and wayward
youngsters and of family
disagreements
YO wen u gettin da ps4 tings in
moss side? Aint waitin no more.
Plus da asian guy whu works
dere got bare attitude
#wasteman
I'm not even thinking of
weddings, cause obviously I'm
not divorced from the 'tranny' yet
WHO SAID WHAT?
SIDE EFFECTS:
By looking at the words people choose we
can:
Learn about them
Group them
Engage them by using their language
13
14
GROUPING OF FOLLOWERS BASED ON WORDS
ANALYSIS
HOW IT WORKS: DATA RETRIEVAL
15
COLLECT FOLLOWERS DESCRIPTION AND STORE THEM IN A
TABLE
HOW IT WORKS: WORD FREQUENCY ANALYSIS
16
TRANSFORM EACH DESCRIPTION INTO A TABLE OF WORD-
FREQUENCIES
HOW IT WORKS: SEMANTIC CLUSTERING
17
COMPARE THE WORD-FREQUENCIES OF EACH FOLLOWER IN
ORDER TO GROUP TOGETHER PEOPLE WHO USE SIMILAR
WORDS
HOW IT WORKS: CREATION OF PERSONAS
18
FOR EACH GROUP CREATE AN EXAMPLE PERSONA
Avg. #Followers: 350
Avg. #Following: 675
Avg. #Tweets: 2300
Avg. #Followers: 2000
Avg. #Following: 8500
Avg. #Tweets: 17K
Avg. #Followers: 680
Avg. #Following: 3200
Avg. #Tweets: 1500
Some examples of how to use
the results
19
20
DESIGN SPECIFIC MESSAGES
DESIGN RELEVANT CONTENT/MESSAGES FOR THE IDENTIFIED
PERSONAS TAILORING TOPICS, WORDING, POSITIONING, ETC.
QUANTIFY AND PRIORITISE
PRIORITIZE CATEGORIES WITH BETTER CHANCE TO SPREAD YOUR
MESSAGE AND MONITOR FOLLOWER ACQUISITION
21
SELECT ADVOCATES
22
ENGAGE WITH DIGITAL ADVOCATES ONLINE (@MENTION) AND
OFFLINE (EVENTS, PARTNERSHIPS, PRESS RELEASES, ETC.)
REGIONALISE STRATEGY
23
OPTIMISE REGIONAL ACTIVITIES IN THE LIGHT OF
GEOGRAPHICAL DISTRIBUTION OF FOLLOWERS
Want to discover more?
Get in touch
Mail: igor@nvalue.net
Twitter: @NValueAnalytics
24
25

More Related Content

NVALUE Analytics - Words, tweets and personas

  • 2. NVALUE: THE PROCESS OF ADDING VALUE 2 ADDED VALUE
  • 3. NVALUE: THE PROCESS OF ADDING VALUE 3 Rough diamond Polished diamond Data Insights
  • 4. Some examples of how we can your data 4
  • 5. Cross-sell well Cross-sell poorly CROSS SELLING ANALYSIS 5 IDENTIFY CROSS-SELLING PATTERNS AND OPTIMIZE THE STRATEGY (BUNDLING, CO-MARKETING, RECOMMANDATION, ETC.) Sales data SAP, Webshop eCommerce, etc. Business Reports & Dashboards Operational focus Cross-sell Matrix Strategic focus
  • 6. CAMPAIGN PERFORMANCE ANALYSIS 6 IDENTIFY WHICH PARAMETERS PREDICT BETTER CONVERSION RATE IN ORDER TO OPTIMIZE YOUR CAMPAIGNS MID-RUN Campaign data CRM, Marketing automation, etc. Business Reports & Dashboards Operational focus Conversion Analysis Strategic focus
  • 7. 7 GROUPING OF FOLLOWERS BASED ON WORDS ANALYSIS SEGMENT YOUR TWITTER FOLLOWERS TO IDENTIFY PERSONAS TO TARGET WITH YOUR MARKETING MESSAGE Social media data Twitter, Facebook, LinkedIn, etc. Social Media Dashboards Operational focus Semantic clustering Strategic focus
  • 8. AND MUCH MUCH MORE Basket analysis Clustering of customer based on buying patterns Competitors data analysis Trade-fairs/events analysis All aiming to your data 8
  • 10. KEY CONCEPT: The words we choose when communicating paint a picture of ourselves, even if we dont realise it 10
  • 11. 11 Like all the best families, we have our share of eccentricities, of impetuous and wayward youngsters and of family disagreements YO wen u gettin da ps4 tings in moss side? Aint waitin no more. Plus da asian guy whu works dere got bare attitude #wasteman I'm not even thinking of weddings, cause obviously I'm not divorced from the 'tranny' yet WHO SAID WHAT?
  • 12. 12 Like all the best families, we have our share of eccentricities, of impetuous and wayward youngsters and of family disagreements YO wen u gettin da ps4 tings in moss side? Aint waitin no more. Plus da asian guy whu works dere got bare attitude #wasteman I'm not even thinking of weddings, cause obviously I'm not divorced from the 'tranny' yet WHO SAID WHAT?
  • 13. SIDE EFFECTS: By looking at the words people choose we can: Learn about them Group them Engage them by using their language 13
  • 14. 14 GROUPING OF FOLLOWERS BASED ON WORDS ANALYSIS
  • 15. HOW IT WORKS: DATA RETRIEVAL 15 COLLECT FOLLOWERS DESCRIPTION AND STORE THEM IN A TABLE
  • 16. HOW IT WORKS: WORD FREQUENCY ANALYSIS 16 TRANSFORM EACH DESCRIPTION INTO A TABLE OF WORD- FREQUENCIES
  • 17. HOW IT WORKS: SEMANTIC CLUSTERING 17 COMPARE THE WORD-FREQUENCIES OF EACH FOLLOWER IN ORDER TO GROUP TOGETHER PEOPLE WHO USE SIMILAR WORDS
  • 18. HOW IT WORKS: CREATION OF PERSONAS 18 FOR EACH GROUP CREATE AN EXAMPLE PERSONA Avg. #Followers: 350 Avg. #Following: 675 Avg. #Tweets: 2300 Avg. #Followers: 2000 Avg. #Following: 8500 Avg. #Tweets: 17K Avg. #Followers: 680 Avg. #Following: 3200 Avg. #Tweets: 1500
  • 19. Some examples of how to use the results 19
  • 20. 20 DESIGN SPECIFIC MESSAGES DESIGN RELEVANT CONTENT/MESSAGES FOR THE IDENTIFIED PERSONAS TAILORING TOPICS, WORDING, POSITIONING, ETC.
  • 21. QUANTIFY AND PRIORITISE PRIORITIZE CATEGORIES WITH BETTER CHANCE TO SPREAD YOUR MESSAGE AND MONITOR FOLLOWER ACQUISITION 21
  • 22. SELECT ADVOCATES 22 ENGAGE WITH DIGITAL ADVOCATES ONLINE (@MENTION) AND OFFLINE (EVENTS, PARTNERSHIPS, PRESS RELEASES, ETC.)
  • 23. REGIONALISE STRATEGY 23 OPTIMISE REGIONAL ACTIVITIES IN THE LIGHT OF GEOGRAPHICAL DISTRIBUTION OF FOLLOWERS
  • 24. Want to discover more? Get in touch Mail: igor@nvalue.net Twitter: @NValueAnalytics 24
  • 25. 25