Hi - This sounds interesting and is an area that has fascinated me for quite some time. I think it is possible to build an agent whose intelligence emerges from preserving viability, learning which perceptions/actions are worth their metabolic cost, and grounding concepts in sensorimotor experience. I have been inspired by the ideas of Karl Friston, Anil Seth and the like. Following!
Analysts and consultants who use excel frequently are poorly served. Valuable time is lost in pivot tables, filtering/refreshing data, and formatting slides.
We recently launched http://plot-ai.com to tackle this problem. It is a no-code excel add-in for data analysis, visualization and publishing (slides/web).
We have pilots going on and a lot of interest from folks in data science, consulting, strategy, finance, BI.
If this excites you as an end user or a developer I would love to chat. Ping me - [email protected]
We are solving this problem in a way with our product plot-ai.com. It is essentially a full blown data visualization platform within Excel with publishing capabilities to PowerPoint and the Web.
Would really love your take on this and open to chat. [email protected]
We traveled to Japan for 3 weeks and used Airbnb exclusively. 2 out of the 4 places we visited were sub par. While the images and the reviews made us believe that we were getting an awesome place, reality was starkly different. In one of the places we left in the middle of the night with a 1 year old in tow! The bugs, general cleanliness of the place, lack of AC got to us. Airbnb CSR was not cooperative at all saying, despite me juggling between international calls/messages and we lost the full fee for 3 days. Because we didn't give the host a chance to fix the issue, a situation we thought couldn't be fixed. Mind you we spent ~$5K with Airbnb on that trip alone. Though I live in the Bay Area and have friends working at the company, I was done. I will stick to hotels unless Airbnb develops better mechanisms to handle these situations or builds consumer friendliness into its ethos.
To illustrate - email has its own folder structure, slack has its own channels, confluence has its own pages and jira dances to a different tune. A better way to organize work would be around topics that are important to us, while these apps are relegated to the background.
We have wondered about this long enough that we decided to take a shot at solving it. Signup if you are interested. http://kraft-labs.peek.link/YE5
In this case Starbucks paid for it. But something similar could happen with a Square transaction where they may be held responsible. Was just making a point that there are costly lessons to be learnt in the world of processing.
A combination of things are responsible - a super old/competitive market (processing), the ever changing risk dynamics and Square's targeting
Let me illustrate points 1 and 2 with an example.
a) Square earns $2 in revenue per $100 of transaction volume b) $0.5 would be their gross margin accounting for expenses i.e. bank/visa fees c) The next biggest line item to subtract would be risk. If there is a fraudulent transaction of $100, then square has to compensate it with 200 transactions of $100 each. (200 txns *0.5 gross margin). Net effect is not only do they have to deal with low margins, they also have to manage risk very well. And this is not something you accomplish overnight especially as you enter new markets. Personal anecdote: I had $120 or so disappear from my Starbucks wallet a couple of months ago and the merchant had to eat the loss.
Lastly, large payment processors get out of this loop because they manage the big (Targets and Walmarts of the world) with the small and over time have fine tuned their risk engine. Square went into it in the reverse order, targeting mom-and-pop stores first (which have low volumes and high acquisition costs) and then trying their hand at large merchants (Startbucks in this case and they lost a ton doing this). In the process they never got risk management right for either segment. And oh, they did not go with an online first strategy which PayPal did in the 90s and the likes of Braintree and Stripe did recently.
A combination of things - a super old/competitive market (processing), the ever changing risk dynamics and Square's targeting
Let me illustrate points 1 and 2 with an example.
a) Square earns $2 in revenue per $100 of transaction volume
b) $0.5 would be their gross margin accounting for expenses i.e. bank/visa fees
c) The next biggest line item to subtract would be risk. If there is a fraudulent transaction of $100, then square has to compensate it with 200 transactions of $100 each. (200 txns *0.5 gross margin). Net effect is not only do they have to deal with low margins, they also have to manage risk very well. And this is not something you accomplish overnight especially as you enter new markets.
Personal anecdote: I had $120 or so disappear from my Starbucks wallet a couple of months ago and the merchant had to eat the loss.
Lastly, large payment processors get out of this loop because they manage the big (Targets and Walmarts of the world) with the small and over time have fine tuned their risk engine. Square went into it in the reverse order, targeting mom-and-pop stores first (which have low volumes and high acquisition costs) and then trying their hand at large merchants (Startbucks in this case and they lost a ton doing this). In the process they never got risk management right for either segment.