Chatbots: Stop with the technology talk, it’s all about the Conversation, dummy

Chatbots: Stop with the technology talk, it’s all about the Conversation, dummy

With AI chatbots, the conversation is the hard part, not the frameworks and tools.

Think about prototyping an insurance bot, integrating IBM Watson, Facebook Messenger, and claims databases. The most challenging component, by far, is the conversation. We have to understand the audience and their communication patterns. Talk to customers who have filed claims or are filing claims over the phone and online. Talk to customer service reps and claims adjusters. Listen to logged phone calls and read case emails. Researching conversation patterns in messaging.

Right now chatbot developers are treating the conversation component like a simple decision tree. But people don’t talk in decision trees. It’s a back and for negotiation. Non-developers are using the “no coding frameworks” and they are disappointing. When developers try to integrate conversational intelligence, they fail. When there is a problem definition, developers are great at integrating tools and frameworks. But mapping out how ordinary people converse or within a domain is a different skill set. Most UX professionals aren’t prepared for that either, so there is a talent gap.

tl;dr: There are lots of good use cases for AI chatbots, but too many people see it as a gold rush. Most are not doing the work to solve real problems.

Why Chatbots are Hot

Why Chatbots are Hot

Chatbots are hot right now. It has all the hype. That and virtual reality. But let’s talk about chatbots right now. I’m writing a post on the company blog about chatbots, but I thought I’d put down some thoughts here. Figuring out why chatbots even matter is an interesting exercise. It seems obvious: communications on the Internet is an utter disaster at the moment.

There are four factors. First, online ads downright suck. The ads themselves are not creative, and they are in your face. That is a terrible combination. Agencies and marketers don’t get excited about banner ads and text ads. Unless they are “digital” marketers, and that is all they know. Video and native ads have possibilities, but those executions are bad. Our brains have become dulled from creating banner and text ads. What’s wrong with interrupting the user experience with overlaying ads and tiny close buttons?

More important question is why TV and radio get away with it instead. My main theory is even though we all grew up with interruptive ads on those mediums, we had no alternatives. There was no Facebook or Snapchat to occupy our attentions during commercial breaks. Would TV and radio ads be possible if created today? Another theory is that Americans like breaks in stories. Look at popular American sports: football, baseball, and basketball. They are all situational. One team gets the opportunity to score, and then the next team tries to score. While not entirely applicable to basketball, timeouts make it situational.

If a viewer has watched a few shows on Netflix, how can they go back to live TV? It is painful. The only reason to watch live TV is live events like sports. But social media, which is taking eyeballs from TV ads, makes live TV worth watching. Sharing thoughts on social networks when watching live TV makes it more enjoyable.

Back to why chatbots are huge right now. The second factor is messaging is such a big deal. Business Insider created the chart above showing how messaging usage has surpassed social networks. It’s understandable. As soon as a social network gets big, mom & dad come, the advertisers move in. Then the cool kids migrate to another social network. But the way messaging works, you don’t have to see your dumb uncle’s Trump posts. Messaging allows users the one on one interaction, while interacting with separate small groups. Kids are comfortable with the UX and asynchronous nature of messaging. They are the perfect audience for chatbot-type interactions.

Next, websites and apps are painful. Marketers and agencies design websites and apps without putting the customer at the center. Look at any “digital” agency website. Stuff is flying around, punch the visitor in the face annoying. The websites are a demonstration of how they can grab the visitors attention. Sure. Throw in the bloated nature of websites due to ads and trackers. Why are we surprised ad blocking is on the rise?

The last factor, AI is more accessible. Computing and infrastructure costs have plummeted. The resources to build AI and machine learning are available to anyone. Granted, a lot of what we see are more of decision trees than AI, but it is a start in the right direction. The investments in technologies are already evident. Siri (ok, not so much), Alexa, the multitude of Google products. Plugin architecture of these platforms increases the impact and value of what AI provides. Once users are comfy with AI as their primary interface, they will demand it everywhere. More on this next time.

Let’s hope brands don’t build crappy chatbots and screw up this game-changing opportunity.

Brand as a Service

Why don’t we have Brand as a Service companies? Over at Redef, there is an article about Disney as a Service. In the IT world, the “as a Service” concept isn’t new. Most know about Software as a Service (SaaS),  a cloud-based web application having a monthly or yearly subscription fee. Think Salesforce or Basecamp. 

FYI, I’m using product and service interchangeably. 

In the 20th century, manufacturers and creators have been walled off from the end users. Industries have layers and layers of middlemen who are responsible for distribution and retailers getting the product into the hands of the buyers. This wall has created a supply chain that is problematic in many industries. Media brands have had content creators, distributors, and publishers (TV, radio, newspapers) or retailers (Best Buy, theaters).

As everyone knows, the Internet changed everything. Non-publisher media brands can now engage with customers with no middlemen. Why bother dealing with them now? Part of it is that a shift to directly interfacing with end users is daunting for several reasons:

  • The brand becomes responsible for the whole stack of content creation, marketing, distribution, and post-sale engagement. The financial investment is significant.
  • Such a change will not happen over time. All the partners in the supply chain will voice their concern since it has the potential of cutting into their revenues and they can threaten to boycott the brand. Financial pain for everyone.
  • Even if the investment is possible, the organization structure of brands has to change. Companies optimize for what they are good at, creating content or creating a widget, and pushing the goods downstream. Corporate cultures will have to change, different types of talent will be required. 

But transforming into a Brand as a Service is worth it. Control is lost with each extra layer between the brand and customer and increases the probability of chaos. The brand needs to connect directly with the consumer to provide the best possible customer experience. 

The question is how does a brand decide if it wants to become a hub consumers will seek out, willingly pay for their products and services over and over? The most straightforward answer is that a brand must have more than 2 or 3 products that consumers will be interested in coming back for over and over. 
That’s where Walt Disney was a visionary. He envisioned a set of content and products that would feed, support, and reinforce each other. 

Brand as a Service

Films are the core product, and they feed to create TV, music, and other publications. It is a portfolio of content that is connected and builds on each other. 

Going back a few years, Disney becoming a stand-alone hub would be impossible without the Internet. Still today, such a transformation will be challenging, but it is finally possible for brands that have the connected portfolio of products to control their destiny.

Bot Metrics

These are bot metrics I found Googling. It will be updated as I find more.

While bot metrics can include KPIs like daily active users, being in the early days of bots, it would be better to focus on how well bots work in fulfilling the customers’ experience. Most other metrics related to funnels and sales should still be applicable.

From “Towards a Method For Evaluating Naturalness in Conversational Dialog Systems”

Metric Type Data Collection Method
Total elapsed time Efficiency Quantitative Analysis
Total number of user/system turns Efficiency Quantitative Analysis
Total number of system turns Efficiency Quantitative Analysis
Total number of turns per task Efficiency Quantitative Analysis
Total elapsed time per turn Efficiency Quantitative Analysis
Number of re-prompts Qualitative Quantitative Analysis
Number of user barge-ins Qualitative Quantitative Analysis
Number of inappropriate system responses Qualitative Quantitative Analysis
Concept Accuracy Qualitative Quantitative Analysis
Turn correction ratio Qualitative Quantitative Analysis
Ease of usage Qualitative Questionnaire
Clarity Qualitative Questionnaire
Naturalness Qualitative Questionnaire
Friendliness Qualitative Questionnaire
Robustness regarding misunderstandings Qualitative Questionnaire
Willingness to use system again Qualitative Questionnaire

From “Different measurements metrics to evaluate a chatbot system”:

  • Dialogue efficiency in terms of matching type.
  • Dialogue quality metrics based on response type.
  • Users’ satisfaction assessment based on an open-ended request for feedback.

It’s a fact: customers like to gripe

They found that customer response to positive and negative customer experience is in direct contrast to customer response to a positive or negative customer service experience. Fully 71 percent of people who have positive product experiences engage in word of mouth, while only 32 percent of customers with a negative product experience want to tell other people about it.