The modern information economy is a complex interconnected system of brand and consumer interactions. Social media forms the heart of this ecosystem, where consumers constantly share organic opinions and feedback on brands, products, and experiences across a dizzying array of online channels. In a way, this makes social media the world's largest and most diverse focus group, providing organic unstructured data that has immense value for brands – but only if they can effectively listen to what is being said. Effective social listening helps brands identify and capitalize on feedback - opening up new dimensions for innovation, competitive differentiation, and consumer interaction in real time.

Why is social listening so difficult? Consider that there are roughly 6,000 Tweets and 293,000 Facebook updates posted every minute. This amounts to roughly 1.145 trillion megabytes of social media data generated daily. Conventional methods of social listening struggle to keep up with this flood of data and are further bottlenecked by the need for human review. Luckily, a new generation of AI-powered social listening tools is emerging as a more capable alternative in this area. These AI-powered tools can analyze huge amounts of data from multiple online channels in real time, aiding leaders in making crucial decisions with up-to-the-minute information. The value of real-time insights is often linked to how current they are, and this makes AI-powered social listening a far superior option for businesses. As industries across sectors are growing more data-oriented than ever before, AI systems can not only execute everyday social listening, but also distill insights such as sentiment analysis from the incoming petabytes of information and make recommendations for the business - from instantly identifying key trends to flagging developing areas of concern.

The Capabilities of AI-based Social Listening

While automated tooIs have long been a part of online reputation and data management, AI-based social listening unlocks a new dimension of management for businesses. As noted above, the volume of online conversations to be monitored is staggering. For large public brands, effectively monitoring these even with automated parameters would be next to impossible without the help of AI. With the right training and development, a scalable AI program is able to learn constantly from past actions to analyze as well as respond to the new data being generated every minute. A successful example of this is Microsoft’s Social Intelligence Practice (SIP) team, which uses Sprinklr’s Social Listening solution to pull in more than 8 billion mentions annually from diverse online sources, including product reviews, and feeds those insights back to marketing, development, product and research teams. The SIP team has developed research methodologies that look beyond brand mentions, volume, and sentiment — expanding into industry landscape and broad conceptual context of topics, taking into account social behavior and language. This multidimensional view sets the stage for deep learning and insights that are critical in the decision-making process of teams across the organization. This includes emerging products, where the SIP team listens to the real-time voice of customers and shares insights with product marketing and product engineering teams to build, create, or modify those products based on customer need.  

Translating AI-systems into Business Impact

We’ve touched on what AI is capable of, but what are the results businesses can expect? Some of the results that businesses are already seeing today are better analysis of their consumer sentiment and corporate reputation; as well as spotting opportunities where they (and competitors) fall short. Given the memory and training of the AI tools, these insights are also created in light of historical performance and results - letting businesses identify any major deviations on a macro scale.

We saw a promising display of this capability when a leading travel website used AI-powered moderation and response tools on their social media channels online. These tools automatically analysed comments for sentiment, routed and personalised issue resolution, and extracted contextual insights at scale.  Similarly, a leading conglomerate in the American food sector successfully used an AI-powered platform to unify the management of 40+ social media accounts across brands, as it learned from previous posts to identify and maintain the individual brand tonality across platforms.

On the consumer front, AI social listening also tremendously improves response time to consumer complaints online - by up to 70%, by either highlighting negative conversations or automatically responding to them. These alerts can often prevent crises before they happen - preserving corporate reputation on social media. Each recognised threat can then be further analyzed and turned into insights to resolve the situation with data-backed solutions.

The Conditions For Effective Performance of Social Listening Systems

While new AI capabilities are immensely useful, they do require a significant startup investment. Most AI platforms require a large amount of historic data to be trained on before they are ready to deploy, with the involvement of an AI or Machine Learning professional to refine the process. Additionally, the development of a customized AI tool for a business may also require a significant upgrade to existing tech infrastructure for system compatibility. Finally, the current level of reliability for AI in business is directly proportionate to the quality of data it is trained on - while AI systems are excellent at pattern recognition and analysis, inputs of flawed or poor quality training data can reduce the effectiveness of the software as a whole.

Fortunately, AI tools are constantly self-improving by design, and can turn data inputs into business value at a scale never seen before. Businesses that are able to effectively adopt AI systems for social listening keeping in mind the above parameters can be assured of guaranteed value from them. Through the analysis of insights in real time, AI has the potential to improve business and marketing strategies by a significant amount, offering specialized solutions to each business and unlocking room for more innovation in these fields.

The potential for AI to revolutionize social listening lies in its ability to continually evolve and adapt to the ever-changing digital landscape. Existing AI technologies have already raised the bar, and are expected to become the new standard for high performing businesses. Brands can expect tools that offer not only deeper insights on consumer behavior, but also personalised engagement suggestions per customer and market based on historic data. One of the leading areas of improvement at present is training AI to better recognise image, audio, and video posts, which can improve its utility on social media platforms like Instagram and YouTube. Finally, as Natural Language Processing (NLP) capabilities of AI improve, properly trained tools will be able to create customized responses and strategies automatically to suggest the best way to deal with crisis situations while preserving the brand identity. Thus, the marriage of AI with social listening is not only something that will help businesses keep pace in the evolving data economy, but to proactively shape its future.

Disclaimer: The views expressed here are solely those of the author and do not in any way represent the views of pitchonnet.com.