Do you know what the ‘problem of generic flow’ is?
It’s a solution that serves a generalised objective. It’s the average of group behaviour. It’s guesswork. And, more often than not, it is theoretically perfect but practically lacking.
When the shoe doesn’t fit, that guesswork leaves you a customer short.
Marketers are more actively trying to target the smallest niche possible, to curate a message that directly speaks to a customer. AI personalisation proposes to solve problems for an individual instead of a group.
But, while AI, chatbots, and predictive machine learning are collectively capable of understanding consumer behaviour, striking and maintaining a conversation, and suggesting personalised recommendations, is the industry proficient enough to harness this opportunity?
What Proactive Marketers Have to Say about AI-Driven Personalisation?
Jake Bennett, CEO, POP, believes that there is a silver lining here.
As per Bennett, utilising AI-driven personalisation is a challenge for most companies right now. Whoever breaks into the tech first will get a competitive advantage over others.
Plus, you don’t need expert status to extract profitable advantage with AI-powered personalisation. With cloud providers like Amazon, Google, and Microsoft investing heavily in machine learning, the time is ripe for testing the waters.
Super Bots, Personalisation, & AI Content and Flow
Meet LegDay. This gym chatbot makes it easier for customers to interact with a business. It can be employed to make appointments, book and pay for services, communicate announcements and streamline operations. It uses landline texting. No downloads needed.
While LegDay presents a reasonable example of what the current chatbot technology is capable of, it still involves some homogenization. Chatfuel and Botanalytics are a few steps ahead in this case.
For a bot to be great, it needs more than one or two flows. The multiple flows are all supposed to be tailored per individual, giving the bot enough options to offer when a customer initiates interaction. It needs a context for every consumer, to leverage user inputs, to filter and push heavily personalised content to the user. It needs to offer a scalable experience that caters to its target’s needs as acutely as possible.
It’s not merely about the message that a user is most likely to respond to, says İlker Köksal, CEO, Botanalytics. It’s also about the right time to deliver the message, Köksal stresses.
AI Personalisation Using Chatbots- Where Is This Headed?
In 2015, Pinterest introduced AI-powered visual search. It used deep learning. You could zoom in, find a pattern, colour and view other pins with these similar components.
Sunglass, the eyewear retailer, created its interactive product curation tool using machine learning. The device matches objects from the inventory based on how the search vectors look and not by how they are described.
CamFind, a mobile app, runs on an AI-enabled image recognition API from CloudSight. Users are allowed to take a picture using the app and get a detailed description of the object, including product recommendations.
Pizza Hut’s Facebook Messenger Bot can accept orders. H&M, the clothing service, lets you buy products directly via the conversation chatbot. The seamless integration of Amazon’s Alexa in the world of conversational e-commerce is no news.
All these examples, all from some of the leading organisations in their respective industries, speaks volumes about the future of AI-driven personalisation that we are headed to. If you consider the improved language, increased access to vast data repositories, and technologies that help in creating more efficient, accurate, and faster responses, you’ll discover the prospects that the combined principles of AI personalisation & chatbots bring to the table.
