Folashade Uba

Analysing Customer Insight, Unleashing SEO Potentials From Support Queries.

Transcript

1. Analysing Customer Insight, Unleashing SEO Potentials From Support Queries. Folashade Uba AP MOLLER - MAERSK @folashadeuba Speakerdeck.com/folashadeuba
4. Contact Form Email Calls
5. How to Leverage Customer Insights in Our SEO EFFORTS
6. “It’s important to remember that your website is not about YOU. It’s about YOUR VISITORS and their NEEDS” Derek Halpern
7. VALUE SALES CONVERSION
8. Why should we CARE about this as SEOs?
9. Understanding Customer Needs and Pain Points
10. Understanding Customer Needs and Pain Points Keyword and Topic Discovery
11. Understanding Customer Needs and Pain Points Keyword and Topic Discovery Improving Search Intent Alignment
12. Understanding Customer Needs and Pain Points Keyword and Topic Discovery Improving Search Intent Alignment Reduce bounce rate and increase conversions
13. Understanding Customer Needs and Pain Points Keyword and Topic Discovery Improving Search Intent Alignment Reduce bounce rate and increase conversions
14. SEO used to be a race to the top of the leaderboard
15. Now… it's about building a user-friendly map that guides customers directly to the solutions they need.
16. Now…We have all these customer feedback WHAT’S NEXT?
17. Large Dataset. Machine Learning Analysis. Case study: Qatar airways Customer review dataset gotten from Kaggle.com
18. Customer Pain Points Analysis(We are looking out for ….)
19. Customer Pain Points Analysis(We are looking out for ….) Uncover Search Intent
20. Customer Pain Points Analysis(We are looking out for ….) Uncover Search Intent Discover Themes and Patterns
21. Sample Data frame
22. Sentiment Analysis
23. Negative- 270 Positive- 2091 Neutral- 8 Reviews
24. Focus on the Negatives
25. Part-of-speech (POS) tagging What is POS and how is it to be used in the analysis? • Noun • Adverb • Adjective • Verbs Lemma
26. Negative Reviews (Adverbs and Adjectives) 114 110 102 80 63 61 55 53 45 39 39 36 0 20 40 60 80 100 120 when very so only then again even back just however never later Adverb Lemmas Contact Form Data 72 51 44 39 38 36 32 31 27 26 26 0 10 20 30 40 50 60 70 80 next other good more first last old poor full long uncomfortable Adjective Lemmas Contact Form Data
27. Negative Reviews (Verbs and Nouns) 2228 992 991 852 650 642 624 509 499 487 457 350 0 500 1000 1500 2000 2500 have fly get be check make take give serve go use offer Verbs Lemma Contact Forms Data 4391 2176 2027 1580 1552 1171 1109 1072 1014 891 879 864 0 1000 2000 3000 4000 5000 flight seat service time food staff class hour crew business cabin airline Noun Lemmas Contact Form Data
28. Sentiment analysis on Lemma
29. Negative Lemma
30. Words associated with these Negative lemma
31. Words associated with these Negative lemma
32. Filtering into the Dataset
33. Bring Back the data frame and perform POS * Focus on things, not strings.
34. Top Nouns(100)
35. Topic Modeling
36. Top 100 Nouns Semantically Categorized
37. • Airplane • Comfort • Flight • Meal • Time • Trip Themes 5 7 10 14 17 47 0 5 10 15 20 25 30 35 40 45 50 Trip Time Meal Flight Comfort Airplane number_unique _lemma Theme Number of Unique Lemmas per Cluster
39. SYNTAX SHOWING USER’S INTENT VERBS NOUNS +
40. 12 12 13 14 19 49 56 62 116 0 20 40 60 80 100 120 140 have time cancel flight use Qatar cdg doh fly experience fly business take care fly Qatar connect flight Most Common Action Pairs(Lemmatised Bigrams)
41. Why use Machine learning and not AI?
42. What do we then do with these INSIGHTS?
43. Develop new content pieces to fill these gaps
44. Optimize your content to address these needs
45. Incorporation of keywords into existing content
46. Leverage Social Media and other Channels
47. Utilise Structured Data
48. Enhance Local SEO
49. Content isn’t KING; it's the CUSTOMER who’s the KING KEY TAKEAWAY
50. Designing CONTENT tailored to their NEEDS and PROBLEMS will guide them smoothly toward CONVERSION. KEY TAKEAWAY
51. Let's Connect on LinkedIn!