Places.
This is a product case written up about an imaginary app (that I came up with) with the main use case of making it easier to hang out with your friends.
Description and Vision
Going out to eat or for drinks is an almost daily activity for those living in cities, especially ones as large as New York City. When deciding on where to go the internet has become the go-to resource for searching and referrals. With bars and restaurants greatly expanding their online presence, information and recommendations have become scattered throughout numerous websites with each individual having their favorites sources. When surveyed people in NYC ages 23-35 (post-college and pre-family) go out to eat about 3 times a week and meet friends or colleagues for drinks 2 times a week. Of this group, the average person spends roughly 30 minutes looking for a place to go each time. With a plethora of options to choose from people enjoy trying out places they have not been to before. On the other hand, when trying to decide quickly due to time constraints, 2/3 of interviewees simply choose a place that they have gone to before rather than waste time searching for a new one only to be disappointed. People rely heavily on curated lists, Instagram pictures and Google Maps/Yelp reviews when judging the quality of a place often checking around two sources before choosing.
When trying to decide on a place to go only 16.67% of those surveyed said that they specifically look for a place geographically convenient to all parties, however, 83.33% said that they try to consider everyone’s preferences when deciding. It is very common for groups of people to have varying dietary needs and restrictions that are often uncomfortable to bring up. According to survey results, out of those who have dietary restrictions (vegetarian, vegan, gluten-free, religious etc.), 80% expressed discomfort voicing their needs when part of a group going out to eat. These people would like to voice their needs, but it is not always easy, and risk being ostracized by the group. 100% of dietary-restricted respondents said that they wish there were a way to eliminate the awkwardness of voicing their concerns.
Along with dietary restrictions, people also weigh other features as part of their decision-making process. The next highest weighted feature was deals. This group of people is relatively price sensitive and in an expensive city such as NYC people will often seek out bars offering $4 beers versus drinking the same beer at almost double the price somewhere else. This, however, is not always an easy task as this information is often only found on bar/restaurant websites and is not aggregated anywhere else. It is then usually up to the individual to scour these websites looking for best option.
In summary, people search for places on an almost daily basis weighing a few key features as part of their search:
Quality (how is this place compared to others)
Restrictions (can everyone be accommodated)
Deals (is it better to visit on Tuesday vs Wednesday?)
Hypothesis
People will meet up with their friends or colleagues more often on a weekly basis
Due to ease and quality people will meet up with their friends 1-2 extra times per week on average
People will spend less time searching for places to go and stop using other methods
Majority of people (>50%) will stop using other services as sources of recommendations
User and Need
Users could save up to 3 hours per week by eliminating the time spent around searching for places to go
Users constantly have plans fall through with friends due to issues around selecting places to go and will benefit from a one-stop-shop application
Primary use cases
User wants personalized recommendations of places they should try
User logs into app and sets up a profile
Could use login by Facebook/Google for ease
User navigates to explore menu
Can view as list or map
User is asked how the place was and given option to rate
User wants to meet up with a friend
User searches for the friend they wish to meet up with
User selects area of choice
User ranks their provided suggestions
User is provided with the mutually decided best option
User is given the option to navigate to the place
User is asked how the place was and given option to rate
User wants to meet with a group of people
User adds all of the people involved into a Group
User browses for an area of interest
User sends suggested places to the Group
The Group votes on sent places and a “winner” is decided
User is given the option to navigate to the place
User is asked how the place was and given option to rate
A user’s friend wants to meet up with them
User is alerted via a push notification that their friend wants to meet up
User is provided the suggestions and ranks them
Mutually decided best option is given
User is given the option to navigate to the place
User is asked how the place was and given option to rate
A user wants to see places that they previously have gone
User logs into app
User clicks the menu button and then selects History and is provided a list of all the places they went
User can either search or scroll through all of their places
User can also click “Search for friends”
After searching for a friend’s account, the user can see their public Place list
Storyboard
Comparators
Applications which give you information about bars and restaurants are a dime a dozen. A few of the main ones being Google Maps (specifically business pages and reviews), Yelp, Trip Advisor and The Infatuation. Each one of these can be used to find recommendations suited to your likes and tastes. A big differentiator is that none of them consider anyone other than the user themselves. Each of these are slightly different from each other. Google Maps has become a go-to for people when searching places to go as right from inside the app they can get transit directions, reviews and sometimes a menu. The Infatuation is interesting because they offer curated lists of recommendations. They also have an on-demand texting service where you can text a number and where you are located, and they will respond with recommended places to try.
KPI’s
Acquisition
How many users sign up for the platform?
How many bars/restaurants sign up to partner?
Conversion
Do users still use other services for recommendations?
Is our service their first point of contact?
Engagement
How many reviews are people leaving?
How many messages are being sent/received?
Retention
How often are users utilizing our service?
DAU vs MAU
Satisfaction
What is the NPS of the recommendations?
What is our app store rating?
Fail rates
How often do people not follow suggestions?
Key Constraints & Risks
Market: This is a highly segmented market. There are lots of other players who have a much further reach. How do we convince people to break their current habit and switch?
User adoption: This application relies heavily on network effects. While network effects are strong how do we get the flywheel going? Users only stand to benefit if the users they want to interact with are on the platform.
Operational: The base of the product will rely heavily on outside partnerships (ex. Google for mapping service). This may be hard to break reliance from.
Legal & regulatory: While no direct legal risks can be thought of, it is possible that something goes wrong while a user is following our recommendation and this potentially risk should at least be thought of. While rare, it could pose an issue (ex. Allergic reaction).
Financial: As with most mobile applications there is relatively little financial risk. If the network effects take off quickly than we will need to be prepared for the financial implications of scaling quickly in order to match the demand whether it be through cloud services or internal hardware. This cost can add up quickly. Since the goal is to not place standard banner ads due to UX reasons it is possible that this app will have difficulty monetizing.
Technology: The core technology being used is AI based recommendations. The algorithm behind this will be quite complex and will need to be refined at launch. If the system does not output quality recommendations from day 1 then we may never obtain a user base.
Go-to-Market & Value Proposition
Go-to-Market: As the algorithm becomes suitable for an individual’s tastes/preferences based on their profile, roll the application out to the “early adopters” for personalized recommendations. Once a significant user base is created, expand to connecting multiple individuals at a time, starting with just pairs and working towards groups of 3+ being careful that the algorithm can keep up with the added complexity.
Value proposition: Save time looking for places to go and be given high quality recommendations that all parties will enjoy.
Monetization & Key Cost Considerations
The initial plan will be to monetize via bar/restaurant partnerships. Bars/Restaurants can partner with us to be promoted on our platform which will then drive traffic to the partner establishments. On top of these partnerships we will release two versions of the applications, free and paid. Taking the Spotify approach, the free version will have limited functionality only allowing users to find recommendations for themselves while the paid version will open up group features, history, sharing etc. (basically all community features). This will allow users to get a taste of the personalized recommendations without much input but in order to access premium features they will need to pay. This will be a one-time unlocking payment for $5. It can be assumed about a third of all users will be premium.
Main cost considerations revolve around hosting and software development. A highly skilled software engineer costs around $200,000 per year all things considered. As this application takes off and services more and more users, cloud computing costs will increase. Initially, cloud computing costs for hosting and service such on AWS can cost upwards of $1,000 per month for a few thousand users. As our user count increases, this once we reach upwards of millions of users this number can get as high as $10,000 per month.
| # of MAU users |
Developer Cost |
Cloud Computing Costs |
Revenue from partnerships |
Revenue from premium users |
Profit (or loss) |
|---|---|---|---|---|---|
| 1,000 |
(1) $0 |
$50x12 |
$1,000 |
$1,500 |
$1,900 |
| 100,000 |
(1) $0 |
$500x12 |
$100,000 |
$150,000 |
$244,000 |
| 10,000,000 |
(2) $200,000 |
$10,000x12 |
$1,000,000 |
$1,000,000 |
$1,680,000 |
*Developer costs are assuming founder as one
Recommendation and Plan
During research, nine individuals were interviewed in a one-on-one setting. Overall the response was quite good with all nine saying that they would indeed use an application such as this. Everyone had comments about different features that they would prioritize but none quite out of the scope. This, however, can only be taken at face value. Throughout all of these interviews it became apparent that questions revolving around the specific idea (towards the end of the interview) were answered almost always in a positive way. Interviewees were quick to say this is an application they would use often when in reality they may not.
Another concern is obviously adoption. In order for this application to be a success, widespread adoption is necessary as a lot of the value derived is from network effects. The personalized recommendation engine is a large part of the application but the real value and separator in the product is the ability to loop in others, either colleagues for friends into the decision-making process. When a person is searching by themselves (either for their self or a group) the incumbents (Google Maps/Yelp) are typically more than sufficient. Looking at the competitive landscape for group interactions, Instagram is by far and away the top option. Now even though Instagram does not deal with bars and restaurants directly like Google Maps/Yelp, food Instagram pages have become insanely popular and users often direct message posts of dishes to others with captions such as “we should go try this!!”. This is where this application can insert itself as a way to make these interactions come to fruition more often. In order to do so, either Places will need to overtake Instagram in this space (unlikely) or needs to partner with Instagram and connect the two (also unlikely).
Lastly, one use case that has not been touched on as a part of this is dating. When looking at dating and also dating apps this application could be very useful as typically it involves two strangers meeting up and either grabbing drinks or dinner somewhere that both of them would hopefully enjoy. This plays well into the products algorithm considering preferences and removing some awkwardness from this interaction. The reason dating was not touched on was because 1) the vision is not of setting yup another dating app and 2) this type of functionality as actually already being built into current dating app leaders. An example of this is Hinge (a popular dating app) has just started a partnership with OpenTable (dinner/table reservation service) where right from inside the Hinge app someone can answer basic questions about preferences (cuisine, price, location, vibe, etc.) and OpenTable will then suggest a place for the two parties to go to, also allowing for table reservations all from in the app.
It is with all of the above reasons that I suggest going forward with this product but with a caveat. Although there seems to be a want, the need just cannot be verified. Some interviewees (3/9) actually expressed enjoying the process of searching for a place to go and thus the time saving portion does to give these people much value. The required strong network effects are also worrisome as getting over the chasm from the early adopters to the mass will be quite difficult. The best play would be to try and develop the algorithm for group profiles/recommendations and then try to be acquired by Google and integrated into Google Maps. Google Maps already has a large user base and connections to businesses and could really utilize group features. This will also allow the product to piggyback off of Google Maps’ monetization strategy which is much stronger than this product’s standalone strategy.
