The target in conversational interfaces is to achieve a user manual that just says, “Type or say what you want.” The reality is that natural language processing technology and tools have some limitations. What do you view as the current state of natural-language technology available for commercial use?

One limitation of NLP tools today is that they don’t always work for less popular languages. So, people needing to support those languages often need to scrape together an NLP solution on their own.

Also, NLP tools can have intent “drift,” meaning that as the number of intents a bot supports grows, misclassification of intents is more likely. This misunderstood-request problem is solvable but will take more time.

A related problem is missed user requests where the NLP engine misses an opportunity to map a request to an existing intent. Chatbase is working on exposing both types of issues, regardless of the NLP engine used.

Finally, even if you have a perfect NLP tool that can correctly extract intent and entities, you still need to respond to user requests helpfully. So, NLP alone is not sufficient for producing a delightful bot experience.

What are the motivations for moving to conversational interfaces? What is the payoff for companies moving to support this technology for customer interaction, employee support, analytics, or other goals?

We’re aware of many companies working on customer-support bots to reduce costs as well as increase customer delight. There are many common and simple questions that bots can handle 24x7. In fact, according to a recent study from Drift, Salesforce, and other companies, the top capabilities people expect from bots are non-stop availability and ability to quickly answer simple questions.

Other compelling business use cases for bots include lead gen, enabling companies to get qualify leads in a more enhanced way versus using forms. That approach helps the customer get quick answers 24x7, and makes lead qualification more adaptive to customer requests.

How fast do you think core natural-language technology will improve? And what are some of the promising directions of research?

At Chatbase we’re automating bot analysis and optimization to help make bot NLP seem more human. Whereas in the past, optimizing a bot required teams of analysts to comb through conversation logs to find errors and correlate them.

We’ve identified three types of common errors that can be automatically fixed. We call them “UMM errors,” which stands for unsupported, misunderstood, and missed user requests. We helped one large retailer that buried 5 of its business analysts in logs automate that work instead, and now it’s re-assigning most of them to other more strategic tasks because Chatbase finds errors more accurately and efficiently than the humans do.

If voice interaction is used, what are the current limitations and likely trends in speech-recognition technology?

Voice speakers are starting to have screens, so expect multimodal interactions that might start with voice, then disambiguate to a screen, then back to voice, and so on.

What do you see as trade-offs and differences between voice interaction and text interaction?

Today, we’re seeing that text bots are much more widely used than voice bots. That might be because discovery, while still not great with text bots, is easier than for voice bots where the user has to think of the name of a service to trigger it versus searching a directory. I expect that to change over time, but it’s the reality today.

Also, with most text bot platforms, the only way to have a conversational interaction is by triggering a third-party bot. Some voice platforms provide an intelligent assistant with which a user can interact, so some users might never trigger a third-party bot if the assistant handles the head use cases well.

Another difference between voice and text bots is that most voice bots are single-turn interactions (e.g. “play Lady Gaga”), while text bots on average have three turns.

What categories of tools are available to a company or developer that wants to create a conversational interaction without a major R&D project?

There are lots of tools that help with bot building; some help you build a decision tree that isn’t so flexible with requests it handles (i.e. they don’t do type-ins well), others provide an NLP engine and thus more flexibility in ability to handle requests, and yet others focus on marketing automation to segment and notify users (who respond at a higher rate to bot notifications than emailed ones).

Plus, there are analytics and optimization tools (like Chatbase and others) that help a bot builder track growth, engagement, and retention, and guide optimizations to improve those metrics.

Finally, there are also early-stage ad products that help with bot discovery and monetization.

Automation of customer service is a major application category for conversational interaction. What trends do you see in this area?

There are a number of companies trying to generate customer-support bots based on an existing knowledge base and support logs. The benefit of such an approach is that it increases the odds that a bot will cover the top questions asked and provide well-honed responses. Theoretically it’s faster to build a bot this way, but in practice maybe not given the work of parsing through past data.

Do you have any specific recommendations on practices that will result in effective conversational systems?

My top recommendation is to hire a copywriter at an early stage to create a compelling conversational experience. Many bot teams forget to do that and take a hit on the user-experience side.

Any final comments?

According to Gartner, more budget will be spent on bots by 2021 than on mobile apps. So there are major investments that will be made, and are already being made, by companies to build delightful bot experiences. The tools will just get better and best practices will form. It’s exciting to watch this space mature and become mainstream!

About Chatbase

Chatbase gives builders of conversational interfaces (or bots) sophisticated tools for creating better, and stickier, consumer experiences than ever before — leading to better conversion rates and retention. Chatbase is a cloud service that easily integrates with any bot platform and type, voice or text, and is free to use.

Among other features, Chatbase uniquely relies on Google’s machine learning capabilities to automate the identification of bot problems and opportunities that would otherwise take a lot of time, leading to faster optimizations and better bot accuracy.

Chatbase is brought to you by Area 120, an incubator for early-stage products operated by Google.