1. Future-Oriented Benchmarking through Social Media Analysis

2. Collect Big Data Customer Intelligence
Businesses need to listen to the voice of the customers to understand how they’re doing, and how to improve.
Social media is a great source to collect real-time customer voice. Online review forums (e.g., Yelp, Foursquare, Skytrax) and social media platforms (e.g., Facebook, Twitter) contain rich sources of customer feedback, far exceeding traditional ways of customer surveying methods (e.g., comment cards) by volume, timeliness, content, and interactivity.
Organizations can use such data to analyze customer experience (good and bad), and to understand “wishes” of the customers for predictive analysis.
However, social media data has been under-utilized in business analytics.

3. Not sure if we want to specifically mention MCO or make it more generic.
If do specifically work on MCO, this slide will introduce the project at a high-level to set the stage and give students background info.

4. Social Media Analysis Why? Why? Why?
(snapshot of reviews on MCO from Google review) An example – customers commenting on negative (slow security) and positive (friendly staff) aspects of their own experience. Aggregating customer comments to get a general sense of how the organization is doing – evaluating performance.
What’s more – analyzing the review content and the emotions expressed, businesses can understand why their customers are not satisfied, and identify the root causes to take actions.
More importantly, businesses can compare how they’re doing with their competitors – transitioning to benchmark analysis.

5. Benchmark Analysis
Benchmark Analysis is the process of comparing the performance of one business to that of similar businesses. Helps organizations understand where their business is performing well and where it is underperforming. Also helps businesses identify trends/issues BEFORE they become a problem.
Not just understanding how things are in the past, but changing the focus of the data to future-oriented analyses. Use available social media data to know what delights the organization’s and its competitor’s customers before they do. Understand the “wishes” of the current customers to take proactive actions.

6. What we want
A system that automatically pulls and analyzes social media data for future-oriented benchmarking.

7. Desired Functions Live web-scraping of customer reviews
Text classification with machine learning algorithm
Sentiment analysis
Content analysis & Latent Semantic Analysis
E.g., Natural Language Processing for Named Entity Recognition (e.g., locations), Point of Speech (e.g., action verbs, adjectives), and relationships in texts
Reporting dashboard/platform with infographics
Alex used R programming and Microsoft SQL Server
Students can use any platform/software they prefer, such as R language, Python, SQL, IMB Watson, etc.

8. The Team
Industrial Organizational Psychologists
Data Scientists
Programmers
Linguistics
Customer Experience Experts
Airport Design Experts

9. For questions, please contact:
Dr. Richard Griffith,
Hairong Jiang,