How To Go Beyond The Bot
The bots are here: How to satisfy user habits in the new service economy
From Sephora and Barbie, to Burger King, the Washington Post, Poncho and Microsoft, brands in varying verticals are building AI bots that can ‘meet’ and be of service to the billions of users on mobile messaging apps and social platforms.
What is driving the rise of the bot, and how can brands go beyond the bot to satisfy today’s consumer?
The Perfect Storm
There is no question that language and communication have evolved. Long-form content has given way to short form, the phone call to the text, SMS to MMS, and most recently, typed text to emojis and other pictorial moment to moment communications like Snapchat.
These simplifying shifts in on-demand communication coincide with the ‘uberfication’ of consumers that crave products, information, and services that can meet them in moments of want or need.
The growth of natural language processing and artificial intelligence driven by machine and deep learning neural networks couldn’t be better timed: It can automate the previously impossible task of meeting consumers in each and every single on-demand moment.
These on-demand moments increasingly take place on messaging and social platforms like WhatsApp, Kik, Slack, or Twitter because they have become the dominate way users consume content online. The rise of responsive site design has improved mobile browsing, but users increasingly experience the web via mobile social and messaging apps that give context to content from friends, celebrities, influencers, and brands.
With so many of these moments taking place on messaging platforms, brand marketers and tech startups alike are looking for automation and integration opportunities that provide frictionless consumer experiences and improve customer service. Cue the rise of the personalized messaging bot.
Simple Is Best, But People Are Complex
Domino’s made headlines last year with their Twitter order by emoji campaign that allowed anyone to order the real thing by simply tweeting a 🍕 @dominos.
The campaign’s success stems from the ubiquity and simplicity of the pizza emoji. The emoji is a universal part of a shared online language, and best of all, it is easy to automate ‘one-tap’ convenience when all a computer has to understand is if someone sends a pizza, a real one needs to be sent back.
The simpler the communication, the easier it is for a bot to understand, interpret, and anticipate what a consumer wants.
But what about more complex human-computer interactions?
The growing science at the intersection of AI and linguistics that enables software to understand human language is called Natural Language Processing, or NLP.
NLP is by no means new, but the rise of open source libraries like OpenNLP. NLTK, and LingPipe is powering a resurgence in tech as startups build products that are able to automate simple tasks.
An increasing number of open-source platforms are leveraging the power of the development community to build NLP integrations. Slack’s ecosystem, for example, has no shortage of API’s that empower amateur coders to create a bot and enjoy their “hello world” moment.
With today’s technology, computers, devices, and the internet of everything can come close to being automated.
Why visit a URL and comb through a user experience when you can ask Siri, command Google Now, or even shoot a fast text or Snap to a bot that automates your life?
Our machines are about to get smart(er).
Of Bots and Businesses
A bot is simply a software application that runs automated tasks, or scripts.
AIM users in the early 2000s will likely remember SmarterChild as their first interaction with a primitive chatbot. A message sent to SmarterChild triggered a human-like response back.
Whether SmarterChild ever passed a Turing Test, it is a precursor to today’s bot that doesn’t simply respond or provide content, but can be programmed to automate more complicated tasks.
Using evolved natural language processing that is programmed to understand behaviors, wants, and needs, today’s bot can evolve into an entirely new way to drive purchase and automate the internet of everything. In fact, we’re already seeing this evolution from Apple’s Siri, Google Now, and even Amazon’s Alexa.
These bots can and should inspire us to think about how we can use natural language processing to automate and connect businesses and technologies together to ultimately connect people together.
Ecosystems of Connection
Workplace platform Slack isn’t the only messaging service fostering a bot ecosystem: Kik’s just-launched bot store includes tools that allow any developer to create a bot to join those already in the store from brands in entertainment (Vine, FunnyOrDie), news (The Weather Channel), and even cosmetics (Sephora).
Like any great startup, Slack and Kik are exposing their APIs to allow entire businesses to form within their ecosystem — this creates platforms within platforms that only drive more value to their business.
The launch of Apple’s App Store, Amazon’s Web Services, and Facebook’s Open Graph provided APIs to engineers, developers, and problem solvers that created new products that continue to help us communicate, start businesses, and connect.
Understanding and Tapping Into Consumer Demand
I challenge marketers to ‘go beyond the bot’ by recognizing that the growth of natural language processing empowers us to not only improve and automate consumer experience, but to truly understand and predict demand.
By using machine-learning to analyze the millions of interactions between your consumer and automated digital software, you can analyze competitor and brand discourse, consumer psychology, and ascertain a deep and relevant profile of your consumer.
The persona will become a relic when IBM Watson or TensorFlow is given the chance to analyze these interactions and let you know in extreme detail what your consumer wants or needs, what their pain points are, what resonates with them, and just as importantly, what doesn’t. Try that on for a CRM approach and watch the numbers fly.
Every emotion is a trigger, and with the power of AI and NLP we can take customer experience to a whole new level informed by a deep understanding of our consumer.
The clues that will unlock better brand experiences are all around us, we just need to move past simply storing ‘big data’ and start applying it.
For example, I think it’s safe to assume that almost all customer service teams record their customer facing calls. Why not analyze these calls with natural language processing to extract value for product and marketing teams? With machine learning, hours and hours of conversation can be quickly sifted to reveal the gold that will change your brand.
So what are you waiting for? Put natural language processing to work to automate better experiences and unlock a deep understanding of your consumer.
Originally published at www.psfk.com on April 13, 2016.