Every company is a data company. Ridesharing Economy by the numbers.

In this example, we’re looking at 3 companies in the ride sharing space: Uber, Lyft, and Gett.  Once these names are configured in iTrend, you can track various public communications to and from each company, automatically classify them by subject (category), sentiment (positive, negative, neutral), and detect customer service or PR issues in real-time.

Summary screen shows you how much data exists for the selected time period (this example uses last 7 days):

Ridesharing Stats

Selecting a particular company (e.g. Uber), will show current consumer sentiment (when talking to the company) and company’s overall responsiveness to service issues and customer feedback:

consumer sentiment Uber

‘Customer’ tab will let you detect most frequently occurring word combinations – and see if there’s a common theme.  In Uber’s case, we see that “promo codes” play a big role, as they are very often discussed and shared on Twitter:

promo codes

‘Market Feedback’ has a section that will show you any unresolved complaints. These may include product questions, suggestions, or service issues – where the customer is expressing their concern and the company hasn’t responded to them yet:


Overall, as evidenced by the screen above, customer service remains a big challenge for the ride sharing companies.  Further research shows that when people don’t get a response quickly (within a day at most), they get frustrated and publish negative comments.


Some issues are serious (incorrect billing, cancellations), and should be addressed as quickly as possible.

Last 7 days of data includes almost 800,000 tweets – this is statistically meaningful, and can provide very valuable insights into company’s quality of service, public support, and its overall image:

800k tweets

If you are interested in seeing more information, feel free to contact us on Twitter @iTrendHQ.  We are also offering a free 14-day trial of our data platform for Competitive Intelligence here: www.itrend.tv

Technologist, parallel entrepreneur. Interests: travel, photography, big data, analytics, predictive modeling.

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Posted in lyft, marketing, ridesharing, Technology, uber

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