Social Intelligence

Consumers today make purchasing decisions based on the research they conduct on the internet. Post purchase feedback on the web has become very important in consideration cycle for a prospect. Research suggests four out of five consumers reverse their purchase decisions based on negative online reviews.

A typical customer review provides plenty of information for a business to take note of. For example, here is a review from a Yelp customer that gave a rating of 3 stars to a restaurant. “Food and ambience were outstanding. Since it is a popular location, the waiters can be very slow, so that aspect isn’t good. Overall I give it 3 stars”.

Most review monitoring websites and tools currently in the market only provide an overall rating, of 1 to 5 stars. This type of general feedback does not really help you to understand why the review is only one general number, it currently does not provide any actionable information. If you read the review sentence by sentence, you would score food at 5 stars, service at 2 stars and general aspect at 1 star (based on the comment about the location). This is exactly what Netisen business attribute analysis feature does, it breaks down reviews for deeper insights. Our proprietary natural language processing algorithms work as shown in the picture below. We take the review process sentence by sentence and give an attribute for each part that is broken down. We then provide a score for each attribute level.

Typical YELP review
"Food and ambience were outstanding. It is in populated location which is no good and the waiters are very slow sometimes. Overall I will give it 3 stars."
netisen Algorithm
Data Cleansing
Sentence Analysis
Sentiment Scoring
Attribute: {Keyword, Modifiers, Rating}
  • Food: {food, outstanding, 5}
  • Experience: {ambience, outstanding, 5}
  • General: {location, (populated, no good), 1}
  • Service: {waiter, (slow, sometimes), 2}
  • General: {overall, stars, 3}

With our dashboards and analytics, we enable you to make service adjustments and product adjustments based on customer feedback on easy to read dashboards.