AI Pricing Analytics for Fashion Industry
Originally Published on: Quantzig AI Pricing Analytics
Game-Changing Solutions for the Fashion Industry
The fashion industry is highly volatile, which makes it necessary for players in this domain to have a strong hold over pricing and trade promotions to remain competitive. Most retailers provide huge discounts to get into the customers’ hearts and shopping baskets. However, this results in negative marketing spending and pricing issues as discount pricing is counterproductive.
In such a scenario, pricing analytics solutions help retailers stay afloat in the digital world where shoppers can compare prices and deals. The adoption of AI in the fashion industry and the prediction of fashion trends with machine learning are a couple of technological innovations gaining prominence.
The Challenges of the Client
Our client is a leading fashion designer from Spain and has a successful line of trendy products. However, the client wanted to launch a new design using a powerful algorithm that analyzed past designs and future trends. The client was also struggling with other issues such as transportation bottlenecks, inaccurate apparel pricing, tracking of unessential competitors, and a lack of insight into the competitors’ prices and discounts.
The client approached Quantzig for tailor-made pricing analytics solutions capable of optimizing price, performing competitive benchmarking, and monitoring the price gaps in real-time. The client also wanted to understand the pricing strategies of their rivals to help them adopt suitable pricing analytics models.
Impact Analysis of Quantzig’s Pricing Analytics in the Fashion Industry
The solution offered by Quantzig helped the client develop effective fashion design, merchandising, and marketing plans. In addition, our pricing analytics solutions enabled our client to get a holistic 360-degree view of competitors’ assortment, promotion, and pricing methods. The client was also able to able to reap the following benefits:
Improved sales margins from 4% to 13%
Standardized discounts and improved global sales by 37% and 13.0%
Developed a robust marketing plan
Increased retail pricing transparency by 21%
Evaluated client’s pricing, marketing, and sales data to derive consumer preferences
Devised a roadmap for value-based product categorization
Improved the understanding of rivals’ pricing strategies
Improved standardized discounts by 37%
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