The main difference is in the linguistic complexity. 

People express themselves differently when they speak compared to when they type. When we speak we use more sentences and we make our sentences longer. 

As a result a voice bot needs to have better AI compared to a chatbot, in order to handle a conversation and deliver the same customer experience. 

If your business model allows it, is better to start with a chatbot and add a voice bot on top of it.

This way you can gradually increase the complexity of your AI without compromising on your customer experience. 

nmodes is the only platform offering businesses their own AI that represents their brand. Other platforms are one-size-fit-all solutions that make it difficult for businesses to let AI know their sales process, customers, product details. nmodes gives every company an AI assistant that knows their business and grows together with it. 

In addition, nmodes AI is laughingly easy to use - there is no need to be technically savvy because all communication happens in natural language. 

Also, nmodes platform offers data privacy better than any other AI solution. nmodes unique architecture makes us the only conversational AI company that does not own customers' data.  

Conversational AI is the use of speech-based assistants (such as chatbots, voicebots) in order to create an easier and more user-friendly experience for the customers.

In May 2019, the Collision Conference  took place in Toronto for the first time, and we couldn’t miss it! It was a great opportunity to meet amazing people, learn from great companies and showcase our own capabilities.



Attending the first day’s talks at the Collision center stage

AI solutions naturally attract attention

Lots of interest interest in our conversational AI

For a business to grow successfully, it needs to scale its sales, customer service, marketing.

The only sustainable way to do this is to introduce an automated sales and customer experience service.

Conversational AI is the single available method to automate customer experience without reducing the quality of service. It comes in a variety of forms such as a chatbot, voice bot, virtual assistant, cognitive agent. They all share the scaling ability and the ability to deliver human-level quality of conversations. 

While Toronto is charged with hosting the Collision - "North America's fastest-growing tech conference" this year, nmodes is excited to make its first appearance among designated start-ups who have been selected to demo their products to conference visitors, potential investors, tech-enthusiasts and business executives.

nmodes, a year and a half in the market, offers a conversational product that uses AI to provide its customers with a scalable solution to execute 24/7/365 marketing acquisition and customer experience programs. While nmodes has already garnered its global presence with 40+ clients, North American market continues to be most enterprising for AI Chatbots and Voicebots.   Collision Tech Event offers an exciting opportunity for nmodes team to take its networking game a notch higher and pitch it to businesses looking to catch-up with the AI space and be early adopters of hottest AI products available in the market.

How nmodes is different than other chatbots?

AI space is nothing new to the tech world as chatbots, virtual assistants and voice bots are finding their commercial contribution toward improving the customer experience of brands. nmodes continues to work closely with the businesses focusing on helping brands drive double digit growth in lead conversions and engagement rates.

Three key market differentiators for nmodes:

1. 1. Interlacing marketing and customer experience

nmodes chatbots are custom built for the brands.  nmodes solutions support full customer lifecycle from lead generation to marketing campaigns to scheduling demos, to gathering feedback and understanding engagement patterns of existing customers.

1. 2. Lifetime AI training

nmodes solutions promise to work with progressive AI capabilities that are built to recognize old and new communication patterns and form a sensible response template that is malleable and fulfills the intent of desired conversation for the customers.

Nmodes solutions work on three principles while conversing with the customers.

A) Keep business context

nmodes solutions remember the customer’s history and their presence in the sales cycle and hence conversations are based upon the context of customer for the brand.

B) Data personalization

personalization of conversations focuses on collecting different data points from all internal and external data sources, helping brands deliver tailored and one-on-one predictive interactions.

C) Easy to use analytics

nmodes advanced dashboards uncover detailed analytics and insights on customer conversion rates, engagement rates and listen upon most common conversations to help brands better align their marketing communications and customer experience strategies.

nmodes AI is based on semantic algorithms. They require significantly less computational capacity compared to standard machine learning algorithms used by a majority of conversational AI systems today.

As a result, the infrastructure requirements are drastically reduced. In simple terms, what Google Home or Amazon Alexa do with the help of supercomputers or advanced computer farms, nmodes AI can do on a basic server.

And it gives nmodes ability to delegate conversational capacity to the users.  With the help of nmodes AI every business (and individual) can create their own AI to handle the details of the business (products, customers, etc) not accessible from the outside.

We are experiencing a strong demand for conversational AI solutions. It is coming from every corner of the B2C market. It is growing by the day.

Conversational AI is becoming increasingly popular among the consumer facing business community. It is easy to see why - AI offers sales and customer service scalability and therefore is critical for the long-term success of a business.

Conversational AI solutions such as chatbots, voice bots, and virtual assistants provide much needed speed and efficiency, in an age where the rapid advancement of technology makes them virtually the only sustainable customer service solution.

Bu there is a catch - AI is complicated. Mainstream businesses do not have in house AI expertise. And it is not part of their business model to develop such expertise.

Today’s market offer several good conversational AI solutions, such as IBM Watson or Google DialogFlow. However, getting a business value out of them requires the very AI expertise that mainstream companies do not possess.

So what can be done?

Any AI solution should follow these three steps in order for the mainstream business community to fully benefit from it:

1. Conversational AI should come as a service,
2. The service should be available in natural language,
3. The service should be fully personalized.  

So you decided that your business needs a chatbot.

And you’ve probably heard conflicting opinions on chatbots - some praise them for the ease with which they can offer customer service, others criticize for their lack of true intelligence.

How to proceed?

At nmodes, we have been working on chatbots longer than most, starting long before they became popular.

Here is how we advise mainstream businesses to approach the chatbot quandary.

1. SET YOUR BUSINESS GOALS  

Remember that users prefer to spend less time talking to your chatbot, not more. A user wants to resolve specific issues related to your brand, not engage in a soul searching chit chat about the meaning of life, politics or sports. A user expects your chatbot to provide the answer to a particular question, and the faster the chatbot can do it the more satisfying customer experience it will create.  

All that means is that your chatbot does not need to have the capabilities of a Siri (generic conversational AI solution). Instead, it has to understand really well the conversational domains related to your business. It does not need to support much of the rest of the language.

And so you need to decide which business related topics you want your chatbot to cover and not to venture outside of these topics.

Typically chatbot topics revolve around sales process, customer support, sometimes they include lead generation, FAQs, problem resolution, and reputation management.

2. DEFINE THE DIALOGS

Chatbots are about conversations. After you have decided what kind of topics you want your chatbot to support it is time to get a bit more specific and define the dialogs. Ask yourself the following question: what do you want to achieve at the end of the chatbot’s interaction with the customer. For example, if you are dealing with the sales process, the end result could be a customer making a purchase, or a customer providing contact information for the sales team to follow up on, or  when a customer indicates what product he or she is interested in.

Build a dialog with the end result in mind.

We sometimes call this creating the conversation flow.

Of course, you can create as many conversation flows as required to support your business model.

3. DECIDE IF YOU NEED AI  

The are two types of chatbots - based on multiple choice buttons and based on natural language conversations.

Don’t discard buttons. Remember that a chabot is expected to make the user experience as enjoyable and as friendly as possible. Buttons often make conversation super easy and fun (the user simply clicks a button, what can be easier?).  In many business cases buttons provide a fast and efficient way to ask relevant questions and keep the conversation flowing towards the desired conclusion.

Using buttons also makes chatbot development simpler and reduces the development costs.

The second option is to make a chatbot support natural language conversations, in which case you will need AI.

Pick the AI solution you want to work with.

The good news is that there are several decent products in the market so you have a choice.

The not so good news is that they all are relatively complicated and require a certain level of technical knowledge.

(And you can always talk to us - we provide AI solutions that do not require any technical knowledge).

4. DECIDE IF YOU WANT TO DEVELOP YOUR CHATBOT IN HOUSE OR OUTSOURCE

Unless you want to position your business as an AI company you likely do not want to develop it on your own. There are several reasons for that.

First, AI technology is complex and its complexity if often underestimated. You will need top AI expertise and will probably need more of it than you anticipate.

Second, as Cameron Schuler recently observed, there is a significant shortage of AI experts and it will be difficult for you to find one.

Third, and perhaps most importantly, if you are a mainstream business developing in-house AI expertise is not part of your business model.

Bringing in an AI partner to help with your AI needs is a reasonable option for many businesses. Of course, the downside is additional immediate costs.  

Following the simple steps above and answering these questions will help you navigate the sophisticated world of AI, decide what kind of chatbot does your business require and how to approach the process of creating it.

NMODES chatbots and conversational AI solutions come with a unique set of features.

Our goal is to make it easy for businesses to create and manage chatbots. The features we offer are important for successful implementation of a chatbot not only because they significantly improve its quality, but they also allow you to edit your chatbot in natural language, without any need to have technical knowledge or AI specialist in the team. 

1. Free AI Training 

Our chatbot solutions come with free AI training for life.

We will train your chatbot and continue to enhance it indefinitely.
It is our responsibility to ensure that your chatbot has the updated natural language processing capabilities. It is also our responsibility to guarantee that it understands not only common language but also language that is specific to your business, such as names of the products, terminology used in your industry, inventory list, and more. 

Your chatbot will be interacting with the customers all the time. We will enable it to learn from these interactions continuously and improve its language understanding and responses as a result of this learning.

2. Editing in natural language 

We realize that AI is a complex body of knowledge and one of your biggest concerns is that you are not familiar with it well enough. We made sure that you don’t need to be technically savvy to successfully manage a chatbot. Using our simple and friendly online interface you can control your chatbot in real time using common natural language. No technical knowledge is required.

At NMODES we continuously improve our AI capabilities. We use our AI not only to make the experience of your customers, conversing with your chatbot, better, but also to make your own experience, conversing with our platform, better.

Eventually the platform will be able to interact with you fully in natural language. We are not entirely there yet (it is an immense task). Still, we hold true to our promise that there is no need in being technically savvy to operate our platform even today. When the platform does not understand natural language our highly trained specialists are always ready to take over and provide support.

3. Real time connectivity 

Often there is a need for chatbot to access structured data (such as inventory database) to answer customer’s question. We made it easy for your chatbot to create external queries in real time and modify the responses accordingly. Your chatbot is able to decide in the middle of the conversation, based on the information it received from your database, how to respond and how to proceed with the conversation.

These are the most exciting among the features we created so that our customers have easy and enjoyable chatbot experiences. But there are other features available: conversational templates, dynamic AI Engines clustering, multiple widget skins and more! Let us know if want to see the full list of features.

To learn about the core technologies required to build a chatbot check out this post:

Artificial Intelligence, more commonly referred to as simply AI, has been, since it’s early days, changing our lives in many ways. AI has become one of the greatest inventions of the human mind. When thinking of AI, we do not normally associate AI as being involved in helping farmers grow more crops to feed the exponentially growing population, or helping develop cancer treatment, or even keeping kids safe from trafficking and abuse by finding improper online activities. Instead we think of computers to phones, to self-driving cars and robots. However AI doesn’t just power the gadgets that we have grown so accustomed to in our daily lives, but it is increasingly being used to help solve impending social challenges.

One of these impending social issues is the quite literally hot topic – global warming. The challenges of global warming are growing by the day, as its impacts are becoming more severe and harder to manage. Melting ice caps, severe sever weather changes, extinction of species, are just a few of the consequences of the manmade climate change that is plaguing our world today. Despite widespread acceptance and awareness, the rate at which the world is embracing positive change is unfortunately not fast enough.

Fortunately there are many large companies that are setting an example by using AI to develop new ways in which to battle global warming. In fact, it seems as though AI is the only solution we have. It is helping us not only track and our present data, but also analyze our past data so that we can make informed decisions about the future. One such example is the use of AI to collect large amounts of data on land, animals, weather, ecosystems, etc… and organize it, so that scientists and governments can then determine what needs to be done, and the most cost effective ways to engage conservation methods.

We are quite surely seeing more and more AI initiatives being undertaken to help create a more eco-friendly world.

Whatever the downfalls of AI may be, its ability to help us against destroying our planet is perhaps its most important trait – because as hard as it may be to accept, our planet is dying and AI can help us prevent that. 

Our company (www.nmodes.com) is a bit like Croatia (#WorldCup): Everybody pay attention to other teams with shiny and glitzy products. But we work our hearts out, deliver quality results, humbly and respectfully advance forward. And at the end of the day we are winning. 20+ customers, full trust and glowing testimonials from customers, 200% growth in the last year. All is achieved completely organically.

                                                                                                                                         

All quality conversational AI solutions such as chatbots, voice bots, virtual assistants are customized. The reason is because conversational AI solutions have a component called AI training that has to be individually tailored to the needs of each business. Currently AI industry does not have a suitable solution to automate this component.

There are, of course, easy-to-use, scalable products such as Chatfuel, ManyChat and others, but they do not provide sufficient quality and therefore do not add value to the professional sales or customer service process.

The next generation of conversational AI solutions will be scalable, while capable of delivering the level of quality required by businesses and professional organizations. nmodes is among a limited number of AI companies, with sufficient level of technological knowledge and deep enough understanding of underlying linguistic processes. working on delivering this kind of solution to the market as quickly as possible. In the meantime, customizable AI solutions, with personalized AI training component, is industry's best option.  

AI training is a critical part of conversational AI solutions, a part that makes AI software different from any kind of software previously created.
AI training is not coding.
Unlike all other existing software which is fully coded.

Let us consider a simple example:
We create chatbots for two companies, one company is selling shoes, another is selling cars. From the software standpoint it is one chatbot solution running as an online service accessed remotely or a program available locally. In both cases they are two identical instances of the same software (one instance for the shoes company, another for the cars company).
Yet, for the first company the chatbot is supposed to talk about flip-flops, summer shoes, high heels and so on. For the second company, however, the chatbot is not expected to know any of that. Instead, the chatbot should be able to support conversations about car brands, car models, should know how to tell Toyota Camry from Toyota Corolla, etc. This shoes and cars knowledge is not programmable. It is trainable. It is not coded, instead it is a part of language processing capability that AI solutions like chatbots have. And herein lies the major differentiation and advantage of the AI solutions compared to traditional software.

How to train AI?
There are several ways to do it. Sometimes AI system can train itself, improve its linguistic ability over time. It also can be trained by professional linguists. And in some cases, by the users. The latter is the desirable scenario because businesses know better than anybody else what they want their chatbot to talk about.
It is not easy, given the existing state of AI technology, and usually requires a high level of technical knowledge. You may have heard mentions of intents and entities in chatbot discussions. These are examples of linguistic elements AI training is currently based on.
Without proper understanding of what these linguistic elements are and how language acquisition process works in existing AI systems it is better to leave AI training to professional linguists.

   
Chatbot platforms are essential tools if you need to build and run a chatbot.
There are many available on the market, big and small, popular and not so much.

Here are some useful thoughts that should help you navigate the complex world of chatbots and conversational AI solutions.

All chatbot platforms can be split into two categories: those that let you create chatbots without any programming, and those that require programming. Now, the idea that you don’t need to possess technical knowledge to build a chatbot seems appealing but the reality is not so rosy. In fact, I have yet to see a professional chatbot created without coding.
Chatbots rely on sophisticated algorithms and advanced knowledge of linguistics. These technologies are so complex that at the moment there are no plug-and-play solutions available. The companies like Chatfuel, Manychat, Flow XO and many others are attempting to fill that void and offer chatbot platforms that are simple in use. The best way to make the chatbot creation simpler is by dropping the need to code them. However this simplicity comes at a price: chatbots made without coding are limited, rigid and in general, primitive.
So to summarize: if you want to impress your girlfriend use Chatfuel. If you need a professional chatbot that delivers on your business goals and provides customer satisfaction use advanced chatbot platforms with programming capabilities.

One of the main, if not the main, tasks of the chatbot platforms is to connect your chatbot to the user interfaces. There are many ways for your chatbot to interface with the world: on Facebook messenger, on the website, on the mobile app, via SMS, on Twitter , on Skype, on Slack, on Telegram, and more. A good chatbot platform should seamlessly connect the chatbot to most of these channels. Chatbot platforms do not make your chatbot smarter. For this you need AI Engines (brief disucssion on AI Engines: http://nmodes.com/entry/2018/03/29/what-are-ai-engines-and-how-choose-one/).

For best results create your chatbot on a chatbot platform, then connect it to AI engine.

One of the top chatbot platforms on the market is Microsoft Bot Framework. It is robust, powerful, with a wide variety of useful functionality built-in. Another good chatbot platform is DialogFlow. DialogFlow has a slightly different architecture in the sense that it is a chatbot platform and an AI Engine all in one interface.

Chatbot platforms can be used to create conversation flow for your chatbot. There are several schools of thought here: some prefer to delegate conversation flow to AI engines. Chatfuel and other tools with the emphasis on simplicity (build your chatbot in minutes, no coding necessary) offer easy graphical interfaces for conversation flow creation. And there is always a reliable option to create conversation flow in an old-fashioned way, programmatically.

Which option to choose? Depends on your chatbot requirements and the business needs the chatbot is expected to address.And if you have questions feel free to ask: http://http://nmodes.com/contact-us/

Chatbots and assistant programs designed to support conversations with human users rely on natural language processing (NLP). This is a field of scientific research that aims at making computers understand the meaning of sentences in natural language. The algorithms developed by NLP researchers helped power first generation of virtual assistants such as Siri or Cortana. Now the same algorithms are made available to the developer community to help companies build their own specialized virtual assistants. Industry products that offer NLP capabilities based on these algorithms are often called AI engines.

The most powerful and advanced AI engines currently available on the market are (in no particular order): IBM Watson, Google DialogFlow, Microsoft LUIS, Amazon Lex.

All these engines use intents and entities as primary pnguistic identifies to convey the meaning of incoming sentences. All of them offer conversation flow capability. In other words, intents and entities help to understand what the incoming sentence is about. Once the incoming sentence is correctly identified you can use the engine to provide a reply. You can repeat these two steps a large number of times, thus creating a conversation, or dialog.

In terms of language processing ability and simplicity of user experience IBM Watson and Google DialogFlow are currently above the pack. Microsoft LUIS is okay too; still, keeping in mind that Microsoft are aggressively territorial and like when users stay within their ecosystem, it is most efficient to use LUIS together with other Microsoft products such as MS Bot Framework.

Using AI engine conversation flow to create dialogs makes building conversations a simple, almost intuitive, task, with no coding involved. On the flip side, using AI engine conversation flow limits your natural tendency to make conversations natural. The alternative, delegating the conversation flow to the business layer of your chatbot, adds richness and flexibility to your dialog but makes the process more comppcated as it now requires coding. Cannot sell a cow and drink the milk at the same time, can you?

Amazon Lex lacks the semantic sophistication of their competitors. One can say (somewhat metaphorically)  that IBM Watson was created by linguists and computer scientists while Amazon Lex was created by sales people. As a product it is well packaged and initially looks pleasing on the eye, but once you start digging deeper you notice the limitations. Also, Amazon traditionally excelled in voice recognition component (Amazon Alexa) and not necessarily in actual language processing.

The space of conversational AI is fluid and changes happen rapidly. The existing products are evolving continuously and a new generation of AI engines is in the process of being developed.

Chatbots and conversational AI solutions offer easy and reliable sales channel.

Chatbots are helpful because they not only engage with your customers, they also retain them. This means that unlike other forms of marketing, chatbots keep your customers entertained for longer. For example, when a user has a customer care inquiry, the chatbot can answer it accurately and take the conversation a step further by offering personalized shopping advice based on the customer's purchase history and preferences.

Chatbots make communication with your brand proactive, thus enhancing your brand's reputation. By increasing interactions, they make a positive effect on your sales numbers.

Chatbots allow your business to reach out to a wider audience. They can gather and analyze customer feedback and data. Send relevant notifications. Make communicating with your brand more fun.

Chatbots help with top of funnel conversions. Offering your online customers a highly personalized experience of your site with the addition of a chatbot can help guide consumers down the sales funnel, and ultimately influence their buying decisions. 

To summarize, chatbots opens new opportunities for you to increase sales. Opportunities that are easy to use and are not costly. Better not to miss them!

Conversational Artificial Intelligence solutions can communicate with people in their natural languages. The interactions happen via speech or text – our most common forms of interaction.

The most popular example of a conversational AI solution is chatbot.

The chatbot popularity began in 2016 with Facebook’s announcement  of a developer-friendly platform to build chatbots on Facebook messenger. Soon, chatbots became the buzz of the technological community and spread across various industries. As a next step, toolkits that helped build a bot in five minutes grew popular, companies raced to the market with new bot announcements and the world woke up to a new chatbot-based reality.

A well developed conversational AI chatbot is able to interact on a near-human level. If we think about it, most companies’ customer service and sales centers deal with a core of 6-12 repeating issues. conversational AI software allows companies to develop an intelligent response channel that can cover the most common customer interactions.

Another advantage in using Conversational AI is in the marketing and branding domain. Chatbots allow the companies to stay on their message without veering off course . With AI, the scripts are all written and approved in house. Even when the AI system learns, when the appropriate training techniques are implemented, the system will adhere to the required profrssional verbiage.

Many of us get the understanding of artificial intelligence from the film industry. It creates an image of smart, humanized machines that are helpful, efficient and omnipresent. It is true that AI has seen rapid advances in the past several years, to the point that it became an integral part of our everyday life.  In real life, however, AI is far away from the level portrayed in sci-fi movies. And yet there are affordable AI tools and solutions that can make a significant impact on your business.

Here are three main reasons why a company, especially if it is a B2C company, should consider integrating AI into their business process.

AI makes your sales process scalable

AI solution dealing with your prospects and customers works 24/7 without sick days, holidays and breaks. It can handle any level of traffic, incoming inquiries and conversations. It does not need to be trained. It does not have personal issues or bad days. It is always polite and uses professional jargon. It is fast.

AI creates better user experience

Some might find it surprising but this is only because they have experienced low quality AI solutions. A professional AI solution makes customer experience better primarily because it delivers the results with a minimum of fuss and maximum efficiency. A good AI eliminates bureaucracy, makes customer experience speedy and seamless, and that’s what consumers are looking for today.   

AI offers sustainability

Adding AI to your business model creates long-term sustainability for the business. It allows your business to grow while controlling, or even minimising the costs. More importantly, it ensures that the business remains competitive in providing the level of customer service consumers became accustomed to. Lastly, it creates platform for future technical improvements and integrations which, without a doubt, will be based on Artificial Intelligence components.

nmodes recently introduced new feature for our family of Advanced AI solutions – Arabic language support. 

nmodes provides conversational AI solutions to address the needs of customers around the world. We deliver chatbots, integrated AI solutions, omni-channel AI systems and more. Our solutions are professional, efficient, scalable. 

We believe this is the beginning in the marketplace for advanced conversational AI solutions that help improve and scale current sales and customer service business process. With Arabic support, we’re creating new opportunities for an important, fast-growing market segment.

Additional native language support is an integral part of our strategy of bringing a full spectrum solution to the AI market.  We continue to define new solutions, add new languages to enable businesses and organizations around the world more easily benefit from the advantages of  using solutions powered by advanced AI technology.

These are early days for travel bots. They mostly specialize in customer service, customer information and sometimes online booking. Advanced AI technology is good and getting better by the day, but it does not replace a person. And that's unlikely to change for a while.

In order to create a positive and enjoyable experience it is imperative to have a clear understanding of what bots do well and what they don’t.

One area where they have clear advantage over humans is response speed. Using bots makes your travel business scalable. Bot can handle mutlple user conversations simultaneously and replies instantly.

The part of the bot technology that needs significant improvement is understanding of the meaning of what customer said. The solution is to take the user off the bot when this stage of the converastion is reached. One of the popular techniques is to redirect the user from bot to the website when the questions get complicated. The majority of users are at ease with website navigation where they find themselves in the familiar environment.

This approach allows to utilize the scalability of the chatbot while maintaining the high level of customer service.

Is this the dawn of the chatbot era?

Microsoft product strategy has always been and still remains that of ‘zero alternative’. Their ultimate policy is for their customers to have no choice but to embrace only Microsoft products. Consequently they created and are offering products and solutions in (almost) every segment of IT enterprise and consumer market, including, but certainly not limited to, their own data base, their own cloud services, operating system, office tools, programming language, and many more.

Not only do Microsoft offer wide variety of products, they tie them up together in a unified ecosystem that makes it easy for components to connect and interact. At the same time, this ecosystem is hostile to non-Microsoft products.

Microsoft strategy for the burgeoning, fast growing AI segment is similar:

Create products to address all parts of the AI market, add them to the ecosystem to ensure easy compatibility from within and difficulty of use from outside.

Currently the products on offer are:

- Microsoft AI engine, called LUIS. It is supposed to compete with other major industrial AI systems such as IBM Watson, and has similar training methodology. It offers webhook interfacing via endpoints.  

- Microsoft chatbot building platform, called, surprisingly, Microsoft Bot Platform. It addresses the popular demand for easy chatbot design and provides seamless connectivity with main user interfaces, such as web interface, SMS, mobile, and messaging platforms.

- In addition Microsoft offers their own messaging platform in Skype.

The main advantage of  using Microsoft AI products is the built-in connectivity with user interfaces.

The main disadvantage is in their ‘zero alternative’ policy - once you’ve chosen a Microsoft product you are likely will be forced to choose only Microsoft products for the duration of your project.

There are lots of written tutorials and online videos on this subject.

Yet many of them omit important details of the bot building process. These details may vary from one user to another and are difficult to describe in a unilateral fashion. Consequently it is easier for tutorial writers not to mention them at all. We try here to fill the gap and provide some additional clarity.

1. Creating Facebook app.

One of the first steps in building a Facebook Messenger bot is creating a Facebook App. It requires a business Facebook page. This might seem obvious to avid social users yet worth mentioning: a business Facebook page can only be created from a personal Facebook page. If you already have a business Facebook page move on to the next step. If you have a personal Facebook page go on and create a business page. If you are among the lucky ones that live without Facebook presence now is your chance to become like everybody else.

2. Getting SSL certificate.

Next you need to setup a webhook. Your web application is hosted on a web server and the webhook’s role is to establish connection between Facebook and your web application via your web server. In order for the webhook to work you need SSL certificate because Facebook supports only secure connections (HTTPS) to external web servers. So first, you need to purchase it. The costs change from one company to another but it is important to buy a reliable certificate otherwise Facebook might reject it. All major ISP companies offer SSL products. Second, you need to install it on your web server. The installation process can be tricky. Sometimes you can get technical help from the ISP company that sold you the certificate (as a rule of thumb, the bigger the brand the better their technical support is supposed to be. But the cost may be higher too). You can also rely on popular tools, such as keytool command utility, assuming you know how to use them. In any case, it might be a good idea to allocate several days, up to a week, for this step when planning your project.

3. Choosing the server environment.

Your options are (almost) unlimited. Many online tutorials use Heroku which is a cloud-based web application platform, but a simple Tomcat web server would suffice too. Your decisions should be based on your business requirements.  A lightweight server such as Tomcat is a good fit when it comes to web centric, user facing applications. If backend integration comes into play, a web application server should be considered.

Your choice of programming languages is also broad. PHP is one popular option, Java is another but the list by no means ends here. Your chatbot app communicates with Facebook using POST requests, so any language that supports web protocols will work. Again, make decisions having your business goals in mind.

There is a growing demand in the industry for Artificial Intelligence products, from simple chatbots to conversational ecommerce solutions to advanced intelligent systems.

And there is a growing number of AI companies offering such products.

One of the problems however is that AI products currently available on the market require technical sophistication on behalf of the user, such as familiarity with APIs, communication protocols, XML, etc.

nmodes aims to solve this problem. Our position is that the users do not need to be technically savvy to enjoy AI capabilities. We offer our AI solutions as a service, fully hosted, fully supported.

We do not ask for any technical knowledge from our customers. We only want them to tell us the details relevant to the business process they are looking to implement or support and we will take care of the rest.

In particular

1. We train AI to understand and support their own use cases.

2. We host the entire solution, without claiming the ownership of the data we process or use to train our AI.

3. We support all user interfaces ( UI ) required by our customers.

4. We connect to third-party APIs and integrate our AI with third-party components.

Artificial Intelligence as a Service ( AIasS ) that we offer makes new AI technology easier to use increasing its exposure to businesses and organizations worldwide.  

A close look at the history of humanity leaves us with no choice but to admit that the majority of jobs as we know them now will be transferred to automated systems. This is part of the technological and scientific progress our civilization is undertaking and it is irreversible.

Artificial intelligence became mainstream in 2016. For the first time artificial intelligence is not only available to big companies like Google, Amazon or Apple, but to the majority of businesses worldwide.  Startups have started building products and services using artificial intelligence en masse.

The essence of artificial intelligence is massive, intuitive computing power: machines so smart that they can learn and become even smarter.  The machines are becoming quicker and more nimble. They cover wider range of conversation topics. They now connect to robotic systems and online interactive systems. There is literally very little they cannot, or will not be able to, do as applied to industrial workforce.

With all the good that’s going to come with automation, we are suddenly faced with a new problem: the elimination of many low and middle class jobs. Many jobs that have already been severely impacted by computers (manufacturing, administrative support, retail, and transportation) will continue to diminish. In the nearest future routine-based jobs (telemarketing, sewing) and work that can be solved by smart algorithms (tax preparation, data entry keyers and insurance underwriters) are most likely to be eliminated.

What to do? It is fruitless to fight automation, we need to find ways to work with automation rather than against it.

The solution is to become more creative as species. Creativity is the natural advantage of humans over machines. Automation is about to change the course of the world, it’s going to be a great disruptor and impact the workforce like nothing we’ve seen before. We can sit around and gradually become obsolete, or accept the challenge and use the tool of creativity, which we are in unique possession of, to maintain our superiority.

Although it doesn’t feel like all that long ago, way back in the 90s one of the most important factors to a call center’s success was the ability to route a customer to the right support agent with the IVR (Interactive Voice Response). Countless hours were spent identifying the most efficient call routing patterns and expert agent capabilities to ensure that your request reached the right person quickly. This technology is still widely used today and there are still teams in the largest companies programming IVR systems to accomplish pretty much the same goal.

As the standard for customer support evolved there have been many attempts to improve the function and the customer experience associated with IVRs to reduce hold times and provide more relevant support faster. Even today some companies will use their IVR system as a way to keep a customer on hold, rather than provide a solution, when agents are inundated with calls.

For those of us who’ve worked in the voice industry for some time, we’ve seen first-hand the attempts to accomplish a customer’s need before reaching an agent. First there was expert agent routing that delivered your call to the agent most qualified to help you. Then came advances in voice recognition, which today has evolved to be a very effective tool to increase containment rates and deflect calls from reaching a live agent. My two favorite examples of the power of voice recognition are Cox Communications and Capital One, two examples of great voice recognition and routing.

Our memory, however, is short. It wasn’t so long ago that we were all pulling our hair out punching digits into the phone or constantly repeating “agent”, “Agent”, “AGENT”, AGENT!!!!!”.

Whether it was a limit of computational power or the sheer cost of developing and implementing advanced call center technology, it took decades for phone systems to be able to front end the customer support process as efficiently as they do today. Thankfully we all survived to see it without boiling over from the hypertension usually associated with calling with a customer service department.

Bad customer experience is definitely not the case with Chat Artificial Intelligence (Chat AI). While we seem to hear about the shortcomings of Chat AI like the disconnected conversations and the robotic like responses, these experiences are usually the product of Chatbots with limited AI functionality or early stage deployments. The increases in both computational power and the massive advancements in machine learning are driving excellent customer experiences that improve over time.

When was the last time you heard of technology actually performing better, on its own, without a ton of additional development work or continuous updates? Well, that’s the case with Artificial Intelligence. Like a person, the more experience it has interacting with customers and information, the better it performs with little need to be manually improved or fine-tuned.

Today, AI Chat can be used to answer a large majority of customer requests and because Artificial Intelligence learns as it is used, customers prefer to interact through AI chat to avoid all of the frustrations commonly associated with calling a contact center agent. 

We’ve all seen the numbers and they tell us that customers are more likely to make a purchase if they’re able to speak to a representative at the time of purchase. Study after study shows that if you can prevent even the smallest percentage of customer defection revenues and profitability can literally skyrocket as much as 80%. Just as important, the faster is your service the better is customer experience.

The same can be said for customer support. More than 70% of customers say that responsive customer support providing fast, courteous, relevant and contextual answers to their inquiries are the most important factors in determining the quality of customer service and the likelihood of that customer doing business with the company in the future.

As our world becomes even more “on-demand” and global, providing around the clock sales and customer support is quickly becoming a key differentiator. Customer’s desire to do business with companies on their own schedule and terms are driving financial growth and customer loyalty across all sectors and industries. Companies that neglect this “always on” requirement not only lose out, but need to find ways to be competitive.

Unfortunately, only the largest companies have the financial resources to deliver 24x7 customer support and sales operations. Still many of the largest companies can’t justify the expense of building out and staffing a 24 hour contact center. While outsourcing to a BPO is always an option, statistics show a diminishing return for outsource customer and sales support operations.

As customers continue to drive up the use of chat and social communications for customer support and sales, along with the incredible growth in Artificial Intelligence technology, smart companies on the forefront of customer service now have the ability to offer around the clock service for a large portion of their customers.

Think about this: While the average phone support call has previously been measured at almost 6 minutes, the average chat session lasts just 42 seconds, indicating that the vast majority of customer support issues are simple and only require limited information in order to leave a customer informed and satisfied with the interaction.

Today Artificial Intelligence can deliver a personalized, informed, and contextually relevant response to just about any question related to most customer inquiries. Add on the fact that AI actually “learns” as it interacts with people and information and the value to the customer and the vendor actually increases over time.  Wouldn’t we all like to have immediate service with zero wait times and fast, courteous response that immediately addresses our needs? I know I would.

Implementing Artificial Intelligence for customer service comes down to an application cost that, when amortized over the number of chat or social sessions it can handle, reduces customer support costs to as little as 10% of traditional contact center and agent expenses.

The one objection to relying on Artificial Intelligence in the contact center is the customer experience. There’s enough bad press out there about Chatbots and broken, robotic responses that are sometimes irrelevant that some customer support professionals are wary of any form or automation. My response to that is, while those were valid concerns; just take a look at Siri today vs. 2 years ago. The quality of responses has dramatically improved, as has the customer perception and usefulness.

What are your thoughts about Artificial Intelligence in the contact center? We’d love to hear from you.

While Chatbots have been around for a little while now, their presence is more noticeable thanks to Facebook and Microsoft’s recent advancements.

Initially customers complained about the robot-like experience and the limited functionality of first generation bots and rarely found them useful. The customers were skeptical about how valuable in practice chatbots actually are, which has left recent AI vendors like nmodes with the task to combat the leftover stigma from the poor customer experiences and shortcomings of these initial offerings.

Chatbots, like an IVR?

We’re all used to calling into a contact center and punching numbers into a menu to be routed to the correct agent or service to address our needs. Interactive Voice Response solutions (IVRs) drive this interaction and are basically If/then routing trees that “listen” to the digit entered and “transfer” the user to the appropriate next step. While tremendous advancements in technology have brought voice recognition capabilities, those first generation IVRs were all about automated actions based on prompts.  Enter your account number, press 1 to speak to an agent, etc…

The first generation Chatbots are just like an IVR. They can respond to prompts to progress through a predetermined process or display some canned information like pricing, a contact number, route to an agent, etc., but that was about the extent of it. Still 1^stgeneration Chatbots came with 4^thgeneration expectations. While these basic functions have tremendous value to a business, the customer expectation is very different when dealing with a phone call vs. a chat session. Consumers have experienced IVR routing for decades whereas chat is still relatively new and is perceived as a conversation with a person, rather than interacting with a machine. Add on the fact that many vendors and consumers mislabeled Chatbots as Artificial Intelligence in the beginning and the expectation of a dynamic, responsive customer experience is even greater.

So it’s no surprise that customers were less than impressed with “Artificial Intelligence” that could only display simple answers and basic information. We were expecting Hal from 2001: A Space Odyssey or KIT from Knight Rider, and we got a pixelated PONG instead.

Let’s talk…

Now, Artificial Intelligence has evolved to be integrated into Chatbots to deliver a more powerful user experience.  While these new versions of Chatbots coming out are powered by Artificial Intelligence, AI powered chat also exists independent of bots in some instances. Confusing? Yeah, I was too.

The beauty behind true Artificial Intelligence is its ability to recognize the context of a conversation and respond with relevant, contextual information dynamically. A customer can now “speak” to technology the same way they would hold a conversation and the AI has the ability to “read” the customer’s intent to provide information quickly and efficiently. No more are you limited to a set of canned responses. The AI can reach in to a wider array of relevant information to craft unique responses based on any number of criteria. While in most cases AI is still limited to a few topics per use case, the technology is growing quickly, making almost daily improvements in functionality and customer experience.

What is even cooler is that the longer the AI is deployed, the more it “learns” and improves the speed and quality of responses. So while the scope of AI interactions is limited at first, the maturity curve is quick, delivering an ever-improving customer experience without having to invest in additional people, processes, or technology. It really is like a “growing up” of technology, right before your eyes. 

1. Zero Wait Times

Do you suffer from long chat queues? How about any chat queues? Can you imagine a world where your customers are either immediately engaged in a proactive chat, or can easily decide to engage in a on-demand chat and get immediate service, no matter how busy your contact center is? Well, that day is today. With AI chat integrated into your contact center customers can be served immediately with no additional agent resources required. Now you can change your focus from calculating the time before a chat session starts to hcounting the number of chat sessions escalated to a live agent (We’ll give you a hint: It’s a lot less with Chat AI).

2. The High Efficiency Rate

The average chat session is completed in 42 seconds. With most questions being answered in just a few short seconds (Unless you’re Zappos, of course), Chat AI can quickly and effectively address most problems today, without having to engage a live agent. The savings in time and abandonment rate combined with the increased customer satisfaction, not only reduces costs, but delivers a measureable improvement in performance and customer perception.

3. It Really Understands Language

True Artificial Intelligence is not the robotic, sometimes irrelevant interaction of yesterday.  Answers are more personalized, relevant, and complemented by the newly acquired ability to access multiple data sources to deliver the best possible responses to inquiries. The technology “learns” the longer it is deployed, as a result customer  experiences improve with time without the need for additional investments in technology, people, or processes.

4. No such thing as a “Sick Day”

AI doesn’t sleep, it doesn’t get sick, you don’t need to train it, and it’ll never quit in the middle of a seasonal rush. You don’t even need to give it lunch, breaks, or let it go to the bathroom. Imagine how easy Workforce Management becomes when all you’re doing is flipping a switch. Scheduling becomes less and less of an issue over time, as the application continues to learn, making your investment more valuable over time.

5. 24x7x365

Imagine the increase in sales volume and support cases your business could handle if you’re able to offer 24x7x365 chat. And cart abandonment plummets when customers are offered chat. Today Chat AI can answer anywhere from 70-90% of customer inquiries meaning that only the most verbose requests require a human, and at an operational cost that is often 4 times less than the cost of staffing a contact center.

Meet Eliza, the Mother of AI..

Today, Artificial Intelligence seems to be the buzz of every major enterprise. Salesforce is formally announcing Einstein this fall, IBM has worked on Watson for years now, and after 20 years of working with AI, Microsoft has made a few attempts to bring the technology to the market. With all this activity, you may be asking yourself what kind of impact AI will have on you and your business, and where you might want to look to investigate the possibilities Artificial Intelligence represents.

Before we discuss how AI will impact customer support and consumer experience, and how you may leverage it in your contact center, I thought it would be fun to take a look where AI got its start.

The term AI was coined by computer scientist John McCarthyin 1956 who subsequently went on to create the Dartmouth Conference to advance the ideas and technologies associated with machine intelligence. While this collective of thought leaders and scientists made huge advancements through programs at MIT and others, most of their work was only circulated in academic fields.

Not many people were aware of Artificial Intelligence, how it worked or its potential uses, until around 1964 when MIT computer Scientist Joseph Weizenbaumwrote Eliza, a program based on Natural Language Processingthat was able to successfully question and respond to human interactions in such a way as to almost sound like a real human being. Eliza, with almost no information about human responses was able to use scripts and pattern  matching to simulate responses that might occur between two people.

The most famous of these simulations, highlighting  AI ability to intersect with modern needs and technology, was DOCTOR. DOCTOR was able to question and respond to a human in such a way so as to almost sound like an actual psychotherapist. As the human subject made statements, DOCTOR asked questions and made statements relevant to the conversation as if it were a present and conscious being… almost.

Over the years  computer scientists, whether academics or industry professionals,  have worked tirelessly to improve upon these developments with the hope of delivering a computer program capable not only to ask and respond, but to understand the context of a conversation. A program that can relate relevant data to responses, thus providing value to the human it’s conversing with, while helping to chart the course of the conversation, just as if you and I were talking over a cup of coffee or across a conference room table.

Why is this important, you may ask? With the introduction of Chatbots, we began to see some of the potential in Artificial Intelligence. Companies could now front-end customer chat interactions that allowed the company to be more responsive to its customers while shortening wait times and deflecting inquiries from the call center, which as we all know are hugely expensive.

The one problem with Chatbots? Customers hated dealing with limited technology that was cold, often incorrect, and frustrating. People are accustomed to dealing with the cold, sterile nature of technology when they type numbers in a phone to be routed but expected a human to be chatting with them. These negative experiences have made a number of companies a little gun shy about implementing true Artificial Intelligence. The last thing a business wants is a customer complaining, especially on Social Media, about a poor customer experience due to a bad interaction with technology.

There is a significant difference between Chatbot technology and true AI, consequently the outcomes and customer experience are proving to be very different. Where a Chatbot is more like an IVR, answering simple questions and routing customers to the correct agent, Artificial Intelligence is aware of the conversation and able to present relevant responses, thereby providing a faster response and shorter customer interaction times and better customer service. I mean, if Eliza’s DOCTOR could simulate a psychotherapist in 1964, what can AI do for your contact center in 2016?

How to offer businesses and organizations a solution that personalizes and scales consumer interaction process at the same time?

Personalizing the user relationship process. Today end users and consumers demand to be targeted individually and to be approached based on their actual interests. nmodes AI (Artificial Intelligence) powered solution helps organizations accurately identify user needs in real time. Our solution delivers information on each user individually thus providing the necessary level of personalization required of the successful customer service.

Scaling the user relationship process: Once the organization identifies a user and a problem that needs to be addressed, next step is reaching out to that user individually. Currently this is a manual non-scalable procedure. nmodes AI (Artificial Intelligence) solution provides automated assistance to human personnel, including substitution when deemed appropriate, thus making the entire process scalable.

Today more than 90% of all organizations and businesses rely on solutions based on keywords, even though these solutions provide low quality results not sufficient for the new generation of personalized scalable services.

nmodes solution enables sustainable delivery of high quality results, with x5 costs reduction and up to 45% increase in conversation (engagement) capacity.

Digital industry is changing rapidly.

For the last decade analysis of social chatter and capture of consumer sentiment was considered the cutting edge of the marketing strategy.  In these early days of the new era of digital information businesses were told to listen to what market is saying about them. They were educated on the importance of media monitoring and the advantages it creates for strategic growth.

This picture has become outdated.

Listening to Big Data, in all its aspects and forms, is no longer enough. After you successfully listened and understood what customer said the next natural step would be to act, or respond. And so the digital domain is now spreading to include responses, with a host of innovative technological solutions reshaping the field rapidly.  Advances in artificial intelligence in particular create disruptive scalable opportunities in the space traditionally known for its slow manual progression.

Facebook was among the firstto enter the market, introducing bots into the process of connecting users with brands. Then there was Microsoft's turn.

Following these developments bots became the hottest trend in Silicon Valley in 2016.

nmodes fits seamlessly into this new world order. We deliver AI solutions that power business sales process. Our listening solution accurately monitors and captures real-time needs and interests of individual customers within the defined audience. And our Intelligent Assistant solution brings scalability to responses without compromising on quality.  

How to make content more relevant and appealing to the content consumer?

This is a problem that has been on the mind of content creators for some time now. In our age of information abundance it is not easy to stand out and make your voice heard. The competition for the consumer’s attention is escalating, and with the number of information sources ever increasing, it will only get tougher.

Traditionally, a content delivery does not change across the target audience. A commercial, or a blog, looks and is experienced in the same way by all viewers and readers. We are entrenched in this paradigm, and can hardly imagine it being otherwise.

It turns out, the advancement of new technologies capable of capturing individual intents in real time brings up new opportunities in creating personalized experiences within the framework of content delivery.  

This is how content can become more relevant - by becoming more personalized.

In a rudimentary form, we are already familiar with this approach as seen in online advertising. Some web and social resources aim at personalizing their promotional campaigns based on whatever drops of behavioural patterns and interests they can squeeze out of our web searches.  The problem, of course, is that the technologies used to power these campaigns understand human behaviour poorly and results, therefore, more often than not leave a great deal to be desired. To put it mildly.

nmodes has been working on semantic processing of intent for several years. We now can capture intent from unstructured data (human conversations) with accuracy of 99%. (Interestingly, many businesses do not require this level of accuracy, being satisfied with 90%-92%, but we know how to deliver it anyway).

We recently started to experiment with personalizing content by using available consumer intent.

We used Twitter because of its real-time appeal.

We started by publishing a story, dividing it into several episodes:

And we kept the constant stream of data flowing, concentrating on intent to dine in Paris:

We then merged the content of the story with consumer intent to dine in Paris as captured by our semantic software. Like this:

This merging approach shows promising results - the engagement rate jumped above 90%.

Overall we are only at the beginning of a tremendous journey. We know that other companies are beginning to experiment, and the opportunities from introducing artificial intelligence related technologies into content delivery are plentiful.

There is a long road ahead, and we've made a one small step.  But it is a step in a very exciting direction.

There are many travelers looking for hotels and places to stay on social web. Every day.

Take Twitter, for example:

Or this:

People are genuinely looking for help. Surprisingly though only few are getting it. According to nmodes data less than 12% of Twitter travel  requests are being answered. The rest - lost opportunities for hotels and businesses in the hospitality industry.  

 And how big is this opportunity anyway?

nmodes Twitter data shows that every 15 min somebody expresses intent of going to, or visiting New York. Most of these travelers need a place to stay there.

Every 33 min - intent of traveling to London.

Every 54 min - intent of traveling to Paris.

We started Twitter recommendation service @nmodesHelps and were encouranged by the results. 72% of those that received our travel recommendations reacted by thanking us and expressing their gratitude. This reinforced our assumption that people seek travel advice on Twitter, accept it as an instant value, and are prepared to act upon it.

The hotels that are ready to move fast to monetize this opportunity will benefit the most.

One of the most central causes of missed growth opportunities and overspending is a failure on the part of businesses to create strategies that are tailored to the intent of the consumer. Recognizing and harnessing visitor intent brings increased engagement with relevant messages and calls to action.

Once a business identifies purchase intenders it can create content that aligns with their needs and desires in order to increase the likelihood of conversion. Consequently it can pick up on pre-sale signals from visitors in the research phase and drive lead-nurturing initiatives accordingly. The ability to identify this spectrum of visitor intent is key to creating relevant engagement campaigns that drive sales.

nmodes has been at the forefront of delivering consumer intent to businesses.

We sort the intents based on conversation topics, called ‘streams’.

Here is a stream of people looking for a hotel:

A stream of people who are getting married:

A stream of people thinking of going on a cruise:

A huge number of travel related conversations is happening every day on social networks.

Based on nmodes Twitter data (averaged over 1.5 years of observations) there is

- 1 conversation every 15 minutes in which people notify that they are going to NYC;

- 1 conversation every 43 minutes in which people from the USA express intent to go to Europe;

- 1 conversation every 4 minutes with interest or intent to go on vacation;

- 1 conversation every 3 hours in which people are asking for hotel recommendations.

And this is just a tip of the iceberg.

(nmodes currently has 70+ travel-related topics and intents, and growing.)

For travel companies all these are qualified leads, potential customers, and attentive audience.

Reaching out to these potential customers results in a positive consumer experience, brand recognition, and, yes, sales!

In its very essence social marketing is based on one simple foundation - give first, take later.

This concept of giving to the community is hardly possible to overestimate. It defines the way social networks operate and goes even deeper, to the basic principles of social interactions among humans.

In fact it is a much healthier foundation for business than traditional one, based on advertising.

Yet it runs contrary to what many entrepreneurs and business people perceive as a proper marketing approach.

Traditional marketing, such as billboards, radio ads, posters, banners, emails blasts, etc is based on two principles, a) the statistical law of big numbers, aiming to reach out to as large audience as possible while knowing that only a small percent would become interested, b) message of self-promotion and self-advertisment.  

Social marketing negates both of these principles.

Social marketing is personal, it operates individually, and in a personalised way. Which makes perfect sense from a common perspective. Would you rather be bombarded by the generic ads that in most cases have nothing to do with your interests and desires, or approached on a one-on-one basis with a chance to discuss your specific needs?

Social marketing is directed towards promoting the interests of others, not yours (or your business). Again it makes sense as we are a social species, we live in societies and rely on communication. The most successful communication strategy is the one that takes care of the needs of your communication partner.

And so, opposing the traditional marketing approach, social marketing is based on the idea of giving to the community. Which makes it more efficient than traditional marketing, if measured against the effort applied. In other words, taken 100 random prospects, we are more likely to convert them into customers if using social marketing than traditional marketing.  

But is it scalable?

(to be continued)

Going after investors? Do you know that less than 1 percent of startups actually raise VC (or angel) capital, which means that the vast majority are self-funded. Yet the main reason for it simply lies in the inability of most companies to find investors.

Bootstrapping, however, has several strategic advantages for your company's future growth. Perhaps the biggest is retaining the majority of shares and control over the strategy and direction your company is moving towards.

It also teaches financial discipline. Bootstrapping at the start helps to understand the importance of  revenue and cash flow, as opposed to unabridged product development, and keeps you connected to your company's financial reality. Only when profitability increase do you then green-light new opportunities, increased risk-taking, and growth acceleration.

In reality, the founders are expected to be flexible.  While entrepreneurs have certain intentions and philosophies when they are starting out, a hallmark trait for successful founders is the ability to adapt to changing environments and opportunities.

Sometimes, that means waiting a long time to generate the financial metrics that really matter, revenue and profit. By challenging your leadership team to focus on building the business organically and figuring out how to make the company consistently profitable on a model that can scale without VC capital, you make your company more valuable to future investors.

Automation drives forward the economy. It allows businesses to scale and service large groups of customers. Automation first appeared in traditional industries, such as cotton production in England in 18th century or car conveyors in the US in early 20th century. The automation replaced physical labor.

With the invention of computers automated systems began to replace intellectual labour such as math calculations. Most of the software applications we use today can be described as automation. Online payments processing, online tickets purchasing, tax returns software, computer games, search engines, and endless other programs are all examples of software automation system.

As a next step we are now aiming at automating human decision making processing and high-level intellectual activities, historically considered to be sole domain of humans.

One interesting aspect of automation is lesser quality of service compared to manual service.

This is to be expected. If we gain in quantity we lose in quality.The gain in quantity is what automation is about - it allows to reach out to a large number of customers. Manual product or service can reach out to individuals only. The price we pay for the ability to deliver product or provide service en masse is the drop in quality.

Sometimes automation is an obvious choice. This is when the gain, the scalability, hugely outweighs the costs, lower quality. Search engine is a popular successful example. In other cases, the advantage in not so obvious. Online travel booking offers fast service without leaving the comforts of the home, but it does not often deliver the best option, such as finding the cheapest flight, and therefore many people still use ‘manual’ travel agents.

There are obviously some key differences between Facebook and Twitter that make them appealing to different people as well as businesses. If possible, businesses should try to leverage both networks in their marketing and sales efforts.

But marketing approaches for each network differ.  Consequently social selling approaches differ as well. Here are some major differences of the two networks that impact sales strategy:

- Twitter lets all the accounts commingle, Facebook makes a definite distinction between business and personal. This can be an issue because a business page cannot proactively connect with individuals with personal profiles. Individuals have to first like a business page and still the business can’t reach out to them directly unless they message first. This is not the case with Twitter, as anyone can follow pretty much anyone.

- Facebook preferred way to market products and promote online sales can be compared to a showroom. The prospects can see the product and purchase it through some other channel, however engagement (with prospects) is limited to friends and followers. Hence growing the number of friends and followers becomes a critical task on Facebook.  Twitter does not offer promotional capabilities but engagement activity is not limited to followers. The engagement on Twitter is therefore more straightforward and can lead to direct sales.

- Facebook user data is typically open to friends or followers. Twitter data is typically open to the entire world.

- Twitter is fast (minutes). Facebook is slower (hours and days).

- Twitter is more about building a brand identity. Facebook is more about business relationships.

To summarize, a direct timely engagement could be a good strategy on Twitter. In a typical scenario a user tweets that she needs a taxi or asks where to dine tonight. A taxi company or a relevant restaurant engages in a conversation and secures a customer. It is an efficient approach with immediate ROI.

On Facebook a good strategy is to grow and educate a community of followers. Facebook is excellent for promotional campaigns. This is a longer-term strategy with effects not visible until after several months.

Social selling is one of the hottest buzzwords in the technology market. The popularity of social networks made the customer interaction and buyers hunting easier than before. More and more consumers are using social media to find deals, research products and make recommendations.

From the seller’s perspective the efficient use of social media is based on the mastery of following two major steps:

1. Finding the relevant audience,

2. Engaging with that audience.

The first step should be automated. This is exactly where the promise of Big Data, or Smart Data, as they now begin to call it, is supposed to come into fruition. Finding relevant information in the ocean of social data is the poster example of how Smart data can help businesses in the new world defined by computerized systems and networks. The companies should be able to use programs and solutions that accurately and efficiently deliver relevant data. If the company is spending time to sift through the ever increasing informational stream without automating the process, it is wasting precious time thus compromising its business growth and eventually losing competitive edge.

 The second step however is inherently manual. it is not a good idea to automate the engagement process. Social networks are designed to build trust, and trust cannot be won automatically. So it requires time and effort and knowledge. It also requires patience - trust cannot be built in minutes.

It is important that businesses looking to add social media into their arsenal of revenue channels, and we believe that all businesses should do just that, grasp this two-steps process. A clear understanding of the nature and requirements for each of the steps helps to plan strategically, manage the resources properly and avoid costly mistakes.

There is a huge amount of information out there. And it is growing. To make it efficient and increase our competitive advantage we need to evolve and start using information in a smart way, by concentrating on data that drives business value because it is accurate, actionable, and agile. Accuracy is an important measure that determines the quality of data processing solutions.

How accuracy is calculated?

It is easy to do with structured data, because the requirements are formalizable. It is less obvious with unstructured data, e.g. a stream of social feeds, or any data set that involves natural language. Indeed, the sentences of natural language are subject to multiple interpretations, and therefore allow a degree of subjectivity. For example, should a sentence ‘I haven’t been on a sea cruise for a long time’ be qualified for a data set of people interested in going on a cruise? Both answers, yes and no, seem valid.

In these cases an argument was put forward endorsing a consensus approach which polls data providers is the best way to judge data accuracy. This approach essentially claims that attributes with the highest consensus across data providers is the most accurate.

At nmodes we deal with unstructured data all the time because we process natural language messages, primarily from social networks. We do not favor this simplistic approach, as it is considered biased, inviting people to make assumptions based on what they already believe to be true, and making no distinction between precision and accuracy. Obviously the difference is that precision measures what you got right, and accuracy measures both what you got right and what you got wrong. Accuracy is a more inclusive and therefore more valuable characteristic.

Our approach is

a) to validate data against third party independent sources (typically of academic origin) that contain trusted sets and reliable demography. Validating nmodes data against third party sources allows us to verify that our data achieves the greatest possible balance of scale and accuracy.

b) to enrich upon the existing test sets by purposefully including examples ambiguous in meaning and intent, and providing additional levels of categorization to cover these examples.

Accuracy is becoming important when businesses move from rudimentary data use, typical of the first Big Data years, to a more measured and careful approach of today. Understanding how it is calculated and the value it brings helps in achieving long-term sustainability and success.

We are inundated with information. There is so much information around us they coined a special term - Big Data. To emphasize the sheer size of it.

It is, of course, a problem - to deal with a large amount of data. Various solutions have been created to address it efficiently.  

At nmodes we developed a semantic technology that accurately filters relevant conversations. We applied it to social networks, particularly Twitter. Twitter is a poster child of Big Data. They have 500 million conversations every day. A staggering number. And yet, we found that for many topics, when they are narrowed down and accurately filtered, there are not that many relevant conversations after all.

No more than 5 people are looking for CRM solutions on an average day on Twitter. Even less - two per day on average - are asking for new web hosting providers explicitly, although many more are complaining about their existing providers (which might or might not suggest they are ready to switch or looking for a new option).  

We often have businesses coming to us asking to find relevant conversations and expecting a large number of results. This is what Big Data is supposed to deliver, they assume. Such expectation is likely a product of our ‘keyword search dependency’. Indeed, when we run a keyword search on Twitter, or search engines, or anywhere we get a long list of results. The fact that most of them (up to 98% in many cases) are irrelevant is often lost in the visual illusion of having this long, seemingly endless, list in front of our eyes.

With the quality solutions that accurately deliver only relevant results we experience, for the first time, a situation when there are no longer big lists of random results. Only several relevant ones.  

This is so much more efficient. It saves time, increases productivity, clarifies the picture, and makes Big Data manageable.  

Time for businesses to embrace the new approach.

I am regularly approached by businesses that sell to other businesses to help them market and promote their brand on social networks.

And so I noticed that some of them have a vague idea of how social media works and the sustainability it offers. They often see social marketing as yet another way to advertise and sell their products, in the same manner they are accustomed to do on traditional marketing mediums. Not surprisingly it usually results in frustration.

While I saw companies successfully sell on social, they are typically limited to mass consumer oriented B2C verticals, such as fashion and apparel, travel and hospitality. There is a segment of online shoppers, sometimes called ‘impulse shoppers’, that makes purchases straight off the Twitter timeline, yet the majority of us go to social networks for different reasons. Certainly no one is buying an insurance policy, or a house, or a CRM solution there.

The success of social media and its importance for business is in its unique ability to build trust.

For B2B, as well as for the majority of consumer-oriented businesses, this is where the real value of social marketing lies. A more detailed discussion here

And so that means approaching social media strategically.  First know precisely why you want to engage, understand clearly how it will help you grow the business. Then, if you are convinced of social media’s importance for the success of your business, start taking practical steps.  Obviously very company is different, but here are some observations that are pretty generic:

- Plan long-term. Don’t expect results after one month. Not even after two months.

- Do not do social media just because ‘everybody’ is doing it.  When people have strategy their choice is between social tools X or Y or Z. It typically comes early in the conversation. And when people say ‘I’ll try it for a month and see if it brings results’ or ‘I want to see how my friend/my competitor is making out before deciding’ it usually indicates a lack of strategy, because it implies a choice between tool X and doing nothing. In that case, better do nothing.  

- Social media does not substitute sales. It is however one of the most efficient ways to grow sales Here is a good explaination

Social media’s importance for B2B business is increasing. More and more owners and executives are inquiring how they can succeed in the new environment. As usual, the earlier you start the better are the chances.

Right now we're in a world that sees  transparency as the new form of integrity. Right now we're in a world that understands that reputation is everything. Loyalty is somewhat fleeting as consumers, armoured with this incessant flow of knowledge from the web, have the ability to make swift  judgements and decisions about individuals, companies and governments, often times to the detriment of the target.

The emergence of social media has forced companies to stop hiding from behind that veil of corporate spin and address the very things that the web has thrown at them. Nothing is secret any longer. Even secrets that were once held secure behind invulnerable fortresses now have a strong probability of materializing today.

Is transparency as a norm working? Or, are the results of transparency surfacing a new order that will create yet another tier of acceptance from the masses?

"Anonymity is Authenticity"

Following the death of Rahteah Parsons, who, after being assaulted by 4 boys, was tormented relentlessly by classmates and other kids on social networks; and also following the suicide of Hannah Smith, who experienced the same torment, it's clear the internet has evolved to an era that has given free reign to voice an opinion and use like-minded affiliations to express and further spread that opinion. In these cases, anonymous profiles proliferated the incessant stream of hateful attacks that eventually wore down both girls' defences.

And while I originally argue that anonymity was a cowardice state that allowed people to be and feel comfortable being the anti-self that runs away from accountability, my stance has seen another side of this coin.

Anonymity is Safe

It becomes clear that humans, while inherently social, are discriminating of the things we disclose and to those to whom we share. 

If transparency breeds contempt, then anonymity should build acceptance

The freedom to express opinion and judgement without feeling guarded, or without fearing others linking you to a statement is indeed liberating. And while this free reign may take the form of a soapbox soliloquy or criticisms (and perhaps bullying attacks) against opposing views, there is a large segment of users who want the ability to share a secret, or have a place to vent their frustrations or challenges -- without the fear of reprisal.

Despite revelations from Snowden and the NSA that nothing on the net is private, this does not stop the wave of user adoption for applications like SnapChat, Whisper or Secret.

Here are some recent stats for Snapchat from Mashable

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I've recently downloaded Whisper and my experience has been more than liberating. It has allowed me an outlet to record my hopes, desires and more importantly, my anger and not-for-public emotions. Being judged in real life or on social takes its toll. If my reputation precedes me, then I will be discriminating about what I say in places where my content and identity are linked.

Popular opinion just doesn't matter. It's irrelevant. But I want to track progress in my life: my emotions, my dark moments, my personal observations, my milestones -- all in my own digital diary.

Why shouldn't users have the option to keep part of their identities secret and separate?

It's up to the next generation

This new medium has created is an endless volatile loop of positive and negative reinforcement. While transparency has extreme benefits, there are just as many negative consequences that have come as a result of creating this honesty within social channels. Society continues to send the wrong message to Millennials and GenZers, warning them to be more discerning and to suppress who they really are as individuals... warning them of the potential consequences should they venture down the wrong path.

How we communicate today poses tremendous issues for this younger generation. Their experiences are grounded in the fear of being vulnerable... fear of being misjudged... fear of not being accepted... fear of being punished. When the next generation grows up, it'll be up to them to shape the landscape and determine how to balance the impacts of transparency and anonymity.

Much is being said about social networks and their importance for businesses. The amount of analysis, explanations, and advice keeps on growing, while the matter is being investigated from every possible angle, real and imaginary.  

As for me, the need for businesses to market and sell on social can be explained by a simple argument.

Here it is.

The principle advantage of social networks for a business over other mediums is in the social networks’ potential to build trust. Traditional marketing mediums, such as TV, newspapers, internet, radio, etc. are not designed to build trust. They are information channels, or scaling vehicles, or sales means, but their primary goal is not to build trust. Social networks, on the other hand, are exactly this - a trust building tools.

And herein lies their biggest advantage in today’s market. The endless variety of options consumers have and the ever growing dissatisfaction with traditional aggressive marketing methods, such as commercials or banners, means that creating trust between businesses and their audiences is now the most efficient way to attract customers. The way that guarantees long-term sustainability and growth.

This is, simply put, the reason for businesses to embrace the social.

It’s clear that marketing has drastically changed in the last decade. The rise of digital, accompanied by its ever-evolving technologies in mobile and advertising will build a perpetual environment of test and learn. As well, continuous emergence of audience platforms will create a nomadic culture that follows the fickle consumer paths. Ultimately, this will dictate the sustainability of platforms.

Marketing has been one organizational function that has succumb to tremendous pressure to evolve in the last decade. It’s turned both ad agencies and companies on their ears, furiously attempting to learn and adapt, while desperately hanging on to what they already know.

Perhaps it’s time to let go. If there ever was a time to accept change it’s now. In my personal experience, and from what you’ll read,

- I’ve witnessed an incredible evolution in the digital space by way of technology and targeting,

- I’ve also witnessed rapid changes in consumer consumption and the increasing fragmentation of media,

- Adapting and learning has been integral in helping me evolve with the market demand.

Consumers have changed the game for marketers.

No longer do we have only a few mediums for content consumption. In as little as 2 decades we’ve moved beyond just TV, radio, print, billboards. We’ve also raced beyond the standard network channels, the key national newspapers.

As consumers our attention has moved to sites that speak to our own areas of interest. They may not necessarily be as popular or as known. Our peers greatly influence what we do and where we go. But, our ever trusted smart phones gives us access to inform us about the things we want, when we want them and where we want them.

This always-on economy is not about to die down. The growing consumer expectations will mandate companies to have greater visibility into where their customers are, what they’re saying, their preferences, their preferred channels and modes of communication. The growing pressure to keep the “owned” and “earned” channels “on” will challenge the business to become much more responsive than ever before.

Marketers are slowly becoming obsolete.

As marketers, our roles have been forced to evolve. It hasn’t been easy. Coupled with this consumer evolution we’re witnessing, the economic times have changed the way we operate. No longer is marketing a cost centre. We are now more accountable than ever. The old performance measures which we were accustomed to need to change. We need to evolve beyond the mindset of traditional mediums, and embrace the inherent benefits of digital and where it’s going.

Becoming obsolete is a reality in today’s fast-moving environment. Yes, today’s marketer needs to leave their comfort zone and venture into an environment that does not seems to want to sit still. Luckily, it doesn’t necessarily mean abandoning the principles they’ve learned along the way. It just means evolving their thinking and applying these same principles to the new mediums.

1. Data is the new norm: The promise of big data brings with it enormous benefits that can now inform customer preferences, propensities; identify relevant prospects in real-time; distill meaning from reams of information where it impacts competitive or brand reputation. The opportunities to target more granularly beyond just “company”-collected transactions provides profound instances to find the right customer, at the right time, in the right channels, with the right message. The need for strong data analysts to compile this information across multiple platforms and mediums will be an essential component to effectively target for acquisition; improve retention rates and optimize for real-time performance.

2. Agility is imperative: Gone are the days of relying on historical data. These days, any data point longer than 30 days is too old and therefore, irrelevant. Gone are the days when media plans or strategies are “baked”. No longer are we required (or should we be required) to sit and wait for results. With data becoming more embedded in our daily work, marketers must work towards a more agile environment: This means becoming more data responsive to an increasingly  fragmented and splintered market,  having the structures and processes to change tactics on the fly.

3. Value is the new currency: One of the hardest lessons for marketers to have learned was to refrain from leading with overt company or product messages. “Leading with value” has become a difficult principle to adopt, after years of “me-me-me” communications. Declining performance of digital ad units means marketers must rethink content from the position of the customer. The rise of editorial as an essential function within marketing will be necessary to instil this new discipline.

4. Customer convergence has arrived: All mediums are converging. Appointment TV is dead. The customer dictates the content they want to consume, across multiple mediums, the times they want it.  On-demand mediums will challenge the marketer as consumers move swiftly between tablets to smartphone to television. The new ways of targeting customers across multiple-platforms now allows the marketer more long-tail opportunities that will augment and support traditional mass targeting.

5. Customer experience mandates an always-on presence: A more informed customer expects consumers today an optimal experience that “allows them to shop and receive their purchases where they want, when they want and how they want.” This means providing the ‘continuous experience’ across brands, devices and format: mobile internet devices, computers, brick-and-mortar, television, radio, direct mail, catalog etc. Today’s marketer is channel-agnostic and is aware of sites, platforms and channels the customer is researching, eliciting recommendations, price-comparing and ultimately, buying.

6. Sustainability, not campaigns: The value of social media as an open channel two-way conversations now provides brands with the ability to not only build relationships, but benefit from the effort and commitment to nurture customer relationships through these channels. Word of Mouth and Advocacy are strong indicators of brands doing it right. The value of organic traffic that results from content value, social consistency and customer-commitment, will surpass the more costly campaign-driven ad-buys and promotions.

7. Social cannot be outsourced: Agencies will never be able to truly be able to build effective community management services. This function needs to live within the organization. Customer relationships with brands cannot be fostered via surrogate means, and then adopted into the organization. Only employees within the organization, with the proper knowledge and solutions, can effectively troubleshoot customer complaints and provide the right responses in the expected timeframe. An emerging discipline in community /customer relationship management will be critical to gauge the pulse of the community and to bridge the gap with the organization.

8. Context is key: Google has gone beyond just keyword and now tries to extract real meaning from what people search or speak about. Semantic algorithms go this one step further and now give marketers the tools to truly understand what people need and want. It’s here that will help predict and define areas the brand can connect and provide value to customers.

9. Customer-centric needs to be the standard: As digital grows up, the areas mentioned above will move companies to start to shift in ways that puts the needs of the customers at the centre of the organization. One-to-one marketing will a reality as data allows us to truly customize experiences for each customer. Retention will get increasingly harder as mediums and platforms rise and fall with the nomadic consumer and Facebook and Twitter become less standard platforms. Where pundits have prophesied the death of marketing, a more responsive, dynamic and collaborative organization will take its place.

10. A dynamic organization is a social organization: The result of these changes will inevitably move away from marketing and become embedded in all parts of the organization. A responsive, dynamic organization means that PR, HR, Product development, Inventory Management, Operations will need seamless communication channels to properly receive and disseminate information intra and outside the company to stakeholders and customers. The future CMO, in my opinion, will become more operations-minded but will rely on the collective organization to function effectively.

Marketing is no longer a discipline with best practices and tried and true techniques. As long as technology exists, and media evolves, consumers will continue to find new ways to connect and consume information. What’s clear is that these days our traditional definition of longevity is short-lived. Not only does the marketer need to morph with the times, the organization does as well.

With the launch of the new dashboard nmodes is helping businesses to drive traffic and grow sales.

For example, One of our clients needed to increase traffic to their website while at the same time improve the conversion rate. In other words, they wanted to see more quality traffic.

The client made a concentrated effort on social media, however they were having difficulties in finding the target audience - traditional keywords search resulted in too much noise and did not produce desired outcome.

nmodes dashboard simplified this client’s engagement process. We created a dedicated stream that accurately addressed their targeted audience.  nmodes dashboard is actionable, so their engagement became easy. nmodes technology identifies potential customers accurately, and so their engagement became efficient.

As a result, the click thru rate rose up to 65%, traffic quality improved by 25%, and conversion increased to 6-8%

Another client relied heavily on mainstream dashboards (such as Hootsuite) These tools do not do a good job finding relevant conversations, in the process producing too much noise and forcing client’s community managers to spend long hours manually identifying these relevant conversations. The client manages multiple social account and this type of manual labour was impeding the business, both in terms of costs and efficiency.

nmodes produces highly accurate results in finding relevant conversations that do not require manual clean up. The client started using nmodes solution, and immediately freed a substantial amount of hours which enabled them to consecrate on servicing their customers and acquiring new ones.

nmodes ability to accurately deliver relevant messages and conversations to businesses is based on its ability to understand these messages and conversations. Once a system understands a sentence or text, it can easily perform a necessary action, i.e. bring a sentence about buying a car to the car dealership, or a complaint about purchased furniture to the customer service department of the furniture company.

Understanding sentences is called semantics. nmodes has developed a strong semantic technology that stand out in a number of ways.

Here is how nmodes technology is different:

1. Low computational power. We don’t use methods and algorithms deployed by almost everyone else in this space. The algorithms we are using allow us to achieve high level of accuracy while significantly reducing the computational power. Most accurate semantic systems, e.g. Google’s, or IBM’s, rely on supercomputers. By comparison our computational requirements are modest to the extreme, yet we successfully compete with these powerhouses in terms accuracy and quality of results.

2. Private data sources. We work extensively with Twitter and other social networks, yet at the same time we process enterprise data.  Working with private data sources means system should know details specific only to this particular data source. For example, when if a system handles web self-service solution for online electronics store it learns the names, prices, and other details of all products available at this store.  

3. User driven solution. Our system learns from user’s input. Which makes it extremely flexible and as granular as needed. It supports both generic topics, for example car purchasing, and conversations concentrating on specific type of car, or a model.

Most of the online search is keywords-based. Same in social domain, a vast number of analytical tools, networking platforms and mobile apps use keyword-based technologies as well.

There is a difference, of course, between traditional internet search and social search. The former finds websites. The latter finds conversations, messages, posts. Keyword-based internet search is doing a decent job for us for over 20 years. Keyword-based social search is not doing a decent job at all.

Consider a basic example: finding on Twitter who is interested in buying jeans. We can start by typing ‘jeans’ but that brings up too much noise. Maybe ‘need jeans’? Less noise but then we  people who use expressions like ‘looking for jeans’ or ‘want jeans’ or shopping for jeans’. Not to mention those who use ‘denim’, or brand names. So we have to run multiple searches or create a complex search string using logical AND and OR and hope it works. Neither option is simple, or convenient, and certainly not efficient.

The above example highlights the major flaw with keyword search - it does not capture the meaning of social conversations, and therefore cannot be a reliable source of information about conversations.

It does not provide too much of correct information. And it does provide lots of incorrect information. But the biggest problem is that it has extremely limited potential for improvement.  

So as long as we stick with keyword-based social search the results are destined to be limited.

Why, then, we stick with keyword-based search in social search? Simply because there is no good alternative. Until recently, that is.  

The advanced semantic technologies capable of capturing the meaning, or intent, of conversations are now offering an exciting alternative.

I will discuss these technologies on my next blog.

1. Mental, or psychological, state of your team is important: you can put so much pressure on people before they crack. Brazil players didn’t become unqualified professionals overnight. They failed because they were overwhelmed by their country’s expectations, distorted sense of history, and the right to win considered divine. They were too emotionally charged, not in the proper state of mind to compete. So better keep calm, relaxed atmosphere in your team even before launch, or important deadline.

2. Manage customer expectations. Brazil were ramping them up unreasonably. Aggressive messages like the 6^th[title] is coming, statements by their coach about two more steps to heaven massively backfired by creating an unhealthy emotional frenzy in the society, which in return influenced the players (see 1.)

3. Logic, organization is the key to successful execution. Germany are not a great team. But they are very well organized. They had a detailed game-plan where every team member knew his task and several different scenarios where prepared. They were able to adjust when the situation on the field changed to squeeze maximum advantage. Sounds simple? That’s because it is. 

Social media generates a vast amount of data. There are 500 million daily messages on Twitter alone. Still more data on Facebook, Google+, LinkedIn and other social networks. Some of this data is useful to businesses, in fact, it is extremely useful.

A business can use social data to generate actionable insights about customers, competitors and their company strategy. Social information empowers departments and teams, and when used correctly, creates a strong sustainable bond between businesses and their customers.

nmodes Intent API helps businesses to execute their social strategy efficiently. Here are the major elements of social strategy Intent API contributes to:

1. Listening. Intent API finds customer intent with any level of granularity. You might want to know who is looking to buy shoes in general, or looking to buy flip-flops in particular, or interested in buying only Nike footware, or interested in buying sneakers in New York region.

2. Sales and marketing.  Intent API understands what stage in the purchase process your customer is in. Intent API tells if a customer is ready to buy, or is in the awareness stage, or considering the purchase but not ready yet, and so on.

3. Social intelligence. Intent API delivers meaningful intents and behavioral information on a large scale and for all verticals. Any insights and topics, as long as somebody is conversing on this topic, are available.

4. Teams and projects. Intent API channels information to the relevant departments within the company. Sales prospects should go to sales department, complaints to customer service, brand conversations to the marketers, and technical issues to tech support.

A massive change has occurred in the world during the last ten to twenty years. Until recently and throughout the history of mankind information was hard to access. Obtaining and sharing information was either a laborious process or impossible, and the underlying assumption was that information can never be enough.

Today, of course, we have the opposite picture. Not only information is easily available, it keeps pouring in from a growing number of sources, and we continuously find ourselves in situations when there is more information than we want or able to process.

A major task we, as species, are facing is therefore how to reduce or filter out relevant information. It is, to repeat, in direct opposition to the task we’ve been accustomed to during all previous centuries, which was how to obtain information.

Since this change took place only recently, within a lifetime of one generation, we didn’t have time to develop efficient set of procedures to address the new problem. But the work has started and will only accelerate with time.

What is intent? It is the reason behind the sentences we say. Behind posts and messages, as they appear on social networks. For instance, the intent of the tweet ‘I am going to buy a new car soon, my old car is entirely broken’  is buying a new car. The intent of this one however ‘ Need to buy me a car, got things to do lol’ could be anything from killing time by posting randomly to impressing friends, but not buying a car.  

During the time when most customer activities online happened on search engines (e.g. Google) understanding of intent was predominantly the task of these search engines.  So when I type ‘typical menu of Chinese restaurant’ and the search engine displays the list of local Chinese restaurants clearly in this case it did not understand my intent.

Nowadays, when an ever growing part of the consumer related activities is happening on social networks the task of understanding the customer intent becomes responsibility of a business.

Here are three reasons why this task is essential:

1. Marketing is personalized. Email blasts are a thing from the past. Today to stay completive your business should be able to target individually. And that means knowing what each of your potential customers needs in real time. The best way to know this is to understand customer intent. The numerous analytical and measurement tools available today exist only because until recently we didn’t know how to capture customer intent properly.

2. Knowing intent allows efficient and timely service across your company’s departments: those interested in the product belong to marketing department, purchase intent goes to sales, unhappy customers go to customer service, and so on.

3. Knowing intent offers long-term sustainability to your business because it reduces the noise. Unlike the previous generations, when the problem was a lack of information, today’s problem is the abundance of information. Business can function efficiently and be sustainable only when a competent model of finding the right information is in place. Understanding of intent is the best model available