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AI Chatbots: Next-generation Tool to Help Businesses Reduce Operational Costs & Improve Customer Service

July 18, 2018 Written by Andrei Klubnikin, Senior Content Manager
In case you consider joining the team of e-commerce and customer service companies that invest in chatbots, check out our new article highlighting the benefits of AI-powered virtual assistants. P.S. You’ll find some useful tips for building a smart bot there, too!

Types of Chatbots You Can Use to Streamline Business Operations

Suppose you have an online gadget store. As shopping apps’ usage grew by 54% last year, you decide to build an mCommerce app to expand your brand presence and reach. Since 51% of Americans do not download new apps for months in a row, you need a feature that will help you stand out from the competition. Having a smart assistant running inside your app is a great way to help customers navigate your product catalog and make shopping easier; why not build a chatbot then?
Every item in your catalog has a detailed technical description, including a gadget’s operating system, screen size, internal memory, manufacturer, etc. If you decide to add a chatbot to your app, how will it function? Most likely, the bot will be triggered by “keywords”: when a customer types in the basic characteristics of the gadget he’d like to buy, the not-so-smart program will simply surf through product descriptions, filter out the ones that do not match the query and display the relevant ones to the user. Similarly, your customer can narrow down the search a bit further by specifying additional details.
Wait, but what about virtual assistants that make suggestions based on customers’ shopping history, offer legal advice on flight delays and parking tickets or figure out what you want to buy by interpreting an emoji?
Oh, that’s a different type of virtual assistants; in fact, there are four major types of bots – and they are not created equal.
Apparently, chatbots are not created equal

What Kind of Chatbot Would Deliver the Most Value to Your Customers?

  • Flow-oriented bots. Like the mCommerce assistant from the example we’ve given above, flow-oriented bots follow an algorithm set by a software developer. Such bots require users to go through a number of questions and provide relevant answers based on user input.
  • AI bots. While flow-oriented bots rely on buttons and very little text (and will probably start apologizing if you write a lengthy reply!), Artificial Intelligence chatbots are powered by Natural Language Processing (NLP) algorithms. Such bots are capable of analyzing longer text-based replies and understanding a customer’s intentions. Technology-wise, AI chatbots most often use open-source NLP engines like Wit.ai, Api.ai and Program-O.
  • Hybrid bots. These chatbots are semi-intelligent applications that follow some predefined logic path and possess limited NLP capabilities. However, hybrid bots will only turn to text analysis when they need to get users back to one of the topics they’re familiar with.
  • Human-assisted bots. Such bots are powered by artificial neural networks; however, there’s a human support specialist who oversees conversations and takes the matter into his own hands if required. Recent studies show that 86% of customers expect bots to connect them to human workers once a problem occurs.
General Chatbot Architecture
Which way should your business go?
It depends on several factors, including your target platform (bots can be integrated into a website, app or popular messenger like Kik or Telegram), your goals (smarter navigation vs. personal recommendations vs. smooth customer service delivery) and the level of automation you’d like to achieve.
Needless to say, you should not ignore your customers. Although 65% of consumers do not mind engaging with a company via a chatbot, only three in ten Americans have actually used bots. When it comes to Millennials and Gen Yz, however, 60% of younger customers have already interacted with a virtual assistant – and 70% of those report positive experiences.

A Glimpse Inside AI Bot Development

As you see, it’s not that difficult to develop a linear question answering algorithm; what IS difficult, though, is to create and train a neural network which processes data, gets smarter over time and adds a hint of personality to your chatbot.

How do Neural Networks Powering AI Bots Work?

In 2017, we published an article offering a little insight into neural network development. In order to explain the process in simple terms, we told readers about a C++ employee bonus system which had been created by Pavel, our head of iOS Development Unit.
The system is powered by a neural network which takes Redmine data on employee attendance, overtime, task updates and completion as binary inputs (“0” or “1” standing for “yes” or “no”). Then the hidden layers of the neural network multiply the input numbers and sum up the results to make a decision. For instance, employees who worked regular hours during the previous month, but mostly spent their time fixing bugs, are not entitled to a bonus. The same goes for employees who worked extra hours, but also had some idle time.
Have any questions? Ask our team!
What makes such systems different from flow-oriented and hybrid bots? Obviously, it’s the ability to make data-driven decisions on its own rather than rely on default “if-else” statements defined by a software developer.

Why are AI Bots Better than Traditional Virtual Assistants?

A couple of years ago (and many months before chatbots became a thing!) the R-Style Lab team worked on a period calculator app that predicts fertile days, ovulation and menstruation cycles and tracks users’ eating habits and daily lifestyle. The app is powered by a neural network which was trained using actual medical data and is thus capable of making up to 54% more accurate predictions than other women calendar apps. Furthermore, the app features a smart assistant that helps women identify the causes of painful ovulation and successfully overcome them.

Here’s how it works:

  • Once a user opens up the app, she is greeted by a chatbot. The virtual assistant asks the user how she’s feeling, etc.
  • If the user is not well – for example, she’s suffering from abdominal pain – the bot asks her questions to identify its cause. The smart assistant offers both multiple choice and open cloze questions, as well as interactive pictures of a female body so that users could click where it hurts.
  • Having processed the replies, the bot suggests the diagnosis (“it looks like indigestion”) and comes up with health advice (“take activated charcoal”). The same logic is applied to the prediction of fertile periods.
Thanks to the custom-written neural network, the AI bot is able to engage in non-linear dialogs as opposed to standard messenger assistants like those of Sephora and H&M which spot keywords (“blue jeans”, “nail polish”, “Monsieur Big Mascara review”) in customers’ messages, search for relevant answers in a brand’s online catalog and send links to products that match the given query to happy users. Example of a simple flow-oriented chatbot
Needless to say, there is a qualified physician somewhere in the background, ready to answer tricky questions and make sure the bot provides accurate healthcare information.

How to Avoid Possible Pitfalls when Operating an AI Bot?

The program we’ve built is a classic example of a human-assisted bot, and – considering the fact that it took Twitter trolls less than 24 hours to turn Microsoft’s Tay into a Nazi – it might be the only approach you can take right now. There are lots of open-source libraries including Python’s PyBrain and NumPy that allow making a chatbot in mere days. However, you need months (and tons of verified data!) to train a neural network and put it to work – and the task should be trusted to a reliable custom mobile apps development company with previous AI experience.

Does Your Business Need an AI Bot?

Ok, now you know how AI-based virtual assistants function under the hood and what makes them superior to linear chatbots. The question is, do you really need to create one?
  • Recent studies show that chatbots – even linear ones! – can perfectly deal with 80% of tier 1 customer support questions.
  • According to IBM, chatbots can help companies reduce customer support expenses by up to 30%.
  • In just two years, up to 85% of customer interactions will be handled without human assistance.
  • More than 50% of consumers believe a business should be available 24/7.
  • 46% of customers prefer to communicate by messaging rather than over the phone.
At this point, it’s fair to say the future of customer service is not exactly human.
Technology advancements have made customer journey mapping trickier that ever; there are just too many points of interaction between brands and users to offer comprehensive “always-on” support, let alone impact their buying decisions. No, you can’t simply build a chatbot and shut down your sales and customer support departments; however, a smart virtual assistant would free up your employees’ hands and let them focus on creative work instead of dealing with routine enquiries.

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