Empower Your Business With The Magic Of Machine Learning!
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Empower Your Business With The Magic Of Machine Learning!
Recommendation Engines are the key feature for the success of e-commerce , e-shops , content companies, providers and businesses such as Alibaba , Ebay , Google , Amazon , Netflix , Baidu, Youtube and others.
Scientists from JD.com one of the largest Chinese e-commerce websites describe the following to an academic journal
Many market surveys show that customers prefer personalized options.
People want to choose!
Bezos observed in the early days of Amazon .
If I have 2 million customers in the e-shop I must have 2 million shops there.
One for every customer!
Today it has 300 million customers and 300 million online stores.
Whether it’s shopping, texting, traveling, working, socializing, people want their devices to suggest places to eat, photos to share, friends to contact, things to buy.
The truly successful Recommenders go beyond mere commercial transactions.
Achieve Personal Curiosity and Discovery.
You may have noticed that many times you want to buy a pair of pants.
You want to put it on your night out. You’ve kind of imagined what it will look like but you don’t know exactly!
Recommenders often discover and recommend a pair of pants that might be a little —advanced- for you!
Something you might say …this is not for me…but in your heart you would like to try it!
It would take you out of your comfort zone!
It would take you a little further!
Successful recommendations make users feel good about their choices.
Suggesting an emotionally evocative spotify playlist is about the same as trusting a talented chef to prepare a special meal for you and your family.
What would you say if you don’t like it after all?
How about you finally get something amazing?
Something you never dreamed of?
Recommenders depend on Buyers’ shopping data, algorithmically enriched , to make recommendations that either pleasantly confirm or pleasantly surprise the buyer!
GroupLens , started by Paul Resnick of MIT and colleagues at the University of Minnesota in 1992, is the first Recommendation engine with collaborative filleting code
He was one of the early pioneers of Recommendations Engines.
It was a research effort to support the rapidly growing Usenet news reader community.
The GroupLens used quick ratings from one to five to evaluate news articles, combine ratings statistics and generate recommendations based on user profiles and preference similarities
The system had a scalable architecture and allowed for scalable recommendations.
The system automatically identified similar interests among a growing number of users, without them having to know each other.
GroupLens acquired a small client in 1995, a start-up that sold books over the Internet, Amazon.com
GroupLens was Amazon’s portal for Recommendations.
Amazon.com bookseller soon offered –If you like this author–
This feature that encouraged customers to click to literally expand their knowledge.
From the beginning, Amazon has taken mass personalization seriously.
The purpose of Amazon was to bring the customer new experiences and discover new things.
It then used product similarity algorithms (item based Recommenders ) based on product characteristics.
Similarity recommendations based on product features could compute better, faster, and cheaper than User Based Recommenders
For example if I have bought a 10-speed mountain bike and then I have bought a mountain biking book and then I have bought mountain exercise clothes and if there are many users who have also made the above purchases then you create a correlation between the products.
To a customer who buys a mountain bike, the recommender would then recommend a book on mountain biking or mountain exercise clothing.
Instead of finding purchase correlations through profiles of people, they started to find correlations between product purchases.
Bezos’ passion for personalization would be emulated by Netflixs Reed Hastings , facebooks Mark Zuckerberk , Linkedins Reid Hoffman, StichFixs Katrina Lake, Alibabas JackMa, Airbnb Brian Chesky, Spotifys Daniel Ek and many others.
Amazon has turned to another recommendation innovation that marries sales with customer experience!
Place recommendations in the shopping cart.
The idea of recommendations at the checkout is very old!
Food stores place candy and other items in the payment lanes.
But Amazon goes one step further.
It’s personalized!
Recommends items you will probably buy!
But the implementation did not go smoothly.
The main objection was that the recommendations would distract consumers!
The project has been stopped!
But then Amazon used the experimental culture it has developed!
As Bezos says, if you double your experiments you will double your ingenuity!
The project will proceed on a trial basis!
The results were amazing and the basket recommendations stayed in the basket forever!
In addition, Jef Bezos presented product reviews, something many retailers hate.
In a Harvand Business review, Jef Bezos was quoted in a prominent publication of critical product reviews…
Why do I allow negative customer reviews?
In the long term, a trusting relationship is more valuable to any retail business than a short-term profit.
Long-term customer value for the business is more important than a transaction or two!
The history of recommendations is the history of how people seek and perceive advice.
Whether offered by Pythia, or by the gods, by wise men, the stars or the dice, the recommendations should not command, but should present plausible paths and possible options.
That’s why they matter.
To learn how the top companies in the world increase revenue up to 30% from existing customers using Recommendation Systems and Personalization read the book below.
https://mobiplus.co/ebooks/customer-rediction/
Netflix and Hastings announced the $1 Million Netflix Prize in 2006.
The money would go to the contestant who would predict consumer ratings 10% better than Netflix called Cinematch.
They gave all contestants and the public over 100 million movie views with one to five stars for 17000 movies from 500,000 users.
Netflix realized that everything about their business would improve if they improved their recommendations.
The competitive publication of this commission contest was a smart bet on the innovation of the recommendations.
To understand the nature of the task imagine a giant spreadsheet with a row for each user (500,000 users) and a column for each movie (17,000 movies)
If every user rated every movie, the spreadsheet would contain 8.5 billion ratings.
The spreadsheet contained only 100m scores and filled 1.2% of the cells.
Therefore most of the cells were empty.
Amazing!
Three years later, Bellcores Pragmatic Chaos Recommender exceeded the 10% threshold and earned $1 million.
To do this, they created alliances with major competitors in the challenge and used their proposal algorithms.
So they combined their approaches.
The success of the competitions surprised both data scientists and digital innovators alike.
People, machines, online stores, companies need to adopt new ways of learning from each other and from data.
Dramatically improve their collaborative skills to anticipate what people really want.
The same data and the same Tools that Netflix used to help subscribers decide what movies they’ll watch helped Netflix decide what movies will produce.
The analysis provided by Recommenders was key to the company’s willingness to bet $100 million dollars to take the production of the series and now enter the film production business.
Recommenders suggest to Netflix managers what shows are most likely to win the curiosity, loyalty and money of consumers.
Recommendations are made up of algorithms.
Algorithms transform the data into relevant recommendations by finding, calculating and ranking the most interesting associations and co-occurrences for users.
Greg Linden who successfully pioneered Amazon’s first recommendation engines reports in 2017.
Recommendations and Personalization live in the sea of data we all create as we move through the world, including what we find, what we discover and what we love.
The vision… to offer an experience to every customer. ….that surprises and excites…..is still open!
The companies of the world should be recommender innovators like Spotify, Stitch-fix , Bytedance and thousands of others.
They should learn to continuously use Machine Learning and provide fairness and surprise for their users.
The companies that will succeed will be those that passionately embrace Machine Learning and AI as essential to their strategy.
Lartigiano increases revenue and cart using Artificial Intelligence!
See how he did it here!
Contact us now to see how the mobiplus shopping recommendation platform can give you the above features and increase your revenue. 30%.
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