Businesses today are only in the early stages of realising the cognitive computing potential for marketing and branding purposes. The implications for increased accuracy, efficiency and ultimately customer satisfaction ensure that 'self-learning' data technology will be a true game-changer across the branding spectrum.
Having promised so much for decades, artificial intelligence (AI) has in many ways only begun to show its worth in recent times. In 2015, IBM Research called these “landmark years” for AI in terms of its impact on marketing, business and society in general.
Cognitive computing represents one way in which artificial intelligence can make a practical and applicable difference to business branding. Cognitive computing allows a company to extract patterns and insights from huge volumes of data that will allow insights into, for example, consumer sentiment or popular topics. Companies can therefore engage with an audience and adapt their messages like never before.
What is cognitive computing?
Cognitive computing is the computerised simulation of human thought, analysis and understanding. This technology is self-learning and utilises algorithms designed to replicate the way the human brain works.
For systems such as IBM’s Watson, the more information it processes, the more accurate it becomes, developing ‘neural networks’ that can interpret and assess large amounts of data as a human would. The key processes for cognitive computing systems are data mining, pattern recognition and natural language processing.
So how can brands take advantage of cognitive computing?
Cognitive computing has the potential to greatly enhance how brands use a sentiment analysis – the analysis of blogs, social media, discussion forums, comment boards and so on for positive or negative opinions about a certain product or topic.
The accuracy of automatic sentiment analysis up until now is debatable, and it is clear that human intervention is still necessary to interpret the subtleties and nuances of language, which vary according to demographic, location and culture.
But with a system that is self-learning as it consumes data, these manual requirements can be taken out of sentiment analysis, possibly allowing companies to engage with consumers in a meaningful, even personalised way on a grand automated scale. This could be applied to social media, email or even the phone.
The personalisation that can result from cognitive computing can add additional dimensions to a company’s marketing and branding. If a company has access to a potential customer’s information (preferences, location, etc.) as well as real-time details of stock and pricing, special offers and discounts can be tailored to the individual. This relies on the speed of cognitive analysis of big data as well as its ‘intelligent’ insights.
Predicting consumer preferences
The ability to glean patterns and trends from large, intricate masses of data has the potential to give marketers and brands that most sought-after marketing tool: a crystal ball.
Using cognitive computing with a predictive intention has already been employed for anticipating natural disasters, with the possibilities from a marketing point of view largely untapped but tremendously exciting.
This could work in a number of ways. For example, a company could predict what an individual customer might be interested in based on previous purchases and social media input. And perhaps more importantly, predictive cognitive computing can anticipate trends, ensuring brands are ahead of the curve at all times.
The rise of cognitive computing and its application to data from social media, mobile technology and other sources of consumer information has the capacity to identify and engage with audiences and customer bases in new ways. It may also ensure a brand is prepared for future developments and changes in style, values, entertainment and technology.