Demand planning is one of the most critical capabilities for a 21st-century retailer or a brand that seeks to stay relevant as the digital revolution sweeps the global retail industry. However, fashion’s success rate is in the same space as the software industry, with two-thirds of products not meeting the True-Demand™ of the consumer. Current prediction models are designed assuming products can be understood through attributes. However, it’s time we change this old way of engaging in a one-way conversation with the consumer for accurate demand planning through advanced predictive analytics. We will put the existing predictive analytics methods through a litmus test right now. Can you narrate what you are wearing or what you are having in your home or your shoe in language? 

Whatever narration you get, if you give it to a designer, do you think they would recreate the True-Demand™ of the consumer? Your response to this question will be a “no.” This means that the so-called existing prediction models use transaction data and attributes that carry signals that are insufficient for an industry like fashion and lifestyle. Hence, the need for a new thinking “Apollo,” a one-of-its-kind predictive analytics engine that uses both images and textual attributes for top-hole demand planning. With Apollo, a brand or a retail organization can produce on-trend styles by evaluating its past sales data and analyzing True-Demand™ data specific to the brand. The brand’s buyers, planners, and merchandisers can use Apollo to augment their decisions using demand planning that helps them take appropriate actions and track the performance of styles launched.

Apollo has demonstrated best in class increase in forecast accuracy of anywhere between 20% and 40% from the current baseline with our clients. This in effect creates an opportunity to double your profits.