For good sentiment analysis, you need data and tons of it. What better way to gather this data than through live-chatting up your customers and what better means than chatbots.
Anybody into data analytics will want to tell you that social media has made the gathering of data relatively easy. However, the data you gather from this source can be misleading if you want to use it for sentiment analysis.
Compared to decades ago when the only ways to gather insight on what customers wanted and how they felt about a brand were by knocking on their front door and asking, cold-calling them on their wall phone or sending them a survey with a self-addressed stamped envelope, the social media has brought about a revolution and deserves accolades for that.
From any angle, you look at it, the job of gathering data before the advent of social media was labor intensive but the methods employed yielded accurate information. We can’t possibly go back to those days taking into consideration, the global population and technological advancements we can reap from.
Taking social media monitoring (SMM) into account, it’s easy for you to conclude that today’s ready access to customer sentiment would mean that the resulting customer data analytics are more accurate. After all, you are getting your data from very reliable sources such as Twitter and Facebook.
The first bottleneck of sentiment analysis culled from SMM is the overriding belief that chat stats are the same as survey results. This is definitely not the case.
Curating brand sentiment data from social media is relatively easy, but a number of times you can only succeed in making the most casual assumptions about the demographics of the chatting customers. Without demographic data, it’s almost practically impossible for your brand to convert customer sentiment into actionable insight.
Chatbots to the rescue!
According to Larry Kim of Mobile Monkey in an interview granted to Nature Torch, “Chatbots are going to be an amazing source of business information for companies. For example, you’ll be able to analyze exactly what people are asking for, what their issues are, etc. It’s a bit like how websites transformed businesses over 2 decades ago, and how an entire discipline of marketing evolved to support that infrastructure.”
Thanks to advancements in technology. We now have not just ordinary Chatbots but also AI Chatbots.
This innovation did not only give us the long-awaited opportunity to chat with customers at scale but to also do it in real-time. A chatbot is a two-dimensional conversation-catcher.
It’s able to either address customers pain points or re-direct them to a human assistant. Also, the ability to record all the interactions creates a repository of real data that can be used for accurate sentiment analysis.
Coupled with natural language processing (NLP) this way of collecting data can help us in not just learning more about how people communicate but also do the sentiment analysis of what they say. By looking at the words they use and analyzing their demographics, it is easy to detect trends and to personalize messages.
Brands that engage chatbots can use the new insights gained from sentiment analysis to better relate with customers and also greatly improve on their ROI. Going by the fact that Big Data has been defined by the 3Vs: volume, velocity, and variety, by looking at the conversation logs of chatbots we can fairly conclude that the definition is appropriate.
In terms of volume, chatbots can communicate at scale. The velocity of the information is also awesome, while customers who engage in a conversation with the Chatbots come with variety, all the time seeking for different pieces of information and doing so in entirely different ways.
Carrying out the sentiment analysis of some recurring issues collated from live-chatting with Chatbots puts brands in good stead to determining which products are not clearly understood by customers or constitute their central points of attraction and hence re-channel their marketing efforts and resources accordingly.
This will go a long way to help your brand to figure out which products to market differently, which to market aggressively, and which to redevelop for a relaunch.
Retrieving and organizing data is just the first step in leveraging benefits. The actual value of Big Data comes from the in-depth sentiment analysis. In the case of chatbots, this can be viewed as creating dedicated Business Intelligence tools.
The core algorithm of chatbots border on pattern recognition. When we speak, our word choices and patterns of self-expression depict who we are.
Our speech and text go a long way to affirm our composure, engagement, intent, forthrightness, and other important but concealed personality traits; on the other hand, they can also reveal our state of uncertainty, self-doubt or boredom.
AI chatbots can also recognize groups of words in context at scale and give meaning to an individual feeling by analyzing the way a customer talks. This is the basis for sentiment analysis and helps brands to retain customers or employees (in the case where the chatbots are used for internal purposes) by identifying traces of frustration at the on stage and nipping them on the bud.
With 24/7 services of chatbots, you are assured of real-time and accurate sentiment analysis of your customers which ups your competitive advantage and translates to a better ROI.
