Anushruti Singh
Food retail revenue is enormous, at US$ 905.20 billion in 2023. According to Statista, this market is expanding at an 8.40 per cent CAGR. Although the sector is predominantly dominated by unorganised players like mom and pop stores, there will be an increased need for organised retailers to differentiate themselves through tech-driven innovations.
However, the sector has long been hampered by issues that have existed since the days of the traditional ways of doing retail, such as demand and supply shortages, an unorganised supply chain, and food waste management.
As online shopping takes over, it is predicted that 1.2 per cent of the total income in the food retail business will be generated through online sales by 2023. However, simply using internet platforms will not fix the problem. This is where demand intelligence solutions come in.
Demand intelligence tools leverage cutting-edge technology to collect and analyse data on consumer behavior, demand patterns, and supply chain dynamics. By harnessing this technology, retailers gain the power to make informed and strategic decisions regarding product assortment and inventory management.
In an interview with SME Futures, Avinash Kasinathan, CEO, CensaNext Systems, highlights the crucial importance of demand intelligence technologies in making the Indian retail environment better.
Edited Excerpts:
What are some of the new-age tech innovations that can help reinvent the Indian retail segment?
There are various new-age tech innovations that are helping to reinvent the Indian retail segment.
To begin with, digitisation of the end-to-end ecosystem across producers, processors, warehousing, logistics, retailers, and consumers enables clear tracking of information, material, and money. Then comes strong planning, comprising demand forecasting and converting it into supply and processing plans while ensuring reliability in quality and fill rates at every stage.
Creating a connected digitised ecosystem to suggest alternative optimised networks for running an efficient ecosystem.
Utilising long-term demand forecasts for crop cultivation planning, mapping farms based on soil, water, and resource conditions, and providing detailed cultivation plans to farmers.
Leveraging advanced demand forecasting solutions that capture multiple parameters, including seasonality, spikes during weekends, festivities, weather events, and other factors, to manage day-to-day demand forecasts for various products.
What are the current challenges in the retail sector that can be addressed through demand forecasting?
At CENSA, we have developed our very own demand forecasting solution that captures more than 25 parameters across 105 SKUs to manage day-to-day demand forecasts. With our extensive experience in the entire value chain, we believe the following concerns can be addressed through demand forecasting:
Firstly, the supply-demand mismatch and price volatility. By shifting the focus to demand-driven supply chains, demand forecasting enables better alignment between supply and demand, reducing the mismatch and price volatility in the market.
Secondly, it can resolve the high food wastage issue. Demand forecasting helps optimise the production and distribution processes, ensuring that food is grown and processed as per the anticipated demand, thus minimising wastage.
The third is the solution to the problem of low remuneration for farmers. By accurately predicting long-term demand and aligning crop cultivation plans accordingly, demand forecasting helps farmers to produce crops that align with the demand at the time of harvest, increasing their income potential.
It can optimise shelf space and merchandising operations. Demand intelligence tools provide insights into the potential of various stock-keeping units (SKUs) at a hyper-local level. This information helps retailers to optimise their shelves, ensuring the best return on investment and improving their overall merchandising operations.
Why is predictive analysis critical for building customer insights?
Predictive analysis and technology are crucial in building customer insights by leveraging data and advanced analytics to anticipate customer behavior and preferences. Here are some insights on predictive analysis and its role in building customer insights:
Understanding consumer behavior: By analysing past data and identifying patterns, predictive analysis helps uncover valuable insights into consumer behavior. This includes identifying purchasing trends, preferences, and factors influencing buying decisions. The ability to study past data carefully and develop tailored models for each product category enables businesses to gain a deeper understanding of consumer behavior.
Hyper-local view and personalised recommendations: Predictive analysis provides a hyper-local view by analyzing data at a granular level. This helps retailers understand the potential of various stock-keeping units (SKUs) in specific locations. By leveraging this insight, retailers can tailor their product offerings to meet the specific demands of different customer segments, enhancing customer satisfaction and driving sales.
For example, according to Censa Insights, the recommended rice variant for a store in a Telugu-dominated colony in Bangalore will differ significantly from the popular Sona Masuri rice consumed in other parts of Bangalore. This information is particularly valuable to retailers, who often view their stores as valuable real estate spaces. By understanding the specific preferences and demands of the local community, retailers can make informed decisions about the types of rice they stock, ensuring that they cater to the unique needs of their customer base.
What is the role of technology in helping retailers to decide their product mix?
Technology plays a significant role in helping retailers to decide their product mix by providing valuable insights and optimising their merchandising operations.
With the help of technology, retailers can identify products that are frequently purchased together or exhibit complementary buying patterns. This knowledge allows them to create an effective product mix, cross-selling opportunities, and promotional schemes. Retailers can enhance customer satisfaction and drive sales by offering relevant and attractive product bundles.
