After my last blog post on the mobile app ecosystem, I received a lot of positive feedback and had some very interesting discussions. I decided to do a followup analysis that digs deeper into the install behavior of the Apple App Store. Using install data for the last year, I looked at trends for the top 5 apps by installs and analyzed how the data fluctuated over time. Installs spiked during Christmas and Valentine’s Day as smartphones and tablets are often gifted during those holidays. School seasons also had a large impact particularly to social apps as students are a big part of their demographic. Aside from seasonality, the App Store is also more active on the weekend than on weekdays, which is likely due to people spending more time on their phones and tablets. What this all means is that when developers plan launches or promotions, they need to be aware of the install behavior on the App Store. Different strategies can be used to either target hot periods where there are a lot of eyeballs or to target days where the competition is less fierce.
During Christmas, I was traveling and noticed that numerous people, from young to old, were addicted to mobile games, ranging from casual to educational. This prompted me to wonder what types of apps have the highest revenue and popularity. I pulled 2015 U.S. data from App Annie that includes app installs and revenue. Gaming apps dominated the top grossing charts while social apps led the free chart with the most installs. Another interesting difference is that in the top free chart, the installs were evenly distributed. In the top grossing charts, gaming apps have a lion share of the overall pie. There is even a stark difference in revenue between number 1 and 2. For example, Clash of Clans ranked first and made $507M compared to Game of War which earned $389M. It is astounding how much money these games pull in considering only a small percentage of users pay. This might explain why a lot of people want to make apps and get a share of this app gold rush.
With the Powerball jackpot approaching the $1.5B, friends, family, and coworkers are all in a lottery craze. The odds of winning the jackpot is 1 in 292,201,338. At $2 a lottery ticket, simple math suggests that a jackpot of at least $584,402,676 would yield a break even expected return. But the world is not that simple and straightforward. In this article, it accounts for various factors such as tax, multiple winners, lump sum discount, and non – jackpot prizes. The jackpot has to be at least $2.3B for the expected value of a $2 ticket to break even. It’s rare that the jackpot exceeds $1B. The odds are very much in favor of the lottery company but yet for that hope of an overnight fortune, people are still willing to take chances.
Another interesting phenomenon is that people tend to be biased towards winning the lottery versus the probability of unfortunate life events. Here is a table that shows a list of events that have a higher likelihood of happening than the lottery.
Golden State Warriors is on pace to be the best team in NBA history overtaking the 1995 – 1996 Chicago Bulls. Stephen Curry is leading the charge for the Warriors on this journey. Young basketball players across the nation are all aspiring to be the next Stephen Curry. What are the chances of a high school basketball player becoming the next MVP? I took a look at the number of basketball players at different levels starting from high school to NBA.
Silicon Valley has always been known as the home to tech behemoths that produce gadgets used worldwide. Recently, it has also produced million dollar shacks. Home prices in the valley have always been above average but in recent years, affordability is at an all time low with even more demand than the pre – 2007 Housing Bubble. There are two reasons behind this phenomenon. Tech IPOs have increased the purchasing power of many individuals, allowing them to afford homes at higher price points. Also, the increase in foreign investments from China have fueled the spike. However, with the number of tech IPOs decreasing and the Chinese economy slowing down, there will be a correction soon.
Most of us have experienced the dreaded feeling of sitting in traffic and wondering how long it takes to go home from work. On a daily basis, I notice that in the morning, going southbound from San Francisco is congested and this pattern is reversed in the evening after work. This triggered the following question: which city has the shortest and longest commute in the Bay Area? Why does one side have worse traffic than the other? Here is a map that shows the average commute time by area of residence. East Bay and San Francisco residents experience the longest commute (~31 minutes). This could be attributed to how most companies are located on the peninsula, so naturally people across the bay would have longer commute times. Furthermore, there are much larger tech companies (Google, Apple, and Facebook) in the South Bay. Next time, if you are making dinner arrangements on a weekday, do consider the flow of traffic and plan accordingly.
In the past two years, there has been numerous food delivery startups. They range from on-demand restaurant delivery services (i.e. Doordash, Fluc, and Postmates) to prepared grocery subscription based orders (i.e. Blue Apron). With food delivery currently being one of the hottest VC sectors, who will win and dominate the market?
Of the numerous food related companies, Blue Apron seems to be promising. Given how competitive the food start up scene is, their growth and revenue figures are impressive. They did not release revenue figures but revealed that they deliver 3M meals every month. They grew from delivering 1M meals monthly to 3M in seven months. Assuming the same growth rate, their revenue will reach $334M for second half of 2015. Blue Apron has the potential to expand as Americans are trying to eat healthier. The switching costs for prepared grocery subscription based service is higher because there are more added value factors such as type of recipes, difficulty of recipes, and the way ingredients are packed. This is in contrast with services like Doordash and Fluc where switching costs are low. If they are to succeed, then they need a way to differentiate themselves. However, all of these services face the same challenge of being able to scale logistically. It will be interesting to see how they will be able to efficiently scale with growth.
Instagram studied the rising popularity of emojis and claimed that they are becoming a common form of expression. 50% of posts from Western countries now contain the use of emojis.
Communication has evolved over time and the trend is that words are becoming minimalistic. People’s attention spans are shortening. In the early days of Internet, people wrote emails. Shortly after, texting arrived. Furthermore, Twitter allowed a maximum of 140 characters per tweet. And now, more than half of the text on Instagram contains emojis.
This is not surprising given how Instagram is so visually focused. The selection of users can partially explain the trend. People who like to visually express themselves choose to use Instagram in the first place. Emojis allow users to take that a step further.
I am curious of the uses of emojis in texting and messaging apps (WhatsApp and LINE). Since the nature of those apps are not as visually focused, would those users still utilize emojis as frequently as those who use Instagram? Would we observe the same trends?
Many companies like Google and Tesla are investing in self driving cars. Self driving vehicles allow everyone to travel easily regardless of their driving ability. Because they prove to be safer, they will have a large impact on the society.
Fatalities are the largest cost in car accidents. On average, 92 lives are lost daily in the United States in incidents related to distracted drivers. Since self driving cars would have prevented these deaths, they would have saved 33,580 lives yearly. That also translates to $519M in savings related to medical costs. Driverless cars would have a dramatic improvement on the society. However, even if the technology become scalable, it would take time for people to adopt. In the past, we have seen government subsidies for new technology such as electric vehicles. If we expect mass adoption, then the government is likely to provide incentives to compensate for switching costs.