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What is the hottest trend in artificial intelligence right now? Machine Learning is the right answer! Thanks to technological advances and emerging frameworks, Machine Learning may soon hit the mainstream. Because of new computing technologies, Machine Learning today is not like Machine Learning of the past. While many Machine Learning algorithms have been around for a long time, the ability to automatically apply complex mathematical calculations to big data – over and over, faster and faster – is a recent development. Every single day it’s become clear that Machine Learning is already forcing massive changes in the way companies operate. Every Fortune 500 company is already running more efficiently — and making more money — because of Machine Learning. But how this “phenomenon” helps business bring money and attract new and new customers?

Problems that can be easily solved using ML

Every single business some time or other can face with definite problems. But there are some kinds of business problems Machine Learning can prevent if not handle at all:

Email spam filters
Some spam filtering can be done by rules (IE: by overtly blocking IP addresses known explicitly for spam), but much of the filtering is contextual based on the inbox content relevant for each specific user. Lots of email volume and lots of user’s marking “spam” (labeling the data) makes for a good supervised learning problem.

Speech recognition
There is no single combination of sounds to specifically signal human speech, and individual pronunciations differ widely – Machine Learning can identify patterns of speech and help to convert speech to text. Nuance Communications (maker of Dragon Dictation) is among the better known speech recognition companies today.

Face detection
It’s incredibly difficult to write a set of “rules” to allow machines to detect faces (consider all the different skin colors, angles of view, hair / facial hair, etc), but an algorithm can be trained to detect faces, like those used at Facebook. Many tools for facial detection and recognition are open source.

Credit card purchase fraud detection
Like email spam filters, only a small portion of fraud detection can be done using concrete rules. New fraud methods are constantly being used, and systems must adapt to detect these patterns in real time, coaxing out the common signals associated with fraud.

Product / music / movie recommendation
Each person’s preferences are different, and preferences change over time. Companies like Amazon, Netflix and Spotify use ratings and engagement from a huge volume of items (products, songs, etc) to predict what any given user might want to buy, watch, or listen to next.

Here is enumerated not all but just a few problems that can be solved. And with the course of time this list will only expand.

Industries that already use ML in action

Most industries working with large amounts of data have recognized the value of Machine Learning technology. The adoption of Machine Learning is likely to be diverse and across a range of industries, including retail, automotive, financial services, health care, and etc. By gleaning insights from this data – often in real time – organizations are able to work more efficiently or gain an advantage over competitors. In some cases, it will help transform the way companies interact with customers.

Retail industry
Machine Learning could completely reshape the retail customer experience. The improved ability to use facial recognition as a customer identification tool is being applied in new ways by companies such as Amazon at its Amazon Go stores or through its Alexa platform. Amazon Go removes the need for checkouts through the use of computer vision, sensor fusion, and deep or Machine Learning, and it’s expected that many shopping centers and retailers will start to explore similar options this year.

Financial services
Banks and other businesses in the financial industry use Machine Learning technology for two key purposes: to identify important insights in data, and prevent fraud. The insights can identify investment opportunities, or help investors know when to trade. Data mining can also identify clients with high-risk profiles, or use cyber surveillance to pinpoint warning signs of fraud.

Health care
Machine Learning is a fast-growing trend in the health care industry, thanks to the advent of wearable devices and sensors that can use data to assess a patient’s health in real time. The technology can also help medical experts analyze data to identify trends or red flags that may lead to improved diagnoses and treatment. Machine Learning can be used to understand risk factors for disease in large populations. For instance, Medecision company developed an algorithm that is able to identify eight variables to predict avoidable hospitalizations in diabetes patients.

Oil and gas
Finding new energy sources. Analyzing minerals in the ground. Predicting refinery sensor failure. Streamlining oil distribution to make it more efficient and cost-effective, and many others thing that you can do using ML. For example ExxonMobil, the largest publicly traded international oil and gas company, uses technology and innovation to help meet the world’s growing energy needs. Exxon Mobil’s Corporate Strategic Research (CSR) laboratory is a powerhouse in energy research focusing on fundamental science that can lead to technologies having a direct impact on solving our biggest energy challenges.

Government
Government agencies such as public safety and utilities have a particular need for Machine Learning since they have multiple sources of data that can be mined for insights. Analyzing sensor data, for example, identifies ways to increase efficiency and save money. Machine Learning can also help detect fraud and minimize identity theft. Chicago’s Department of Public Health is early adopter. It used to identify children with dangerous levels of lead in their bodies through blood tests and then cleanse their homes of lead paint. Now it tries to spot vulnerable youngsters before they are poisoned.

Marketing and sales
Websites recommending items you might like based on previous purchases are using Machine Learning to analyze your buying history – and promote other items you’d be interested in. This ability to capture data, analyze it and use it to personalize a shopping experience (or implement a marketing campaign) is the future of retail. PayPal, for example, is using Machine Learning to fight money laundering. The company has tools that compare millions of transactions and can precisely distinguish between legitimate and fraudulent transactions between buyers and sellers.

Transportation
Analyzing data to identify patterns and trends is key to the transportation industry, which relies on making routes more efficient and predicting potential problems to increase profitability. The data analysis and modeling aspects of Machine Learning are important tools to delivery companies, public transportation and other transportation organizations. In some cases, mathematical models are used to optimize shipping routes. By honing in on excessive driving routes, drivers can see a reduction of nearly one mile of driving every day. For a company like UPS, a reduction of one mile per day per driver would equal a savings of as much as $50 million a year in fuel.

Have you ever worked with ML? Was it useful for your business? Or maybe you are still thinking about whether it costs to implement Machine Learning in your business? Will it be relevant and defensibly? If you have an answer on at least one question – share with me your experience. We will be happy to discuss it in comments. But if you don’t have an answer, always remember – Big companies are investing in Machine Learning not because it’s a fad or because it makes them seem cutting edge. They invest because they’ve seen positive ROI. And that’s why innovation will continue.

 

Yuliya Poshva

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Artificial Intelligence, Machine Learning are new buzzwords that are actively discussed in the tech world. Do you remember how our future was described in the movies some time ago: Terminator, Skynet, AI rules the world? General AI machines have remained in the movies and science fiction novels however narrow AI technologies are gradually evolving from the science fiction era to the reality and are already around us. Google uses Machine Learning to filter out spam messages from Gmail. Facebook trained computers to identify specific human faces nearly as accurately as humans do. Deep Learning is used by Netflix and Amazon to decide what you want to watch or buy next.

AI, machine learning, and deep learning are not quite the same thing but these terms are often used haphazardly and interchangeably, and that sometimes leads to some confusion. So let`s see what is the difference between each type of technology.
 
Artificial Intelligence (AI)

Artificial intelligence, which has been around since the 1950s, has seen ebbs and flows in popularity over the last 60+ years. But today, with the recent explosion of big data, high-powered parallel processing, and advanced neural algorithms, we are seeing a renaissance in AI—and companies from Amazon to Facebook to Google are scrambling to take the lead.

AI is the broadest way to think about advanced, computer intelligence. It can refer to anything from a computer program playing a game of chess, to a voice-recognition system like Amazon’s Alexa interpreting and responding to speech. The technology can broadly be categorized into three groups: Narrow AI (that is focused on one narrow task), artificial general intelligence or AGI (a machine with the ability to apply intelligence to any problem, rather than just one specific problem), and superintelligent AI (when its equal to humans or even surpasses them).

Pardoe believes that “we’ve just entered the “Fourth Industrial Revolution”, and while the adoption of AI has just started, the next few years will transform many sectors.
 
Machine learning

Machine learning is one subfield of AI. Or let`s say it`s the field of AI which today is showing the most promise at providing tools that industry and society can use to drive change. The core principle here is that machines take data and “learn” for themselves. Unlike hand-coding a software program with specific instructions to complete a task, ML allows a system to learn to recognize patterns on its own and make predictions.
 

 
Here are some of the popular machine learning methods:

-supervised learning: the “trainer” will present the computer with certain rules that connect an input (an object’s feature, like “smooth,” for example) with an output (the object itself, like a marble), and the algorithm learns by comparing its actual output with correct outputs to find errors. Supervised learning is commonly used in applications where historical data predicts likely future events. For example, it can anticipate when credit card transactions are likely to be fraudulent or which insurance customer is likely to file a claim.

-unsupervised learning: the computer is given inputs and is left alone to discover patterns. The goal is to explore the data and find some structure within. Unsupervised learning works well on transactional data. For example, it can identify segments of customers with similar attributes who can then be treated similarly in marketing campaigns.

-reinforcement learning: the algorithm discovers through trial and error which actions yield the greatest rewards. This type of learning has three primary components: the agent (the learner or decision maker, for instance, the driverless car), the environment (everything the agent interacts with, for instance the road) and actions (what the agent can do).
 
Deep Learning

Deep learning is a brunch of Machine Learning, let`s see it as the cutting-edge of the cutting-edge. It uses some ML techniques to solve real-world problems by tapping into neural networks that simulate human decision-making.

Deep Learning involves feeding a computer system with a lot of data, which it can use to make decisions about other data. This data is fed through neural networks. These networks are logical constructions which ask a series of binary true/false questions, or extract a numerical value, of every bit of data which pass through them, and classify it according to the answers received.

Text-based searches, fraud detection, spam detection, handwriting recognition, image search, speech recognition, Street View detection, and translation are all tasks that can be performed through deep learning. Deep Learning is used by Google in its voice and image recognition algorithms, by Netflix and Amazon to decide what you want to watch or buy next, and by researchers at MIT to predict the future.

The machine revolution has certainly started and the AI revolution is sure to pave the way for some significant changes in our lives. Machines will gradually improve, slowly replacing jobs that require repetitious behavior. But what happens when one day the machines become smarter than us?

 

Anna Kozik

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altabel

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During the annual Health Information and Management Systems Society conference, IBM CEO Ginni Rometty declared that the era of cognitive computing in healthcare is upon us.

“It actually is an era that will play out in front of us, which is what we call the cognitive era,” Rometty said. “I hope to persuade you … that this idea of cognitive healthcare, systems that learn, that this is real and it’s mainstream and it is here and it can change almost everything about healthcare.”

The official IBM website says that IBM Watson Healthcare mission is to empower leaders, advocates and influencers in health through support that helps them achieve remarkable outcomes, accelerate discovery, make essential connections and gain confidence on their path to solving the world’s biggest health challenges.

Let’s look into what IBM Watson is and what exactly it will bring us.

IBM Watson is an advanced artificial intelligence program that is transforming healthcare into a quantifiable service where every bit of information is available and physicians only have to go through their personalized reports instead of reading through dozens of papers for every patient’s case.

Here are just some upgrades that IBM Watson will bring to healthcare.

Your doctor will be well-informed

At the moment one of the most significant challenges in healthcare is the huge amount of information available. Your doctor can not be aware of all the information that has been published recently. Watson however is able to search all the information, so doctors don’t have to spend hours and hours on reading and investigating.

It’s currently being used in genome analysis research at a hospital in the US where it found a third of patients were affected by information published in articles since their treatments began.

You’ll be recommended better treatments

If, for example, you’re diagnosed with cancer, you might benefit from the platform, Watson for Oncology. Usually the doctor meets with cancer patients and spends time reviewing their notes – which would be presented in paper format or in a list of emails. It turns out that A doctor’s decision will be made basing on his individual experience and the information available in front of him.

IBM Watson takes all those unstructured notes and restructures it in a way that the doctor can check easily, with treatment recommendations of which drug to give, which radiation or dosage.

You will be prescribed better medication

A very important aspect of IBM Watson is medication. Generally it takes about 12 years to produce a pill, but recent tests at the Baylor College of medicine in Houston, Texas, has reduced significant parts of the research process to weeks, months, and days. IBM Watson is able to accelerate the discovery of new treatment by streamlining research processes. As a patient, you will benefit from having more appropriate treatments available for you when you need it.

It’s clear that IBM Watson is already transforming healthcare, but much progress still lies ahead.

“We’re just at the beginning of something that will be very big and very transformative over the next 50 years,” said Watson Healthcare Executive Lead, Thomas Balkizas.

Feel free to share your thoughts about IBM Watson prospects for the near future in comments below!

 

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Yana Khaidukova

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Digital health is dramatically reshaping and redefining how healthcare is delivered. And here are some new trends that we can observe now and which are expected to change the future of eHealth.
 
Distributed Healthcare

New technological aids has changed the relationship between patient and doctor. Patients can now google information about illnesses and treatments, read their digital patient journal online, learn of their doctor’s findings and take responsibility for their own care in a completely different way than in the past.

The use of digital and mobile IT solutions in healthcare means that care is no longer available only in a specific location. Nowadays, patients have the right to choose where they wish to be treated and, in the future, this will not only include choosing which hospital to visit, but also whether to hold their appointments via video link or to treat their depression using online therapy.
 
Smart Devices

Apps and mobile technology are already a natural part of our everyday life.
There is a number of eHealth applications now available and one of them is the digital diary which allows patients to record measurement data and appraisals or to note down their general physical and mental states during the day. As a next step they forward this information to their doctor.

Apps like this also give patients a simple means by which to take greater control over their own well-being, whether related to blood-sugar levels, blood pressure, or mood.
At the moment, healthcare do not use all the rich data that this type of smart device can provide. However, through projects such as the Swedish eHealth Agency’s Health for Me and other platforms that allow patients to collect their health data, an attempt is being made to both understand and find ways to utilize this digital “treasure” for the benefit of both patients and providers.
 
Interoperability

One major feature of eHealth is large IT systems. These are designed to suit a broad user base, however, which invariably makes it difficult for them to cater specifically to any one user. The future lies in creating smaller, customized systems that can communicate with one another through their interoperability. Custom-designed digital solutions entail opening up the market to small-scale actors and utilizing the entire ecosystem during development.
 
Big Data

Big Data has changed the way we manage, analyze and operate data in any industry. Healthcare is obviously one of the most promising areas where Big Data can be applied to make a change. In future perspective healthcare analytics can reduce costs of treatment, predict outbreaks of epidemics, avoid preventable diseases and improve the quality of life in general. Treatment delivery methods face new challenges today: average human lifespan is increasing together with the world population. Healthcare professionals, just like business entrepreneurs, are capable of collecting massive amounts of data and look for best strategies to use these numbers.

Even if healthcare services is not something that exсites you, still you are a potential patient, and just like everyone of us you should be aware about new healthcare analytics applications and how they can help you.
 
Artificial Intelligence

Anytime a new technology enters healthcare, there are a number of challenges it faces. Common setbacks of artificial intelligence in healthcare include a lack of data exchange, regulatory compliance requirements and patient and provider adoption. AI has come across all of these issues, narrowing down the areas in which it can succeed.
The most popular use of artificial intelligence in healthcare is in IBM’s smart cloud, where Watson lives. The Watson platform has been used in a number of disciplines within healthcare including with payers, oncology and patient risk assessment.
 
To know more about the way IBM Watson works and its perspectives for the future please check out my new article “IBM Watson. Future is closer than you think” next week.

 

yana-khaidukova

Yana Khaidukova

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As The Internet of Things continues to grow, huge amount of data is going to be generated. How huge is the “huge”? Really huge. I do mean that.

Physical devices across the globe are consuming and creating data to drive a continuously connected world. David Booth, CEO at BackOffice Associates believes that currently we are at the tipping point of the Internet of Things. He says, “It was not a big leap for the industry to realize that an IoT global network of continuously connected devices would mean that data would not only be created at geometric rates, but that it would become one of the most valuable commodities in the world.”

Alongside the fact that year 2016 was declared to be the year of the first Zettabyte in internet traffic, Cisco report says the number will reach 2.3 ZB by 2020. Before long we will be transferring this much data annually.

If it does not say anything to you, imagine a byte equals 1 character of text – a zettabyte would cover War And Peace by Leo Tolstoy(which is about 1,250 pages) at least 325 trillion times. Or if 1 gigabyte can store 960 minutes of music – technically a zettabyte would be able to store just over 2 billion years of music. If that still isn’t illustrative enough, let’s measure in cups of coffee. Cisco states that if the 11oz coffee on your desk equals to one gigabyte, a zettabyte would have the same volume as the Great Wall of China. This amount of information is mind-blowing. Zettabyte transformed Big Data into enormously Big Data.
 

The Internet of Things (IoT) is expanding rapidly and relentlessly. And as IoT grows, so do the volumes of data it generates. Ignoring this fact is not an option, and companies will do so at their own peril and risk.

Though there are many new start-up companies storing, analyzing and integrating massive amounts of big data created from the IoT, not many of them have actually considered how the IoT can and will transform organization thinking by implementing data quality and information governance.

With so much data being created, companies must understand what they want to do with it, what are their data requirements and ensure that they have access to the right data. Unless a company can find a way to accumulate, manage and, most important, monetize their data storage, data hoarding can be a real issue for them. Put simply, while the value IoT brings is in the information it creates, innovation gold lies in the filtered data an organization has extracted from the intermediate layer between the devices and the cloud (so called “fog”).

Obviously, data provides powerful potential for boosting analytics efforts. And analyzing the amount of data that is going to be created by the Internet of Things requires new, advanced analytic techniques. The good news is, artificial intelligence and cognitive computing are maturing at a fast pace.

When used properly analytics can help organizations translate IoT’s digital data into knowledge that will contribute to developing new products, offerings, and business models. IoT can provide useful insights into the world outside company walls, and help strategists and decision-makers understand their customers, products, and markets more clearly. It can drive so much more — including opportunities to integrate and automate business processes in ways never imagined before.

Rowan Trollope, Senior Vice President and General Manager of Cisco’s Internet of Things (IoT) and Applications, told participants at the Cisco Live conference, “One of the biggest mistakes you could make now is to underestimate the Internet of Things. This is a life or death issue for most of our customers. They have seen what has happened with Uber and taxi companies and with Netflix and Blockbuster”.

The bottom line is that IoT and Big Data can either disrupt your business or help you become more competitive compared to other businesses that are about to be disrupted.

 

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Alexandra Presniatsova

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Machine learning

A breakthrough in the technology of artificial intelligence and its active use in practice is the trend of the last two-three years. If earlier the creation of a high-quality machine translation system required a decade, now startups can offer consumers quite a competitive product in this area within one year.

Machine learning is a new approach to information processing, it turns the machine into an intelligent device very fast. In many ways, the development boom based on machine learning programs happened due to the fact that almost everything you need can be found among free software. It is enough to download the development environment, a number of libraries and read the manual. For a week or two, you can write, for example, a program recognizing wine labels or even individuals.

AI opened a completely new universe that humanity will explore for centuries. This means that robots are getting smarter and can learn independently. They are even capable of transmitting their knowledge to each other. To do this, of course, communication infrastructure is necessary. With its help, the program, which has recently invented a new universal language, could teach the other machines.

By the way, people did not expect artificial intelligence to create a new language, it was a by-product performed while teaching machines to translate from different languages. The program has learned how to translate from the languages it hadn’t been asked to by itself. Hence, the researchers concluded that a computer system uses meta-level language for communication, a new sort of Esperanto, a universal language.

 
Robots and VR
 

Analytical agencies called 2016 the year of virtual reality technologies. According to the Digi Capital forecast, by 2020 the virtual reality market will come up to $ 30 billion. Today we have every reason to believe that in 2017 VR-technology will finally become mass.

This trend has affected robotics as well. Complex machine control via VR-helmets and screens shows that augmented reality is gaining popularity. At MWC in Barcelona 2016, all visitors were offered to try themselves as excavator operators, controlling real excavators via Oculus Rift helmet.

This is one of the main scenarios of applying VR in industry and business, which will be used in a variety of situations: unmanned vehicles control (trailers, drones, trucks), surgical operations, exploring out of reach places (the ocean bottom, mines, permafrost). However, the automation trend of the last decade is increasing in order to completely avoid people’s participation in these processes.

 
Artificial Intelligence
 

The idea of intelligent robots has been exciting minds for a long time. We are used to different fiction anthropomorphic golems, androids, perfect voice assistants. Moreover, the success of HBO Westworld recent show demonstrates that the interest in artificial intelligence is rapidly increasing.

Meanwhile, the representatives of different professions were asked to imagine AI as a professional assistant at work or even in the role of a leader. Intelligent Apps have the potential to transform the workplace by making everyday tasks easier and its users more effective. The prospect of getting help from the robot frightens 25% of people, 40% are against the robot leader. However, the majority of people can easily imagine robots among their colleagues- 35% want to see a robot as a personal assistant. Every fourth looks positive on robots to take a leading position.

 
Internet of Things

The internet of Things has been labeled as “the next Industrial Revolution” because of the way it will change the way people live, work, have fun and travel, as well as how governments and businesses interact with the world.

Most of us are used to applications, which allow us to switch tracks on the audio system, to open our cars, turn on the lights, change the temperature in the room. According to Ericsson ConsumerLab research, two out of five people expect applications to remember users’ preferences and configure home appliances in the nearest future. It is as a good way to save personal time that can be spent on tasks that are more important.

 
Unmanned vehicles
 

They can either be remote controlled or remote guided, or they can be autonomous vehicles which are capable of exploring the environment and navigating on their own. With the right technology, multiple cars could “talk” to one another and reduce the chance for crashes.

Every fourth interviewee said he would feel safer if all the cars would be driven by robots. Meanwhile, 65% said they would prefer to have an autonomous vehicle rather than drive themselves.

Self-driven cars – futuristic, comfortable and safe. However, at the moment none of the existing systems can completely take over driving. Even the most sophisticated systems can fail.

 
Augmented reality
 

Approximately four out of five users believe that a complete blending of real and virtual worlds will happen just within three years. Half of the respondents are already interested in buying special gloves or shoes that would control VR-objects (for example, for playing virtual instruments).

A well-known game Pokemon GO is a good example to demonstrate the real potential of augmented reality. Many people want to use similar possibilities not only in the games but in real life as well. More than half of users would like to have AR-glasses to see better in the dark and, for example, to be able to observe criminals. One out of three would like to use augmented reality to get rid of unpleasant elements of their landscape, such as graffiti and litter. Many people dream of not seeing street signs, uninteresting shop windows and billboards.

 
Security Paradox of “smart” devices
 

More than half of the respondents use applications and trackers that transmit alarm and danger warnings. Using such apps people expect to increase their personal safety level. The paradox is that 60% of those who feel more secure with a smartphone admit that would try to avoid those situations while not having a phone in the pocket. People rely on their smartphones capabilities too much. Meanwhile, they won’t know what to do if they lose the device or the battery dies. Three out of five people, who believe that the smartphone makes their lives safer, are in a bigger danger.

 
Social fragmentation
 

For every third respondent social networks have become a main source of information. However, social networks do not connect people from all around the world, on the contrary, they form small groups and communities. There is a chance that this fragmentation will only increase: every week, every day individuals exclude each other from friends or refuse to accept connection requests based on the opinions of other people.
 
We all know that making predictions about the course of technology’s future is challenging. Surprises can appear in any direction. Now we can only imagine those amazing opportunities we are going to explore in the nearest future.

Feel free to share your thoughts about technology prospects for the near future in comments below!

 

Darya Bertosh

Darya Bertosh

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Artificial intelligence gives us more and more opportunities every day. Chatbots and their developing for client-communication are the hottest topics today. Many experts say that chatbots are the future. Let’s get a short review.
 
Who are they?

Chatbots are essentially programs pretending to be people that you can interact with through text or even voice. They are closely connected with a wide messenger’s distribution and I believe everyone has been already acquainted with them. It’s obvious that chat- bots’ popularity is starting to take off.
 
How can they enhance e-commerce?

1. First of all chat bot doesn’t get tired, being late or absent – it assists your clients 24/7 365 days per year and get significant workload on its shoulders. That is why they seem to be the best answer for e-commerce business owners to manage thousands of one-to-one conversations with customers.

2. The second point is that chat bots can understand natural language and communicate with people in the same manner making conversations realistic and trustworthy. So chat bots give online shop owners an opportunity to provide pleasant shopping experience for customers.

3. Chatbots are proactive. They can understand what the particular customer wants not only through a simple conversation, but also through analysis and collecting of personal and profile page data ( smartphone data, cookies and so on). All this information might help to improve the marketing strategy and provide customers with the best user experience.

4. Additionally, chatbots simply save customers’ time. You don’t need to waste time for searching the appropriate item, click on mass of characteristics, study a market and etc. You can simply say : Hey, I want to buy an efficient computer for my child. Bot does it for you with a great pleasure and at a quick pace.

5. Finally, all this points give more advantages for businesses: services become much more better and faster, the conversion becomes higher, sales increase, operational costs and salary charges reduce.

 

 
Fresh examples of using

The most outstanding example is already occurring in China, where consumers are using WeChat to fulfill their daily living and commerce needs. Soon, this system will gain prominence in the United States.

Further more brands like Walmart and Hyatt are testing customer service and shopping within apps on the new Facebook Messenger for Business App. A free messaging app, called Kik, has a bot shop for companies such as Sephora, Vine, and H&M. So it can be said that the part of the technology is already there.

Amazon’s voice bot messaging has brought the idea of personal assistant to the next level. Customers can talk to the bot and ask it to order items through Amazon Prime, get a pizza, purchase flowers and call for an Uber. Voice commands are making shopping easier than ever, but there are still many issues to work out.

 

 
What’s Coming in the Future?

Voice-based AI and mobile chatbots are the dominant trend right now, but it’s not crazy to think that this could evolve into something more personal and user-based. E-commerce data is there to help you segment and automate email messaging to certain customers, so it’s not silly to assume that this data will eventually merge with bots.

Imagine a personalized bot that talks to you, and only you, when you shop on the Target website. They’d know the last items you bought, deliver the most relevant products and even stock your shopping cart with suggestions. The days of taking hours to shop online will soon knock the pad. In the near future, all the work will be done for you.

Are you ready for this?
 

Kate Kviatkovskaya

Kate Kviatkovskaya

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