"Predictive Analytics: The future of social media"
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David Uribe emphasizes the transformative potential of predictive analytics in social media marketing, arguing that current metrics often overlook the qualitative aspects of audience behavior. He advocates for mixing quantitative and qualitative data to uncover valuable patterns that can guide actionable strategies. The document presents various metrics and models to better assess engagement and effectiveness in digital marketing.
"Predictive Analytics: The future of social media"
- 1. Predictive Analytics The future of Social Media David Uribe - Global VP/Creative Director at Asylum Marketing LLC Co-founder & CMO at smartBeemo LLC Miami Ad School Professor @david_digital
- 2. Digital made everything measurable…
- 3. Then it became a constant transformation
- 4. Now, we just cannot agree on what success is…
- 5. Because we’re focused on numbers, forgetting than numbers are the consequence… not the cause.
- 6. Do we want to know/see numbers or identify behaviors?
- 7. How do you measure qualitative behaviors then?
- 8. BIG DATA “Extremely large data sets that may be analyzed computationally to reveal patterns, trends, and associations, especially relating to human behavior and interactions.”1 1: Source: Google
- 9. What is the right method to find those patterns? We’re not even looking for them. Client/Boss happy = Happy agency.
- 10. Digital Mktg 2015: Data rich - Insights poor
- 12. Intelligent Data vs Big Data Data - Patterns - Actions
- 13. How do we find valuable patterns?
- 14. Key: Mathematical Attribution Value to qualitative data (behavioral metrics) and enter the equation. To find patterns: Mix quantitative (n) data with qualitative (b) data by attributing a value to what you can’t directly measure / timeframe (t) P = n * b (f) / t
- 15. Existing metric vs Creating a new metric Welcome to the metrics revolution
- 16. Example I The social voice of a brand model Content Pillar A Content Pillar B Content Pillar C Content Pillar D 30% 30% 30% 10% Timeframe = 7+% Sustained Engagement
- 17. Example II Dynamic Fluctuating Weighing System (DFWS) Topic A: Product 33% Topic B: Gym 33% Topic C: Lifestyle 33%
- 18. Example III smartBeemo’s scoring metric Likes = 1 Comments = 3 Shares = 7 Now predict B*#ch!! Fav = 1 Rep = 3 RT = 7
- 19. Example IV View-ability engagement metric VEM = topic (x3) * ER * ATS / Tribe ER = L + C Avg time spent = #” Tribe = Targeted audience
- 21. Predicting is better than guessing
- 22. Science/Math rule the world It’s time for scientific social media marketing
- 23. Predict influential content Set the trend $
- 24. Let’s not forget the essentials...
- 25. Content: SoMe Marketing has to be
- 26. Meet the Bee-mo Team www.smartbeemo.com