The concept of extracting valuable insights from consumer behaviour in marketing did not receive much attention until 1974. This eventually paved the way for the emergence of the all-encompassing discipline of ‘consumer research’
With the arrival of technology, theorists shifted their focus from randomised experiments to a more consumer-oriented and financially informed perspective. To remain relevant in the face of constantly evolving consumer behaviour, and effectively tap into what works, brands are tasked with the daunting challenge of detailed research.
They must analyse vast numbers of surveys, customer support emails, social media mentions, feedback and data from pretty much every customer touchpoint they set up. AI and digital-native marketers are leveraging advanced and self learning AI models and algorithms to analyse massive amounts of unstructured data.
Yet, despite the abundance of available data, many brands utilise only a small fraction due to unclear goals and limited familiarity with relevant technology and AI, leading to a dependence on manual processes.
To address this issue, numerous enterprises are developing full-stack marketing product suites that incorporate a diverse range of AI-driven tools, meticulously tailored to leverage the potential of consumer insights in record time.
These suites enable marketers to achieve their goals swiftly and efficiently, completing tasks in one-third of the time it would take with traditional methods. These AI-driven tools are poised to witness innovative advancements catalyzing a revolution in the marketing landscape as we hurtle towards the halfway mark of this decade
Here’s a bird’s-eye view from my perspective. Large language models (LLMs) will distill voluminous amounts of consumer insights into condensed synopses.The sheer number of brand mentions, which can be in the millions, makes manual analysis impossible.
This is where LLMs step in as an alternative to help summarise the sentiments of these mentions and provide consumer insights. It takes on the tedious task of reading through millions of surveys, feedback emails, reviews and more.
All the vital information is gathered and an overall summary is provided in the form of compact reports or crucial takeaways for marketers to act on. These reports and insights will enable brands and marketers to take action faster.
Low code/no code development will provide the necessary boost to brands’ to gauge consumer intelligence.
Brands are increasingly adopting low/no code automation systems to automate manual work or data collection.This facilitates a head start for multiple forthcoming and novel brands to establish channels for gauging consumer intelligence,encompassing evaluations, feedback, customer support emails and an array of other measures.
A formidable amount of data is collected through enabling brands to develop resources such as help centres, FAQ pages and community forums. This helps customers to discover solutions and answers on their own.
Also, brands will be able to utilise the gathered consumer intelligence to improve their products and services, and ensure that customer support agents are mapped to more high-level matters.
Adaptive AI will potentially improve retail operational efficiencies by analyzing real-time shopping behavior.
In the near future with the aid of Adaptive AI, retailers will possess the capability to provide highly personalised recommendations and seamless customer experiences.
These will be made possible through real-time insights acquired via video analytics, which will be powered by cutting-edge on-prem infrastructure. In-store analytics will intelligently process customers’ dwell times in various sections of the store, generating invaluable insights
Consumer data, such as past shopping history across multiple channels, demographic profiles, reviews and feedback can be recorded and stored in data banks. By harnessing these multi-dimensional insights, retailers will have the means to create an enriched and immersive shopping experience.
Conversational AI will provide a multi-dimensional mode in gathering consumer insights customers want to be able to engage with brands by being allowed a flexibility to switch modes and channels for a more immersive experience.
Brands must develop a strategy capable of automating across various modalities, be it voice or chat across web or social or other channels, like WhatsApp or Instagram. By adding a layer of data analytics to these channels or modalities, they can be used as seamless touchpoints to generate consumer insights for enabling a seamless touchpoint.
This can be achieved by leveraging conversational AI solutions to deliver differentiated Dynamic AI agent initiated conversations through these channels. This helps brands to reach out to customers on their preferred channels at the ideal time and with their desired communication.
But there is still a significant awareness gap among the masses in understanding the real potential of AI. One of the reasons for this is the technical aspect of adoption, requiring a significant investment of time to comprehend before implementation.
It’s crucial for businesses to recognise that AI is anasset for revenue generation, with limitless capabilities that can provide a competitive advantage.
By Vrushali Prasade, Co-Founder and Chief Technology Officer at Pixis
