Comparing the User Interface in Indian Websites in ECommerce

I did a  search for ” samsung 4g mobile” across the following – Google Search, Askmebazaar.com, Amazon.in, Snapdeal.com, Flipkart.com and got following results.

I wanted to compare user interface, the search results, the navigation experience, and lastly any price arbitrage opportunities. Part One of this series just looks at initial search results for a single keyword and compares it across websites.

Findings-

  1. Apparently FlipKart does not believe in Adwords, or SEO

https://www.google.co.in/search?q=buy+a+samsung+4g+mobile+in+india&oq=buy+a+samsung+4g+mobile+in+india&aqs=chrome..69i57j0.4451j1j7&sourceid=chrome&es_sm=122&ie=UTF-8

Screenshot from 2015-10-17 09:36:262) Amazon has a clean interface and good graphic icons for the product

http://www.amazon.in/s/ref=nb_sb_noss?url=search-alias%3Daps&field-keywords=samsung+4g+mobile

Screenshot from 2015-10-17 09:34:45

Surprisingly it suggested in associated products – a beard trimmer for me. Amazon also prompts saying how the item is limited and only X are left in stock.

3) Flipkart sells FlipKart itself (of apps etc) in half the page and has no associated results ( you may also buy this- or add to cart)

http://www.flipkart.com/search?q=samsung+4g+mobile&as=on&as-show=on&otracker=start&as-pos=1_q_samsung+4

However I liked the ordering of the filters in left margin, with price on top, then brand etc. Quite clearly price is the top filter in such sites.

Screenshot from 2015-10-17 09:33:29

4) Askmebazaar has a menu layout in horizontal rows and columns than a list layout. Interesting to see the Indian origin interfaces had menu layout while the US derived websites had a list (top to bottom layout)

It was interesting to see that every price in it had a discount with a strikethrough in the initial prices. Striking and interesting as its different.

http://www.askmebazaar.com/index.php?

Filters in right margin were Category first, then brand ( both irrelevant since I am askign for Samsung Mobile) and price at bottom. Price was a dropdown filter than a radio button or checkbox

filterapp_data=c2VhcmNoX3F1ZXJ5PXNhbXN1bmcgNGcgbW9iaWxlJmRlZlNlYXJjaD0w

5) Ebay has really awesomely long URLS and is a mix of Amazon and Askmebazaar interface

http://www.ebay.in/sch/i.html?_from=R40&_trksid=p2050601.m570.l1313.TR10.TRC0.A0.H0.Xsamsung+4+g+mobile.TRS0&_nkw=samsung+4+g+mobile&_sacat=0

Screenshot from 2015-10-17 09:52:14

6) Lastly Snapdeal had a nice clean interface and it was less cluttered than some of the other sites. I was also interested that they showed a prompt to buy iPhone so an interesting association analysis result.

Which e commerce company in India has the best user interface? You decide. Beauty and ease lie in the eye of the beholder.

Screenshot from 2015-10-17 09:33:59

AI versus BI -Machines can’t learn but Nerds hate Humans

Beer is an alcoholic beverage produced by the saccharification of starch and fermentation of the resulting sugar. The starch and saccharification enzymes are often derived from malted cereal grains, most commonly malted barley and malted wheat.

Organizations especially in technology consist of a mixture of business managers and technology managers. Bizops and Techops. Jocks and Geeks.

Business managers like to think of using strategic as a prefix to anything. Strategic thinking, strategic planning, strategic campaign , strategic project. Strategic is a word to make things look more important than they are. Anything that has the word strategic should have more than a 12 month gestation period.

I come across machine learning often. It is a cute term and it ranks in terms of abused terms often. Algorithm is another abused term. You say algorithm but what you mean is you need a function.

Basically machine learning means your machine can learn. That by itself is an oxymoron two words that are self-contradictory juxtaposed together. It should be program learning or code learning. Your code or program is trying to approximate learning by taking in outputs  in a previous step as inputs in a future step. Learning is presumed to you can learn only from past experience.

My Dell Laptop has not learned anything. My Dell Laptop is the machine. Sometimes I use functions from packages in languages. These functions are based on well-established algorithms like apriori, k means , and even math theorems like Bayes. I am not learning anything. My machine is not learning anything. the function converges to a solution based on math written by a human, coded by a human, implemented by a human and finally called by a human. Where did the machine in the machine learning come from?

It is derived from Artifical Intelligence. That is another oxymoron. If something is intelligent what is artificial about it. Isn’t a work created by a human as natural as a work created by Nature.

Most nerds and geeks  I have met are not sorted emotionally. They can tell you half a dozen algorithms to sort data, but they don’t know when to shut up, when to listen, when to say yes and when to keep quite than just say no. These are humans that are full of knowledge but won’t learn about human behaviour as fast as they learn about algorithms. Geeks dont make good business managers. Atleast most geeks. I personally cant make out the difference between a geek and a nerd and who gets paid more and who has more fun.

But geeks are incredibly focused people doing tasks that are incredibly boring to normal humans. The reason they dont gel with other humans is because hey they didnt spend time with other humans – they retreated to the safety of the machines and computer screens a long time ago in their formative years, and it paid them enough to keep staying and spending more time with machines than humans

Humans are just as important as machines, maybe more. The geek writing the algorithm can learn more much more about psychology, philosophy and creative liberal arts that affect human behaviour.  That human behaviour generates the data that the algorithm runs on eventually. Cross training your geeks to get insights about humans and cross training your business managers on the difference between AI and BI, machine learning and statistical modeling, what is an algorithm and what is a package.

Artificial Intelligence AI versus  Business Intelligence BI is the new phenomenon. Tech teams brought up on dashboards and reports have to adopt to machine learning algorithms incorporated as decision-making assistance tools. More AI in your BI maybe.

Cliches and buzz words and hype jargon are pick up lines in business world. They can get you some action for sure but they cant get you into Heaven. Sort your geeks emotionally and your business managers algorithmically. Beer is a great way for lonely introverts to bond with flashy extroverts.Now that is just my personal beer algorithm. You may discover your own beer game for your organization.

 

 

 

 

Introduction to Sufi Music

Why Sufi Music is the best example of two different religions and people can coexist and have a big party.

Sufi Music

Sufi music is the devotional music of the Sufis, inspired by the works of Sufi poets, like Rumi, Hafiz, Bulleh Shah, Amir Khusrow and Khwaja Ghulam Farid. Qawwali is the most well known form of Sufi music, and is most commonly found in the Sufi culture of the Indian Subcontinent.

yeh jo halka halka suroor hai                        – This slight intoxication that I have
ye tere nazron ka kasoor hai                         – Is all the fault of your looking at me
ke sharab peena sikha diya hai                     -That has taught me to take up drinking
tere pyar ne tere chahne                                                                            —– Your love and your wanting
teri bhehki behki neegah ne mujhe ek sharaabi banadiya                 —–Your intoxicating  looks has made me an alcoholic
pilaadi hai apni kiss nazar se ke mujhe hosh nhi hai                          —– You have served the drink with a single look and now I am not in my senses

 

 

Eleven lessons for Startup teams from Ocean’s Eleven

Creating a startup is like giving birth to a baby. Sustaining the baby you need a team of concerned protective disciplined and skilled people. Skillset and mindset are the operating words. The skills and the minds of the diversity in the team must gel.

Danny Ocean had an excellent idea to repay his debt to society. He set up a team of skilled hackers and scientists to achieve a common goal. No birds were hurt in Danny’s startup. Even the bad guy won his money in insurance and the French guy lost his money in the sequel. But this not about Ocean’s Twelfth  where clearly Danny and his merry men went overboard and hired a bridge too far. This is about the original play on the original startup. So here are the eleven lessons.

  1. Skillsets should be complementary- The grease monkey and the smooth talker are different skill sets.
  2. Team members should have cross-functional skills. In a crisis you need backups. In terms of volatility you need options. Suppose your Hadoop guy called in sick. Cross train your tech team into different technologies and cross train your business team into different skills like leads. deal origination,
  3. The big guy who lends his name to the team needs to have Charisma to do the recruitment for first few chaps
  4. You need a smart guy to handle the Operations like Rusty and you need a guy with a lot of money with nothing to lose like Ruben. Ops and Finance are the first few hires you will make Danny Boy.
  5. Leader needs to have integrity. If that is questioned Leader needs to be flexible. If Leader has a secret personal reason to do the op in a startup, it goes better if that is known to the top guys in the startup team, but not all the members in the startup team have the same need to know.
  6. You need young men who are hungry and eager to prove their mark in the world like Matt Damon. Young men who are skilled but not known well enough are easier to recruit to your startup team . With a limited budget you need a few good young men in your team.
  7. Diversity happens by karma not by design. Choose people based on skills and mindsets. Ethnic orientations should not paly any role in positive or negative decisions in the recruitment. If the startup idea is good, the black guy and the chinese guy and the jewish guy and old man and the young man will play fine. If the idea is bad, not all the liberal books you read can set it right.
  8. Be ruthless when dealing with ruthless situations but be classy always. Class shall always count. You may think of your startup idea all the time but it is most probably not your first and not your last job. Most of us dont get the luxury od dining on the Job like Steve Jobs did. your classy behvaiour will help you in your career beyiind your startp team and even in your personal life.
  9. Dont forget the little guys. The little guys help with blueprints, intel, product design and product delivery. Dont forget them.
  10. Be open to hire more.
  11. Be loyal to the team, and dont fire your people when you are down. Dont crap on your team when the shit hits the fan. Take the shit and swallow it and smile back. that’s what a fearless leader does.

You be good to your team, and your team will rob the casinos and get you girl. BE nice and BE Kind to your team. In case of doubt, watch the DVD again. It’s excellent motivation.

Fashion Models, Role Models and Regression Models

During my stay in Hauz Khas Village I used to come into contact almost on a daily basis with fashion models. I had friends in the business both on the designer and on the modeling side. Ok my friends were not so big, and I never got no party tickets. Who has time to party when one is writing a blog? Work-life balance for a blogger is just sleep-shit-eat-write-read-think-procrastinate-sleep.

Fashion models have a tough profession. They are expected to be nice else they are a bitch or a brat. There are more catty bitches and more bratty snitches in the fashion business than in a movie on Whitey Bulger. Fashion models are expected to be thin. Smoking cigarettes and drugs helps them stay that way. But in the public they will say they were born to be thin. They attend amazing parties with amazing free food but are not expected to eat or swallow more than a morsel or two. Fashion models have a short shelf life and get not much respect in the unfair game of life.

Role models used to be sportsmen like Joe Dimaggio, Jackie Robinson , Kapil Dev and Tendulkar. Tendulkar who? Don’t do that Russian roulette? it will give you tennis elbow in the chin. Ok, ok. Tendulkar just is the man who made the largest number of runs in cricket. Think Babe Ruth multiplied by 20 in statistics and divided by 5 in terms of appearance and size. Role models used to be businessmen who gave jobs. Role models used to politicians who actually wrote the speeches they were going to say and actually kicked bureaucrats to do the work the politicians promised in their speech. Remember Kennedy and the moon shot speech. Now thats a role model for me and Happy Birthday to you Mr President. My role models were Charles Dickens who wrote himself to death and Churchill who won the Nobel Prize for Literature after winning the World War and turning down the Dukedom of London. Churchill before Sartre turned down the Nobel, when Brando still accepted Oscars, when Ike was still building and not talking about the mil-ind complex and Churchill a few years away from the crazy last years and the truth on how we overlooked a few things like genocide, famine, holocausts in his quests for Anglo Saxon supremacy. Churchill was my role model because he could write his own speeches and get his team deliver on those speeches.

My role models got obsolete. My role models got old and died. My role models sold out for cash from the advertisements on Television.

Role models for the next generation are who made the quickest billion in technology, who dated the most in the least time in acting and no politician makes the role model list unless you wanted to be a politician yourself. Paris Hilton gave us a night in Paris, Kim Kardashian who gets invited to the Presidents dinner with journalists, Olympic medallists and hacker snitches like BradleyManning,Snowden and the rappers dating J Lo are the role models of the next generation.

The nerds, the geeks, the quants, the scientists rebelled against this and made their own models. Their regression models helped the money bags sell the right kind of ad on the best kind of content so the middle of the intelligence curve sheep can keep spending money in debt in debt in debt.

Fashion models look good but are broke. Regression models make you rich but can get ugly. Role models are the ones that help you balance your needs with your greeds, with your hunger and ambition to look good and look glorious and your enduring need to balance your energy with long term non-burnout success.

Marilyn Monroe, Churchill, Einstein were the old models. Kim Kardashian Jenner West, Barack Obama, Zuckerberg are the new models. Choose your models wisely. Or enjoy the hetero scedasticity that follows.

Post Script-

(image from

http://rarehistoricalphotos.com/ticket-to-armistice-japanese-leaflet-dropped-on-allied-troops-1942/

Lieutenant Colonel Mahmood Kan Durrani who was an Australian prisoner of the Japanese quotes a lecture given by a Japanese officer on how leaflets should be prepared. One of his six recommendations was:

“The leaflet should have, if possible, the picture of a beautiful woman, after the method used by the Germans in the First World War. This device would insure that the soldier would be attracted and would be unable to resist looking at the picture over and over again. This would rouse his passion, and his heart would be inclined for love and to hate fighting.”
Sex and Psychological Operations by Herbert A. Friedman

Screenshot from 2015-10-13 20:58:33

Nobody sees the invisible data scientist

You are a data scientist if you help turn data into decisions. This may be a non-glamorous excel sheet, Python or R, or writing one more query to your RDBMS. Data to Decisions is the key. Don’t turn data into just  one more Powerpoint or one more spreadsheet  for management to say hmm, interesting and then ignore. Be a  data scientist for the next epoch not just for surviving the next meeting.

Don’t believe what they told you in Harvard Business Review on competing on analytics. I am trying to talk you into competing within analytics.

Learn one new thing a day. This may be trick in coding, a function in R or a library in Python. Read a lot of technical blogs. Write one  blog post a week. Clarity of thought is only proved when you can write clear words. Blogging is a great way to build your personal brand.

You are not a data scientist if you are in the middle of Drew Conway’s Venn Diagram, that lovely impossible sweet zone of being balanced in business, coding, and math. You are a data scientist if the world acknowledges you as one.

So do those free courses or MOOCs, and do those hackathon contests, but write one blog post a week and learn seven new things in data science in a week. Learning one thing every day. That’s just it. Look at babies. They learn so many things rapidly.

Suspend your cynicsm and your greed for a year or two. Focus on the knowledge. Knowledge shall set you free, but getting paid is what makes you rich. People pay data scientists for their skills, but also their branding. No one wants to lose their job because they hired a sexy data scientist. Help your client or your boss look good in front of their bosses and clients. The one way you can do that is excellence.

Like Bill and Ted, focus on your excellent adventure in data science. Tools- check. Techniques -check. Business reading- check. Blogging-double check. Make a checklist of things you need to learn every day, every week, every month, every year.

Go to meetups, you putz. Dont just sit home on the weekend. Go shake hands with your fellow data scientists. Only way you can beat them is learning more things faster, being branded as a better data scientist by your writing and social media, and finally by being known in the data science fraternity as the person to go to when stuck.

Data scientists are fire fighters with code. Fight the fire in the business and a day will come when they celebrate you as a hero. Put in 10,000 hours of practise in data science. Start from giving half an hour to blogging every week, and half an hour to reading about code and techniques every day.

You give nine hours to the job, two hours to your commute, three hours to the family. You even give six hours of sleep as your brain reboots. Give yourself half an hour.

No one sees the invisible fire fighter. No one sees the man who knows a lot but is too shy to explain , share or give away a part of his code, his knowledge and his wisdom.

Code well. Dig Data Hard. In the future everything related to a decision will have a data scientist lurking somewhere. Be the guy they trust for decision making assistance.

Dont try to maximize your brilliance in your go to visible efforts, instead focus on minimizing your incompetence. Curious people often find solutions wise men overlook . What made DJ Patil, Hillary Mason, Hadley Wickham celebrated  data scientists. Not just Their ability to learn and create, but also  their ability to expand and share their learning. Surely you can share some stuff and improve your visibility.

Screenshot from 2015-10-13 08:18:53

 

 

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