AI in sentiment analytics has heralded a paradigm shift. It is a brilliant way to extract useful insights out of random and unstructured text, voice and image data. It brings in unprecedented cost advantages because it runs on its own, learns on its own, and saves precious human effort. But the very possibility of Artificial Intelligence (AI) delivering all those lovely outcomes in sentiment analytics, hinges closely on the way you design it, use it, maintain it, train it, and watch it.

If we look at a survey of 500 Indian CXOs on AI confidence, done by NASSCOM and EY, we can see that as much as 60% of these business leaders believed AI will disrupt their businesses within three years, yet only 25% of enterprises have deployed AI solutions. What was holding these otherwise-buoyant CXOs back? Note that 56% blamed it on low external ecosystem maturity (technology and service providers), 53% could not quantify benefits, and 40% of business leaders have issues in talent shortage for implementing and scaling AI solutions.

So just opting for AI will not complete the entire curve. You would have to make sure that you have the approach and partners to build it in a way that is relevant and useful for your specific problem-area of need. You will also have to gauge its maturity and its level of sophistication on some key parameters.

What we actually need is expertise and planning. While there are a plethora of tools in the market with more on the way, implementing those tools come with its own set of challenges around adaptability, scalability, and expertise requirements. For something as new and nascent as AI for sentiment analytics, it may make sense to look at a partner who have sunk their teeth, and legs, deep into the actual action areas here?

Benefits of choosing a partner over a sentiment analytics tool

The benefits of choosing a partner over a box are multi-pronged:

  1. You can make sure that you have the right training data – in terms of scale, context, availability, and quality
  2. You can align sentiment analytics with exact business and strategic goals by sketching a proper plan and blueprint
  3. You can design the AI algorithm for your unique needs and constraints
  4. You can take time in integrating it properly with existing systems and processes – because your partner will help you with on-ramping, with skills and with tools.
  5. You can have someone who can monitor, maintain, and rectify the models consistently
  6. Your partner can advise on the mistakes to avoid – like not training the model on a wide base and diverse range of data. This can help you arrest myopia and shocks from the model
  7. Your partner can offer recommendations based on the versatile spectrum of deployments their team has been undertaking in other enterprises. They know the space well, in and out, not just at the starting-point but till the actual ground work
  8. You can have a collaborative team that includes your IT resources and partner experts for taking the level of innovation and confidence several notches up
  9. You can rest easy about integration, ROI, skills and learning curve – with the right partner to support you

The idea is to create a tool that is going to help you with real-world business outcomes. It should be capable enough of adapting to unique scenarios and changes that are hard to predict but are still hard to ignore. You would need to have an approach where context and speed can be balanced in the most optimum way possible.

Factors to consider while building an AI-powered Sentiment Analytics tool

Just check for these signs and you would know if the AI tool you have built, or are building, is a wise bet or not.

  1. Does it help to cover all kinds of data that is relevant for sentiments – cold, warm, hot, unstructured, structured, and latent?
  2. Does it cover all possible sources of sentiment-related data – social media, facial gestures, customer feedback, purchase behavior, support cycle data, and brand-related conversations?
  3. Does it align well with the systems already in place or the ones you intend to bring in ahead – are integration dead-ends or roll-out challenges properly accounted for?
  4. Does it deliver insights that help the actual decision-maker? Sharing an insight about another product or segment may not be of any use for a marketing person who is busy working on some different product.
  5. Does it help to avoid costly mistakes and warn of red flags in advance?
  6. Is it predictive and prescriptive enough?
  7. Is it capable of cutting down costs that were otherwise spent in this area? Is its overall impact affordable, sustainable, and useful in the long run?
  8. Is it simple to administer?
  9. Is it getting good adoption from the end users or is there something about it – like complications, lack of familiarity or a different language – that is leading to resistance?
  10. Is it going to turn metrics and data into business outcomes?
  11. Is it capable enough to cut down training time, skill requirements, and data-migration hurdles?
  12. Would it be transparent and easy to configure, and correct if there is ever a need?
  13. Is it going to be cognizant of and responsible about key AI concerns like bias, privacy of user data, compliance needs, unwarranted commercialization of data, and unethical exploitation?

If you can work with smart and experienced partners like us you would be able to accomplish all these benchmarks and with impressive simplicity and speed. Do not be ignorant of the challenges around sentiment analytics with AI. Be prepared and go forth with the right tools and approach. Be emotional. Be excited. After all, it is a space about sentiments.

Learn how we helped a flagship retail brand drive efficiency with AI-powered Store Image Analytics.