HOW TO MAKE A SUCCESSFUL CHATBOT (BUSINESS TIPS)

sasha uritsky

Nov 23, 2018

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.

HOW TO CHOOSE AI SOLUTIONnmodes.com

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

FINDING AI ENGINEnmodes.com

(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.

CHATBOTS BRING GROWTHchatbotsmagazine.com

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.

Interested in reading more? Check out our other blogs:

Social selling. Difference between Facebook and Twitter

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.

Integrated Real-Time Data Boosts Content Delivery

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.