Aditya Birla’s TMRW will close FY24 with $150M annual revenue run rate: CEO Prashanth Aluru

TMRW CEO Prashanth Aluru said capital is not the only moat, and capabilities and ecosystem are also important, at TechSparks 2023 in Delhi.

Aditya Birla’s TMRW will close FY24 with $150M annual revenue run rate: CEO Prashanth Aluru

Wednesday November 29, 2023,

2 min Read

Aditya Birla Group’s house of brands company TMRW will close FY24 with an annual revenue run rate of $150 million across its portfolio of fashion and apparel brands, said its CEO and Co-founder Prashanth Aluru.

“We have eight brands in our portfolio, and three of them are in the Rs 200 crore - 350 crore zone, and five of them are in the Rs 50 crore - 100 crore zone. A lot of them have grown significantly since we have come in. Midsize brands are in the 5-10X zone and large brands are growing at 20-30%. So, we will close the year at a $150-million run rate,” said Aluru, at YourStory's startup-tech event TechSparks 2023 in Delhi.

At a time of muted funding across the startup ecosystem, Aluru said that capital is not the only way with which brands can be built.

“One of the biggest learnings over the last 18 months is that capital can be a moat; capital is important, but fundamentally the TMRW thesis is all about capabilities and the ecosystem,” Aluru said.

“Of course, some brands have found a way to growth hack their way in a more capital efficient way and some brands have not found that way.”

Aluru said brands that are solving for the short term, like investing excessively in performance marketing, are the ones that would be capital-starved.

For TMRW, using data science is one of the key ways to build a moat. Aluru said the company has two data science stacks.

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“The moment you join TMRW, you join the homogenous stack, and a lot of the complex problems in fashion, whether it is recommendation engine, search, all those algorithms are built on the back of what Bewakoof has,” he said.

TMRW’s portfolio of brands include Bewakoof, The Indian Garage, JuneBerry, and Nauti Nati.

“The second part is data science led, data-driven led from trend spotting, demand sensing, how do you find your bestseller, and the right pricing,” Aluru said.

“It is humanly impossible to decide when the range gets really broad. How do you get the machine to decide based on the way you want your P&L?” he added.


Edited by Kanishk Singh