How Motivi Achieved a V-shaped Sales Rebound Without Adding Discounts or Markdowns
Slower Product Sales Post-Crisis
Project Results€8.8M immediate impact opportunity €3.1M extra seasonal revenue €5.5 sales potential increase
Motivi is one of Italy’s leading fast fashion brands for women’s apparel, with over 200 stores across Italy. Each store traditionally offered similar products and layouts, with new products released on the same date.
The strategy had been working: Motivi was a popular brand, comfortably maintaining its distinctive niche in the industry. When the Covid-19 pandemic hit, however, sales dropped drastically, threatening Motivi’s survival.
Even as stores began reopening, growth was sluggish. Customers were less likely to wander into stores for a casual shopping trip, especially since they could easily anticipate what would be available in their local Motivi store.
Slower Product Sales Post-Crisis
Nicoletta Greco, the Chief Project Officer for Motivi, needed to mitigate the impact of depressed store traffic quickly. She believed that varying product assortments and shortening product life cycles could motivate customers to visit stores and buy more often.
“Fear of missing out is a crucial driver of customer behavior in the fashion industry today. Personalized assortments for each store would potentially provide an extra incentive to visit and purchase frequently. Figuring out which assortments would appeal to which customers, however, was critical. We needed to ensure that the new assortments would generate opportunity without cannibalizing existing sales.”
We needed to ensure that the new assortments would generate opportunity without cannibalizing existing sales.”
– Nicoletta Greco, CPO of Motivi
Additional, critical problems stood in her way:
- Demand on Staff: New pandemic regulations had already increased the staff workload in stores; they could hardly be expected to adjust to new display rules, too. Assortments would need to comply with existing rules.
- Unknown Demand: To make assortments dynamic enough to make a difference, they would need to include new items.
It wasn’t clear, however, if there were a way to estimate local demand for items never sold in a particular store.
- Store Manager Buy-in: Store managers had been using homogeneous assortment rules for years without problems. They would have to be convinced that any change would be worth the effort.
Responsive Store Assortments with Simple Rules
Motivi partnered with Evo, a ToolsGroup company, to implement Evo InventoryAI for more dynamic assortments. Prescriptive artificial intelligence allows the tools to adapt to crisis conditions and deliver valuable recommendations from the start.
“We could not afford a slow recovery, which meant that we needed to find a strategy that would successfully increase sales as quickly as possible. Evo had proven that they could develop a viable system, launch a pilot, and get results in weeks, not months or years, so we trusted them to find an innovative solution to turn KPIs around in record time,” said Greco.
Evo implemented a responsive assortment system: personalized product assortments for each store, differing over time to reduce the shelf life of slow-moving products.
This approach relies on:
- Tracking Company and Market Data
Evo combines historical sales data with extensive market and competitor data to understand local demand better. Impact was magnified by monitoring sales of over 300,000 market products and the consumer behavior of 22% of the Italian population.
- Estimating Sales Potential
The Evo system uses carefully calibrated attribute-matching to estimate the sales potential of new products. The algorithm filters this through Motivi’s visual display rules, which are translated into simple system inputs.
- Moving Inventory Dynamically
Evo InventoryAI recommends new assortments and autonomously directs inventory movements from warehouses and between stores to match local demand dynamically.
Evo’s responsive assortment was initially deployed using 85 SKUs across 19 stores paired with controls in two regions during a systematic, four-week A/B pilot test.
“I was hopeful that the new system would help us recover from the crisis, but the evidence would have to be compelling. This was a big change for our team,” said Greco.
I was hopeful that the new system would help us recover from the crisis, but the evidence would have to be compelling.”
Initial Impact: Exceptional V-Shaped Recovery of Sales for Slow-moving Products
The new assortment system freed up traditional constraints for products with more than three weeks of lifecycle, so the system could autonomously identify products with slowing sales and move them elsewhere.
This agility gave products a V-shaped rebound in sales post-transfer. Sales did not just recover; they quickly exceeded the previous baseline.
“The immediate impact exceeded our expectations. We weren’t just surviving the pandemic; we could start to fight back, to try and thrive despite it,” said Greco.
Although the Evo system meant that the assortment of products at a given store could change, store displays and layout required no significant adjustment. The algorithm was designed to respect Motivi’s overall display rules for any given product set.
Replenishment proceeded much as usual, with alternative products slotting into the displays vacated by slow-sellers.
“I was wary of adding too much logistical complexity at the store level during this difficult time. Evo recognized this and carefully followed our existing display requirements so that dynamic assortments had minimal impact on staff,” said Greco.
Long-Term Results: €8.8 Million Impact Opportunity
After the successful pilot, Greco designed the implementation route to roll out the more responsive assortment across all stores. The global implementation plan revealed an overall €8.8 million impact opportunity.
Better assortments were estimated to generate €3.1 million in extra revenue every season by satisfying sales potential across the store network. Aligning granular sales potential with purchases would create a further €5.5 million impact.
“This has been a transformative project. For the first time, we successfully tested machine-driven assortment decisions. Evo showed us that a responsive assortment could better serve demand, capturing incremental margin and revenue,” said Greco.
This has been a transformative project.”
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