4. THE PROBLEM
Too many social media posts to track and read.
Many of our customers/prospects are feeling
neglected because we don’t have the resources to
read and respond to all of them.
Need a way to filter them down to only the ones
where the sender is expecting a response.
6. DETERMINING THE ANSWER
Is the sender…
● Making a request
● Asking a question
● Reporting a problem
● Angry or Unhappy
● None of the above
Actionable?
Yes (1)
Yes (1)
Yes (1)
Yes (1)
No (0)
7. THE SOLUTION
Use Amazon Machine Learning to analyze a Twitter
stream in real-time and make a determination about
whether or not a tweet requires a response.
(binary classification: yes or no)
Then route the positives to a customer service agent.
Based on: github.com/awslabs/machine-learning-samples/tree/master/social-media
14. 10xnation.com/social-customer-care-amazon-machine-learning
STEP BY STEP GUIDE
● Step 1: Requirements
● Step 2: Gather training data
● Step 3: Prepare raw tweets for labeling
● Step 4: Submit job to Mechanical Turk
● Step 5: Format labeled data
● Step 9: Upload training data to S3
● Step 7: Generate the Model
● Step 8: Configure Machine Learning
● Step 9: Configure Kinesis
● Step 10: Configure IAM
● Step 11: Configure SNS
● Step 12: Configure Lambda
● Step 13: Configure Twitter
● Step 14: Fire it up
16. EXTENDING THE SOCIAL MONITOR
Let’s make our new social media monitor even better…
● Wrap a UI around it
● Pre-populate a tweet response
● Categorizes topic of each tweet
● Determine sentiment of each tweet
● Provide insight into personality of sender
17. THE SOLUTION
Use IBM Watson to analyze a Twitter stream in
real-time and determine…
● Sentiment
● If response required
● Type of response required
Based on: github.com/watson-developer-cloud/social-customer-care
23. STEP BY STEP GUIDE
10xnation.com/social-customer-care-ibm-watson
● Step 1: Requirements
● Step 2: Configure Natural Language Classifier
● Step 3: Configure Alchemy Language
● Step 4: Configure Personality Insights
● Step 5: Configure Tone Analyzer
● Step 6: Configure Twitter
● Step 7: Train the Natural Language Classifier
● Step 8: Create the application
● Step 9: Fire it up
24. ENDLESS POSSIBILITIES
● Give customer service agents a way to provide
feedback on the system’s accuracy
● Capture the agent’s feedback and tweet responses
● Use new data to further refine prediction accuracy
● Automate more and more as system gets “smarter”