This guide is an overview of the mega-popular technology, that is Artificial Intelligence. It includes key concepts, use cases, and risks of joining the AI movement. 

To dive deeper, let’s define AI. From a business standpoint, Artificial Intelligence (AI) refers to using intelligent machines or systems to solve business problems. The machines can be trained to perform tasks by processing a massive volume of data to identify patterns. 

СTO Vision names several elements that are the enablers of a good AI case. They are divided into two groups: technology and business. 

Technology side

• ML algorithms
• NLP
• Robotics
• Computer Vision
• Data Engineering
• Sensors
• Hardware architectures

Business side

• Business strategy
• Cybersecurity 
• Ethics
• Legal and regulatory regimes
• Training and testing
• Operation and maintenance

The role of Artificial Intelligence

• AI lets automate repetitive processes of research and learning through the use of data. The goal of AI isn’t to merely automate human labor, but to perform large-scale digital tasks. Automatization of this kind suggests bringing a human into the loop at the initial stages to set up the system. 
• AI turns your products into intelligent ones. Generally, AI technology is not implemented as a standalone application. It’s integrated into existing products, allowing you to boost their capabilities. Think Siri, a voice assistant that was added to Apple devices of the next generation. Automation, communication platforms, bots, and smart computers, combined with large amounts of data, can improve various technologies we use at home and the workplace, ranging from security data analytics to investment analysis tools.
• AI adapts through progressive learning algorithms so that farther programming is done based on data. AI identifies patterns in data that allow an algorithm to learn a particular skill. Like an algorithm can learn how to play chess, it can also learn to recommend suitable products in an online shop. At the same time, the models improve as new data becomes available. Backpropagation is a technique that corrects the model by learning from new data in case the original answer is incorrect.
• AI performs an in-depth analysis of large amounts of data using neural networks with many hidden layers. Not so long ago, creating a five-level fraud detection system was an unattainable goal. That all changed with the tremendous growth of computing power and big data. Deep learning models require a massive amount of data since this is their training foundation. And the more data, the more accurate the model.
• Deep neural networks enable AI to achieve the highest levels of precision. For example, Alexa, Google Search, and Google Photos are deep learning tools, and the more we use these tools, the better they become. 

AI and business: Key Points

According to the IBM report, some areas that benefit from AI implementations. We can find good AI use cases in all industries. But IBM singles out several domains where AI has a great potential. 

1. Cognitive engagement to acquire customers: Finding new ways for human-machine interaction so that it could resemble a natural, human-like conversation. 
2. Cognitive insights and knowledge: expanding knowledge and experience to master high volumes of information.
3. Cognitive automation: promoting agility and operational efficiency, which goes one step further than process automation and simulates human capabilities to streamline a decision process within complex tasks.

Other points to consider:

• The ubiquity of cloud computing influenced the advancement of AI
• Some working AI cases can be inscrutable
• AI is too prone to hacking
• AI can be unethical
• AI can be used by competitors to damage your business
• AI calls for the extreme advancement of cybersecurity

What does a successful use case look like? IBM Experience

AI is a viable solution to many problems. Its benefit is achieved in the three domains we described previously. Now let’s have a look at the successful AI use cases from IBM. 

Production of goods: a neural network detects aberrations in the work of machines. The network is trained using data received from sensors placed on the truck engines to identify the deviations from the norm. 

Automotive industry: predictive error detection for welding robots and predictive maintenance assessment. Supervised learning techniques are used to develop predictive models to serve as early warning systems for failures. They continuously receive system messages. This way, the manufacturers can do maintenance work when needed to reduce the downtime, false alarms, and unnecessary workload.

Energy suppliers: A microgrid forecast of energy demand and production optimization. Together with predictive mathematical optimization, predictive ML models show which the power sources will be the most cost-effective. 

Raw material supplier: An informative dashboard. Together with the customer’s procurement experts, IBM analyzed the business dynamics and made a list of potentially relevant data sources. Several models for machine learning were trained to learn price fluctuations and predict future price changes. 

Risks of Implementing Artificial Intelligence

While implementing AI can change your business model, there are many risks to it. 

• Security and ethics: Your company needs to mitigate the possible risks and ensure that the customers regard the use of this technology as ethical. 
• Infrastructure: Your company needs to carefully consider and build an architecture to mitigate risks and avoid detriment. To that end, it’s recommended to use outside services that will handle the verification of AI ethics and security standards and compliance with regulations. 

Key Technologies of Artificial Intelligence

Here are the main AI technologies your company can benefit from, according to CIO. 

Machine learning 

This subset of AI is the foundation of any organization’s AI strategies. Indeed, it’s proven to bring a lot of value, especially in complex solutions. The solution includes algorithms, development tools, APIs, and model deployment, and many more. Machines are taught without explicit programming. 

Deep learning

Another approach to data resembles how the brain works and uses artificial neural networks. The information is flowing through these networks, altering their structure. As such, they can change (and be “trained”) depending on the input and output. The learning is based on observing the data sets. Deep learning is a growing trend that fits in well with Big Data. In particular, it presents value to pattern recognition and classification. 

 Natural language processing

This technology has to do with the interaction between humans and machines. Computers can analyze and understand spoken and written language. Voice assistants such as Alexa, Siri, and Google Assistant all use NLP to handle voice requests. NLP is broadly used in customer service and support as chatbots and voice assistants, but can also bring a lot of value to an organization’s internal processes. 

Natural language generation

The software-based on this technology turns all kinds of data into readable text. This technology can automate business analytics, product description, finance reporting, all sorts of reminders, and more. All in all, it lets you create content instantly. Structured data can turn into text with high accuracy and at a record speed of several pages per second. Companies like Automated Insights, Lucidworks, Attivo are worth watching. 

Virtual agents 

To many people, the difference between a virtual agent, a virtual assistant and intelligent virtual assistant is merely semantics. Some try to distinguish between them and regard virtual agents as customer assistants, while they are more akin to online assistants in reality. Virtual agents are often known as AI-induced bots that can lead a meaningful conversation with a user. One advantage is that the clients can be assisted at any time. But they also have a great potential in other fields such as healthcare and heavy industry. 

AI hardware

These include devices with embedded AI, microchips, and GPUs. The combination of hardware and AI goes beyond consumer devices such as games and entertainment. Hardware with embedded AI will be used to advance deep learning. Key players to watch are Google, IBM, Intel, Nvidia. 

Robotics 

Robots that are controlled by AI programs and are used to automate manual work. This technology is mainly used at warehouses and factories now but showing great potential in medicine as well as business and smart homes. 

Robotic Process Automation

RPA automates business processes intending to redistribute the human resources to perform more complex tasks that bring more added value. It is a technology that allows organizations to configure software (software robots) to perform repetitive, mechanical operations at the user interface level. Software work performs tasks in the same way that people do, receiving, sorting, processing data, and performing specific actions with them, without changing the organization’s IT landscape.

Key AI Methods

Supervised Learning. One of the machine learning methods, where data labeled by humans is fed to a machine so it can be taught. A ready model is used to provide predictions from new data. 

Unsupervised Learning: Opposed to Supervised Learning, models of this type detect patterns in unlabelled data and where outcomes are unknown. 

Training Data: An initial set of data used to develop a model. It helps a program understand how to apply technologies like neural networks to learn and produce results. 

Succeed with NCube

If you want to successfully integrate AI into your business, partner with NCube. We will build a team of highly-skilled data scientists and machine learning engineers to provide you a solution that addresses your business needs. Be sure to contact our team via the form below for more information on building your virtual tech team in Ukraine. Meanwhile, please take a look at our success stories, including our contribution to AI projects. 

 

Machine learning is in its heyday, but the question for many businesses remains: How can it solve the existing problems? In this post, we will look at business areas in which ML could make a good case. Hopefully, it helps you see what can be done so you can figure out how it might be applied to your business. 

Where can machine learning be applied?

Machine learning is applied for complex decisions where a lot of factors are at play. That said, problems that suggest a clear chain of steps to follow in order to resolve them aren’t likely to need machine learning at all. The same goes for having existing cases of successfully addressing problems using ML. In these scenarios, applying algorithms may be a redundant step. 

On the other hand, here’s a list of common business cases where leveraging machine learning is recommended. 

Face recognition

It’s somewhat problematic to come up with a set of rules for the face recognition process, given that there are different nuances like skin and eye color, shapes, and angles that might impede accurateness. Nevertheless, machine learning algorithms can be trained to detect eyes, mouth, nose, etc. Facebook already uses those, and there are also many free face recognition tools. 

Spam filters

Email spam filters go by simple rules, for example, they can identify and block compromised domains and IP addresses. However, these algorithms tend to lack in filtering incoming messages based on a user’s personal preferences. That is where applying machine learning can be the right solution.  

Recommendation engines

The tastes of each person in music, movies, and products are different. Tech giants like Netflix, Spotify, Amazon rely on machine learning and data to predict what a user might prefer. 

Speech recognition

Machine learning makes it possible to translate a spoken language into text. The significant challenges are a variety of accents, loud environments, and the fact that speech recognition algorithms need lots of data. The top companies operating in this area are Nuance Communications, Google LLC, Amazon.com, Inc, Apple, Inc, IBM Corporation, Microsoft Corporation, and Baidu.

Online ads

It’s impossible to predict which Facebook ads will prompt a particular user to click. Machine learning can identify patterns in user behavior and determine which ads resonate with which group of users. 

Fraud detection

Little cases of fraud detection can be fended off with a clear-cut set of rules. That is because fraud techniques are continually evolving, so your protection needs to be adapted all the time. 

Upselling 

Machine learning systems can process data that concerns your buyer profile and types of things they are looking to buy from you. You can tweak the algorithm to target clients that are land concrete pages of your website. For example, if you’re selling phones, you can use machine learning to sell iPhones to people that have reviewed your Apple product articles. 

Dynamic pricing

This one is a good area for implementing machine learning. Instead of offering a fixed price at all times, you could tie pricing to time of the day or a type of user. A good example here is Uber. Its algorithm analyses a destination a user wants to go to and nearby routes to offer a special price. 

Customer service 

Machine learning and customer service are a natural fit. Lots of companies have already improved their services using chatbots to address customer requests in no time. 

There’s no denying it, chatbots boost sales. That can be backed up by the case of Sephora, who increased their sales up to 11% after integrating a chatbot assistant into their Facebook page. The secret is to create a chatbot that would capture your brand voice while providing a service akin to a human assistant. 

What to consider when implementing machine learning?

According to EmerJ blog, several factors warrant the usage of machine learning at an organization. 

Recent and accurate data

Clean data is the cornerstone of any data science effort. But it also needs to be up to date, which is particularly true for the rapidly changing fields. If your data is not recent, it can hardly present any predictive value. From years of operating, you probably have gained a large volume of data. It needs to undergo the cleansing process before your data science team can use it. 

Labeled datasets

Unsupervised learning can be applied in many cases of unlabeled data, but it’s not the best decision to start out with unsupervised ML. More profit can be gained from old labeled datasets. 

Facebook owns a vast amount of labeled data like users’ faces from various angles, in different poses and with different expressions. The company uses this data is used to train algorithms, making them highly accurate. Another case here is Google that analyzes the relevance of search results based on the rates of clicks, the usability of pages, user scroll depth, and other factors. There cannot be a set of clear rules for showing the right results with such a number of factors at play. Supervised datasets are also vital to credit companies. While there’s a huge amount of transactions, it’s possible to track connections in purchases, locations tied back to individual users. Thus, it’s possible to predict fraudulent actions. Machine learning can also streamline customer support by identifying and marking types of requests received via emails or phone, such as technical problems, refunds, shipping problems, and more. 

Room for error

As suggested by EmerJ, machine learning cannot be applied in an environment where errors are intolerable. After all, machine learning is a malleable, adaptive skill that continues to grow as the company endures. An example of such is a system that recognizes an amount or a company name on an invoice and then pays it. An error in such a case results in overpaying the amount or paying the wrong company. In this example, it’s best to apply machine learning to sort types of bills, but the payment part requires human involvement.  

Who is ahead of the curve in the business machine learning? 

Pinterest 

The company has long since implementing ML algorithms to improve user experience. Pinterest uses image search and recommendation scripts developed by Kosei, a machine learning company. Additionally, the social medium leverages various algorithms to filter content as well as for monetization purposes.

Facebook 

The social media giant uses machine learning to take your communication with chatbots to the next level, making it more human-like. There are also algorithms for filtering out spam and irrelevant content. 

IBM

A global giant named IBM is also mastering machine learning. The most prominent project is IBM Watson used in healthcare. 

Twitter

The company uses algorithms to organize the news feed so that users can see the most relevant content. Analyzing tweets and likes of each user helps to achieve that. 

Google

Through machine learning, the behemoth improves search results and page ranking and boosts speech recognition and translation speed. 

Baidu

Baidu, a Chinese search engine, strives to keep pace with Google, investing heavily in machine learning. One of the most significant developments based on machine learning algorithms is Deep Voice, a human voice technology capable of generating such natural voices that are barely distinguishable from natural ones. The technology can even “clon” voices, pronunciation, stress, timbre, and pitch of sounds in spoken language. 

Read also: CTO Guide To The Business of Cloud Computing

Afterword

If you see the benefit of using machine learning to improve your business, it’s useful to consider a few things. Starting out with machine learning doesn’t necessarily take an in-house team of specialized experts. Algorithms can be created by service providers or software developers with knowledge of data science libraries. What you can do on your part is to ensure that the data is recent and accurate. Think about tasks that can be automated to increase speed and scope – that area can be improved by machine learning. Above all, consider that machine learning isn’t there to put humans out of work. Instead, it can be used to reduce routine work, boost speed and improve accuracy. 

Our machine learning and data science specialists from NCube support the data efforts of FRST, a Blockchain analytics and strategy company. We can set up a team of skilled data science specialists and machine learning engineers from Ukraine for your project. Let’s talk?

Artificial intelligence is one of the most significant technologies of recent times. From solutions that recognize symptoms of a heart attack to face aging apps and voice assistants. AI has penetrated every field: medicine, social media, entertainment. A lot of companies, large and small, turn to AI in the hope of expanding the possibilities of their business. The decision to implement AI solutions comes with the need for a relevant framework. In this article, we are going to review 7 popular frameworks for AI. 

TensorFlow 

TensorFlow is used for voice and image recognition as well as for language processing like Google Translate. Works best for complex projects, including multi-layer neural networks. DeepMind, Uber, Airbnb and Dropbox use this platform.

Tensorflow has API in Python on top of C and C++. The framework has a flexible ecosystem of tools and libraries as well as a vibrant community. Tensorflow enables data scientists to use the most recent Machine learning tools. Developers can create and deploy machine learning applications 

Read also:How Big Data and AI work Together 

TensorFlow comes with intuitive API interfaces (for example, Keras) that enable fast model iteration and simple debugging. As a multiplatform solution, it allows to teach and deploy models in cloud and on-premise regardless of the used language. 

There is also TensorFlow Lite, an open-source solution that is used for deployment of machine learning on smartphones or IoT devices. Next, Tensorflow.js is a solution that allows to create and teach a model in JavaScript and deploy it in the browser. 

Pros:

• A large number of manuals and documentation;
• Powerful tools for monitoring the learning process of models and visualization (Tensorboard);
• A large community of developers and tech companies;
• It provides service models;
• It supports distributed learning;

Cons:

• A steep learning curve;
• Low user-friendliness;
• Not the fastest one in benchmarking;
• The need to control video RAM;
• Documentation;

PyTorch

PyTorch was developed by Facebook and used by Twitter and Salesforce among others. It’s a good solution for when you need to teach models fast and efficiently.

PyTorch is an environment for machine learning written in Python. It enables Tensor calculations with GPU acceleration. 

With PyTorch, data engineers can change the network behaviour on the go. The framework offers dynamic calculations graphs that allow you to process input and output of a variable length, which is useful if you are dealing with recurrent neural networks. Developers can use C and C ++ with the help of API extension for Python.

Unlike TensorFlow, PyTorch is less flexible in terms of supporting multiple platforms. It lacks built-in data visualization tools but has tensorboardX as an external alternative.

When deploying networks on the GPU, PyTorch will take up only the necessary video memory.

Pros:

• A modular structure, so you can combine ready-made modules;
• a model creation process is quite simple and transparent;
• The framework supports popular debugging tools such as pdb, ipdb, or PyCharm debugger;
• Simple to create custom layers and work on  GPU;
• Has a wide range of pre-trained models;

Cons:

• Documentation;
• Lack of model support
• Lack of monitoring and visualization interfaces

Keras

Keras is a minimalistic deep learning library in Python that runs on top of TensorFlow, Theano, or CNTK. It is compact, modular and expandable solution that is aimed at accelerating work with neural networks. Keras is chosen for smaller projects due to mediocre performance as compared to Tensorflow. 

Keras supports a wide range of neural network layers: convolutional, recurrent, and dense.

The library contains numerous implementations of widely used building blocks of neural networks: layers, target, and transfer functions, optimizers, as well as a variety of tools that simplify work with images and text.

Keras is one of the best deep learning frameworks for translation, image and speech recognition. 

Pros:

• Fast and simple prototyping;
• Documentation;
• It’s lightweight while enabling deep learning networks and multiple layers;
• Has configurable modules
• Intuitive interface;
• Build-in support for teaching in more than one GPU;
• Built inside TensorFlow
• It can be configured as an evaluator for TensorFlow and trained on GPU clusters on the Google Cloud platform;

Cons:

• Unsuitable for large projects;
• Not easy to customize;
• Not always suitable for a highly specific deep learning model;

 

Caffe 

A deep learning framework in C++ with Python interface. Caffe is characterized by expressiveness, speed and modular structure. Caffe is a good solution for training models, processing images and improving neural networks. It also supports CNN and feedforward neural networks. A few years ago, Facebook released Caffe 2, which is suitable for both mobile and large-scale development in the production environment.

Pros:

• Pre-trained models for creating demo apps;
• Fast, scalable and occupies little memory space;
• It works well with other frameworks such as PyTorch;
• Optimized work with the server;

Cons:

• Little to none community support;
• Not the best solution for complex networks;
• Lacks in debugging tools and tech support;

 

Deeplearning4j

 Deeplearning4j ( DL4J) is a good choice for Java programmers. It is a commercial-grade, open-source, distributed library written for Java and Scala. DL4J provides good support for various types of neural networks, from convolutional to recurrent and recursive. This framework is used for image recognition, natural language processing, vulnerability search and text analysis.

Pros:

• Reliable, flexible and efficient;
• It can process large amounts of data without compromising speed;
• Compatibility with Apache Hadoop and Spark, on distributed CPU or GPU;
• Documentation;
• Community;
• Enterprise option;

Cons:

• The use of Java for machine learning is usually a costly and time-consuming way;

XGBoost

XGBoost is an open-source framework that offers a gradient boosting system for C ++, Java, Python, R, Julia. It boasts high performance, flexibility and portability. XGBoost belongs to the realm of classical machine learning frameworks.

Originally, it was a research project of Tianji Chen and Carlos Gestrin as part of the Distributed [Deep] Machine Learning Community, but later it was expanded and presented to the public at the SIGKDD conference in 2016.

XGBoost focuses on computational speed and model performance and is a suitable solution for solving regression, classification, and sequencing tasks. If the data can be presented in the form of a table, then the accuracy and performance will be significantly higher than that of DeepLearning solutions. 

The framework is compatible with Windows, Linux and OS X. It also supports AWS, Azure and Yarn clusters, works well with Flink, Spark.

Pros:

• A fast and convenient way to train models such as “decision tree”;
• Accuracy;
• Portability;
• Ideal for hypothesis testing;

Cons:

• Field-specific;

Chainer

 One more Python deep learning framework supported by Intel, IBM, NVIDIA, and AWS. It’s a flexible, intuitive solution that runs on multiple GPUs with little effort, supports CUDA and various networks such as feed-forward convnets, recurrent and recursive. This framework is mainly used for voice recognition, machine translation, and key analysis. 

Pros:

• It beats any other Python framework in terms of speed;
• It makes code intuitive and easy to debug;
• supports various network architectures;
• The ability to modify existing networks in the runtime;

 Cons:

• Less popular than other Python networks;
• A small community, so not much help; 

 

AI and machine learning development with NCube 

We at NCube build teams around AI stack consisting of Ukraine’s best machine learning, data science, and data engineering talent. The expertise of our engineers spans cognitive computing, computer vision, machine learning, and deep learning. We can build your data science team in the same way we did for Fetch AI.

Years ago the main communication channels boiled down to phone calls and meetings. Then, the internet has brought us lots of other options, like email, social media, mobile and web apps. The most recent shift in the way people communicate with businesses is the chatbots. 

A chatbot is an AI-powered software that is programmed to simulate a conversation with a real person, be it on a website, app or platforms like Slack, Skype, Facebook Messenger, WhatsApp, and Alexa. Chatbots let customers communicate with brands via text or audio messages and can be easily customized to the needs of any business. 

AI-powered conversations resonate with a vast market of Gen Z and young millenials who are extremely comfortable with chatbot interactions. As such, chatbots are becoming not only a communication means but also a sales channel. 

If you are looking for a way to apply artificial intelligence chatbots to supercharge your marketing, here’s what this technology can do for you. 

Improve your customer service

Customer service is the chatbot technology’s natural habitat.  

Users expect to find information about your product or service quickly and easily otherwise they leave for good. For a small team 24/7 support can be an insurmountable task as well as for companies with millions of customers. Chatbots can be a great solution for both cases.

Salesforce’s research has found that a bot can be a great alternative to a standard contact form, given that 86% of customers would prefer the first option. Take a look at the eBay bot. 

image via techcrunch

Make sure your responses are friendly, written in simple language and resonate with your brand’s tone of voice. In addition, you can customize your chatbot by adding a pinch of character – select an avatar image and come up with a memorable name. This will create a sense of communication with a real person.  Chatbot applications can help you:

• Short wait time for customers
• Be online 24/7
• Reduce the number of service representatives

If you are interested in Big Data and Artificial Intelligence, we recommend visiting https://ncube.combig-data-and-ai 

Capture insights on your customers with machine learning chatbot

If want to know about your customers more, chatbots can make things easier for you.

Just like chatbots can be programmed to interact with customers, they can be also be trained to collect data and monitor users’ behavior on your website. This data provides valuable insights into your customers’ needs and how their purchase decision is made. 

Combined with deep learning, you can use the information on how users interact with your website to enhance the way they work. For example, by reviewing the questions and requests that a chatbot failed to handle, you can prepare new triggers and assign relevant responses. 

Jumpstart the sales cycle

Those who have expressed interest in your product or service can potentially become your customers. This can be a long journey, and chatbots can offer a solution that might take the headache out of it. Chatting is the best way to understand the readiness of the lead to make a purchase, be it with a bot or a human. But rather than filling out a bunch of forms, your customers can get a personalized experience via conversation. You just need to program the bot with a set of relevant questions to understand at which stage of the sales funnel your lead stands. If done right, verifying leads with chatbots saves you time while providing an excellent service to your potential customers.

A great application of chatbots is remarketing or follow-up. How do chatbots work in this area? It’s simple. You can set up a chatbot to engage people who have visited your website or social media accounts in a conversation. Chatbots for remarketing is a much better way to reach your potentials as compared to sending non-personalized messages via messenger or email. 

Augment your email strategy 

While chatbots can’t fully replace email marketing, they can be a powerful tool that helps you get a customer’s response faster than you would with email. Let’s compare a chatbot and email marketing for promoting products and services: 

Delivery speed 

Chatbot: if a recipient is active, a message can be received and seen immediately. 

vs. 

Email: There’s a risk of getting into a spam box where a user might never come by your email. Besides, many clients go through their inbox once in a couple of days. As such, chances are high that it will be days before a lead sees your email. 

Functionality

Chatbot: The chatbot might be able to handle simple, mundane tasks like providing the answer or sending the user to the relevant resource. If the bot can’t solve the user’s problem, a manager steps in. 

vs. 

Email: Your emails can include features such as order or pay online and links to outside resources. When clicking on links and buttons, a new page usually opens, which may be inconvenient for smartphone users. 

Data collection

Chatbots can collect and store customer data by asking questions and analyzing the responses. Chatbots can use collected data in further conversations. 

vs. 

Email campaigns are usually set up according to previously collected data. An email marketing specialist collects insight from campaigns to finetune the strategy for the future. 

The outcome? With chatbots, you stand a better chance that your message will be read by the user. More so, you can increase the volume of marketing conversations in messengers. And finally, based on data that comes from users’ engagement with a chatbot, you can set up more targeted campaigns.  

You may also be interested in How playing video games can help you sharpen your programming skills? Find details here.

Provide better, more personal engagement

Users prefer personalized advertising messages. Chatbots can bring in the personalized conversational element that can help you build a relationship with your potentials at the early stages of the sales process. 

The key to personalized engagement here is to ask customers about their preferences to further turn those into a personalized experience. 

A great example of such engagement a chatbot launched by Sephora on a messaging app Kik. 

The chatbot is programmed to understand the customer’s needs and guide them to the relevant cosmetics. 

In a similar way, CNN and Fox have adopted chatbots to create and deliver a news feed tailored in line with users’ preferences. They have implemented it on Facebook Messenger, Kik, and Line platforms as well as on Alexa, Amazon’s voice-controlled device. 

Both are great examples of adopting chatbots to deliver a personalized experience. In fact, lots of Facebook chatbots can be easily set up to serve that purpose. 

Afterword

According to State of Chatbots 2018, chatbots’ main advantages are as follows: 

• being online 24/7. Using chatbots companies can address customers’ problems in real-time, which has a direct impact on the quality of customer service and business’ success. 
• engaging more people into a sales conversation. If a company wants to sell to more people, using chatbots will be economically viable. 

We can conclude that clients are looking to get a personal experience, no matter who will provide it for them. That being said, marketers can use chatbots to hit both targets: to automate the marketing process while providing that personalized experience. Chatbots can also play a key role in the sales funnel and be the source of valuable customer data you can use to build your marketing strategy.