Microsoft is tapping the power of AI to provide customers of its Dynamics 365 business applications suite with new features. The company announced a series of capabilities at its Ignite conference today focused on bringing intelligence to business processes, including a pre-built chatbot designed to field customer service queries.

Anyone can build a helpful, functioning chat bot, even if you're not a coder. Robin Lord shares an insightful how-to, complete with lessons learned and free code via GitHub to fast-track your own bot's production.

Marketing and sales company HubSpot today announced it has acquired bot creation platform Motion.ai. Terms of the deal were not disclosed. The acquisition will bring bots to HubSpot’s more than 34,000 customers for the first time.

HubSpot’s best-known bot may be GrowthBot, which was made last year by CTO and cofounder Dharmesh Shah. The Motion.ai platform can currently be used to create bots for Slack and Facebook Messenger, and for chat on websites. Motion.ai has been used by brands like T-Mobile, Kia, Sony, and Wix.

HubSpot acquired Motion.ai because the company has come to understand the importance of providing customers with services that reach them on chat apps.

In the latest move to bring more bots to Twitter Direct Messages, Sprout Social today announced the launch of its Bot Builder platform. Bot Builder is geared toward helping social media, customer service, and engagement teams create their own Twitter bots for conversations in Direct Messages.

Like bot-maker platforms for other chat apps, Bot Builder on Sprout Social promises to help people with no coding or technical knowledge create functional bots within a matter of minutes.

Bot Builder access is only available to customers of Sprout Social Enterprise, a $249 a month plan.

What do WhatsApp, Fb Messenger, Slack, Telegram, Kik and WeChat have in common?

They’re all messaging platforms.
They saw more active users than social media did in 2016. If predictions mean anything then 2017 is only going to see this go uphill from here.

With the rise of messaging, the traditional approach of forcing people to fill out forms and following up via phone or email is losing favor.

In order to optimize for long-term growth, marketing and sales teams need to adapt to changing customer communication preferences.

The future of lead generation might not be entirely form-less, but it seems likely that messaging will play a more and more significant role. And thanks to bots, using messaging for lead capture campaigns is not only possible, but scalable.

"Here’s an explanation of the different types of chatbots and what their functions are:

• Chat As Chatbots
• Triggered Task Chatbots
• Interactive Information Access Chatbots
• Complex Task Interaction Chatbots
• Search Chatbots"

Bots are computer programs that simulate conversations with humans, only with no direct human involvement. In other words, there is no “man behind the curtain.” Everything the bot needs to know to converse with a real human is built into its algorithm. This concept is also known as artificial intelligence, or AI.

Essentially, an app (short for application) is any program on a hand-held device. But as the line has blurred between computers and phones, and as almost every type of device has become portable, the term “app” has broadened to describe almost any software program.

Apps are already so entrenched that it would be shortsighted to eschew them as a reaction to the burgeoning chatbot movement, but it would also be foolish to exclude bots from future marketing plans. Rather, integrating chatbots into an existing mobile and web app strategy is the wisest path.

Devices or programs that are multithreaded, or able to remember multiple situations, may be the key to improving conversations with AI bots. Today, a user usually must finish a single use case before starting another one. This is unlike normal language, however, as most people don’t always finish an old conversation before beginning another one. To truly mimic natural language, a user should be able to engage in multiple conversations on multiple topics with an AI bot at the same time.

One current strategy for achieving a true NLI is reinforcement learning: Instead of teaching bots to mimic conversation, this strategy may be able to help to actually comprehend language. Reinforcement learning is perhaps most well-known for its role in AlphaGo, a computer developed by a subsidiary of Alphabet called DeepMind, which mastered the complex board game Go – and beat one of the best human players in a highly publicized match last year. Because of the vast number of possible moves and the need to employ creativity to win, the game was one of the hardest challenges for artificial intelligence. While the technique is still in its infancy, it can drastically change the way bots interact with one another to solve problems and could alter the way we teach bots to converse — and even allow them to participate in conversation as if they were a native speaker!

1. Have a Broader Mobile (Messaging) Strategy

Like any advertising campaign, branded chat campaigns perform best when rooted in strategy—specifically, a mobile messaging strategy. This means ensuring everything from the objectives, key performance indicators (KPIs), key messages, and creative content are aligned and working cohesively.

2. Pick the Right Partner

Pick a chatbot marketing expert who is platform agnostic; work with them to develop a proposal and strategy that ladders up to your campaign goals and can be deployed across the appropriate messaging apps. It’s imperative that this partner can also deliver real-time analytics of campaign performance, including message-read rates, average length of conversation, how many people were funneled through to reach the KPIs, etc. 

3. Layer Your Engagement Experiences

Like any other branding, marketing, or advertising campaign, chatbot campaigns perform best when wrapped with other engagement experiences. At least for now, bots lack the ability to sustain interest and engagement when deployed solo.

Some questions will be easier to answer than other questions. The answering process will require access to large structured data sets (Type 1) or unstructured data (Type 2).

TYPE 1

Type 1, based upon structured information: ‘Where is my order?,’ a very popular retail customer service use case (it is a type of question). A chatbot receives an SMS reading, “Where is my order?” This process has a few steps in the back end (the fulfillment system) but should not be hard to imagine. ‘Look up the user, via phone number, look up recent order in order system, check status in shipping, send back response to chatbot, which makes the answer conversational.’

TYPE 2

Type 2, based upon unstructured information or knowledge: ‘How do I?…’ or ‘What should I do if?…’ A simple example is spilling wine on fabric or your carpet. A chatbot receives an SMS reading, “I just spilled red wine on my new shirt.” First, the system may need to do everything from Type 1 above to figure out what the fabric might be, then converse with the user. The system may then need to search a knowledgebase or unstructured data, like video or other information stores. There is nothing particularly artificial here, but advanced search capabilities will be required.

"Under the new paradigm, conversations — not forms — drive the qualification process. And thanks to the rise of chatbots, sales and marketing teams can now have these conversations at scale.

Chatbots, on the other hand, never have to sleep. Chatbots can continue to ask leads the same qualifying questions that BDRs would ask.

To clarify, the goal here isn’t to replace humans, it’s to help humans manage multiple conversations and qualify leads more efficiently — that way nobody’s time is wasted.

Thanks to chatbots, sales and marketing teams can now choose to interact with only the best leads based on the rules and targeting conditions that matter most to their specific business."

Twitter bots can do a heck of a lot more than just spam trending hashtags and relentlessly follow users.

• The State of Chatbots report, a copy of which was provided to Marketing Dive by Forrester, made it very clear that AI-driven chatbots are currently not overly effective, with messaging as a marketing channel driving app engagement through conversations fueled by people and not bots.
• The report pointed out that simple chatbots with a narrow set of potential responses and commands are currently successful, but that most chatbots are disappointing consumers with poor user experiences such as not setting expectations and acting in unexpected ways.
• Forrester also reminded marketers that artificial intelligence isn’t magic, stating that consumers have to “teach” the technology. “AI is not ready to work miracles or generate magical experiences for customers on its own," per the report. "AI-based chatbots are like seeds. You plant them, but then you need to feed them with high volumes of consumer interaction so they grow.”

What are chatbots?

The explanation is hidden in the name itself. Chat and Bots – Chatting with Robots, that is, in layman terms. To give it a slight formal touch: Chatbot is a basic form of artificial intelligence software that talks with humans and answers their queries in a lifelike manner.  When a customer poses a question, within a matter of minutes, a bot sifts through a reservoir of company data to derive an answer.

Types of chatbots        

Presently, two types of Chatbots dominate the market. One functions on the basis of set rules and the other leverages an advanced version and employs machine learning.

Chatbots based on set rules have limited brain power.  It only answers to very specific commands. In short, the bot functions to what it is programmed for.  Nothing more, nothing less.
Chatbots based on machine learning are powered by artificial intelligence.  You don’t have to be really precise while talking to this bot, because it’s designed to comprehend both language and commands quite effortlessly.
Services you can bank on for bot development  

IBM’s Watson
OpenXcell
Dexter
ai
howdy’s botkit

Bots.directory covers all the messaging platforms mentioned above. It also supports filtering by categories. You choose a platform and category and the bots will be sorted by the defined parameters.

BotFinder is another chatbot store. Apart from the abovementioned platforms, it also includes Skype bots.

BotPages offers a long list of bots that work with the most popular messaging platforms including Twitter, WhatsApp, Viber, Line, and others.

Botlist provides bots that fit every taste. In addition to the above platforms, it supports Android, iOS, email, SMS, and others. This means that the store is not limited to text messengers only, but spills over to bots that can function far beyond what you can do in a chat.

Telegram Bot Store offers a variety of bots for its platform. You can sort them by categories, looking at recently added bots, the best new bots, or the bots that are at the top of the charts.