Article | August 15, 2020
In Augmented Apps, we examine how product teams are exploring AI and Machine Learning to make their products more intuitive and enhance the user experience. Artificial intelligence is transforming products in surprising and ingenious ways. Whether it’s core to the product, as with a stock market forecasting algorithm in Quants, or a peripheral component, such as a healthcare domain chatbot that diagnoses diseases via dialog with a patient, building reliable AI components into products is now part of the learning curve that product teams have to manage.
Article | August 15, 2020
As the world looks for a way to manage the spread of the coronavirus while getting back to a more familiar way of living, how can apps provide a solution that is both effective and respects the privacy of all citizens?It seems that no other app has attracted so much attention over the past weeks as the coronavirus app, described as a powerful tool to curb the spread of COVID-19 so we can go back to normal life.
Article | August 15, 2020
Implementing a marketing mechanism that generates leads and brings in sales is the biggest challenge e-commerce businesses face.
However, Amazon and Alibaba, two e-commerce giants seem comfortable in this aspect.
They are doing something different.
Amazon and Alibaba invest heavily in research and development. Amazon spent $42.7 billion in 2020 on “technology and content." Alibaba spends $8,736 in R&D in 2021.
Alibaba’s Tmall Innovation Centre (TMIC) started mining the data of over 600 million users, from its Tmall B2C e-commerce marketplace in 2017. (Atom Thought)
The AI exploits of these two e-commerce multinationals cannot be overemphasized.
Being a senior software engineer turned management consultant, I can attest to the fact that the majority of e-commerce startups launch and vanish partly because they set out with a vision based on the success of Amazon, Alibaba and eBay.
Most e-commerce startups may not basically just want to take over the world per say but to at least control their local markets. Which is not a bad idea.
Nonetheless, it remains very difficult to get more customers but Amazon and Alibaba make it seem simple. Why and how?
5 practical ways Amazon and Alibaba use AI and data mining to increase sales.
1: Product recommendation
Alibaba and Amazon both use AI and data mining to do effective product recommendations to consumers. Alibaba has developed a software called “E-commerce Brain,” which uses real-time online data to predict consumer wants, and the models are constantly updated for each individual through AI to take into account purchase history, browsing history and online activities.
In fact, a team of professors from the University of Toronto notes in a Harvard Business Review that given additional data (such as that provided by Amazon’s purchase of Whole Foods), the company could eventually become so accurate that it could someday turn a profit by shipping people items it predicts they will need.
This product recommendation makes buyers interactions on their ecommerce platform intuitive and seductive to stay.
2: They use AI to shorten consumers shopping time
AI technologies such as voice and visual search are growing powerfully and most ecommerce giants are making good use of it to shorten consumer shopping time. I enjoy using Amazon’s Alexa on my iPhone 11 pro as it recommends a product or answers a query after a voice search.
The fact that voice search and other sophisticated technologies can predict buyer intent is simply fascinating and that is why they keep acquiring more customers every day.
3: AI, data mining helps with smarter pricing
This is very logical. As a small ecommerce owner, you will definitely face the challenge of formulating your pricing policy that can win the heart of consumers out there. You may have to do the research yourself or pay expert business developers to that. However Amazon and Alibaba don't do all that.
They deploy intelligent services by way of AI and data mining to source different types of products worldwide, compare their prices and come up with the best price among all. This makes their platforms a one stop and reference to all online buying and selling businesses as far pricing is concerned.
4: Amazon and Alibaba use algorithms to manage supply chain
I don’t find it funny to go to an online store, order a product that is said to arrive in 3 days and it ends up arriving in one week. To me it is a red flag on the supply chain process of the online store. With this in mind, Amazon and Alibaba have figured this out by integrating AI in their supply chain which predicts the logistics, delivery date, stock levels per purchase.
Amazon even uses drones for quick delivery. Alibaba is equally exploring the development of a smart supply chain in China through its Ali Smart Supply Chain (ASSC) platform, which predicts volatile buyer trends so sellers can focus on improving their product, inventory and delivery operations. All these make the buyer's shopping experience seamless and enticing.
5: They use AI and data mining to build brand loyalty with consumers
A combination of a seamless buyer's journey, consumer intent prediction, best pricing, smart supply chain, attractive UI/UX coupled with industry experience and brand reputation gives Amazon, Alibaba and other e-commerce giants a big edge over others in the customer acquisition.
The power of AI and related technologies like big data, block chain and machine learning cannot be overemphasized these days. Whether you are running a small scale or a large scale business, exploiting the advantages around these technologies is paramount. There are thousands of companies out there that can see you through with the implementation of these technologies.
Article | August 15, 2020
Artificial Intelligence is empowering business leaders to make better, data-driven, and insightful decisions. It has undergone several evolutions since it burst into the business scene in the 1950s, to the point where several thinkers have already painted a machine that replaces human scenarios for the future. Our view on the future of work has evolved into a zero-sum game, where the result is an either-or.
In my opinion, the view that AI will play a dominant role in the workplace is a little extreme. The fundamental assumption around AI replacing human workers is that humans and machines have the same characteristic. Totally untrue!. AI-based systems may be fast, consistently accurate, and rational, but they are not intuitive, emotional or culturally sensitive. Humans possess these qualities in abundance, and it is one of the reasons why we continue to surprise the world with our advancements.
Intuition is the Mother of Innovation
If we are living comfortable lives today, it’s because some business leaders chose their gut feeling over data analytics on numerous occasions. Some historical examples have been:
1: Henry Ford, facing falling demand for his cars and high worker turnover in 1914, doubled his employees’ wages, and it paid off.
2: Bill Allen was the CEO of Boeing in the 1950s, a company that manufactured planes for the defence industry. One day, he woke up to the idea of building commercial jets for a sector that was non-existent – civilian air travel. Allen convinced his board to risk $16 million on a new transcontinental airliner, the 707. The move transformed Boeing and air travel.
3: Travis Kalanick faced serious pushback when Uber instituted surge pricing. His move seemed to anger and alienate everyone. Travis stayed the course, and Uber modified its surge policy whenever appropriate. Now, dynamic pricing is an accepted aspect of this business and many others.
So the question is, should a competent professional trust their gut feeling or make data-driven decisions?
DATA V/S GUT
Top professionals have repeatedly confirmed that gut feeling is one of the main reasons for their success. Leadership often gets associated with quick responses in unprecedented situations and lateral thinking. Experienced leaders are not only fearless about their instincts but are also proficient at making others feel confident in their judgment. Also, going with our instinct can help us make decisions quickly and more accurately since we tend to make choices based on experiences, values, and compassion. Malcolm Gladwell calls this ‘thin slicing’ in his book, “Blink”. Thin-slicing is a cognitive manoeuvre that involves taking a narrow slice of data, what you see at a glance, and letting your intuition do the work for you. However, he does warn that some decisions are exempt from this rule; it only applies to areas where you already have significant expertise.
Artificial Intelligence and machine learning can support leaders to see complex patterns that can lead to new understanding in this fast-moving, digital era. The contention is that ‘human gut’ feeling can go hand in hand with AI – each supporting the other to achieve balanced outcomes.
A Joint Venture Between Head and Heart
Many see AI as an aid to human intelligence, not a replacement. To be one-step ahead in the AI era, professionals must learn to balance human and machine thinking. Organizations will have to showcase the ability to use the correct information at the right time and take action. It’s about using your instinct to take advantage of data and transforming that information into timely business decisions. AI is not yet ready to replace the human brain, but it has matured into an effective co-worker.
Will intelligent machines replace human workers sometime soon? I guess not. Both have different abilities and strengths. The more important question is: Can human intelligence combine with AI to produce something experts are calling augmented intelligence? Augmented intelligence is collaborative, and at the same time, it represents a collaborative effort in the service of the human race.
Figuring out how to blend the right mix with the best of data-driven deliberation and instinctive judgment could be one of the most significant challenges of our time.Enable GingerCannot connect to Ginger Check your internet connection
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