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Online romance

Online romance

Typically, when a single adult wanted to find a date, he/she would ask friends or co-workers for recommendations. After a few dates they would likely discover their mutual interests and figure out if they want to go steady.

The advent of mobile apps has accelerated the trend of finding the love connection online. There are now a myriad of apps that have fragmented the industry. Some apps have evolved to meet the changing needs of users and new apps have emerged to create new trends (backed by lots of controversy). There exists tons of claim about the prowess of their match making algorithms.

Match, okCupid, Tinder, Zoosk, FriendScout24 and eHarmony are some of the popular names.

These apps/sites are not independent companies for the most part. Match owns OkCupid, Tinder, Meetic and Plenty of Fish among others. Match itself is owned by IAC. Spark Networks owns Jade, ChristianMingle and BlackSingles.com. In this business, scale is very important and companies acquire others to boost customer base. Recent financial results from Match shows credence that online dating apps are making money. Dating revenue for Match for the financial year was $260 million.

Despite seeming consolidation, the acquired properties continue to operate as their own brand. Why could this be? One would think that cost of operations and cost of customer acquisition is probably more efficient with one brand. Likely reasons for the continued separation are:-

  • Each site is targeting to different demographics and risks dilution and customer churn by moving to one brand.
  • They have not yet figured out how to create ONE matching model that works for all demographics, so a technical challenge in integrating new acquisitions.

With machine learning, we assume that with more data set available to the model, the algorithm learns and performs better. If users report on the success or failure of their matches, that provides a feedback loop for the model to improve itself.

For centuries, Indian match makers have followed a match making algorithm. It does not take into account aspects like an individual’s interest and is purely based on positions of the stars and planets at one’s time of birth. Each person gets a grid with planetary positions (called a horoscope). Two people are deemed suitable for marriage if their horoscopes match. Highest number of matches is 10 and is considered potential marital bliss. This is my simple understanding. The horoscope is a worthy consideration as a data point for the machine learning models.

I attended a AI/ML Summit organized by Madrona yesterday. Great event. That got me into thinking about applications where ML is already in use, hence this story.

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Clouds

Clouds

Your origin, I don’t know

Destination unknown.

Carry memories

Of places you visit?

Ever got a Passport or Visa ?

No tolls, no TSA.

Amorphous, yet reflective

of my mood.

I envy

Your ultimate freedom.

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What’ll you have to eat?

What’ll you have to eat?

Food is an essential for life. Over the centuries, humans have innovated with food and the fundamental fact is that one can’t survive without it.

On a daily basis, it takes hours to plan, make, eat and clean up food. Multiple times per day. 365 days per year, barring the days of fasting observed for various reasons. If we can shave off time per day on this activity, we can get to indulge in more TV, online games, social media or hobbies in the garage!

The advent of the mobile phone, sophisticated B2B software and a courier network fueled by the on demand economy has created opportunities for many companies to have a go at your food budget. It looks like we are happy to trade our money for the convenience of food delivery.

GrubHub is a leading Chicago based company in food delivery. They reported Q1 2016 earnings of $112 million (27% increase YoY). They also claim 6.97 million “Active Diners” (ever heard of passive diners?), a 24% increase YoY. Daily Average Grubs were 267,800 , a 14% increase YoY.

There are various models in food delivery.

  1. Company handles ordering and payment, restaurants or chefs do the couriering themselves or use a Courier network. ex: Grubhub, Lish. This is traditional restaurant or chef based fulfillment.
  2. Company handles ordering, payments and delivery — PostMates, DoorDash, Amazon Now.
  3. Company handles everything except delivery — cooking the food with a proprietary menu in modern commercial kitchens, ordering, payments, delivery. May use a courier network — ex: Munchery
  4. Company hires people to move food around town in vehicles, hoping people order and are happy with instant fulfillment . Ex: UberEats.

Would you want to order a Taco that has spent a few hours in the back seat of a Uber Car, regardless of a classy container it has spent time in? May be, if you have a hunger attack and rendered immobile.

There is likely a bunch of hybrid models and experiments being conducted in food delivery.

Businesses are a major customer. If an admin has to feed tens of people in a few hours, a food delivery service is a godsend.

A few food delivery businesses have shut down in the last few months.

What’s a food delivery startup do to succeed? Some common sense thoughts:-

  • Founders must have previous logistics or operations experience. Just not enough to be a foodie.
  • Focus on a metro area and learn, learn learn. Know what KPI’s matter.
  • Move to the next area. Know something unique about that market and deliver to that.
  • Raise a ton of money. Train has likely left the station for food delivery startups so better have done this already.
  • Find out areas or customer segments that the big guys don’t operate in. Breakfast ?

It looks like an acquisitive space too. GrubHub acquired Seamless and has recently acquired LABite.

If you have any thoughts, please comment.

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FANG Power

FANG Power

The acronym FANG, as many investorrati (take on Illuminati) know, stands for Facebook, Amazon, NetFlix and Google. FANG performed very well in 2015. For the Quarter ending March 31, companies reported earnings mid April. I wanted to get a top level idea of the numbers put up by these giants. Here is a table for a handy comparison:

FANG Q1 Comparisn + MS/AAPL

Facebook and Amazon hit it out of the park. Their guidance was also stellar. Netflix faces stiff competition and investing heavily in international expansion. Apple came to grips with law of very large numbers and does not have a hit product in the horizon. Google’s Youtube is still growing. The company seems to be spending more to acquire mobile customers.

The strong dollar also impacts earnings, but likely impacts all equally since they are global corporations.

MS has work cut out to re-accelerate growth. It’s a good value stock.

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Your unique twist…

Your unique twist…

Entrepreneurs love to talk about a problem they are solving, targeting existing inefficiencies or how their product adds value to a consumer.

That’s a great start. It’s also quite likely that incumbents have identified the same problems and inefficiencies and have solutions in the market.

To begin, table stake features are needed to start conversations with investors, customers and partners.

If there is no twist in the story, reception is likely flat.

The twist has to be strong, emphatic and create an “aha” in the listener.

That twist needs to be honed time and again as it gets socialized over and over.

People refer to this as USP — the Unique Selling Proposition aka differentiators. Great CEOs and talented sales persons are relentless in discovering, documenting, socializing and following through on their USP.

Can simply world class execution be your USP? It’s hard to gauge this beforehand unless you have already notched a great backstory. It’s powerful to ground the USP on a solid understanding of the landscape. It makes the conversation very exciting because you don’t have to “follow up”.

Best of luck in differentiating.