While there holds no predefined definition for Artificial Intelligence, AI is commonly considered as the technique based on which machines or products are made to perform intellectual tasks like the human brain does, from pondering upon various aspects while acquiring knowledge on same, to evaluating and solving problems, using reason in conditions of uncertainty.
Such a simulation of human intelligence by machines has had a widespread influence on fields of science, mathematics, linguistics, economics and psychology just to name a few.
The initial operation of AI dates to 1914, in the form of Autopilot in Flights, which tremendously contributed to lessening human effort, while concurrently enhancing flight performance and reducing the occurrence of collisions.
Since, AI has had an extensive sway in our quotidian lives, by means of fraud prevention in mobile apps, phishing and spam detection techniques, to the use of machine learning to comprehend emojis on social networking platforms.
Businesses are, on their part, benefiting most from the advent of Artificial Intelligence. With virtual assistant programs, being in a position to deliver real-time support to customers, to Machine Learning algorithms, easing up recurrent tasks, by routing automated service requests, it is no secret that work is being categorized and monitored in a much efficient way.
Likewise, the replacement of labour-intensive and mundane tasks by industrial robots has certainly enhanced production efficiency. AI, in the form of anomaly detection techniques, has facilitated the prevention of outrages and business disruptions.
In particular, ML algorithms are being involved in scrutinizing and analyzing patterns of online behaviour, which serves of the great purpose to serve tailored products and comprehend demand and supply responses. Two of the most common forms of AI techniques implied in predicting social behaviours are Social Listening and Sentiment Analysis.
Social Listening
Social listening is a two-way process, the first of which refers to the monitoring of businesses and brands’ social media outlets, for customer reactions and/or direct mentions to the brands. Additionally, this technique also helps evaluate debates relating to specific business-related, competitors-linked or industry-associated topics.
The second phase pertaining to social listening is to implement and act upon the decision-making process in the light of responding to customer feedback.
This emerging AI technique allows businesses to benefit in numerous ways, from comprehending the needs of the regular customers, to constantly innovate products with new ideas, based on industry trends and market responses.
Social Listening has also proved to ease up the relationship between businesses and their customers, since it enables direct interaction between the two parties, henceforth enabling an improvement inexperience, while simultaneously allowing for persistent customer strategy shifts to fit the current need.
Aside from the above mentioned, social listening has highly altered businesses’ mode of operation, in a commended way. A key aspect, notably reputation management, which was years ago difficult to kept track of, is now easily assessed.
In turn, reputation crises are easily mitigated while the now-tracked brand health, helps indicate the direction in which businesses are heading towards. More importantly, the brand health metrics of competitors have also kept a clear eye on, the exposed weaknesses of and threats to which, call for rapid actionable capitalization on the part of businesses.
Lead generation, Influencer tracking and inbound marketing, through the process of social listening, extremely enable companies to flourish in terms of both efficiency and productivity. In a similar vein, sentiment analysis is yet another method which works not only towards monitoring the brand but understanding the social sentiment which lies within.
Sentiment Analysis
Sentiment Analysis relates to the appropriate quarrying of text, in the light of recognizing and pooling out information in the source material, so as to assist businesses in comprehending the social sentiment of their brand, merchandise or service. The concept of sentiment analysis is also heavily beneficial in scrutinizing online conversations, as a means to grasp human behaviour.
Advancement in deep learning has rendered algorithms to progressively analyze text, which resultantly allows for classification of customer conversation into two categories, the first of which evaluates the key factors to the brand, that the customer’s concerns themselves with. The second attribute pertains to the customers’ underlying sentiments and intents towards those specific factors.
Sub-categorized into three major aspects, from fine-grained sentiment analysis which distinguishes between good, bad and neutral reactions, to emotion detection which assesses underlying sentiments based on emojis, to aspect-based sentiment analysis, this type of AI process, simplifies customer reactions and boosts business to client relationships.
Other than getting meaningful insights from social media, sentiment analysis streamlines real-time assessment and propels flexible actions to satisfy the ever-altering customer demands. Likewise, this process promotes scalability and helps businesses situate their positions, vis a vis their competitors and in the industry.
Similar to social listening, competition is also highly monitored, and a detailed assessment of competitive products and brands can be made. Sentiment analysis is also important in empowering businesses’ internal team in better promoting their products, altering their means of advertising and fluently tracking trends over time, to which they ought to respond promptly to shifts and signals.
Conclusion
While social listening centres upon brand monitoring and subsequent actionable plans, sentiment analysis relates better to an in-depth evaluation of customers’ reactions and needs. Artificial Intelligence has been on the rise, in promoting and enhancing companies’ way of operating over the years.
Different techniques, ranging from automation to data analytics to natural language processing have all contributed to the development of businesses into cost-effective and profitable empires.
The advent of social listening and sentiment analysis likewise is deemed of much essence however in flourishing these very businesses, as these direct their attention primarily to monitoring consumer behaviour.
Beyond the shadow of a doubt, customer engagement is key to the success of any business and social listening and sentiment analysis facilitate this process.
