Credit Worthiness of Kirana Stores on the basis of Non Financial Data
- 1. Credit worthiness of Kirana Stores on basis of Non Financial Data Rohit Agarwal Chief Data Officer Bizom
- 2. Brand’s Salesman visits outlet Order is punched in Bizom App Order is delivered Brand’s Salesman visits outlet Takes order of inventory using pen & paper Order is manually handed to distributor Distributor Checks Inventory Order is delivered Bizom Simplifies Supply Chain
- 4. What is a Bizom Trust Score? Assessment of Credit worthiness of a retailer based on transactional data Calculates a trust factor for doing the business with that outlet Gives an overview of Business health of outlet and its owner but unlike FICO or CIBIL uses Transactional data of an outlet, instead of financial documents • Lenders (Financial Institutions or distributors) should be able to use Bizom Trust Score to decide on the credit worthiness of the the retailers looking for the loan • We want to make the formal credit more accessible for those who need small and short loans (Sachet Size Loans)
- 5. Bizom Data • Transaction data available for last 2+ years helps in understanding what sells where • The entire geography has been divided into 1*1 km square grids (neighborhoods) which helps in getting hyperlocal perspective • 100K+ SKUs (FMCG) categorized into 50+ categories • The categories are multi level Beverages Commodities Home Care Dairy Products Personal Care Packaged Food Confectionary Carbonated Non Carbonated Tea & Coffee .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
- 6. Category being sold Number of SKUs at different Price points in an outlet Category contribution to the overall sales in this outlet Price wise SKU contribution to the category sales Range of products (product assortments) Category wise monthly sales Number of brands visiting the outlet Repurchase History Frequency of transactions Data points collected from Bizom Transactions We also calculate the hyperlocal value data related to these parameters with respect to each outlet
- 7. Aggregations for calculation of Trust Score
- 8. Bizom Trust Score: Combination of the different scores Create Outlet Score Calculated for the individual store Create neighborhood Score Calculate a composite score of a set of outlets in the neighborhood Locality Dynamics Does the locality have a potential to drive the extra sale and hence lower credit default risk Weighted Sum Bizom Trust Score
- 9. Score Range
- 11. Outlet is selling low-value products across all present categories, which is at odds to neighbourhood price profile and consumption behavior Outlet is selling a small fraction of neighborhood category assortment Outlet is over-indexed on SME brands, indicating low-value and low-recall purchases Credit assessment - High-risk outlet
- 12. Month March 2022 April 2022 May 2022 June 2022 Products purchased? Yes No Yes Yes Month July 2022 August 2022 September 2022 October 2022 Products purchased? No Yes Yes No Month November 2022 December 2022 January 2023 February 2023 Products purchased? Yes Yes No Yes Business Health - Core categories Business Health - Recent categories Outlet Business Health v/s Neighborhood Normalized Neighborhood Growth Normalized Neighborhood Consumption Final Profile Score High Low Low High High 261 Longer credit cycle than industry and neighborhood average Inconsistent buying patterns, indicating over and under-stocking during key buying months Poor business health in a star neighborhood, indicating poor short and mid-term portfolio performance Credit assessment - High-risk outlet contd.. Average purchase cycle length 35 days
- 13. Outlet is selling products in line with neighbourhood price profile and consumption behavior Outlet is selling ~100% of neighborhood category assortment Ideal distribution of company types for urban catchment Credit assessment – Low-risk outlet
- 14. Business Health - Core categories Business Health - Recent categories Outlet Business Health v/s Neighborhood Normalized Neighborhood Growth Normalized Neighborhood Consumption Final Profile Score High High High High High 860 Month March 2022 April 2022 May 2022 June 2022 Products purchased? Yes Yes Yes Yes Month July 2022 August 2022 September 2022 October 2022 Products purchased? Yes Yes Yes Yes Month November 2022 December 2022 January 2023 February 2023 Products purchased? Yes Yes Yes Yes Average purchase cycle length 7 days In line with industry and neighborhood average credit cycle Consistent buying patterns, indicating just-in-time stocking during key buying months Good business health in a star neighborhood, indicating good short and mid-term portfolio performance Credit assessment – Low-risk outlet contd..
- 15. Development Plan
Editor's Notes
- #2: Through the Bizom we simplify the sales force automation. What was traditionally a pen and paper workflow has been digitized and made more efficient. We sell the solution to various brands who give it to their sales force for performing the order taking and fulfillment workflows
- #5: Evaluation of a outlets business is restricted to his neighborhood as that is what is generating the business for him Based on the categories we can understand the consumer patterns …homecare means family are coming while confectionary is more kid, young population A typeical Commodity would be the staples like rice, wheat, pulses Homecare is detergent powder, cleaning products Dairy Products : Milk and its derivatives Personal Care: Soap, Deos. Shampoo Packaged Food: Biscuits , cookies, chips, Confectionary: Chocolates
- #6: Category Being Sold: This indicates the diversification. How many verticals is the outlet is selling is correlated to its business opportunity Category contribution to the overall sales in this outlet: Which categories form the bulk of his business. We can compare how the outlet fairs in his core business compared to others Category wise monthly sales: this is the actual business number. It will tell us the trend of how the various categories have been behaving.. Are there any categories which are increasing on a monthly basis or they are flat line. This is related to the growth number Number of SKUs at different Price points in an outlet : price point assortment of the SKUs for the category.. Basically we want to understand if the outlet is only able to sale the L price point products or everything can be sold Price wise SKU contribution to the category sales : This will help us to know the customer profile that comes. If only L price is selling or everything is selling Range of Products: Different sub categories like in homecare: detergent, dish wash, fabric wash, room freshner, toilet cleaner and so on …is he keeping just one type of products or everythin Number of Brands Visiting the outlet: This gives a sense of the importance of the outlet. Also the type of brands make a difference. Only SMEs are coming or the enterprise are also visiting Repurchase History : This indicates the outlets collection behaviour, If the outlet is placing the repeat orders from the same brand that means he si able to pay the credit back on time Frequency of Transaction : This is the indication of the offtake (how much he is able to sale to the consumer. If the trsansactions are happeing on a regular basis that means the inventory is moving fast
- #7: Diversification: to understand the kind of customers that will come and hence the potential Repayment & Purchase Velocity : This shows the cash flow. Is the owner able to pay back the credit on time or not Business Insights: this is the actual truth.
- #8: We do the previous set of calculations for the individual outlet as well the neighborhood outlets The location Dynamics is about the demographics. Population Economic status , footfall, POIs in and around etc
- #9: Based on the diversification : number of brands Repurchases Business amount