Microsoft AI products

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.

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.