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