In the world of e-commerce retail, many of us have current strategies that can fall under the dynamic pricing model. For example, most eTailers will constantly monitor their competitor’s prices, and price accordingly. Some do this manually, and some have complex algorithms, constantly moving prices up and down, sometimes by increments of pennies, to find the perfect and most competitive price.
Sadly though, it often ends there. A retailer will price according to their competitor and their basic KPIs such as cost and profit margins. But the reality isn’t always that simple, and too much money is left on the table if your only strategy is that of beating your competition.
For example: Your competitor drops their price in half, and begins selling out their stock. What do you do? Basic strategy would have you rushing and slashing your prices as well, in order to stay competitive. But what if that’s wrong. What if this item is trending, and in just a few days, your competitor will have sold out all their stock at a discounted rate, while demand for the product will continue to increase. By holding out, you will be the only one left with stock and have the opportunity to sell your stock at full price, resulting in greater profits.
But how would you know if an item is trending? Sure, some items are easy, like say fidget spinners, which picked up steam quickly and became a worldwide sensation among children and teens. However, as merchandising managers, or purchasing agents, we all know most items never get to that viral point. Instead, we have hundreds and thousands of items we need to make decisions on daily, just to keep our businesses afloat.
That’s where Dynamic pricing platforms come in. Simply put: a technology stack that gives you a command center for everything related to pricing and profitability. By utilizing massive amounts of data, and intelligent algorithms, the platforms give you the ability to make these decisions in real-time, on every single product. By plugging into your inventory systems, ERP, website, reporting and analytics, as well as 3rd party data sources such as competitive intelligence providers, and other big data platforms, it has the ability to find correlations for why products are selling or not selling. It can then offer suggestions for price changes, that you can either choose to ignore, automatically update, or simply receive a daily feed of pricing suggestions.
Pini MANDEL, CEO&Co-Founder of Quicklizard
What exactly is strong Artificial Intelligence (AI)? Strong AI is a term used to define a form of artificial intelligence comparable to that of a human. If such an intelligence were to exist, we would not need to cram gigabytes of examples into computers in order for them to solve complex problems. Moreover, the existence of strong AI would imply a machine capable of deliberate and conscious reasoning, as well as feeling emotions and expressing intentionality and creativity. When will this kind of AI exist? Not tomorrow, that’s for sure. There are those who strongly believe that human-level general intelligence is possible, such as Ray Kurzweil, a transhumanists and Google’s Director of Engineering. While the feat accomplished by the DeepMind’s AI-based Alphazero is impressive (it managed to beat Stockfish, the best chess program, after only a couple of hours of training or learning), this remains a form of weak AI. It simply demonstrates a significant jump in a computer’s ability to learn and deal with combinatorial explosion in the closed world of chess. It has only one objective: to kill. We should remember that the expression check mate comes from the Arab expression al cheik mat, meaning the king is dead.
Mozart teaches us that music and musicians are timeless. This is true in other domains of human intelligence such as the arts, philosophy, ethics, love, etc. These are the abilities that define our lives and give them meaning. That which gets us going, but remains a choice. That which transcends us. A human made up of models and experiences (his/her datasets) would be one-dimensional. We would consider such a person unintelligent..and dangerous.
Spider-man’s uncle said “with great power comes great responsibility”. It is understandable why Elon Musk has warned us about the dangers of using AI for military objectives. His arguments bring forth important ethical questions and convey the need for world-wide regulation on this subject. For me, as the CEO of a technology company and an active participant in this technological revolution I am more concerned with delegating out intelligence to a weak AI, than the emergence of a strong AI that can rival the intelligence of humans. One needs to be realistic; before the arrival of Terminator there are the Trumps of this world. Weakness can emerge from things other than weak AI.
It is easy for decision-makers to be seduced by the spectacular feats accomplished so far by current AI systems. To give into our desire for intellectual comfort and to delegate our conscience to these automated chess-players. There is a need for ethical regulations because weak AI should not be synonymous with comfort or a decline in the use of human qualities for our strategic decisions. These regulations are needed to question and perfect weak AI, in order to make both human and artificial intelligence more intelligent.
Written by Bernard EUVERTE, CEO&founder of WorkIT Software
Machine Learning (ML) is becoming ubiquitous in the business world, leaving us with the impression that humans will shortly be replaced by Artificial Intelligence (AI). While at first glance this seems to be true, upon closer inspection this is not at all the case. Certainly, AI-powered automation will continue to eliminate jobs, such as Amazon’s 75,000 robots used to optimize the company’s warehouse logistics, but this is not new. It is always cheaper and more efficient for robots to move packages around a warehouse. So let’s say that humans are being replaced by automation rather than AI.
On the other hand, AI, and ML in particular help us analyze large quantities of data (or big data if you prefer buzz words). How exactly does ML work? Who does the learning in Machine Learning? The answer is: humans. Yes, Machine Learning algorithms rely on datasets used for learning, which are created and generated by humans. The more complex the problem, the more data the algorithm needs. This is an aspect of AI that we do not talk about very often, since it is not very fashionable. It puts the focus back on artificial, rather than intelligence. In fact, Machine Learning is a form of what we call weak AI. Instead of saying that it is not intelligent, we prefer to say that it is of limited intelligence. Imagine saying to someone that they are of limited intelligence? In fact, this is another way of saying that they are in fact unintelligent. The same parallel can be drawn with computers.
Will AI replace humans?
No, as you have understood, we will always need humans to create datasets and generate data used to teach the machines. For now, AI is not destroying jobs. Rather, it is increasing our capacity for analysis and can even create jobs. Like all technological revolutions, it will change the way we live, learn and work.
Written by Bernard EUVERTE, CEO founder of WorkIT Software