Define the goal using the metrics, Engagement/Revenue. Let’s say it is engagement. I will further define this to identify how success would like: #Time spent searching, Reduce number of clicks by providing appropriate recommendations.
Customer segment: Tech savvy casual diner with interest in trying different cuisines (local and while travelling) who is in the age range of 20-40 years
1. Search a restaurant based on my mood
2. Filter Distance by which I have to travel from my current location: Is the travel worth it?
3. Filter How much to spend?
4. Filter What other people are saying about the restaurants I have short listed
6. Reserve seats
7. Leave a review
Due to time constraint, I will choose 2 stages in journey, search based on mood and what other people are saying.
– Searching restaurants based on mood- celebrating, long day at work, weekend, quite time etc.
– Too many results in reviews sometime so I do not get the crux of the review – except the rating
or many reviews with or without visuals
– Not sure how many people of similar interests have been here – validation
1. Introduce user to search by mood with pre-select options and using AI suggest restaurants based on ambience and reviews. High impact: High LOE, Med. Risk
2. Use data analytics on the review to provide labels on the most used description – Ex. Fast delivery, courteous staff etc. – High impact, Medium LOE, Low Risk
3. Picture speaks thousand words, and videos even more: Instead of reading, video reviews of 10-20 seconds along with text. – Medium impact, High LOE, Low risk
4. More information about the reviewers and giving badges as incentives. So a person with Badge review (equivalent of verified purchaser review) would have more weightage for the person searching. – Med. impact, Med LOE, low Risk.
5. Voice based search – High Impact. Med LOE, Med. risk (visual)
As a short term, I would go with 2. and long term with consider mood based voice based search.
To improve engagement = time spent searching for the right restaurant on yelp I would build a feature to get a overall sentiment of the restaurants without having to check every single review and MVP it with user segment in age group of 21-35.