In the fashion and luxury goods sector, when launching new collections, forecasting the right quantities to be manufactured per item is a major issue for profitability. Sales are driven both by variables internal to the company (sales history, in-store sales force, growth, etc.) and external variables (calendar events (Christmas, Mother's Day, etc.), Black Friday, sales, etc.), but also by factors that are more difficult to measure, such as the year's trends (colours, models, graphics, etc.).
Verteego Brain makes it possible to take into account all of these factors, as well as the characteristics of the different products (colours, design, consistency with trends, etc.) to predict the quantities to be expected with a high degree of accuracy.
Forecasting your sales and operations based on historical data is a method that can quickly show its limitations, especially since the past does not always accurately describe the future. This can also happen when you are trying to predict products without historical data, such as when launching new products, forecasting collection sales, opening new stores or launching a new concept with a modified assortment.
Machine learning offers significant advantages in these different cases because unlike "traditional" statistical methods, the forecasts generated by the machine learning algorithms are based on the characteristics of the different products and points of sale. Thus, to predict the behavior of a new product or shop, Verteego Brain relies on the precise weightings of common characteristics with already known articles or shops.
Predicting sales of slow-moving items is a particular challenge that affects many fields, such as haute-couture, luxury goods, lingerie, leather goods, jewelry and watches. Indeed, even a small change in sales can have a significant impact on the average accuracy of forecasts for low-volume items, and can, therefore, generate shortages or overstock.
Verteego Brain uses different algorithms to train its forecasting models depending on the sales volume of the items. Thanks to its ability to finely identify the characteristics and drivers of each item, Verteego Brain can share its predictions with similar items, build a broader knowledge base, and improve average accuracy.
Disposing of discontinued products during sales, at destocking sites or even at the sell-off, is very costly for merchants, who must pass on the price reduction to their margins. While it is difficult to abandon this distribution method, which has become a key element in the strategy of most brands, controlling the number of sales generated in this way is nevertheless a major challenge for the profitability of the brands.
As the machine learning-based forecasting models built into Verteego Brain significantly increase accuracy, they allow for more precise anticipation of the quantities to be allocated, whether for catalog sales or for destocking.
In "sell-in" logic, your client must make a commitment to the volumes he will be able to sell on the market. This of course presents a risk that you will share in the negotiation with your distributor, often in the form of discounts on your list prices.
Having more precise and detailed forecasts of the quantities you will be able to sell will put you in a better position to negotiate with your distributor and will provide you with quantified information that you can use to facilitate his work in a win-win situation.
Whether it is for your catalogs, new collections, sales or destocking, price is a key element for your profitability. However, the complexity of pricing lies, among other things, in the variability of the sensitivity of sales to the price level (elasticity). For example, a promotion with a price reduction of 30% can double the sales of article A (uplift) and have almost no impact on article B. Therefore, applying the same promotional strategy to both products would obviously be sub-optimal. The complexity increases when one takes into account that the elasticity of the same article is also likely to vary according to outlets, assortment, geographical regions, season, or other contexts.
Verteego Brain allows you to predict, for each of the article/store combinations, the level of sales under different pricing scenarios. In many cases, the accuracy obtained by machine learning exceeds that of a simple elasticity calculation, which does not take into account the multiple factors that may have impacted the evolution of sales in the possible history, nor the effects that a price variation could have on the other articles in the assortment.
Knowing precisely the traffic to be expected in your shops enables you to answer many commercial (items to be highlighted, commercial actions to be favored, etc.) and operational (stock levels, sales force planning, etc.) questions.
Verteego Brain allows you to rely on your counter visits to accurately forecast traffic in your shops, both in terms of time and traffic type.