Using machine learning techniques and merging big data from Google Trends, the first edition of Consumer Querimetrix provides consumer insights into India’s evolving car buying journey.
Kotak Institutional Equities, a division of Kotak Securities Limited, in collaboration with Google, has launched Consumer Querimetrix — a tool that demystifies and predicts near-term Indian consumer behavior by analyzing Google Trends data.
Using machine learning techniques and merging big data from Google Trends, the first edition of Consumer Querimetrix provides consumer insights into India’s evolving car buying journey. Using Google Trends data, the tool enables ‘nowcasting’ (near-term predictions) on consumer activity, capturing inflection points earlier than traditional forecasting tools to give a complete picture — on car launches, last mile hiccups, cannibals and competition.
Each edition of the Consumer Querimetrix series will focus on consumer behavior in a different industry.
Launching the report, C Jayaram, Joint Managing Director, Kotak Mahindra Bank said, “The digital wave is challenging conventional business practices across industries. Ground rules are evolving rapidly along with the consumer and those in the business of business intelligence need new tools to keep up with the changing landscape. Consumer Querimetrix is our step in that direction. Today, the sheer volume of consumer-centric search data available presents a tremendous opportunity to analyze and throw up actionable insights. These takeaways would be useful to both companies and investors.”
The first edition of Consumer Querimetrix which focuses on the passenger car segment highlights the extent to which the Internet is altering the ground rules for vendors of cars and allied products/services. With growing access to easy information online, the Indian car buyer’s journey from a whim to final purchase has changed dramatically. More than 75% of car buyers are researching online for reviews, comparative specifications, financial products and used car markets before making a purchase. The first edition of Consumer Querimetrix also explains how the traditional ‘funnel’ model is giving way to a more complex purchasing path where ‘initial consideration’ may not always guarantee sales. Although higher auto-related searches correspond to higher demand for cars, this does not hold true on a brand-wise basis.
Saifullah Rais, Quantitative Analyst at Kotak Institutional Equities and the architect of Consumer Querimetrix said, “In the absence of conventional rules, traditional decision-support systems are not very effective. They fall short on scalability and adaptability. Machine learning algorithms learn from data and do not rely on explicit rules, making them the most effective method of dealing with data explosion.”