NVALUE Analytics - Words, tweets and personas
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The document discusses the process of utilizing data analytics to enhance marketing strategies, including cross-selling analysis and follower segmentation based on word usage. It emphasizes the importance of understanding customer personas to tailor marketing messages and optimize campaign performance. Additionally, it highlights various analytical techniques like semantic clustering and word frequency analysis to engage target audiences effectively.
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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
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