Menswear brand Indian Terrain successfully raised its sell-through rate by almost 5%, leading to significant cost savings amounting to nearly Rs 6 crores
Bengaluru: Indian Terrain Fashions was founded in 2000 by Venky Rajagopal as a premium smart-casual clothing brand for men. In 2010, the fashion retailer underwent a de-merger process, separating itself from its parent company Celebrity Fashions Ltd., which primarily operated as a textile manufacturing and exporting entity.
In September 2015, the company launched a sub-brand named ‘Indian Terrain Boy’ to meet the increasing demand for smart-casual clothing for boys aged between 4-16 years.
Today, Indian Terrain Fashions has established its footprint across the country with a network spanning over 250 exclusive brand outlets, more than 1000 multi-brand outlets and over 400 large format stores including Lifestyle and Shoppers Stop.
The Chennai-based company also retails through its own webstore www.indianterrain.com and other e-commerce platforms such as Myntra, Flipkart, Ajio and Amazon.
The Need
The merchandising team of the fashion brand faced challenges in collecting and analyzing diverse data and drawing conclusive insights. The company also realized the importance of automating inferences and recognized the requirement for a decision support system that offers prescriptive recommendations.
“We were doing it manually with the whole merchandising team spending days to do it,” said Charath Narasimhan, chief executive officer of Indian Terrain Fashions.
In recognition of the necessity to streamline data collection in a more efficient manner, the fashion retailer joined forces with BIRetail, an integrated cloud-based data analytics platform for retailers. It automates data collection and daily refresh from multiple sources to make a measurable difference to businesses.
The Approach
BIRetail seamlessly integrated data from various sources and presented it as a unified view, facilitating analysis and report generation based on this consolidated information.
It also deployed AI/ML (artificial intelligence/machine learning) techniques to provide pointed prescriptive solutions to specific retail functions:
Merchandising support system: It churns out a daily replenishment list of items to be despatched from the warehouses to each store considering permutations of demand, supply, priorities and preferences.
Personalised recommendation system: Utilizing data science, the system showcases the top items that customers were most likely to purchase.
Transactive analytics: Its proprietary tool, transactive analytics empowers robotic process automation to not only generate recommendations but also directly initiate the necessary actions. Once an action is initiated, the system automatically monitors each step to ensure its completion. In cases where an action remains incomplete, an escalation matrix comes into play, striving to facilitate and empower the execution of the action.
The technology platform assisted Indian Terrain Fashions with dynamic recalibration, an aspect that is crucial for a fashion brand. It also incorporated demographic and category-specific dynamic market trends, to effectively reduce instances of stockouts and enhance sell-through rates.
Business Impact
The collaboration with BIRetail enabled Indian Terrain Fashions to increase the sell-through rate by nearly 5% points, driven by improved replenishment strategies that ensured the right merchandise reached the appropriate stores.
“The entire technological integration seamlessly translated into our brand, allowing for direct transfer orders to the warehouse. What previously required the effort of four or five individuals across all 250 stores, is now efficiently managed by a single person, twice a week,” said Narasimhan.
“With BIRetail, we swiftly fine-tuned the engines specific to our business needs. We incorporated numerous parameters into the recommendation engine, such as sales, rate of sale, stock-to-sale ratio, store profitability, and store visibility. This comprehensive approach not only led to a higher sell-through rate and improved replenishment but also resulted in substantial cost savings of nearly Rs 6 crore,” he added.
