App review: Toutiao

Fig. 1 – Screenshot of www.toutiao.com/ homepage 

When I first heard about Toutiaou I thought it might be just another news app, this coming one from China. I learned, however, very quickly that Toutiaou is much more than just a news app; at the time of writing, Toutiao has more than 700 million users in total, with ore than 78 million users reading over 1.3 billion articles on a daily basis.

Toutiao, known officially as Jinri Toutiao, which means “Today’s Headlines”, has a large part of its rapid rise to its ability to provide its users with a highly personalised news feed. Toutiao is a mobile platform that use machine learning algorithms to recommend content to its users, based on previous content accessed by users and their interaction with the content (see Fig. 2).

Fig. 2 – Screenshot of Toutiao iOS app

I identified a number of elements that contribute to Toutiao’s success:

  1. AI and machine learning – Toutiao’s flagship value proposition to its users, having its own dedicated AI Lab in order to constantly further the development of the AI technology that underpins its platform. Toutiao’s algorithms learn from the types of content its users interact with and the way(s) in which they interact with this content. Given that Toutiao users spend on average 76 minutes per day on the app, there’s a wealth of data for Toutiao’s algorithms to learn form and to base personalisations on.
  2. Variety of content types to choose from – Toutiao enables its users to upload short videos, and Toutiao’s algorithms of will recommend selected videos to appropriate users (see Fig. 3). Last year, Ivideos on Toutiao were played 1.5 billion times per day, making Toutiao China’s largest short video platform. Users can also upload pictures, similar to Instagram or Facebook, users can share their pictures, with other users being abel to like or comment on this content (see Fig. 4).
  3. Third party integrations – Toutiao has got strategic partnerships in place with the likes of WeChat, a highly popular messaging app (see Fig. 5), and jd.com, a local online marketplace. It’s easy to see how Toutiao is following an approach whereby they’re inserting their news feed into a user’s broader ecosystem.

Main learning point: I was amazed by the scale at which Toutiao operate and the levels at which its users interact with the app. We often talk about the likes of Netflix and Spotify when it comes to personalised recommendations, but with the amount of data that Toutiao gathers, I can they can create a highly tailored content experience for their users.

Fig. 3 – Screenshot of video section on Toutiao iOS app 

Fig. 4 – Screenshot of user generated content feed on Toutiao iOS app

IMG_4954

Fig. 5 – Screenshot of Toutiao and WeChat integration on Toutiao iOS app

Related links for further learning:

  1. https://www.toutiao.com/
  2. https://www.crunchbase.com/organization/toutiao#/entity
  3. http://technode.com/2017/06/05/podcast-analyse-asia-187-toutiao-with-matthew-brennan/
  4. https://www.technologyreview.com/s/603351/the-insanely-popular-chinese-news-app-that-youve-never-heard-of/
  5. https://www.forbes.com/sites/ywang/2017/05/26/jinri-toutiao-how-chinas-11-billion-news-aggregator-is-no-fake/#24d401d64d8a
  6. https://en.wikipedia.org/wiki/Toutiao
  7. http://lab.toutiao.com/
  8. https://www.liftigniter.com/toutiao-making-headlines-machine-learning/
  9. https://techcrunch.com/2017/02/01/chinese-news-reading-app-toutiao-acquires-flipagram/
  10. https://lotusruan.wordpress.com/2016/03/20/cant-beat-giant-companies-on-wechatweibo-try-toutiao/
  11. https://www.chinainternetwatch.com/tag/toutiao/

 

 

 

App review: Grip

 

Grip is a London based startup that specialises in “smart event networking software”. That sounds like a relevant problem to solve, because don’t we all have a (secret) love-hate relationship with ‘networking’ at events!?

Yes, I’d love to meet with interesting people at events but I hate approaching people randomly.

Let’s have a closer look at how Grip is looking to solve this problem:

My quick summary of Grip (before using it) – I expect an app that uses clever algorithms to suggest relevant people to meet during events.

How does Grip explain itself in the first minute? – The Grip homepage describes the tedium involved in networking at events, with attendees often failing to make the connections they’d hoped for. Grip’s value proposition is to remove this tedium by unlocking “valuable connections at your event, saving attendees time and hard work. We use advanced algorithms to recommend the right people and present them in an easy swiping interface that your attendees will love.”

Getting started, what’s the process like? – Grip uses natural language processing to connect event attendees based on interest, needs and other things they’ve got in common. I liked Grip’s ability to tell an attendee not just who, but also why they should meet someone, in the form of Reasons To Meet.

Grip users will be able to tailor the real-time recommendations they get by setting their own matchmaking rules. I like the element of Grip not totally relying on machine learning, but also giving users the opportunity to feed their preferences into category rules into the Grip dashboard. This will influence the matchmaking engine in real-time and improve the future recommendations for event exhibitors, delegates and sponsors.

I can imagine that the data around users’ acceptance or rejection of Grip’s suggested matches, will help in further refining the app’s recommendations. This reminded me about the review that I did of THEO recently. THEO acts a ‘robo-advisor’ and uses machine learning to provide its users tailored investment advice.

Integrating the Grip API – Apart from the app, Grip have also got their own API, which makes it easier for companies to incorporate event matchmaking capability into their website or apps.

Main learning point: Grip is taking a significant problem for event attendees and exhibitors, and is using machine learning to solve this problem in a real-time and personalised fashion.

Related links for further learning:

  1. https://grip.events/handsake-event-networking/
  2. https://www.eventbrite.co.uk/blog/event-tech-adoption-at-events-ds00/
  3. https://grip.events/ai-event-matchmaking/
  4. https://grip.events/7-secrets-game-changing-event-networking/
  5. http://event-profs.com/world-first-artificially-intelligent-event-technology/
  6. https://marcabraham.com/2017/04/19/app-review-theo/
  7. https://www.eventbrite.co.uk/blog/event-tech-startups-2017-ds00/

 

App review: THEO

Fig. 1 – Screenshot of THEO – Taken from: http://fintechnews.sg/3137/roboadvisor/robo-advisory-services-asia/

I recently came across THEO, a mobile, Japanese investment service offered by Money Design. THEO acts as a ‘robo-advisor’; enabling users to invest using their smartphone, and applying machine-based learning to offer users investment suggestions. The service allows users to start investment from 100,000 JPY. By answering nine questions (see Fig. 2 below), Money Design’s proprietary robo-advisor’s algorithm selects an optimum combination from about 6,000 Exchange-Traded Funds (‘ETFs’) in about two minutes and provides discretionary investment management to the user.

Fig. 2 – Screenshot of questions asked to THEO users to create their investment profile 

The user’s answers will trigger THEO’s underlying algorithms to deliver the most optimal money management plan for the user (see Fig. 3). At this point, we’ll need to consider the artificial intelligence aspect of THEO. This is where the accuracy of the proposed plan, as generated by THEO’s algorithms, comes into play (see Fig. 3 below). As one Japanese investor commented: “I am an aggressive investor with a long timescale so I was surprised to see how conservative the allocation ended up.”

 

Fig, 3 – Screenshot of sample diagnosis results based on answering THEO’s questions

Main learning point: The key point with apps like THEO is going to be the accuracy and personal fit of the investment plan its algorithms will suggest to investors. I wonder whether any manual ‘tweaking’ is involved in assessing investment profiles and subsequent recommendations.

Related links for further learning:

  1. http://jftoday.com/THEO,+the+robo-advisory+investment+app,+exceeds+5,000+users+for+100days/
  2. https://www.bloomberg.com/news/articles/2016-07-12/hedge-fund-founder-turns-robo-adviser-for-japan-s-cash-hoarders
  3. http://fintechnews.sg/3137/roboadvisor/robo-advisory-services-asia/
  4. http://www.retirejapan.info/blog/japan-robo-advisor-theo
  5. https://theo.blue/
  6. http://www.investopedia.com/terms/e/etf.asp
  7. http://fintechnews.sg/3137/roboadvisor/robo-advisory-services-asia/
  8. http://www.theasianbanker.com/updates-and-articles/robo-advisors-poised-to-take-off
  9. http://uk.reuters.com/article/us-china-wealth-roboadvisors-idUKKCN10S2GT
  10. http://finovate.com/drivewealth-brings-robo-advisory-china-new-partnership-creditease/
  11. https://medium.com/@Mosaic_VC/trust-in-a-robo-advisor-world-62397cbe75fe
  12. http://www.wired.co.uk/article/how-ai-is-transforming-the-future-of-fintech
  13. https://en.wikipedia.org/wiki/Artificial_intelligence