Optimum Width
How many options to have in each cluster
For businesses doing > $50 million annual revenue
Browser based. You only need the internet Data ingestion automation via ETL
Contract based Consulting with in-house merchandising experts for POC, Gap Analysis and system outputs
Performs billions of computations within 2-3 hours of runtime for data processing
Uses patent pending machine algorithms developed over 3 years of R&D
For businesses doing < $50 million annual revenue
Browser based. You only need the internet
Self serve. Pay as you go Customer support is provided with any data uploading issues, bug fixes, feature requests etc.
Performs millions of computations within 10 minutes for data processing
Uses patent pending machine algorithms developed over 3 years of R&D
Longtail Correction
Identify and discard highly discounted or longtail data which distorts true demand
NOOS
Never out of stock products
Product Clustering
A cluster represents a set of similar products. All analysis is done on clusters of products. E.g. victor-shirt-casual-male-lowprice
Longtail correction at store cluster level
Core
Bestseller
Clustering done using business attributes (i.e. brand, category, subcategory, gender, mrp_bucket) and 6 product attributes (e.g. color, pattern, fit, sleeves etc.)
Calculated at store-cluster level
Longtail correction at company level
Bestseller
Clustering at business attributes (i.e. brand, category, subcategory, gender, mrp_bucket)
Calculated at company-cluster level
True demand identified at store+cluster level by analyzing past sales revenue and discounts
Optimum Depth
How much quantity to buy in each cluster
True demand identified at company+cluster level by analyzing past sales revenue and discounts T
True Size Set
How much quantity to buy for every size in every cluster
Size continuity is maintained (e.g. S, M, L, XL, XXL)
Size continuity is not maintained (e.g. system may suggest M, L, XL, XXL), if size S is not selling well
Different size set ratios are predicted for all store+cluster combinations
Pivotal sizes (i.e. most important sizes) predicted for each store
Different size set ratios are predicted for all store+cluster combinations
Pivotal sizes are not predicted
Smart Assortment Plan
Technology Usage Model
True Size Set
How much quantity to buy for every size in every cluster
True Size Set
How much quantity to buy for every size in every cluster 222