Getting One Step Closer to Decoding Your Customers
Decoding customers and improving customer engagement is getting a huge boost through the Bot evolution. Bots are leading to be one of the major industry trends with large scale implications in the next five years. Leading the charge are popular bots like Siri, Cortana, Alexa as well as the multitude of Facebook and messenger bots currently in the market. And who can forget the self-driving cars!
The question that comes to mind with this rapid evolution we have seen in the last couple of years is – what’s a ChatBot, how can we prove value, how scalable it is and what’s unique between one bot to other….
To start off, a bot is a piece of code that interacts with humans in a human-like manner. Chatbot refers to an application that stimulates this human-like conversation between customers and machines. They are coded using the business logic provided and self learns through artificial intelligence. Imagine your own army of personal robots integrated in your day to day life interacting with you in new and different ways!
Chatbots have been one of the most popular segments with interaction via text or voice with multiple applications in social media platforms, customer service, gaming, etc. At a high-level, bot interaction can be traced to be of three types – first being deep on the natural language or conversational bot, second being the business process bot, and lastly logical bot. The key difference is the mystery ingredient – the level of rules and programming we are inputting based on the business rules differentiates the bots. Also, dependent on this is the budget and level of effort, and the data science rigor behind developing the solution.
Reason data scientists are loving bots is the sheer size of data that you are getting to improve your models. The extremely rich customer data showcasing their preferences, behavior patterns, and anomalies is unparalleled. With the development and deployment of bot solution, data scientists are leveraging machine learning techniques to incorporate a self-learning capability to incorporate new insights as well as new connections for customer behavior. Marketers can then leverage this data to understand what triggers will be most effective for customer engagement.
One big dimension to the insights is the emotional part of customer engagement. We are not just looking at customer behavior in terms of clicks or orders but also natural language and extracting the emotional context. This will be a new way of defining customer value as well as triggers for more refined customer analysis. All this information can be leveraged in framing the customer DNA.
Applications of chatbots are immense, with one spectrum to automate the repeatable tasks, create common sets of rules that are standardized, personalized assistants, etc. Some are high on programming the business logic and some are deep in machine learning to understand and respond to customer intent.
As we as data scientists and solution providers work to derive high impact and value from Bot solutions, we need to play the crawl-walk and run philosophy. As Bots are just building blocks based on logic we are providing and integrated by the data examples so richer the data better the bots will be. Expecting to have a full proof solution will be a fallacy as well as using thousands of dollars for a world-class AI for solution which can never be perfected. Instead of using crawl-walk-run philosophy we can identify and scope the Bot, prototype solution, and improvise. Iteration is the key to the goal of achieving perfection!