View from India: Neuroscience taps into AI to engender brand loyalty
Image credit: Mixcder
Can brands enjoy customer loyalty in the digital era? Could it be possible to connect neuroscience with AI and machine learning to generate jingles? Perhaps.
Technologies like artificial intelligence (AI) and machine learning can be leveraged to create personalised experiences. The marketing and advertising industries are relying on them for that. “Brands can build intelligent machines and make it learn using machine learning. This can generate customer data which can be fine-tuned for their preferences. Customer engagement can happen through chat and voice bots, speech recognition, computer vision and natural language processing or NLP,” said Santosh Bhat, head of data science, PolicyBazaar.com, speakingat the ASSOCHAM 'Virtual Summit on Branding and Marketing'.
Customer preference may be followed by customer engagement, but not necessarily brand loyalty. People may not be loyal to a particular brand in the digital age. It’s then crucial to use technology to drive customer loyalty.
“Technology could help personalise messages for clients. For instance, a combination of data points can be used to gauge the customer and, if possible, make an emotional connection with them,” said Manish Gupta, group chief information officer, Aditya Birla Group.
Technology could also help create a seamless coherent experience to meet customer expectations. “Brand awareness generates leads and performance. In the online world, there’s scope for personalisation. This could be viewed as an opportunity for marketers,” highlighted Vikram Sakhuja, group CEO, Madison Media.
To illustrate, an apparel brand that sells online can bring in AI tools to create designs and variations to whet consumer interest and generate value for the brand. When AI is used as a tool for building customer loyalty, the customer becomes the focal point. The customer journey can be mapped out along with touch points. A data strategy can be created and customer interactions built into it. AI 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 basic for which all forms of interactions such as email, chatbot, voice calls and speech to text messages need to be mapped together. The audio signals can also be converted to text to throw light on customer profile, context and intent,” added Bhat.
New-age startups integrate chat-bots into the system to make the product or service more contextual to customers. It could be more like a customer connect programme. “A futuristic outlook could be that performance marketing may be on the rise. The demand for direct marketing could go up as there’s a felt need to know the consumer and personalise the offering accordingly,” explained Puneet Das, president (packaged beverages, India and South Asia), Tata Consumer Products Ltd.
The approach for OTT (over-the-top) platforms is different from that of brands. Once OTT platforms are built they can be re-used. This could be possible through tags that are built around the content, while algorithms may recommend content to watch. The same recommendation engine could probably double up as a marketing tool. To illustrate, OTT platforms like Netflix and Hotstar have repeat customers. People go back to these platforms again and again. The reasons could be attributed to content packaging, presentation and marketing.
From a data perspective, the overall technology architecture may facilitate in building a loyal customer base. Hence the tech team and the marketing team could probably coordinate to define the data strategy. “What could be a game-changer is the manner in which the data is collected, cleansed and made appealing to the customer. Wherever required, algorithms could be used to do the necessary recommendations. Algorithms are derived from content-based filtering, which sheds light on behavioural traits,” reasoned Bhat.
Moving on, how about neuroscience in the world of music? “Our brain associates a particular emotion with a particular sound. So when we make that particular sound, we express that particular emotion. The brain is programmed to detect positive and negative emotions,” added Dr AK Pradeep, founder and CEO, MachineVantage. Similarly think of three-dimensional sounds in space. Algorithms have taken a part of music and can be seen as a curve.
“Algorithms can analyse the mood of the music. The machine is fed with musical notes and it makes music. The machine can even compose melodious, rhythmic notes. In the digital world, refresh the brands with sounds that are brand appropriate. This could be possible by feeding algorithms with specific musical notes,” observed Dr Pradeep. Software could probably help in making the mood of the music sad or happy or any other emotion.
In the digital world, AI and machine learning could help create sonic branding and also verify if it works. Algorithms can measure if the music resonates with the brand. That leads us to neuroscience, which uses AI and machine learning to understand music. A futuristic spin points to machines that could probably make music. What the algorithm generates could be IP protected. Perhaps it could open a new world for musical notes keenly embraced by elevators and call centres, among other options. One needs to be careful that the machine doesn’t overtake the human; in this case, the musician.
I leave you with those resonating thoughts.
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