Why bots were D.O.A but messaging is still thriving.

(Get this article in a podcast here) In early 2016 Facebook released an API for Messenger and introduced the age of bots. Quickly it became obvious to everyone that bots were the next huge trend, startups got funded, Medium posts were written and the space was officially hot. I’d been doing messaging for about 8 years at the time and the it wasn’t clear to me that bots would work. Other people that had experience with messaging didn’t jump on the bot bandwagon either.

It’s safe to say, 3 years later, that the bot era hasn’t happened. I don’t think it’s coming soon. This podcast digs into why bots didn’t make it between 2016–2019, and why messaging is still thriving. The reasoning starts with the boring basics, but gets more advanced. So please stick through obvious stuff.

The first big problem with bots was the hype. You could see it from a mile away. Every tech publication featured articles titled, The Bots are Coming with images showing 1980’s robot toys explaining how every interaction that we were having on the web would now take place with a bot.

Oh you want to buy shoes? Soon you’ll just message into Zappos with your shoe size and their AI will automatically pick out the best shoes for you. Then you can just press a single button and complete the purchase. They hype was outrageous. These interactions would play out everywhere, from doctors offices to ordering pizza.

It was clear that tech was thirsty for the next big thing and the public oversold in the process. Of course Facebook and the promotion around the Messenger Platform launch played the biggest part. But hey, it was early 2016 and Facebook was on a winning streak, riding high. It was still before the other (Russian) bots came.

Which brings me to the second reason that the bot-craze crashed and burned — the word “bots”. I couldn’t think of a name with more baggage hanging on it. Russian bots ruined the American election. Twitter bots are spreading fake news. And both social networks are now cracking down on bots on their platform.

There are also the bots coming for your job (a lot of headlines when I was googling for this post). The robots are coming for your job and if they don’t get you, AI bots surely will. Until Facebook coopted the word, “bots” were just spam accounts on social media.

Did we mention AI? A few paragraphs back I talked about sending a message in to a webstore and that store knows exactly the right product for you. How will that happen? AI. How about when I message in to reschedule my delivery. How will the system understand my request? AI.

The tech press projected bots as the coupling of messaging with AI. The problem is that AI doesn’t exist. For some reason when the interface changed to messaging, AI came closer to possible. But this doesn’t make sense. You’ll know when it’s possible to produce the 1 correct answer to any query because google search results will just include one link.

Positioning bots as AI, spreading this through the hype cycle and at the same time conflating messaging interactions with Russian troll farms attacking American Democracy caused a little cynicism and backlash. But none of these reasons are why bots were DOA and messaging is still a strong and growing channel.

From the descriptions above, a simple explanation of a bot is that a user can text in, there will be some logic to understand what user is saying and the bot will respond back with the correct answer. This type of interaction lends itself to customer service or the actual usage of a product (what I call customer operations). Specifically this interaction is started by the user, it’s not meant to be driven by the organization. Because these bot conversations are directed by the user they aren’t marketing.

I want to quickly distinguish another approach that I call messaging as a marketing channel. The idea of using messaging for marketing means building an opt in list and then sending outgoing messages to this list. This is exactly how email marketing works. From all my experience, this is where messaging makes the most sense. This list building and activation approach is not what people are thinking when they use the word bot.

A simple way to distinguish the two approaches, when a user directs the conversation it’s a bot and when the organization is driving the conversation, it’s a marketing use case.

With this context for bots, here are the specific problems that kept bots from taking off:

It’s extremely complicated to let the user drive a conversation, understand understand that user and respond appropriately. For the user to find value the scope of conversation topics must be wide enough to address anything the user might ask. It’s just an incredibly hard problem to be able to parse and respond to so many potential inquiries.
Compounding this core problem described above is that the bot must be correct. Even a 5% error rate will be noticed. On messaging channels, it’s not particularly easy to help the user when there’s a problem. Sending the user a menu/list is a horrible UX (and contradicts the reason to use a bot) and starting the conversation over would super frustrating.
Focusing on customer service or customer operations (like calling an Uber) use cases are not easy places to build & test MVPs. These use cases can require deep integrations, and the stakes are high in these interactions. A frustrated customer failing to get an answer from the customer service bot is a much worse situation than a marketing lead not converting. Marketing departments are constantly testing and failing ideas — customer service, not so much
Which leads to the final point, customer service (and potentially customer operations) aren’t revenue generating divisions A hit strategy in marketing can drive growth. So marketing departments are looking to innovate, have test budget and will grow the channel quickly when something works. An amazing customer service bot might reduce cost, but it won’t significantly increase growth.
In summary, bots were a new interaction that tackled incredibly hard problems in high stakes company functions with little room for errors. Bot makers were pitching to teams that aren’t built for innovation and even if things work amazingly well, the upside is limited. That’s why bots were DOA.

This was a hard article to write. If you’ve made it this far, listening to the podcast might explain some of the ideas from a different angle. I hope you’ll subscribe on itunes.

Finally, I think that AI and customer service automation will start working soon. I just don’t think it will happen on messaging first. It’s more likely that call centre audio + AI is the place where humans will first start to interact with bots, if we aren’t doing that already.

The Superpower of Messaging = Response

The super power of messaging is response. With a messaging campaign it’s possible to ask people to respond, they’ll do it and the response can be valuable. I don’t know a better way to say it.

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When designing a campaign, most organizations would frame the question as What do we want to tell our list? Better results can be had if the question is instead, What’s the most valuable response someone can reply with?

Let’s take an obvious example, NPS score. We’ve already decided that the user telling us a number 1-10 is what we want. How is messaging superior for this response interaction? Compare the user experience when we do NPS via email. The user opens an email, reads paragraphs, clicks a link, chooses their NPS number and then submits the form. Now compare that to receiving a text and replying with one or two digits. What do you think gets a better response rate?

Now think about the cost and effort for an organization to build each respective NPS collection flow. With the email to webform route, most organizations would write an email that’s too long or includes more than just the NPS score ask. This drives down the response rate. Then a webpage and webform is needed. The organization will also need to make sure the page looks good on mobile where most people will interact.

Messaging is much simpler. The message and response is the email, page and webform all rolled into one interaction – simple and elegant.

This NPS idea is obvious, but it’s not my favorite use case. There is a clear and more valuable example – direct response from media. Suppose an organization buys a TV commercial or a podcast/radio ad and we wants people to sign up for their service. In the commercial the call to action will tell people to go to the URL where the webpage will collect information and turn the visitor into a lead.
Let’s examine this flow

The first issue is that the user might not have their computer next to them. If the target visits the webpage on their phone, they are 50%-70% less likely to convert. Seriously!?!? If the desktop conversion rate is 15% the mobile conversion rate will be between 5%-7.%

Pro tip: Many organizations don’t even track their desktop conversion rate vs. the mobile conversion rate. As more (most) traffic is mobile, this is definitely something that organizations should take a look at.

Even when an organization does a great job converting page visitors, no one is converting the majority of visitors. But people came to the page because they were somewhat interested. Now they are gone. The only way to get them back is to buy another ad and hope they see it again.

Messaging addresses both of the above issues and more. When the organization includes a text call to action, such as Text SIGNUP to 12345, more people will text in than will visit the URL. The value of messaging and response in this scenario is to ask the person to reply with their email address. Convert the user in a conversation rather than driving them to a page. When someone text in and is asked for their email address the average response rate is 80%.

Pro tip: Text messaging is the way to create mobile “visitors”.

There’s more. As soon as the target texts in, they are subscribed to the mobile opt-in list. If they don’t provide their email immediately the organization can reach out and ask again.

This idea of getting more people to start a conversion funnel and provide data like email is a core use case for the messaging channel and we’ll be discussing this in much more detail. We went a little deeper than response being the messaging super power, but that’s ok. This is important.

If you can think about messaging as a response channel, you’re thinking about messaging at a deeper level than your competition. Most people just think about messaging reach and open rates. Although reach and read are outstanding metrics they miss the point. Messaging isn’t a great channel for impressions, there are cheaper and better ways to get eyeballs. There is no cheaper or better way to get response, engagement or conversion – especially on mobile.

Ep 50: Does Size Matter? Longcodes vs Shortcodes in Text Messaging Campaigns

For organizations doing SMS, the default approach should be using a short code. The entire short code ecosystem was created to cater to organization text messaging rather than individuals. Short codes have specific benefits around messaging throughput, deliverability and stability of the channel. If your organization is using a short code, you can be reasonably certain that the short code will work. The same cannot be said of long codes – they are less reliable.

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If you’re a brand, non-profit or business, use a long code unless you have a specific reason not to.

The downside of dealing with short codes is all of the process around them. Whereas long codes are cheap and can be setup in minutes, a short code is costly, requires an application and can take a long time to be set up. In the US, all short codes are acquired from the Short Code Registry. https://usshortcodes.com/ Even if your vendor/partner is providing the short code, at some point they are dealing with the registry.

Once a shortcode is leased from the short code registry, it needs to be provisioned. This process connects the shortcode to the carrier networks. Provisioning is done by aggregators, which are the hosting companies in the SMS space. Getting the shortcode provisioned takes a lot longer than one would like, 8-12 weeks. In the application it’s possible to specify what organization is leasing the code and there can also be a different organization that manages the code. So a vendor should be able to easily help with the application. The applicant also shares some information as to the type of messaging campaigns they plan to run. Pro Tip: Messaging can be a dynamic channel so it’s best to share very broad use cases on the application.

Pro Tip: Most organizations will have a vendor involved in their messaging programs. Have the vendor help procure the short code.

Expected Cost and Timeline: Short codes cost about a thousand dollars per month, but can vary depending on a few variables. Once the application is submitted an organization can expect 8-12 weeks before the application is approved and the short code can launch. In an annoying detail, an organization needs to license the short code before submitting the application. So essentially there is a $3,000 setup cost and a 3 month timeline before a short code can launch.

If this sounds like a dealbreaker, don’t worry. Soon we’re going to explore more options regarding short codes.