There are many mysteries humanity is still not close to solving. But why should the sentiments of your customers and employees be one of them? Especially today when you have AI. In fact, it should be just be tap away.

A lot of ink and money has been poured into the cauldron of the Big Data. It happened only a few years back. And the excitement over Big Data technologies was completely understandable – enterprises had stumbled upon something that could bring them closer to insights and better decisions. But soon, we started realizing that Big Data can never be adequate to accomplish some elusive and ideal business outcomes.

That’s when enterprises woke up to the facet of ‘invisible data’. Yes, data that is around us, all the time. Yet, it is data that escapes our eyes, ears, and attention. Sometimes because we are careless or oblivious. But most of the time because we do not have the right tools at our disposal.

Identifying and analyzing sentiments belong to that dark side of the moon. They are orbiting around business stakeholders and processes all through the day and yet, they get skipped or simply slip away. Artificial Intelligence (AI) fixes that gap. And in a near fail-proof way.

AI for effective sentiment analysis

AI powers many different tools that businesses can deploy to better understand data and use it effectively. Now, it is no longer impossible to decipher customer sentiments from the vast amount of data you gather. They can be understood, and understood well. This can be facilitated through different tools and methods that include:

  • Image analytics
  • Natural Language Processing
  • Cognitive Recognition
  • Machine Learning
  • Neural Networks and specific algorithms

The ways can be many. But what actually happens here is that AI is able to go past the shallow output of text or images; and drill into the actual insight that matters. It can extract emotions – raw and real, wherever they are hiding. It can understand sarcasm hidden behind positive words. It can grasp disappointment in a polite face.

When AI rubs its hands and seriously puts its mind to understanding sentiments, it can:

  1. Reach to honest opinions
  2. Unravel hidden feedback and new product needs
  3. Comprehend tone and nuances of what is said and seen
  4. Create contextualization where it can be powerful and can save time and effort


Where to apply AI-powered sentiment analytics

Sentiment Analytics has found the perfect ground to bloom with the advent of AI. Now, organizations can:

  1. Understand the moods and preferences – spoken and unspoken – of a prospective customer way before s/he arrives at the beginning of the funnel. It also helps to cross-sell and up-sell apart from the ability to sell with more precision and less intrusion. Case in point – Facial recognition tools for retail analytics developed by Staqo for several businesses.
  2. Anticipate complaints and gaps of existing customers to offer proactive action for corrections and conversations. This helps to transform customers into lifelong patrons and increases the overall lifecycle of profitability.
  3. Achieve accuracy in talent acquisition by using AI tools during video interviews and other processes during recruitment and selection.
  4. Gauge as to why, or why not, a certain employee is having a good day. HR and managers can use this feedback for constructive action that augments job satisfaction, productivity, engagement, and loyalty. It can also help to arrest potential conflicts and stress-triggers before they get out of control.
  5. Measure efficacy of training and development sessions in an agile and non-obvious way.
  6. Amplify vantage points during crucial negotiations and strategic discussions.
  7. Capture key recovery points as well as warning signs in healthcare – very crucial for silent and serious diseases/complications.
  8. Find an edge in the investment market.
  9. Manage reputation by studying public opinion, perception of brands, and injecting timely action.
  10. Get in control of social media action by using topic mining, finding patterns and trends etc.
  11. Improve customer support with sentient, adaptive and tireless chat-bots and features.
  12. Design new products and services with a deeper grip on the psychological and latent needs of people, thus, opening new market segments and revenue streams while also helping people find better products.

AI and Sentiment Analytics: The way ahead

This brilliant realm has just started exploring many possibilities and applications. There is so much that waits to be scratched and gained on. The sheer idea of getting towards Affective Computing or Emotional AI is staggeringly-beautiful!

Are we there yet? Not till we solve issues like data quality, ethical dilemmas, and deployment gaps. I was just reminded of some of these while reading the latest NASSCOM-EY survey, where inadequate training data came up as a major challenge as seen by 36% of the CXOs surveyed from January to March 2020. We cannot be spending so much time in planning during AI projects (80% of the time is spent on data preparation and 25% on labelling).

Having the right partners and strategy will help enterprises avoid the pitfalls of privacy intrusion, false positives, aberrations, discrimination, and latency as they pursue the benefits of AI in sentiment analytics. It is a power that has to be applied well, with the right strategy and preparation. It cannot be deployed in a reckless or immature manner. Else, it will create more disadvantages than advantages. Sentiments are great goldmines but they can also be minefields, if not navigated with caution, prudence and planning. I would definitely advise my peers to seek some experts and some apt tools when they embrace AI for sentiment analytics.

AI, when wielded, in the right way can help enterprises in making their customers, employees and stakeholders smile. And not in a cryptic way. But in a happy way. From eye to eye. Or shall I say, from AI to eye.