View from India: Neuroscience harnesses AI to build brand loyalty

Can brands retain customers in the digital age? Could neuroscience be connected to AI and machine learning to generate jingles? May be.

Technologies such as artificial intelligence (AI) and machine learning can be leveraged to create personalized experiences. The marketing and advertising industries rely on them for this. “Brands can build smart machines and have them learn using machine learning. This can generate customer data that can be adjusted based on their preferences. Customer engagement can be done through chat and voice bots , speech recognition, computer vision and natural language processing or NLP,” said Santosh Bhat, Head of Data Science, PolicyBazaar.com, at the “Virtual Branding and Marketing Summit” of ASSOCHAM.

Customer preference may be tracked by customer engagement, but not necessarily by brand loyalty. People may not be loyal to a particular brand in the digital age. It is then crucial to use technology to build customer loyalty.

“Technology could help personalize messages for customers. For example, a combination of data points can be used to assess the customer and, if possible, establish an emotional connection with them,” said Manish Gupta, Chief Information Officer of Aditya Birla Group.

Technology could also help create a cohesive and seamless experience to meet customer expectations. “Brand awareness generates leads and performance. In the online world, there is room for personalization. This could be seen as an opportunity for marketers,” said Vikram Sakhuja, CEO of Madison Media Group.

For example, a clothing brand that sells online can use AI tools to create designs and variations to pique consumer interest and drive brand value. When AI is used as a customer retention tool, the customer becomes the focal point. The customer journey can be mapped with touchpoints. A data strategy can be created and customer interactions can be built into it. Artificial intelligence and machine learning tools can help monitor customer interactions. This could simplify the framework and the variables can be used to cross-sell to customers.

“One could probably start with the basics why all forms of interactions such as emails, chatbots, voice calls and text messages should be mapped together. Audio cues can also be converted to text to illuminate the customer profile, context and intent,” added Bhat.

New era startups are integrating chat-bots into the system to make the product or service more contextual for customers. It might look more like a client login program. “A futuristic perspective might be that performance marketing is on the rise. The demand for direct marketing could increase as there is a felt need to know the consumer and personalize the offer accordingly,” explained Puneet Das, President (Packaged Beverages, India & South Asia), Tata Consumer Products Ltd.

The approach of OTT (over-the-top) platforms is different from that of brands. Once OTT platforms are built, they can be reused. This could be possible through tags built around the content, while algorithms can recommend content to watch. The same recommendation engine could probably serve as a marketing tool. To illustrate, OTT platforms like Netflix and Hotstar have regular customers. People come back again and again to these platforms. The reasons could be attributed to content packaging, presentation and marketing.

From a data perspective, the overall technology architecture can make it easier to build a loyal customer base. So the technical team and the marketing team could probably coordinate to define the data strategy. “What could be a game-changer is how the data is collected, cleaned and made attractive to the customer. If needed, algorithms could be used to make the necessary recommendations. Algorithms are derived from filtering based on the content, which sheds light on behavioral traits,” Bhat explained.

Moving on, what about neuroscience in the world of music? “Our brain associates a particular emotion with a particular sound. So when we produce that particular sound, we are expressing that particular emotion. The brain is programmed to detect positive and negative emotions,” added Dr. AK Pradeep, Founder and CEO of MachineVantage. Similarly, think of three-dimensional sounds in space. The algorithms took part in the music and can be seen as a curve.

“Algorithms can analyze the mood of music. The machine is fed with musical notes and it makes music. The machine can even compose melodious and rhythmic notes. In the digital world, refresh brands with on-brand sounds. This could be possible by feeding algorithms specific musical notes,” Dr. Pradeep observed. The software could probably help make the mood of music sad or happy or any other emotion.

In the digital world, AI and machine learning could help create sound branding and also test if it works. Algorithms can measure whether the music resonates with the brand. This brings us to neuroscience, which uses AI and machine learning to understand music. A futuristic twist points to machines that could possibly make music. What the algorithm generates could be protected by IP. This could perhaps open up a whole new world for musical notes keenly embraced by elevators and call centers, among other options. Care must be taken that the machine does not overtake the human; in this case, the musician.

I leave you with these resonating thoughts.

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