THEORY AND STRATEGIES
Article | January 28, 2021
Since the internet became popular, the way we purchase things has evolved from a simple process to a more complicated process. Unlike traditional shopping, it is not possible to experience the products first-hand when purchasing online. Not only this, but there are more options or variants in a single product than ever before, which makes it more challenging to decide.
To not make a bad investment, the consumer has to rely heavily on the customer reviews posted by people who are using the product. However, sorting through relevant reviews at multiple eCommerce platforms of different products and then comparing them to choose can work too much. To provide a solution to this problem, Amazon has come up with sentiment analysis using product review data. Amazon performs sentiment analysis on product review data with Artificial Intelligence technology to develop the best suitable products for the customer. This technology enables Amazon to create products that are most likely to be ideal for the customer.
A consumer wants to search for only relevant and useful reviews when deciding on a product. A rating system is an excellent way to determine the quality and efficiency of a product. However, it still cannot provide complete information about the product as ratings can be biased. Textual detailed reviews are necessary to improve the consumer experience and in helping them make informed choices. Consumer experience is a vital tool to understand the customer's behavior and increase sales.
Amazon has come up with a unique way to make things easier for their customers. They do not promote products that look similar to the other customer's search history. Instead, they recommend products that are similar to the product a user is searching for. This way, they guide the customer using the correlation between the products.
To understand this concept better, we must understand how Amazon's recommendation algorithm has upgraded with time.
The history of Amazon's recommendation algorithm
Before Amazon started a sentiment analysis of customer product reviews using machine learning, they used the same collaborative filtering to make recommendations. Collaborative filtering is the most used way to recommend products online. Earlier, people used user-based collaborative filtering, which was not suitable as there were many uncounted factors.
Researchers at Amazon came up with a better way to recommend products that depend on the correlation between products instead of similarities between customers. In user-based collaborative filtering, a customer would be shown recommendations based on people's purchase history with similar search history. In item-to-item collaborative filtering, people are shown recommendations of similar products to their recent purchase history. For example, if a person bought a mobile phone, he will be shown hints of that phone's accessories.
Amazon's Personalization team found that using purchase history at a product level can provide better recommendations. This way of filtering also offered a better computational advantage. User-based collaborative filtering requires analyzing several users that have similar shopping history. This process is time-consuming as there are several demographic factors to consider, such as location, gender, age, etc. Also, a customer's shopping history can change in a day. To keep the data relevant, you would have to update the index storing the shopping history daily.
However, item-to-item collaborative filtering is easy to maintain as only a tiny subset of the website's customers purchase a specific product. Computing a list of individuals who bought a particular item is much easier than analyzing all the site's customers for similar shopping history. However, there is a proper science between calculating the relatedness of a product. You cannot merely count the number of times a person bought two items together, as that would not make accurate recommendations.
Amazon research uses a relatedness metric to come up with recommendations. If a person purchased an item X, then the item Y will only be related to the person if purchasers of item X are more likely to buy item Y. If users who purchased the item X are more likely to purchase the item Y, then only it is considered to be an accurate recommendation.
In order to provide a good recommendation to a customer, you must show products that have a higher chance of being relevant. There are countless products on Amazon's marketplace, and the customer will not go through several of them to figure out the best one. Eventually, the customer will become frustrated with thousands of options and choose to try a different platform. So Amazon has to develop a unique and efficient way to recommend the products that work better than its competition.
User-based collaborative filtering was working fine until the competition increased. As the product listing has increased in the marketplace, you cannot merely rely on previous working algorithms. There are more filters and factors to consider than there were before. Item-to-item collaborative filtering is much more efficient as it automatically filters out products that are likely to be purchased. This limits the factors that require analysis to provide useful recommendations.
Amazon has grown into the biggest marketplace in the industry as customers trust and rely on its service. They frequently make changes to fit the recent trends and provide the best customer experience possible.
Article | January 28, 2021
THE CORONAVIRUS PANDEMIC has spurred interest in big data to track the spread of the fast-moving pathogen and to plan disease prevention efforts. But the urgent need to contain the outbreak shouldn’t cloud thinking about big data’s potential to do more harm than good.Companies and governments worldwide are tapping the location data of millions of internet and mobile phone users for clues about how the virus spreads and whether social distancing measures are working. Unlike surveillance measures that track the movements of particular individuals, these efforts analyze large data sets to uncover patterns in people’s movements and behavior over the course of the pandemic.
Article | January 28, 2021
As smart machines, data, and algorithms usher in dramatic technological transformation, its global impact spans from cautious optimism to doomsday scenarios. Widespread transformation, displacement, and disaggregation of world labor markets is speculated in countries like India, with an estimated 600 million workforce by 2022, as well as the global labor market. Even today, we are witnessing the resurgence of 'hybrid' jobs where distinctive human abilities are paired with data and algorithms, and 'super' jobs that involve deep tech. Our historical response to such tectonic shifts and upheavals has been predictable so far - responding with trepidation and uncertainty in the beginning followed by a period of painful transition. Communities and nations that can sense and respond will be able to shape social, economic, and political order decisively. However, with general AI predictably coming of age by 2050-60, governments will need to frame effective policies to respond to their obligations to their citizens. This involves the creation of a new social contract between the individual, enterprise, and state for an inclusive and equitable society.
The present age is marked by automation, augmentation, and amplification of human talent by transformative technologies. A typical career may go through 15-20 transitions. And given the gig economy, the shelf-life of skills is rapidly shrinking. Many agree that for the next 30 years, the nature and the volume of jobs will get significantly redefined. So even as it is nearly impossible to gaze into the crystal ball 100 years later, one can take a shot at what jobs may emerge in the next 20-30 years given the present state. So here is a glimpse into the kind of technological changes the next generation might witness that will change the employment scenario:
RESTORATION OF BIODIVERSITY
Our biodiversity is shrinking frighteningly fast - for both flora and fauna. Extinct species revivalists may be challenged with restoring and reintegrating pertinent elements back into the natural environment. Without biodiversity, humanity will perish.
Medicine is rapidly getting personalized as genome sequencing becomes commonplace. Even today, Elon Musk's Neuralink is working on brain-machine interfaces. So you may soon be able to upload your brain onto a computer where it can be edited, transformed, and re-uploaded back into you. Anti-aging practitioners will be tasked with enhancing human life-spans to ensure we stay productive late into our twilight years. Gene sequencers will help personalize treatments and epigenetic therapists will manipulate gene expression to overcome disease and decay. Brain neurostimulation experts and augmentationists may be commonplace to ensure we are happier, healthier, and disease-free. In fact, happiness itself may get redefined as it shifts from the quality of our relationships to that between man-machine integration.
THE QUANTIFIED SELF
As more of the populace interact and engage with a digitized world, digital rehabilitators will help you detox and regain your sense of self, which may get inseparably intertwined with smart machines and interfaces.
DATA-LED VALUE CREATION
Data is exploding at a torrid pace and becoming a source of value-creation. While today's organizations are scrambling to create data lakes, future data-centers will be entrusted with sourcing high-value data, securing rights to it, and even licensing it to others. Data will increasingly create competitive asymmetries amongst organizations and nations. Data brokers will be the new intermediaries and data detectives, analysts, monitors or watchers, auditors, and frackers will emerge as new-age roles. Since data and privacy issues are entwined together, data regulators, ethicists, and trust professionals will thrive. Many new cyber laws will come into existence.
HEALING THE PLANET
As the world grapples with the specter of climate change, our focus on sustainability and clean energy will intensify. Our landfills are choked with both toxic and non-toxic waste. Plastic alone takes almost 1000 years to degrade, so landfill operators will use earthworm-like robots to help decompose waste and recoup precious recyclable waste. Nuclear fusion will emerge as the new source of clean energy, creating a broad gamut of engineers, designers, integrators, architects, and planners around it. We may even generate power in space. Since our oceans are infested with waste, a lot of initiatives and roles will emerge around cleaning the marine environment to ensure natural habitat and food security.
TAMING THE GENOME
As technologies like CRISPR and Prime-editing mature, we may see a resurgence of biohackers and programmable healthcare. Our health and nutrition may be algorithmically managed. CRISPR-like advancements will need a swathe of engineers, technicians, auditors, and regulators for genetically engineered health that may overcome a wide variety of diseases for longer life-expectancy.
THE RISE OF BOTS
Humanoid and non-humanoid robots will need entire workforce ecosystems around them spanning from suppliers, programmers, operators, and maintenance experts to ethicists and UI-designers. Smart robot psychologists will have to counsel them and ensure they are safe and friendly. Regulators may grant varying levels of autonomy to robots.
DATA LOADS THE GUN, CREATIVITY FIRES THE TRIGGER
Today's deep-learning Generative Adversarial Networks (GANs) can create music like Mozart and paintings like Picasso. Such advancements will give birth to a wide array of AI-enhanced professionals, like musicians, painters, authors, quantum programmers, cybersecurity experts, educators, etc.
FROM AUGMENTATION TO AUTONOMY
Autonomous driving is about to mature in the next few years and will extend to air and space travel. Safety will exceed human capabilities and we may soon reach a state of diminishing returns where we will employ fewer humans to prevent mishaps and unforeseen occurrences. This industry will need supportive command center managers, traffic analyzers, fleet managers, and people to ensure onboarding experience.
BLOCKCHAIN BECOMES PERVASIVE
Blockchain will create a lot of jobs for its mainstream and derivative applications. Even though most of its present applications are in Financial Services, Supply Chain, and Asset Management industries, very soon its adoption and integration will be a lot more expansive. Engineers, designers, UI/UX experts, analysts, auditors, and regulators will be required to manage blockchain-related applications. With Crypto being one of its better-known applications, a lot of transaction specialists, miners, insurers, wealth managers, and regulators will be needed. Crypto exchanges will come under the purview of the regulatory framework.
3D PRINTING TURNS GAME-CHANGER
Additive manufacturing, also popularly called 3D printing, will mature in its precision, capabilities, and market potential. Lab-grown, 3D-printed food will be part of our regular diet. Transplantable organs will be generated using stem cell research and 3D printing. Amputees and the disabled will adopt 3D-printed limbs and prosthetics. Its applications for high-precision reconstructive surgery are already commonplace. Pills are being 3D printed as we speak. So again, we are looking at 3D printers, operators, material scientists, pharmacists, construction experts, etc.
THE COLONIZATION OF OUTER SPACE
Amazon's Blue Origin and Elon Musk's SpaceX signal a new horizon. As space tech gets into a new trajectory, a new breed of commercial space pilots, mission planners, launch managers, cargo experts, ground crew, experience designers, etc. will be required. Since we have ravaged the limited resources of our planet already, mankind will need to venture into asteroid mining for rare and precious metals. This will need scouts and surveyors, meteorologists, remote bot operators, remotely managed factories, and whatnot.
THE HYPER-CONNECTED WORLD
By 2020, we already have anywhere between 50-75 billion connected devices. By 2040, this will likely swell to more than 100 trillion sensors that will spew out a dizzying volume of real-time data ready for analytics and AI. A complete IoT system as we know it is aware, autonomous, and actionable, just like a self-driving car. Imagine the number of data modelers, sensor designers and installers, signal architects and engineers that will be needed. Home automation will be pervasive and smart medicines, implants, and wearables will be the norms of the day.
DRONES USHER IN DISRUPTION
Unmanned aerial and underwater drones are already becoming ubiquitous for applications in aerial surveillance, delivery, and security. Countries are awakening to their potential as well as possibilities of misuse. Command centers, just like that for space travel, will manage them as countries rush to put in a regulatory framework around them. An army of designers, programmers, security experts, traffic flow optimizers will harness their true potential.
SHIELDING YOUR DATA
With data come cyber threats, data breaches, cyber warfare, cyber espionage, and a host of other issues. The more data-dependent and connected the world is, the bigger the problem of cybersecurity will be. The severity of the problem will increase manifold from the current issues like phishing, spyware, malware, viruses and worms, ransomware, DoS/ DDoS attacks, hacktivism, and cybersecurity will indeed be big business. The problem is that threats are increasing 10X faster than investments in this space and the interesting thing is that it is a lot more about audits, governance, policies, and compliance than technology alone.
FOOD-TECH COMES OF AGE
As the world population grows to 9.7 billion people in 2050, cultured food and lab-grown meat will hit our tables to ensure food security. Entire food chains and value delivery networks will see an unprecedented change. Agriculture will be transformed with robotics, IoT, drones, and the food-tech sector will take off in a big way.
QUANTUM COMPUTING SOLVES INTRACTABLE PROBLEMS
Finally, while the list is very long, let’s touch upon the advent of qubits, or Quantum computing. With its ability to break the best encryption on the planet, the traditional asymmetric encryption, public key infrastructure, digital envelopes, and digital certificates in use today will be rendered useless. Bring in the quantum programmers, analysts, privacy and trust managers, health monitors, etc.
As we brace for the world that looms large ahead of us, the biggest enabler that will be transformed itself will be Education 4.0. Education will cease to be a phase in your life. Life-long interventions will be needed to adapt, impart, and shape the skills of individuals that are ready for the future of work. More power to the people!
BIG DATA MANAGEMENT
Article | January 28, 2021
While digital transformation is proving to have many benefits for businesses, what is perhaps the most significant, is the vast amount of data there is available. And now, with an increasing number of businesses turning their focus to online, there is even more to be collected on competitors and markets than ever before.
Having all this information to hand may seem like any business owner’s dream, as they can now make insightful and informed commercial decisions based on what others are doing, what customers want and where markets are heading.
But according to Nate Burke, CEO of Diginius, a propriety software and solutions provider for ecommerce businesses, data should not be all a company relies upon when making important decisions.
Instead, there is a line to be drawn on where data is required and where human expertise and judgement can provide greater value.
Undeniably, the power of data is unmatched. With an abundance of data collection opportunities available online, and with an increasing number of businesses taking them, the potential and value of such information is richer than ever before.
And businesses are benefiting. Particularly where data concerns customer behaviour and market patterns. For instance, over the recent Christmas period, data was clearly suggesting a preference for ecommerce, with marketplaces such as Amazon leading the way due to greater convenience and price advantages.
Businesses that recognised and understood the trend could better prepare for the digital shopping season, placing greater emphasis on their online marketing tactics to encourage purchases and allocating resources to ensure product availability and on-time delivery.
While on the other hand, businesses who ignored, or simply did not utilise the information available to them, would have been left with overstocked shops and now, out of season items that would have to be heavily discounted or worse, disposed of.
Similarly, search and sales data can be used to understand changing consumer needs, and consequently, what items businesses should be ordering, manufacturing, marketing and selling for the best returns.
For instance, understandably, in 2020, DIY was at its peak, with increases in searches for “DIY facemasks”, “DIY decking” and “DIY garden ideas”. For those who had recognised the trend early on, they had the chance to shift their offerings and marketing in accordance, in turn really reaping the rewards.
So, paying attention to data certainly does pay off. And thanks to smarter and more sophisticated ways of collecting data online, such as cookies, and through AI and machine learning technologies, the value and use of such information is only likely to increase.
The future, therefore, looks bright. But even with all this potential at our fingertips, there are a number of issues businesses may face if their approach relies entirely on a data and insight-driven approach. Just like disregarding its power and potential can be damaging, so can using it as the sole basis upon which important decisions are based.
While the value of data for understanding the market and consumer patterns is undeniable, its value is only as rich as the quality of data being inputted. So, if businesses are collecting and analysing their data on their own activity, and then using this to draw meaningful insight, there should be strong focus on the data gathering phase, with attention given to what needs to be collected, why it should be collected, how it will be collected, and whether in fact this is an accurate representation of what it is you are trying to monitor or measure.
Human error can become an issue when this is done by individuals or teams who do not completely understand the numbers and patterns they are seeing. There is also an obstacle presented when there are various channels and platforms which are generating leads or sales for the business. In this case, any omission can skew results and provide an inaccurate picture. So, when used in decision making, there is the possibility of ineffective and unsuccessful changes.
But while data gathering becomes more and more autonomous, the possibility of human error is lessened. Although, this may add fuel to the next issue.
Drawing a line
The benefits of data and insights are clear, particularly as the tasks of collection and analysis become less of a burden for businesses and their people thanks to automation and AI advancements. But due to how effortless data collection and analysis is becoming, we can only expect more businesses to be doing it, meaning its ability to offer each individual company something unique is also being lessened.
So, businesses need to look elsewhere for their edge. And interestingly, this is where a line should be drawn and human judgement should be used in order to set them apart from the competition and differentiate from what everyone else is doing.
It makes perfect sense when you think about it. Your business is unique for a number of reasons, but mainly because of the brand, its values, reputation and perceptions of the services you are upheld by. And it’s usually these aspects that encourage consumers to choose your business rather than a competitor.
But often, these intangible aspects are much more difficult to measure and monitor through data collection and analysis, especially in the autonomous, number-driven format that many platforms utilise.
Here then, there is a great case for businesses to use their own judgements, expertise and experiences to determine what works well and what does not. For instance, you can begin to determine consumer perceptions towards a change in your product or services, which quantitative data may not be able to pick up until much later when sales figures begin to rise or fall. And while the data will eventually pick it up, it might not necessarily be able to help you decide on what an appropriate alternative solution may be, should the latter occur.
Human judgement, however, can listen to and understand qualitative feedback and consumer sentiments which can often provide much more meaningful insights for businesses to base their decisions on.
So, when it comes to competitor analysis, using insights generated from figure-based data sets and performance metrics is key to ensuring you are doing the same as the competition.
But if you are looking to get ahead, you may want to consider taking a human approach too.