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The 5 Branches of Conversational Commerce: A Guide to the Bot World


May 27, 2016

The 5 Branches of Conversational Commerce: A Guide to the Bot World

Home > Blog > The 5 Branches of Conversational Commerce: A Guide to the Bot World

This guest post is written by Jeff Lawson, CEO and cofounder of Twilio.

It’s hard to make a right turn down San Francisco’s crowded streets these days without running into a story about bots, conversational commerce, or conversational commerce bots. Bots have reached kale levels of hype. But amidst the noise, there’s something real going on here — we just have to decipher it.

You may have heard of conversational commerce — it’s a catchall term for a future of technology driven by messaging (and voice) interactions that transcend current communications modalities. It’s a convenient moniker but also confusing because there isn’t one trend to follow. Rather, it’s several distinct trends that overlap to varying degrees. Disecting this “mega-trend” into its underlying trends gives us a look at where technology may be taking us.

I count five different trends converging on conversational commerce:

1. Advanced notifications
2. Bots
3. Chat in apps
4. Apps in chat
5. Humans chatting with each other

So, let’s dig in and take a closer look:

1. Advanced notifications

We’re all accustomed to receiving timely alerts via SMS, push, or even email in the course of our interactions with companies and apps. Whether it’s your car arriving, a delivery confirmation, or a message that your flight is delayed, proactive notifications are a simple and effective means of improving our interactions with companies.

But these notifications are getting more and more interesting. Notifications can include multimedia, maps, directions, contacts, boarding passes, and more. And while the reality of this isn’t exactly conversational, every notification should be an invitation to a conversation. So what happens when the consumer replies to these notifications?

Enter the bot craze.

2. Bots

Bot interfaces have long captured the imagination of developers and geeks, and we’re now seeing folks building bots for all sorts of things. Interestingly enough, bots have been around for more than 50 years, since early Eliza demos, but the growth of messaging, the explosion of machine learning successes, the emergence of huge data sets, and the rise of mobile have created a perfect storm for Eliza to grow up. There’s a wide variety of ways bots may come to do our bidding:

Command line bots: For those of us who love Unix, these bots put a command shell in everything. In Slack, for example, bots have been built for fetching Digg (content), ordering an Uber (transportation), pulling data from Google Analytics (data), and more. Handy? Sure! Why flip over to Terminal when a chat app is already open. But sadly, history does seem to indicate that GUIs provide affordances that command lines generally lack, and that’s a meaningful barrier to adoption for the muggles. I suspect these bots will be as widespread as sed, awk, and grep among mainstream users.

Chatbots: Chatbots are essentially a two-way conversation with a computer — the Turing test in action. Chatbots have received a ton of attention, both positive and negative, largely due to strides in deep learning. In business, the vision is that chatbots enable a company to message one-on-one with customers without exploding human costs.

For example, one use case we’re seeing is companies using chatbots to scale customer support situations. Companies such as Msg.ai, Presence.ai, and PullString allow companies to train AI chatbots with the loving touch of an expert operator to understand the context of questions and converse on-brand. If the consumer stumps the bot, it will interface with a support system like Zendesk or Talkdesk. The conversation can then be passed off to a human when deeper intelligence is required.

Personal assistants: These are bots that can accomplish tasks for a person — answer questions and respond to commands — the promise of HAL-like intelligence. Thanks to machine learning and natural language processing and understanding, these bots know your preferences, integrate with everything,  and can act on your behalf — essentially replacing human work. In the consumer world, we’ve already seen some success with personal assistants through Amazon Alexa, Apple Siri, and Google Voice Search. This model is powerful, and represents a massive battleground. If consumers come to prefer this mode of expressing purchase intent, then the winner will be the next Google.

These products are capturing our imagination — and the demos are always killer. But after the novelty wears off, will the technology prove truly useful or a parlor trick? Continued advances in machine learning are just about guaranteed, and if we couple that with APIs everywhere to interconnect services, we could very well deliver on the sci-fi promise of intelligent assistants. The big question is, if the tech industry fulfills that vision, will consumers actually take to it.

Or will we revert back to old habits after one too many “I’m sorry Dave, I can’t get tickets to the Warriors game tomorrow.”

In short, bots are built to augment and scale human interactions. So what happens when these interactions occur in a native app?

3. Chat in apps

Chat in apps provide a conversational functionality embedded within apps. The promise here is that the communications you have from within an app are smarter and more valuable because they inherently contain the context of the information in the application you’re using. So if you’re in the Uber app and you call your driver, the driver already knows your name, where you are, and what you’re calling about (“I’m outside!”) — which is a huge improvement over calling a taxi dispatcher.

However, we use messaging differently when it’s embedded in an app than when we use dedicated messaging apps (SMS, Messenger). When chat exists in an app,  the only entity a consumer is chatting with in the app is the company. So it will feel less like the long-lived conversational messaging experience we’re used to in our personal messaging conversations. Instead, it will be more transactional: “I need new pillows (Hilton)”, “I’m looking for a camera (Best Buy)”, or “I’m at the corner of 2nd and Harrison (Uber)”. Because apps generally know who you are, the context here is killer — if you have the app installed.

Which brings us to apps in chat — the vision of conversational commerce that’s getting the most attention at this point …

4. Apps in chat

This is all about bringing application functionality into chat, as has been done in China with WeChat. In this model, chat is an identity model and discovery vehicle for connecting people and businesses, with a flexible in-chat interface that can be programmed like an app to enable a wide variety of use cases. It’s not the chat itself that’s powerful, but the time-ordered view of our interactions.

Coupled with discovery, apps in chat have largely replaced (or really, prevented) the predominant role of mobile apps in China as they exist in the West. This model has achieved near complete penetration of the market in China, which is astounding. Will the same thing happen in markets where there’s an entrenched app-store model? There may be some use cases, but I suspect it’s unwise to assume things will unfold worldwide the way they have in China.

5. People chatting

And the last category of messaging is just plain old humans conversing with each other. I believe this is the most important near-term opportunity because it’s truly the basis for human communication. At the end of the day, humans want to communicate with businesses the same way they communicate with other humans. This is what conversational commerce promises –the end of phone trees, account numbers, and security questions and the beginning of real, natural conversations, regardless of who you’re communicating with.

Conversational commerce is a thing, and it comprises these five trends in varying degrees. Which ones hold the truth will be determined by developers in coming years with ample trial and error. The biggest key, though, is the shift to humanizing our communications with businesses. These conversations may take place via a text message or voice, with a bot or a human, within a native app or within a dedicated messaging app — but at the end of the day, you’re still communicating with businesses like you’d communicate with anyone else. As the reality of “human afterall” becomes possible, companies that excel at conversation will drive higher customer satisfaction, engagement, and retention.

About the Author

Jeff Lawson is CEO and cofounder of Twilio.

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