Providing Information Relevant to User Behavior in Real Time
Pleasant Bustle and Less Crowding at Stations, in Town, and in Shops
- LIKEUP Launched on June 26-
NTT COMWARE CORPORATION (Head office: Minato-ku, Tokyo; President: Satoshi Kurishima; hereinafter NTT COMWARE) launched LIKEUP on June 26, a service that avoids crowding and realizes strolls around town optimized for individuals by predicting needs and behavior based on users' attributes, times, positions, and surroundings to provide sightseeing and traffic information relevant to the users in real time. LIKEUP is a service that uses the UX engine, independently developed by NTT COMWARE, that can reduce crowding and realize pleasant bustle in town by increasing the accessibility of stations, facilities, and other such areas when used by railroads, developers, other companies, and local government as they provide services for tourists and consumers.LIKEUP has been positively reviewed*1 by users who have actually tried it out in demonstration trials. It was introduced in advance from March 31 in the "Machibura Concierge" service of "Keiyo Line Plus (provided by the Chiba branch of East Japan Railway Company)," an app for communicating information about the Keiyo Line, its surroundings, and "KEIYO TEAM6," to provide crowding forecasts and information about nearby sightseeing, events, and traffic for Kaihin-Makuhari Station.
*1: 82% users responded "Easy to use" and 81% "I would like to use it somewhere else as well."
NTT COMWARE decided to launch the LIKEUP service to reflect users' opinions and needs with regard to changes in behavior as well as the results of the demonstration trial that was jointly held with the East Japan Railway Company (Head office: Shibuya-ku, Tokyo; President and CEO: Yuji Fukusawa) with the aim of revitalizing areas and reduce crowding as part of the activities of their mobility reform consortium. LIKEUP takes into consideration user attributes, times, and positions as well as information about changes in weather, crowding, and so forth to promote behavioral changes by teasing out individually optimized information for the users from relevant information in the area. Through this, it can even out crowding and provide users with safe, secure, and pleasant experiences of strolling about town. Moreover, by coordinating with existing member service infrastructure of companies and local government, it becomes possible to predict detailed user needs and behaviors based on member information as well as provide information that is even more relevant to users. Moreover, by collaborating with other services and apps using API, we will integrate various pieces of sightseeing and traffic information provided by MaaS operators, thus being able to provide the service to MaaS services users as well.
NTT COMWARE expects to sell for 1.3 billion yen by 2022 by expanding LIKEUP sales to various businesses. Moreover, among MaaS that increase efficiency by seamlessly connecting transportation networks, NTT COMWARE will also take on a role of supporting Beyond MaaS, a concept provided as a series of user experiences to connect the services of multiple industries and business types for the sake of resolving more social issues. This will allow us to contribute to such solutions.
An overview of LIKEUP can be found on the accompanying sheet.
LIKEUP is a service using the UX engine made for providing individually optimized information that anticipates needs and behaviors through information about individual users' attributes, times, and positions. It provides railways, developers, and local government with both revitalization and safety and security in towns, retailers and facilities with effective ways to attract customers, and users with experiences of comfortable strolling in town with uncrowded and pleasant bustle.
1.Future vision of the service
2.Check situation inside stadium and routes through signage
3.Situation inside stadium
6.Shop that suits the mood
7.Favorite food, the mood right now
9.Using photo service
13.Enjoying watching sports
14.Shops recommended according to taste and mood
15.Shared experience with others there, real-time commentary
16.Feelings right now, favorite players
18.Good bargains, getting inside shops smoothly
19.Good coupons, seat reservations
22.Local revitalization and safety and security by changing people flows
23.Railways, developers, local government
24.Effectively attracting customers through direct promotion to nearby users
25.Retailers, facilities, etc.
2.How LIKEUP Works and Its Features
|User type is determined based on several images selected by the user, and their needs and behavior are predicted by managing a variety of data that includes purpose and accompanying people, time and place for using the service, the weather on the day, and nearby crowding, which enables the selection and teasing out of optimal information from the relevant local information.
Without any need to go through vast volumes of past data, data cleansing, or data analysis, optimal information is provided by utilizing data available now.
1.Image diagnosis, user types – Entering purpose and situation
2.Fixed information – User attribute information – "Likes nature," "Calm atmosphere," "Being with family"
3.Dynamic user information – Current location – "GPS positioning" – Time – "18:00"
4.Non-user dynamic information – Other – "Weather," "Crowding"
5.Predicted user behavior – Customer journey map
6.Results –Recommendations for you right now - "Aquarium by the sea," "Restaurant in the woods"
7.Contents information – Local information – "Quiet environment," "Rain OK"
8.Local information – Shops – Events – Crowding – Means of transportation
9.Data across industries and business types
10.*Patent application pending, application no. 2019-061263, application no. 2019-061264
11.Information transmission to the users
*2:"Managing Value" is a technical brand of NTT COMWARE.
|(1)||Engine available anytime
Can be used immediately simply by setting up rules based on a customer journey map from user attribute and change information. No need for vast volumes of past data, data cleansing, or data analysis.
|(2)||Can be provided in two ways
In addition to service provision that includes UI and UX, it can also provide an engine only via an API.
|(3)||Collaborates with member service infrastructure of companies and local government
Attributes can also be obtained in collaboration with existing member service infrastructure. It is set up on a website instead of an app since it can connect with companies' existing services and apps.
3.How LIKEUP Can Be Used
Different information is provided to users in the same location if their attributes, positions, and times differ, so each user will have their own experience of strolling around town.
1.[Examples of how users are guided near a stadium depending on their attributes]
3.Around 10 am
4.Around 1 pm
5.Around 4 pm
8.Got to the stadium early but goes to the convenience store since he has nowhere in particular to go
9.Watches the game in the stadium after moving from place to place
10.Reached the station after the game but it is very crowded. Has to stand for a long time and becomes really tired
11.The fun is all spoilt by the crowdedness going home
12.LIKEUP user Mr. B – "Soccer supporter," "Alone," "Likes photos"
13.Decides where to go based on information about "Places you can relax" – E.g. Cafés that serve breakfast, Walk information, Nearby book cafés
14.Enjoys the game after receiving suggestions on fun spots inside and outside the stadium – E.g. Soccer event information, Stadium eateries
15.Picked a restaurant he likes based on information about "Restaurants when you're alone" – E.g. Soba and ramen restaurants, Soccer goods shops – Displays station crowding!
16.He could take it easy at a café before the game and avoid the crowds when going home!
17.LIKEUP user Ms. C – "Town stroll," "With child," "Likes luxury"
18.Enjoys event after receiving suggestions on "Places to go with kids" – E.g. Kids-friendly restaurants, Events for children
19.Goes shopping based on information about "Shopping that suits your taste/Cafés you can take children to" – E.g. Instagrammable cafés, Stylish childrenfs clothes –Stores are notified, coupons activated!
20.Avoided the crowds on the way home by checking "Information on detours and traffic right after the game" – E.g. Multiple traffic information, Slightly high-end grocery stores – Displays station crowding!
21.Sat down on the train back home after enjoying childrenfs events and small detours!
22."LIKEUP" anticipates each userfs needs and provides the optimal information. People flows in the area are dispersed as each consumer behaves differently, which results in town revitalization and local safety and security.
23.Attributes are determined through a user test (image selection)
24.Picking purpose and circumstances
25.Information provided to each and every user – Providing nearby crowding information
4. Service launch
June 26, 2020
Initial cost: free
Monthly usage fee: 1 million yen / area (excl. tax)
*Images and pictures are for illustrative purposes only.
*The company, product, and service names mentioned are the trademarks or registered trademarks of their respective companies.