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Transform your customer feedback into action - MeaningCloud webinar

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Analyze opinions, perceptions, emotions, and intentions to level up your customer insights.
MeaningCloud webinar, April 29, 2020.
More info and webinar contents https://www.meaningcloud.com/blog/text-analytics-customer-feedback-action
MeaningCloud https://www.meaningcloud.com

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Transform your customer feedback into action - MeaningCloud webinar

  1. 1. Transform your customer feedback into action with deep text analytics April 29, 2020 MEANINGCLOUD – 2020 Webinar
  2. 2. 2 MEANINGCLOUD - 2020 We hope you are safe and well
  3. 3. MEANINGCLOUD - 2020 3 Presenter How to participate • Send questions using the chat feature, or • Click the “Raise your hand” button to speak and we will enable your mic • Afterwards, you’ll be able to access a recording of the webinar and its contents as tutorials on our blog Before we get started… Antonio Matarranz CMO
  4. 4. 4 MEANINGCLOUD – 2020 Why this webinar? Analyzing unstructured customer feedback is a must How to make it more actionable and valuable?
  5. 5. MEANINGCLOUD - 2020 5 Agenda • Leveraging unstructured customer feedback: benefits and challenges • Text analytics to the rescue... but with limitations • How to use deep text analytics to extract more actionable insights – Pre-made Insights – Adaptation – Development • Conclusions and Q&A
  6. 6. 6 MEANINGCLOUD - 2020 How to “focus on the customer”, really? Cambiar en Encabezado / Pie de Pg 6 Define “segment of 1” Iniciatives • Voice of the Customer (VoC) • Customer Insights • Customer Experience Management
  7. 7. MEANINGCLOUD - 2020 7 Unstructured (and unsolicited) customer feedback Interviews • Audio recordings • Transcripts Surveys • Open-ended questions Contact center • Speech • Email • Chat • Bot • Social Social media • Social networks • Blogs • Forums • Review sites • Communities
  8. 8. MEANINGCLOUD - 2020 8 Why analyze it? VALUESpontaneous Sincere, in the customer’s words Contextual Enables to explore and discover Viral capability
  9. 9. MEANINGCLOUD - 2020 9 What are the challenges? Volume Millions of interactions per month Velocity Need for fast response Variety Text, audio, image…
  10. 10. MEANINGCLOUD - 2020 10 The reality of unstructured feedback
  11. 11. MEANINGCLOUD - 2020 11 Text analytics to the rescue Unstrutured text Text analytics Structured data Insights Scaling the analysis of unstructured feedback
  12. 12. 12 MEANINGCLOUD - 2020 How to use text analytics in multichannel scenarios Speech to Text Conversion Text Analytics Analytics & Visualization Phone Email, chat, social, bots Structured data Data Insights Conversion: Speech → Text Text Analytics: Text → Structured Data Text Contact center
  13. 13. MEANINGCLOUD - 2020 13 How to use text analytics in multichannel scenarios Harvesting content from social networks, blogs, forums, review sites… Text analytics Language understanding Actionable insights Media monitoring
  14. 14. MEANINGCLOUD - 2020 14 A “typical” example Type Value Entity TeleCom (company) General theme Telecommunication services General polarity Negative “I'm fed up with these TeleCom people. My mobile is constantly breaking down and I want to cancel the service. But their support center is always busy. They're horrible!” Telecom- munication services Entities, general themes, sentiment are valuable, but...
  15. 15. 15 MEANINGCLOUD - 2020 The big challenge is bridging the chasm between analysis and value 15 Transform analysis into something actionable and valuable Data Value Data Analytics Decision Action Value
  16. 16. MEANINGCLOUD - 2020 16 How to bridge the chasm? Relevant topics • Our brands, products… Actionable categories • Our functions, departments… Targeted sentiment • Our attributes, components… Root causes • Specific to our business Drivers • Satisfaction, quality, purchase Discovery • Emerging, “off the radar” topics Emotions • Joy, sadness, surprise… Customer journey • Information, consideration, evaluation, purchase…
  17. 17. MEANINGCLOUD - 2020 17 Text analytics: much more is needed • Flexible development • Easy embedding and consumption • Adaptation to the customer’s context • Short time-to- benefit Pre-built solutions Customi- zation Agility Integra- bility
  18. 18. 18 MEANINGCLOUD - 2020 MeaningCloud: Meaning as a Service Standard APIs (SaaS and on-premises) Use it free at www.meaningcloud.com
  19. 19. MEANINGCLOUD - 2020 19 MeaningCloud levels up your customer insights Pre-built insights Adaptation and customization Development of new insights
  20. 20. Pre-built customer insights
  21. 21. 21 MEANINGCLOUD – 2020 Attribute-/aspect-based sentiment analysis Yesterday I had the opportunity to test the latest models of Samsung and Apple. The Samsung is more reliable although its screen needs to be improved. The design of the iPhone is unbeatable but it is too expensive. • “Neutral” aggregated sentiment? • Sentiment objects are different PepePerez @Jperez – 1h I love the new iPhone. The limitations of document-level sentiment analysis We need aspect-based sentiment analysis Type Value Entity iPhone General sentiment
  22. 22. MEANINGCLOUD - 2020 22 Topic-oriented sentiment analysis • Identify sentiment (positive/negative/neutral polarity or lack thereof) – At the document level – At sentence level – Associated with mentioned topics (entities/concepts) – Domain-specific topics definable in dictionaries The Samsung is more reliable and the iPhone is too expensive The hotel rooms are comfortable, but the landscape is horrible Standard API Tutorial, Recorded webinar
  23. 23. MEANINGCLOUD - 2020 23 Emotions: the hidden key to profitability ➢(Negative) emotions are more durable ➢(Negative) Emotions are more shared ➢(Negative) emotions shape the experience ➢Emotional motivators influence behavior ➢Customers' emotional connection with the brand is (25-100%) more profitable than satisfaction Joy, Confidence, Fear, Surprise, Sadness…
  24. 24. MEANINGCLOUD - 2020 24 Emotion recognition • Identify expressed emotions: Joy, Trust, Fear, Surprise, Sadness, Disgust, Anger, Anticipation • Based on Plutchik's Wheel of Emotions • It complements Sentiment analysis Vertical Pack
  25. 25. MEANINGCLOUD - 2020 25 Analyze expressed intention to predict the future ➢Predict behavior ➢Detect the customer journey and personalize the response to it ➢Discover business opportunities ➢Give better service ➢Retain customers ➢Foster recommendation Request information, Purchase, Recommend, Cancel…
  26. 26. MEANINGCLOUD - 2020 26 Intention in the customer journey • identify a set of basic intentions throughout the customer journey: Information, Advice, Purchase, Support, Recommendation, Complaint, or Cancellation • Detect Purchase / Churn signals Customer journey Informa- tion Advice Purchase Support Recom- mendation Complaint Cancella- tion Vertical Pack
  27. 27. MEANINGCLOUD - 2020 27 ➢Quality lies in the customer’s perceptions ➢Quality is multidimensional ➢The perception of quality produces satisfaction Quality is a multidimensional perception Conformity Functions Aesthetics Service Features
  28. 28. MEANINGCLOUD - 2020 28 Multidimensional satisfaction (Voice of the Customer) Industry / General Company Company1 + - Company2 + - Channel Web + - Phone + - Customer service Information + - Maintenance + - Quality Functionality + - Efficiency + - Product Product1 + - Product2 + - Operation Activation + - Cancellation + - Satisfaction + - • General and by industry: Banking, Retail, Telco... • Dimension and attribute hierarchy • Polarity by attributes and overall satisfaction dimension Vertical Pack Tutorial, Recorded webinar
  29. 29. MEANINGCLOUD - 2020 29 “I'm fed up with these TeleCom people. My mobile is constantly breaking down and I want to cancel the service. But their support center is always busy. They're horrible!” What we have achieved Type Value Entity TeleCom (company) General theme Telecommunication services General polarity Negative Emotion Annoyance Product Cellular phone Attribute – Reliability Negative Intention Cancellation Service – Support Negative General satisfaction Negative Telecom- munication services
  30. 30. MEANINGCLOUD - 2020 30 Discovering the topic structure of comments • Group similar comments – Aggregate according to significant themes – Relations between groups – Detect duplicates • Discovering topics that emerge from the collection – "New voice" of the customer OJO: ING y español
  31. 31. 31 MEANINGCLOUD - 2020 Text clustering Aggregate similar texts and discover meaningful themes 31 New producto opportunity Dissatisfaction cause • No pre-defined taxonomy required (unsupervised learning) • Text-oriented processing • Grouping of texts based on • Adherence to a topic • Content similarity Standard API
  32. 32. MEANINGCLOUD - 2020 32 Measuring the corporate reputation of the company • Market assessment through a set of relevant corporate dimensions and variables • Sources: – Surveys (typically) – News – Social
  33. 33. MEANINGCLOUD - 2020 33 Corporate reputation Standard API • Inspired by industry standards: RepTrak (Reputation Institute), Merco • Entity - Dimension - Variable - Polarity analysis • Sophisticated analysis involving topic extraction, multi-level theme classification and sentiment • Available for Spanish. Want other languages? Contact us
  34. 34. Adaptation and customization
  35. 35. 35 MEANINGCLOUD - 2020 Opinions The sentence “The highest interest rate in industry!” is… ▪ Positive, if talking about savings ▪ Negative, if talking about mortgages Customized linguistic resources improve accuracy Mentions ▪ Names of banks and financial companies, e.g., Citibank, BBVA ▪ Product names, e.g., Your Way Account. Compass Account… Themes Example: analysis of a bank’s customer opinions Products Accounts Checking Savings Borrowing Credit Mortgage Channel Office Phone Internet
  36. 36. MEANINGCLOUD - 2020 36 For every analysis task | Without coding Graphical customization tools • Entities • Concepts Dictionaries • Polarity of expressions Sentiment models • Machine learning • Rules Classification models • Semantic rules Categorization models Topic extraction Sentiment analysis Document classification Deep categorization 36 Standard Tools
  37. 37. MEANINGCLOUD - 2020 37 Agile model development process Machine-Learning (ML) Categorization Semantic Rule-Based Categorization Rule ModelML Model Input text Intermediate categories Categories Model Training Model Editor Training texts Rule editor Automatic categorization engine Classifier training engine Classifier engine Fast model development and high precision from the beginning Transparency, refinement and adaptation
  38. 38. Development of new insights
  39. 39. MEANINGCLOUD - 2020 39 Brand associations • What entities/concepts people usually mention when talking about our brand • “Semantic footprint” of the brand • Fine-grained, individual perceptual analysis
  40. 40. MEANINGCLOUD - 2020 40 Perception analysis • How customers perceive our brand, with respect to predefined, relevant attributes, and compared to competitors • Aggregated, competitive perceptual map • Foundation for positioning analysis
  41. 41. MEANINGCLOUD - 2020 41 Brand personality • Identification of human characteristics that are attributed to a brand: sincerity, excitement, competence, sophistication, ruggedness… • Example: Aaker’s brand personality model
  42. 42. Conclusions
  43. 43. 43 MEANINGCLOUD – 2020 Conclusions Unstructured customer feedback is more valuable than we might think The right tools to extract all that value are becoming available
  44. 44. Q & A time
  45. 45. MEANINGCLOUD - 2020 45 Stay tuned to our blog and emails We’ll be posting a recording of the webinar and its contents as tutorials soon
  46. 46. 46 MEANINGCLOUD - 2020 www.meaningcloud.com Automating the extraction of Meaning from any information source. +1 (646) 403-31043537 36th Street New York, NY 11106 amatarranz@meaningcloud.com Thank you for your attention!

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