Chatbot solutions: Getting one step closer to decoding your customers

Chatbot solutions: Getting one step closer to decoding your customers

Bot evolution has made it easier for businesses to decode customer behaviors and improve their overall engagement. Bots have found a place in a variety of industries. You have Siri, Cortana, and Alexa on our phones, Smart TVs, and other applications. Bots also have a place in the user experience on websites for customer support and on social media like Discord to create another level of functionality.

So, what is a bot? And how can they be used to provide value to customers, as well as the business?

What is a bot?

To start off, a bot is a piece of code that interacts with humans in a human-like manner. A “chatbot” refers to an application that simulates this human-like conversation between customers and machines. Bots are coded using the organization’s provided business logic and can self learn through artificial intelligence.

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 map to three types:

  1. Natural language or conversational bot
  2. Business process bot
  3. Logical bot

The key difference is the mystery ingredient – the level of rules and programming we are inputting based on the business rules are what differentiate the bots. The differences will also depend on the budget, level of effort, and the data science rigor behind developing the solution.

Insights into customer behaviors

One benefit of using bots is the sheer amount of data that you can obtain to improve your models. Interactions with the bot provide businesses with data – extremely rich customer data that showcases their preferences, behavior patterns, and anomalies.

With the development and deployment of a 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. Marketing teams can leverage this data to understand what triggers will be most effective for customer engagement, product managers can identify new pain points, and so on.

Another key dimension of these insights is the emotional part of customer engagement. When leveraging bots, we are not just looking at customer behavior in terms of clicks or orders, but also natural language and extracting the emotional context. This provides an additional 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, enabling businesses to further personalize and improve the customer experience.

The applications of chatbots are immense, from automating repeatable tasks to free up employees to developing personalized assistants, there are many ways to incorporate bot solutions into the customer experience. Some chatbots rank high on programming business logic, while others go deep with machine learning – yet both can help better understand and respond to customer intent.

Iterating and improving chatbot solutions

As data scientists and solution providers work to derive high impact and value from bot solutions, we need to play by the crawl-walk-run philosophy.

Put simply, bots are just building blocks based on logic we provide and integrate by leveraging data. The richer the data, the better the bots will be. Expecting to have a foolproof solution straight out of the gate will be a fallacy, as well as a waste of thousands of dollars for an AI solution that lacks the proper data or business logic to grow with your business.

Instead, by using the crawl-walk-run philosophy, we can identify and scope the bot, prototype the solution that best achieves the business objectives, and iterate.  Iteration is the key to the goal of achieving (near)perfection!

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This blog has been updated. Originally published 2/15/2017.