As human beings, our brains are hardwired to behave in a contextual manner. We interact with our friends in a different way, build a network with our bosses in a different way, and express our feeling with our families in a different way. Our language and tone changes depending upon the situations we are in. It is impossible to interpret or understand any action or behavior without taking into account any context. This holds true for human behavior, undoubtedly. In the changing technological landscape, this statement has become equally valid in the milieu of customer engagements brought about via chatbots. 

Understanding the context becomes especially challenging and important in the absence of face-to-face interactions and lack of availability of visual cues as in the case of chatbots. So, a contextual chatbot equipped with the ability to perfectly interpret the user sentiments with just the medium of voice and text is highly essential for successful customer-brand engagements. A contextual chatbot is the one which communicates your finest value proposition to your consumers.   

I feel developing contextual chatbots to foster intelligent, meaningful, and personalized conversations between users and chatbots need the utilization of Artificial Intelligence(AI). What sets normal chatbots apart from contextual chatbots is that unlike keyword-based conversations of normal chatbots, the later can even decipher user patterns based on data. By making the use of machine learning and deep learning, contextual chatbots can hone these kinds of self-improvement skills and become smart analytical models. 

To help better understand the usage and advantages of contextual chatbot powered by artificial intelligence, I would like to quote a simple example. We all use food delivery applications to order food from our favorite restaurants. Those chatbots are contextual chatbots because they store your previous order history and based on these previous conversations, they form an understanding of what you like to order and eat. After a few consecutive orders, the contextual chatbot will remember your payment information, your delivery address, and your favorite order/restaurant. So, the next time you place an order, you will just need to confirm these details and your order will be placed!

This basic example shows the power of applying Artificial Intelligence and Machine Learning to the chatbot models in order to make them more contextual, relevant, and personalized. For chatbots to become customized and satisfy the users, it is essential to think from the user’s perspective and understand what makes them happy. Deep Learning technology lets you do just that. It is a subset of machine learning which comprises algorithms inspired by human brains. Just the way humans learn from experiences, deep learning enables applications like chatbots to learn from the repeatedly performed tasks to improve the performance next time. 

Another component of artificial intelligence which I consider worth mentioning in this conversation of contextual chatbots is that of Natural Language Processing (NLP). It is a combination of computational techniques which give applications the ability to comprehend human natural language just the way in which it is spoken. Highly relevant in today’s world, it forms the backbone of numerous applications that we use today – from simple spell checks to translation apps. NLP enables chatbots to extract the relevant data from conversations with users, thus making them smarter and contextual during the subsequent conversations. 

NLP is the technology of artificial intelligence which helps chatbots process the intent behind the user performing a specific action rather than simply following the orders. This component makes the contextual chatbot more humane by training it about the several interactions it may have to have with the users, thus helping in streamlining the responses. In simple terms, NLP allows the contextual chatbot to understand what is being said by the user and what the expected reaction is. Amazon’s Alexa is the best example of how integrating NLP in applications allows chatbots to understand the messages, interpret them, and formulate an appropriate response. 

To cut the long story short, contextual chatbots powered by artificial intelligence are not just impacting personal lives but they are also revolutionizing the way businesses are conducted and the way in which marketing is done. Here are some of the popular use cases of contextual chatbots powered by AI which I think give a fairly appropriate idea of how they are affecting various sectors and industries-

1. Contextual Chatbots in Food Industry

Numerous popular eateries and restaurants have integrated chatbots in their systems which help customers to place orders via their messaging bots like Facebook chat messenger and Twitter. The list of food giants who use contextual chatbots includes names like Pizza Hut, Taco Bell, Burger King, Dominos, and Wings Top. 

Pizza Hut, Burger King and Wings Top don’t just allow you to place orders via Facebook Messenger but they also let you reorder your favorites, let you access the latest promotions, and help you with information such as nearest outlet –  all through the chatbot. Taco Bell uses its own chatbot powered by artificial intelligence – TacoBot – wherein we can place orders directly via the internal messaging app called Slack which is used by many of the business firms nowadays. 

2. Healthcare and Chatbots

When chatbots were introduced in the sphere of medicine, they garnered immense criticism from the medical fraternity. Several doubts were raised regarding their accuracy and authority. However, there are some chatbot applications such as Medwhat, Health tap, UCLA medical center, and Woebot which help patients manage their mental and physical health better by making medical diagnoses more accessible, transparent, and fast. 

Medwhat accurately answers our questions based on its interactions and analysis of human behavior. It is a deep learning based chatbot which also bases its diagnosis on the vast amount of medical data and research that it can access. Woebot is a personalized chatbot that analyses the mood of the user based on the emoticons and texts. It then offers accurate advice and suggestions to the user to improve his or her mental health. Healthtap is a unique chatbot which aims to make knowledge of the healthcare domain more accessible to users. It has a Facebook chatbot through which users can access the company’s knowledge library of articles. 

3. Artificial Intelligence powered chatbots in Travel Industry

I think the travel industry is the industry which can reap immense benefits by integrating personalized, contextual chatbots in their services. There are already a few companies such as Snap Travel, KLM, Skyscanner, Expedia etc which are using bot messengers to interact with customers. 

Snap Travel has an artificial intelligence powered chatbot which browses more than 100 websites to give you the best hotel deal based on your budget. KLM Royal Dutch Airlines offers its customers a bot on Facebook where they can receive all the flight information, reminders, updates, and boarding pass at one place. Skyscanner’s bot on Facebook messenger helps you search for flights online while Expedia’s NLP powered bot helps you book hotels based on your requirements. 

4. Use of AI chatbots in the Fashion industry

Sephora, the popular French cosmetic and Beauty brand, was one of the earliest fashion companies to venture into the artificial intelligence domain when it launched its chatbot on Kik in 2016. The chatbot prompts users to answer a basic quiz about their tastes and requirements. It then provides you with the best product recommendations based on your answers, just like a real salesperson would. 

Similarly, Tommy Hilfiger and Burberry too integrated contextual chatbots in their system, though to serve different purposes. “TMY.GRL,” the Tommy Hilfiger chatbot was developed with the aim of providing users with a concierge-like experience by understanding the context, intent, and habits of the users. The Burberry chatbot for Messenger was developed in order to update the users about the latest collection. Even the fashion brand H&M launched a chatbot on Kik Messenger to allow consumers to access their catalog and purchase products from it. Similar to the Sephora chatbot, the H&M chatbot too begins by asking a few basic questions to the users regarding style preferences and requirements before suggesting products. 

Overall, there are quite a few companies in the fashion industry which are using chatbots to drive user engagement and deliver services that they are seeking.

  

5. Finance Sector and Contextual Chatbots

With a large percentage of the population needing help for financial transactions, there is ample scope for artificial intelligence based tools to solve problems and help people. Some financial institutions and banks have already taken this trend in stride and have come up with contextual chatbots that assist people in financial matters.

Bank of America has come up with a digital assistant in the form of a chatbot named Erica which helps users create healthier money habits by communicating with them about beneficial schemes. Wells Fargo has a Facebook chatbot which addresses queries of customers regarding bank/ATM branches and locations, account transactions, and deposits. Similar to Wells Fargo’s chatbot is the chatbot of Capital One Financial which has been developed to interact with their customers and answer their questions regarding their accounts and help them make online payments. Another financial chatbot powered by AI which I find worth mentioning is that of BBVA. Customers of this bank can transfer money through the messenger chatbots of Telegram and Facebook Messenger.   

6. Smart homes with automation

Though the concept of artificial intelligence powered smart homes has been around for quite a few years now, it is only recently that home automation with devices that anticipate and fulfill your demands has started making its presence felt. Revolutionary ventures by companies like Netatmo are taking us a step closer to realizing the home automation dream. With total connectivity and interoperability which can be controlled by a single messenger chatbot in smartphones, such smart homes are surely on the cards soon. 

7. Personal shopping assistants on E-Commerce Websites

I believe that E-Commerce domain has innumerable opportunities to exploit the technology of artificial intelligence and machine learning and integrate it to come up with new features that enhance the user experience. Though quite a few e-commerce platforms like eBay, Kip, Whole Foods, and 1800 flowers have embraced this technology in the form of their chatbots, most other platforms are yet to grasp the benefits it can provide. 

The eBay shopbot is like a personal shopping assistant that helps you with suggestions, choices, and recommendations on which products to buy. Not only that, but it also informs you about the offers and deals on various products. Thanks to the use of deep learning algorithms combined with Natural Language Processing, the eBay shopbot recommends the closest-fit options to your searches. 

Whole Foods chatbot is the application of artificial intelligence on a whole new level. In conjunction with the Facebook Messenger bot, it suggests recipes for meals based on the food products that you have browsed or shopped for on the Whole Foods platform. That’s revolutionary, isn’t it?

Conclusion

Artificial Intelligence is seeping into our lives at a breath-taking pace, and the day is not far when we will fully integrate contextual chatbots in our lives to reap maximum benefits. AI is making life simpler for us in many ways and contextual chatbots are the best examples of how it is being done. 

05:34

It is told that today the attention span of human is dwindling so bad that its even lesser than a gold fish. In numbers, gold fish attention span is 9 seconds while we humans end up around 8 seconds. I understand and can relate to this where we constantly interrupted by volumes of data coming from different devices and media. This means that to connect with another human and make meaningful conversation or impact, we got only 8 seconds or so. This is very critical in a business world where we are constantly trying to make our messages heard to our leaders irrespective of the what size or type the organization is. Given the amount of data increase, the variety of data involved along with velocity, the role of visualization becomes more and more critical than ever before. In a dumbed-down, device dependent herd-like society, we don’t want to end up with holding huge volumes of data at fingertips, but still unable to process and extract any meaningful information. Visualization is not about fancy charts and colors but its the art of enabling an easier interaction with and understanding of data. It enables us to tell the story of what happened and more importantly, what could we do going forward and share it with others. This involves – storytelling, interactivity, share-ability all to be done in less than 8 seconds before our attention evaporates. So, keep the time sensitivity in mind while portraying any kind of visualization to balance the information conveyed along with simplicity of design.

Who knew that we would be creating visualizations for folks with lower attention than gold fish? Fun, right?

22:39

Defining Digital transformation

Digital transformation is real and is here to stay! In fact, technology is now an inseparable part of all consumer-oriented industries and retail is no exception. Today’s consumer has become extremely demanding and seeks personalization, tailor-made solutions as well as seamless interaction across all possible channels. For the same reason, the 2017’s customers are called omni-shoppers as they are no more visiting the brick and mortar stores only, rather carrying retail in their pockets and shopping through smart devices. The changing habits of digital customers have forced the organizations to take a note and undergo digital transformation and look for different ways to engage different customers.

Key drivers of digital transformation in retail

Here are some key drivers that have propelled the digital transformation in retail industry:

• The growing expectations of omni-channel shoppers
• Various challenges all across the supply chain, which are not possible to digitize
• Completion faced from digital and rising costs
• Demand for having a personalized, relaxed, and seamless shopping experience

Causes and consequences of changing consumer behavior

Today’s shopper is not entirely digital! Do you know 90 percent of the retail sales take place within the stores? Also, more than two-thirds of consumers, who made online purchases, still indulge in store transactions. Knowing the figures, does it make sense to care about the digital behavior of a digital consumer? The answer is yes. This is because the ‘digital consumer’ essentially exhibits the ‘changing’ behavior of today’s consumer.

Why digital transformation is more tangible in retail?

Do you know 90 percent of the research prior to a purchase takes place online? At one point or the other, people use internet to either conduct a research or buy a product. Also, 90 percent of the people use smartphones while they are shopping in a store. Out of these, 54 percent perform price comparisons while in the store, while 48 percent check product information online. Around 2 percent even check online reviews about a specific product when in a store.

Challenges faced by companies with regard to digital transformation

The companies are startled with the growing expectations of a digital consumer. Here are such expectations, which have emerged as challenges for companies:

• No more waiting in queues to save time and inconvenience
• Personalized suggestions based on prior shopping patterns and thus saving their time and hastening decision making
• With integration of social media, the customer now wants more exposure, check product reviews online, and take informed decisions
• Desire to view real-time product information around them though interactive touch points
• Various mobile apps offer a seamless shopping experience and also reward points, which can also be redeemed. Consumers want flexibility and option to redeem reward points, compare products, find stores, and check online reviews.

Spending on R&D by top 10 retailers

The emerging demands of the consumers have triggered the need to develop new technologies to support these behaviors. As a result, the IT industry is under pressure to grow rapidly with regard to retail to match up with emerging trends. This has led to a significant rise in investments on R&D and technology. In fact, nearly 30% of retailers pointed out that their IT budget rose by 5% or more in past few years. Amazon claims to have spent approximately $14.2 billion on “technology and content.” Here is the data for R&D spending of top 10 retailers:

How can digital transformation be more innovative to consumers’ needs?

The good news is that there’s still a scope for retailers to be more innovative in order to catch and retain consumers’ attention. When asked about whether their favorite retailer is also a leading innovator, the highest any of the listed retailers could score is just 17 percent. Here, it’s important to mention that this survey was based on various innovation pointers such as store experience, in-store technology, store layout, delivery options. This simply indicates that there is a room for innovation and impeccable digital transformation for retailers in 2017.

04:12

It is possible to examine with granularity what buys your clients are creating if you look through a magnifier. But examine a prism, and you are going to notice some other spread, impartial activities your clients are producing within their route to buy.

The battle will be to wed the insights from both the prism and the magnifier to develop a more contextual and more profound understanding of your clients and your information. This union between the prism and the magnifier is permitted with stats that were behavior.

Behavior Statistics: An Union between Prism and the Magnifier

As “the study of how and why consumers participate or connect to goods.” behavior statistics is described In its most abundant kind, the procedure includes positioning into designs that help tell the story of specific relationships and aggregating all potential touchpoints in to a holistic perspective for evaluation.

With behavior stats, a consumer may examine information designs to examine relationships and behaviours across on site, off site, and also off line contact points of the purchasing period. Additional power consumers of information and professionals may get to the responses of how and why things occurred by aggregating the measures on a per-entity foundation.

When you a DD your data evaluation procedure that is big and behavior stats, and link the spots in your client trip, after that you can reply queries about retailing, promotion, prices, promotions, etc. Basically, with behavior stats, you answer concerns that have been not formerly easy, or even impossible to reply with datawarehousing solutions and conventional BI.

Below are a few questions you may reply with statistics that are behavior:

Merchandising: Are the adjustments in a footwear classification due to altering preference in our existing clients or instead, a shifting population of clients?

Advertising: When is the best period after the buy to deliver an e-mail that is new?

Prices: Are cheaper things successful at getting new clients that eventually buy more expensive items?

Below are both essential elements that make the basis of behavior statistics:

Occasion Chain Statistics: Identifying unique styles displayed by some thing, over period, like an individual. By way of example, for every client (the thing) of a banking, calculate which clients that received a direct mail advertising for a charge card program, effectively completed the program and were accepted.

Segmentation: The grouping together of related people of things which could share common characteristics and display behaviours that are related. Companies can utilize segmentation to examine and find quality client numbers by examining the behaviours of these sub sets of information to better monitor sequences and group frequent clients and recognize defects.

Related Resources:

DESCRIPTIVE ANALYTICS

DIAGNOSTIC ANALYTICS

PREDICTIVE ANALYTICS

PRESCRIPTIVE ANALYTICS

01:11

Consumer stats isn’t a theory that is new; in its simplest perception, it’s the cause soccer games have children and ale advertising displays that are ’ have plaything advertisements.

Quite simply, consumer statistics is the push so that you can determine how they invest their cash to better understand present and prospective clients. Now, the growth of systems for analyzing and storing enormous amounts of consumer information is changing the way companies think about helping their clients. The notion of a section of one” is fast becoming possible.

Identifying Consumer Analytics

For sometime, companies have utilized the many clear dataset they've – transactional information – to better understand their clients. Comprehend and devotion cards, business cards were employed to produce comprehensive consumer segments, including ”, ” pupil, “mom or retired person.” This segmentation empowered specific advertisements that fell and improved transformation atrophied invest.

Nevertheless, information that is transactional may only actually show a piece of a client’s conversation with a company – the buy. An upsurge in purchasing task and on-line societal, joined with Bigdata technologies like Hadoop, have supplied a treasure chest of information combined with the means to gather and analyze that info.


Understanding Immediate Value from Client Analytics

The quick consequences of implementing Big Data technologies in Consumer Analytics are obvious: a complete background of buying, deserted shopping carts, visited webpages, and interpersonal community task empowers a business to effectively mix-market, upsell, and provide reductions to drive ahead a buy. Realtime capabilities empower businesses to determine the present buy as well as the one that is next. Moreover, a perspective that is strong is enabled by an apparent 360 comprehension of current clients into client profitability, which lets client acquisition attempts to focus on large-value segments.

Constructing Long Term Worth with Consumer Analytics

The lengthier-term outcomes of a robust Consumer Statistics attempt may be actually more fascinating. The conventional obstacles have reduced to client turn. The finest client maintenance method may often be client satisfaction; Consumer Stats may ensure clients have buying experiences that are outstanding through modification. Moreover, the set of every discussion enables the recognition of major indicators of turn, enabling a company to intercede before dropping a customer forever.

01:08

Fairly frequently, then retro fit the superfluity of conventional data modeling resources at fantastic time and disbursement in to the Hadoop surroundings and engineering sections may try to breathe life in to conventional IT assets that are submerged. This strategy should be ignored.

Selfservice information resources that are large, optimizing application shipping, beating moment to worth, and are revolutionizing use of information that is large. Companies are rapidly recognizing that information resources that are large with self service capabilities can aid break to info that is large.

To better understand the way in which your enterprise may raise generate company advantage and bigdata entry, follow these steps under:

Our Website: https://www.intellectyx.com/

1. Company-find your entire Business Information

It's generally understood that business decision making may be better educated with information – particularly information produced from a combination of datasets rather than an old information and solitary resource. Consequently, strategic investments have been produced by many companies in information systems that natively incorporate the resource for storing all structured and unstructured data that was big in a single location, with Hadoop. The app is scalable, adaptable, safe and works perfectly well or on assumption.

2. Prevent Building Signal or Retro Fitting Conventional Technology

Why produce a weight of sophistication that restricts your speed to react to fresh problems as they appear and decreases additional improvement? Signal that is building typically needs a group of information experts with practical abilities to create code for modeling and information preparation to distill an effect; this can not level to supply at a rate that matches with desire that is commercial. Thus, building signal to raise information that is large availability isn't a perfect option.

Because in case the incumbent seller considers the conventional drive information product is the correct strategy in now’s evening and age the chance of the company obtaining access to its info this decade stays slender retro fitting conventional technologies isn't a wise decision possibly.

3. Enable Business Users to Easily Get Large Info

Companies should democratize accessibility by creating it user friendly for anybody to yank large information to function a fresh situation as opposed to depending on IT to somehow expect and shove it (because this latter strategy requires time and frequently results in misinterpretations). Also, information penetrations that are simpler, quicker, may enable a company to create better, informed choices. By enabling more consumers to locate penetrations and decreasing cycle times, companies may obtain information that is vital to immediately rotate and out perform their peers.

01:07

Business intelligence (BI) provides data driven insights for decision assistance.

The conventional heart of BI is shine consolidation, querying on line analytic processing, and historic coverage. Nevertheless, several BI surroundings may provide an assortment of abilities that are added.

Several of those BI surroundings include stats that are higher level for modeling, prediction, and what if evaluation. BI surroundings that are additional may also interface to complex event running and supply calculating backend for realtime stats. Nevertheless the others provide accessibility to backend data that is big analytics programs that integrate data stats, machine understanding, natural language running, and additional algorithmically penetrations that are distilled.

Frontend BI programs may make use of many backend information sources, including bigdata systems such as datawarehousing systems and No SQL, Hadoop, OLTP sources, and also stuff like that. This opens fresh layers of information entry for investigation, but in addition, it presents the danger of assessing varieties and older variants of information with changing degrees of truth and age.

Across their life cycles to guarantee a variation of which is, information resources should be regulated because of this:

Merged: all pertinent information was merged in to an incorporated bodily and rationally, database that was analytic

Followed to language, semantic design, a standard data product, outline, measurements, and hierarchies across all areas

Cleaned: before launching in to the analytic database all pertinent information was transformed, fit, united, fixed, and improved

Present: all info was pulled, prepared, and sent in real time from resource programs to additional programs and questions

Multi dimensional: all info was provided in to combination and the full variety of problem, reporting, dashboarding, research, visualization, predictive designs, and additional statistics programs to get adaptive multi dimensional investigation

By implementing these guidelines, companies may efficiently control BI insights to push large-assurance decision assistance across all subject domain names.

01:02