Datasheet
[gusta_post_date vc_id=”date-20426415355fe07776e4838″ date_type=”updated” sub_groups=”label_text” label_text=”Last updated on”][gusta_post_title vc_id=”title-16746549515fe073375c905″]Algorithmic inputs for precise and effective assortments to drive profitability and greater customer experience.
Avoid Blindfold Buying and Adapt to Smart Buying
Greater assortment effectiveness
Leading to better fullprice-sell-through
Improved customer experience
Leading to repeat purchase and customer loyalty
Localized assortment mix
Leading to increase in profitability
Algorithmic Assortment Planning
That yield assortment aligned to meet key merchandising objectives of full-price-sell-through and resultant profitability
Demand prediction and merchandise planning
Machine Learning based prediction models to accurately predict sales based on seasons and trends with the inclusion of causal variables that impact sales like discounts, markdowns, events and holidays
Store clustering
Identify store patterns and buy better for similar behaving stores based on performance and non-performance based variables. Performance based variables – category sales, brand sales, attributes performance and sales by demographic profile of customers. Non Performance variables – Store size, store type, weather and demographic profile. to create store clusters with similar behavior and demographics that aid in localized assortment and inventory allocation policies.
Assortment depth & breadth
Determine the number of product lines and the variety within each line to purchase based on business goals.
Size pack
Obtain recommendations for optimized size packs by region, clusters, etc. to reduce opportunity loss and excess stock situations.
