Market Validation : CMS for Location-Based Apps

OverviewWe are trying to determine if there is a market for the content management system (CMS) that powers the Hipster Bait app. And, if a market exists we need to determine:The size of the addressable marketThe competitive landscape The profile of the prospective buyerBackgroundThe Hipster Bait app runs on a sophisticated content management system (CMS) that allows administrators to created and manage augmented reality game pieces (ex: songs) as well as locations (pins) that then get published to the app. Users are then encouraged to follow the map inside the Hipster Bait app to locate these songs.Try it for yourself:Android:https://play.google.com/store/apps/details?id=com.hipsterbait.hipsterbaitandroidiPhone: https://apps.apple.com/app/id1518649365How the CMS WorksEach location in the Hipster Bait app is entered into the CMS. Locations are curated (carefully researched) by our staff to ensure they're interesting and match the rock n' roll theme of the app. Locations consist of a geocoordinate (latitude/longitude), a photo and a text description of the location. Together these elements form a location. The admin saves these locations in the CMS and when published the locations display as pins inside the connected mobile app.In the case of the Hipster Bait mobile app, more than 50,000 unique locations have been created, saved in the CMS and deployed to to the app as shown in the example below.The image above shows the CMS in the web browser as well as the map from the Hipster Bait mobile app. Additional product screenshots of the CMS have been attached.Value PropositionWe believe there are other location-based apps that could use a similar CMS. If we can validate the market and prove demand for our CMS we will improve the features, separate the CMS from the Hipster Bait app and license it as a SaaS product to other app developers.If we prove demand, and commercialize our CMS, app developers can use our growing inventory of 50,000 curated locations, create their own, or accept user-generated locations from their end-users.The CustomerThe customer will be app developers. More specifically location-based app developers who use a map to drive users to a location to capture a virtual game piece or a physical item. Location based apps fall into two categories: games and apps.Game examples:Hipster Bait is a location-based game that allows users follow a map to find free unreleased rock music. Pokemon Go is a location-based games that allows users to follow a map to find Pokemons.App examples:Gas Buddy is a location-based app that allows users to follow a map to the cheapest gas stations. Foursquare is a location-based app that allows users to check into locations such as restaurants.The above apps all have one thing in common -- they all drive users to a pin on the map. And they all need to load locations into a database (aka CMS) to make this work.

Geospatial Awareness : Location Based Music App

OverviewHipster Bait is a location-based music discovery app that deploys game pieces (songs) on a map interface. Users follow the map to find unreleased rock songs in cities across North America, and soon the globe. When a user finds (captures) a song, it gets removed from the map. The user is then encouraged to rehide (bait) the song in a different location at which point it's committed back on the map.We need a computer science team to help us design a system of automated guardrails (geospatial awareness) to prevent users and admins from hiding songs on private property or in unsafe locations such as a freeway or body of water. BackgroundThe Hipster Bait app is free for iOS and Android. Android:https://play.google.com/store/apps/details?id=com.hipsterbait.hipsterbaitandroidiPhone: https://apps.apple.com/app/id1518649365How it Currently WorksEach location in the Hipster Bait app is entered into the CMS for major cities across North America. Locations are curated (carefully researched) by our admin staff to ensure they're interesting and match the rock n' roll theme of the app. Admin staff do not physically go to each location that they enter into the game. Instead, they use a Google Maps interface to select and save locations.Locations consist of a geocoordinate (latitude/longitude), a photo and a text description of the location. Together these elements form a location. The admin saves these locations in the CMS and when published the locations display as pins inside the connected mobile app. In the case of the Hipster Bait mobile app, more than 60,000 unique locations have been created, saved in the CMS and deployed to to the app as shown in the example below.Users also hide songs on the map. When a song is found (captured) by a user, it gets removed from the map. The user is then encouraged to rehide the song in a new location and in doing so recommits the song back onto the map which in turn creates a new location in the database.At current, we don't have any automated guardrails (geospatial awareness) that prevent an admin, or user from creating locations on private property or unsafe areas.Geospatial AwarenessAs mentioned above there is no automated system in place that blocks the user from creating a location on private property or in an unsafe area.We want to design a system of geospatial awareness that would identify when a song is being hidden on private property or an unsafe area, after which the system would either:A) Automatically move the song to the nearest safe spotorB) Reject the user from creating the locationWe're not sure the best way to tackle this. The map inside our app is powered by Google Maps so one idea is to use image and pattern recognition software to identify private property, bodies of water and roadways in a given city and automatically generate geo-fences. If a user attempts to create a location in a geofenced area scenario A or B above would be applied. The screenshot above shows a location outside the Beverly Hills hotel and below is a screenshot from the location on Google Maps. The hotel is private property and marked by the color beige (A). Adjacent buildings (B) are also private property. Roadways (C) are dangerous locations. Safe areas include sidewalks and pathways (D), common areas and parking lots (E) and parks (F). Another idea we had was to use image and pattern recognition software to identify safe locations, such as sidewalks and parks, in a given city and only allow locations to be created in these safe areas.A third idea, is to locate another map database or system that already has private property and dangerous areas identified. We could then query this database at the point of location creation to ensure the location is safe.We're open to creative solutions and we need a team of computer scientists and possibly GIS experts to design a system that we can implement into the CMS and database.

Game Piece Density Optimization AI : Location Based Music App

OverviewHipster Bait is a location-based music discovery app that deploys game pieces (songs) on a map interface. Users follow the map to find unreleased rock songs in cities across North America, and soon the globe.We need a data science team to help us analyze current game data, and develop a ML model for optimizing our the density of game pieces in our app with the goal of maximizing user retention and gameplay for each major city that we deploy songs to.BackgroundThe Hipster Bait app is free for iOS and Android. Try it for yourself:Android:https://play.google.com/store/apps/details?id=com.hipsterbait.hipsterbaitandroidiPhone: https://apps.apple.com/app/id1518649365How it Currently WorksEach location in the Hipster Bait app is entered into the CMS for major cities across North America. Locations are curated (carefully researched) by our staff to ensure they're interesting and match the rock n' roll theme of the app. Locations consist of a geocoordinate (latitude/longitude), a photo and a text description of the location. Together these elements form a location. The admin saves these locations in the CMS and when published these locations display as pins (bait) on the map inside the mobile app.In the case of the Hipster Bait mobile app, more than 60,000 unique locations have been created, saved in the CMS and deployed to to the app as shown in the example below. We're constantly adding new locations.The image above shows the CMS in the web browser as well as the map from the Hipster Bait mobile app. Additional product screenshots of the CMS have been attached.Density Optimization A.I.As mentioned above the locations are curated by admin staff and are not optimized for density in our active cities.We want build an A.I. to provide recommendations for admins to ensure we set the perfect number of game pieces in a specific region or city.We know from qualitative surveys and user interviews, that if we space the game pieces (songs) too far apart, users won't travel to find them. Conversely, if the songs are too close together, there is not enough scarcity and gameplay suffers. We don't know the ideal density, nor do we know how the optimal density differs between cities. We don't know the density optimization in order to program intelligence into the CMS to guide the administrators and ensure each city has the perfect number of game pieces to maximize retention and gameplay.Game mechanics, scarcity psychology and data science will be required for this component of the platform and we do not have this expertise in-house.