Garbage in, garbage out, presented by David Rabjohns
•
1 gefällt mir•839 views
The document is a presentation about improving social media measurement accuracy by focusing on data quality. It discusses how understanding data sources, organizing data taxonomies around consumer interests, using advanced linguistic analysis of text, and statistical modeling of relationships between advocacy, sentiment, and real-world outcomes can provide better insights from social media for brands. Garbage in leads to garbage out, so high quality data processing is key to accurate social media measurement.
Empfohlen
Empfohlen
Weitere ähnliche Inhalte
Ähnlich wie Garbage in, garbage out, presented by David Rabjohns
Ähnlich wie Garbage in, garbage out, presented by David Rabjohns (20)
Mehr von SocialMedia.org
Mehr von SocialMedia.org (20)
Kürzlich hochgeladen
Kürzlich hochgeladen (20)
Garbage in, garbage out, presented by David Rabjohns
- 1. SOCIALMEDIA.ORG/SUMMIT2016ORLANDOJANUARY 25–27, 2016 Garbage in, garbage out: What you don’t know could be destroying your measurement accuracy DAVID RABJOHNS MOTIVEQUEST
- 2. GARBAGE IN GARBAGE OUT What you don’t know could be destroying your measurement accuracy. Social Data
- 4. 3©MOTIVEQUEST 1/25/16 MOTIVEQUEST: We have been doing this for over ten years. I am going to share the secrets we have learned. COVERAGE MODELS CUSTOMER SERVICE The most strongly impassioned conversations about carrier loyalty were about customer service,models and coverage. Shhhhh don’t tell anyone……
- 5. Q. What affects accuracy?
- 6. 5©MOTIVEQUEST 1/25/16 CONTENT Data TermsOrganization Analytics HIGH QUALITY HIGH QUALITY INSIGHTS =
- 7. 6©MOTIVEQUEST 1/25/16 DATA SOURCES: Better inputs give better insights.
- 8. 7©MOTIVEQUEST 1/25/16 SOURCE UNDERSTANDING: Helps improve insights and media strategy Social Networks Best for understanding how consumers interact with brands Micro-blogs Best for monitoring news, memes, and content of interest Personal blogs Fairly good for deep behavioral insights, but watch spam Multi-media sites Best for complementary behavioral insights Best for deep behavioral insights Forums, Message Boards Photo sharing sites Best for complementary behavioral insights
- 9. 8©MOTIVEQUEST 1/25/16 SOCIAL MEDIA: Consumers are discerning in where they talk. We must be discerning in where we listen.
- 10. 9©MOTIVEQUEST 1/25/16 KEY QUESTION: What is the data being used for? SOCIAL MONITORING HUMAN INSIGHTS Focuses on Twitter and Facebook Tracks and measures shares, likes, retweets Emphasizes real-time updates Informs PR and social strategy Looks at the “now and ongoing” Uncovers social media usage patterns SOCIAL MEDIA MEASUREMENT Focuses on forum, board, and blog communities Tracks and measures opinions and attitudes Emphasizes deep understanding Informs brand positioning and communication Allows historical trending and comparisons Uncovers attitudes and emotions VS
- 11. 10©MOTIVEQUEST 1/25/16 CONTENT Data TermsOrganization Analytics HIGH QUALITY
- 12. 11©MOTIVEQUEST 1/25/16 CONSUMER-LED TAXONOMY: The data also needs to be organized to help with the analysis. LIFESTYLE 34 million PARENTING 40 million AUTOMOTIVE 43 million GAMING 68 million TECHNOLOGY 42 million CAUSES 23 million HOBBIES 29 million OUTDOORS 25 million FITNESS 20 million TRAVEL 15 million PROFESSIONALS 26 million PERSONAL FINANCE 14 million Our 20-cohort taxonomy includes groupings such as the following (shown with annual conversation volume):
- 13. 12©MOTIVEQUEST 1/25/16 CONSUMER COHORTS: By following consumers’ own natural clustering behavior, you can quickly identify unrecognized segments and product opportunities.SHAREOFPICKLE-TALK
- 14. 13©MOTIVEQUEST 1/25/16 BEYOND BRANDS: By harvesting communities in their entirety, you are able to look beyond brands, to conversations about behaviors. Brand Mentions 5% No Brand Mentions 95% FOOD Brand Mentions 50% No Brand Mentions 50% FINANCIAL SERVICES No Brand Mentions 89% Brand Mentions 11% HEALTH & MEDICAL No Brand Mentions 86% Brand Mentions 14%
- 15. 14©MOTIVEQUEST 1/25/16 WordMaps surface words that characterize conversation about a chosen topic by comparing the frequency of word usage in conjunction with the chosen topic with the words’ prevalence in the broader conversation. The relative size of the bubbles indicates the relative frequency of word usage. Proximity to the center term indicates strength of correlation. NEW CONTENT: For example Kraft-Heinz’ brands identify new consumer behaviors and create new content around them.
- 16. 15©MOTIVEQUEST 1/25/16 CONTENT Data TermsOrganization Analytics HIGH QUALITY
- 17. 16©MOTIVEQUEST 1/25/16 LINGUISTIC STRINGS: Strong search terms filter out false positives and incorporate consumers’ misspellings and acronyms. bacon cholesterol AND returns will include “bacon” and “cholesterol” bacon sausage OR returns will include either “bacon” or “sausage” bacon kevin AND NOT returns will include “bacon” but not “kevin”
- 18. 17©MOTIVEQUEST 1/25/16 ((("Canon" OR canons) AND (camera OR cameras OR "point and shoot" OR "slr" OR "dslr" OR mirrorless OR cam OR cams)) OR ((A1100 AND (Canon OR Canons OR camera OR cameras)) OR A1100IS) OR ((A3000 AND (Canon OR Canons OR camera OR cameras)) OR A3000IS) OR ((A3100 AND (Canon OR Canons OR camera OR cameras)) OR A3100IS) OR (A480 AND (Canon OR Canons OR camera OR cameras)) OR (A490 AND (Canon OR Canons OR camera OR cameras)) OR (A495 AND (Canon OR Canons OR camera OR cameras)) OR (D10 AND (Canon OR Canons OR camera OR cameras)) OR (G11 AND (Canon OR Canons OR camera OR cameras)) OR (G12 AND (Canon OR Canons OR camera OR cameras)) OR ("PowerShot" OR "powershots" OR "power shot" OR "power shots") OR (S90 AND (Canon OR Canons OR camera OR cameras)) OR (S95 AND (Canon OR Canons OR camera OR cameras)) OR ((SD1200 AND (Canon OR Canons OR camera OR cameras)) OR "SD1200IS" OR ("sd 1200" AND (Canon OR Canons OR camera OR cameras)) OR "sd 1200IS") OR (("SD1300" AND (Canon OR Canons OR camera OR cameras)) OR "SD1300IS" OR ("sd 1300" AND (Canon OR Canons OR camera OR cameras)) OR "sd 1300IS") OR ((SD1400 AND (Canon OR Canons OR camera OR cameras)) OR "sd1400IS" OR ("sd 1400" AND (Canon OR Canons OR camera OR cameras)) OR "sd 1400IS") OR (("SD3500" AND (Canon OR Canons OR camera OR cameras)) OR "sd3500 IS" OR ("sd 3500" AND (Canon OR Canons OR camera OR cameras)) OR "sd 3500IS") OR ((SD4000 AND (Canon OR Canons OR camera OR cameras)) OR "SD4000 IS" OR "sd 4000 is" OR ("sd 4000" AND (Canon OR Canons OR camera OR cameras))) OR ("SD4500IS" OR (SD4500 AND (Canon OR Canons OR camera OR cameras)) OR ("sd 4500" AND (Canon OR Canons OR camera OR cameras)) OR "sd 4500is") OR (("SD780" AND (Canon OR Canons OR camera OR cameras)) OR "sd780IS" OR "sd 780IS" OR ("sd 780" AND (Canon OR Canons OR camera OR cameras))) OR (("SD940" AND (Canon OR Canons OR camera OR cameras)) OR "sd940IS" OR ("sd 940" AND (Canon OR Canons OR camera OR cameras)) OR "sd 940is") OR ((SD960 AND (Canon OR Canons OR camera OR cameras)) OR "sd960IS" OR ("sd 960" AND (Canon OR Canons OR camera OR cameras)) OR "sd 960IS") OR (("SD980" AND (Canon OR Canons OR camera OR cameras)) OR "SD980 IS" OR ("sd 980" AND (Canon OR Canons OR camera OR cameras)) OR "sd 980IS") OR ((sx120 AND (Canon OR Canons OR camera OR cameras)) OR "sx120is") OR (("SX130" AND (Canon OR Canons OR camera OR cameras)) OR sx130is) OR (("SX20" AND (Canon OR Canons OR camera OR cameras)) OR sx20is) OR (("SX200" AND (Canon OR Canons OR camera OR cameras)) OR sx200is) OR (("SX210" AND (Canon OR Canons OR camera OR cameras)) OR "sx210is") OR (("SX30" AND (Canon OR Canons OR camera OR cameras)) OR SX30IS) OR((Rebel OR Rebels) AND (EOS OR Canon OR Canons)) OR (t1i OR t1ief OR t1iefs) OR (t2i OR "t2ief" OR t2iefs) OR ("rebel x") OR ("Rebel XS") OR (EOS OR EOSs) OR ("EOS 1D" OR (Canon AND 1D)) OR ("EOS-1Ds" OR (Canon AND 1Ds)) OR ("EOS-50D" OR (Canon AND 50D)) OR ("EOS-5D" OR (Canon AND 5D) OR 5D2) OR ("EOS-60D" OR (Canon AND 60D)) OR ("EOS-7D" OR (Canon AND 7D) OR 7D2)) EXAMPLE: It’s not for everyone, but it’s worth spending a lot of time on linguistics and creating an internal library.
- 19. 18©MOTIVEQUEST 1/25/16 CONTENT Data TermsOrganization Analytics HIGH QUALITY
- 20. 19©MOTIVEQUEST 1/25/16 ANALYTICS: Lots of data does not predict behavior. MODEL B ADVOCACY & SALES RELATIONSHIP Model D Units Sold Model D Share of Advocacy MotiveQuest Online Promoter Score Developed in collaboration with Northwestern University.
- 21. 20©MOTIVEQUEST 1/25/16 $150,000 $160,000 $170,000 $180,000 $190,000 $200,000 $210,000 $220,000 $230,000 $240,000 Actual Average Store Sales Advocacy-Driven Sales Prediction *The P-Value of the relationship between sales and advocates is 0.002 RECOMMENDATIONS ANALYSIS: Works in low interest categories also
- 22. 21©MOTIVEQUEST 1/25/16 ADVOCATES CORRELATED BEST WITH SALES: Our regression suggests that Advocacy is far more likely to drive sales than Detraction or Net Sentiment. Variable measured against MONTHLY SALES P-Value Monthly Advocates .04 Monthly Detractors .11 Monthly Net Sentiment .40 • A P-Value is a measure of how likely it is that the relationship between the variables could simply be noise in the data. Lower is better. • The higher the value, the more likely it is that the relationship is attributable to randomness, and relationships where the value is greater than 0.1 are considered spurious. • When the P-value is between 0.01 and 0.05, there is a strong presumption that there is a real relationship. Better
- 23. 22©MOTIVEQUEST 1/25/16 REMEMBER: Approach research like scientists. They look for the presence or absence of evidence supporting hypotheses, rather than just reporting the news.
- 24. 23©MOTIVEQUEST 1/25/16 “There is nothing new under the sun, only old things we don’t know.” Ambrose Bierce
- 26. Learn more about past and upcoming events SOCIALMEDIA.ORG/EVENTS SOCIALMEDIA.ORG/SUMMIT2016ORLANDOJANUARY 25–27, 2016