- The impact of covid-19 on food retail.
- Indentify new customers segments.
- Profiter du ralentissement du renouvellement de la gamme pour rationaliser l’assortiment.
Distributors of "basic necessities", including food distribution, are among the fortunate few who were able to limit the damage of the health crisis, thanks in part to the growth in sales for part of their range. However, the post-covid transition period that is being prepared will confront them with a series of challenges, particularly in terms of managing their assortment.
Choosing the right assortment is all the more important as it allows retailers to prepare for new complicated episodes in the future, whether in the form of pandemics or other disruptive events
The impact of covid-19 on food retail.
As already mentioned in various recent publications, the impact of covid-19 on retail has been considerable. With regard to mass distribution, we have made several observations :
- Sales increased in many categories, as did operating costs and out-of-stocks.
- The 2020 demand history is riddled with anomalies, making it difficult to use in its raw form to feed automated systems. A real problem for retailers who do not have assortment management systems based on machine learning.
- Inventories have become a much less reliable source of data to drive store performance.
- Availability problems related to local changes in containment measures, as well as the relaxation of restrictions, lead to a need for increased adaptation of short-term inventories.
- Price increases linked to rising costs at manufacturers and transporters, as well as the unavailability of certain products, make pricing decisions more complex.
- The composition of customer baskets has become highly uncorrelated to promotions.
- The actual impact of the pandemic on 2021 sales has yet to be seen, notably due to potential reconfinements and new traffic restrictions to be expected.
In this context, the optimization of the assortment using sophisticated algorithms represents a strong and sustainable differentiation axis.
Indentify new customers segments.
Increased scarcity in a large number of product categories has prompted customers to try new products, brands, channels and retailers. This has led to definitive changes in their preferences. As a result, retailers must rely on more sophisticated analysis to identify higher value-added customer segments and re-evaluate the decision-making processes of existing customers.
This update, which can be supported by machine learning algorithms to achieve greater finesse, will help to identify new purchasing methods and the preferences of the best customers.
Profiter du ralentissement du renouvellement de la gamme pour rationaliser l’assortiment.
Due to the uncertain nature of the next 6 to 12 months, new product introductions will slow down. Manufacturers will focus more strongly on their historical business and try to integrate changes in consumer behavior into their strategy before innovating again. Retailers need to take advantage of this momentum to optimize the current assortment based on revised customer segmentations. This will make it possible to design a tighter assortment, adapted to customer needs and ready to accept future product launches while avoiding an increase in stocks.
Adapt the packaging of products driven by e-commerce.
The explosion of e-commerce has increased the need for packaging that is both more durable and recyclable, but also less visually appealing, in order to better match the demands of online presentation. It is then essential to be able to analyze more finely the penetration of e-commerce channels for each item, to synchronize this data with manufacturers and to search for packaging alternatives.
Define the specific assortment for each point of sale.
Experimenting with new point of sale formats can be an opportunity to rethink and reduce the assortment in order to adapt it to changing customer demand. Artificial intelligence enables retailers to develop assortment plans at the level of each channel or point of sale and achieve high levels of consistency between product availability and customer demand.
In view of the increasing complexity of inventory management, not least due to the growing diversity of customer contact points, customer-centric assortment management must be orchestrated by planning processes based on artificial intelligence.
Indeed, AI can significantly reduce excessive inventories and thus dramatically improve free cash flow to finance investments (digital among others).