Like other fashion brands, Motivi was hit hard by the Covid-19 pandemic. Lower store traffic significantly depressed revenues, especially because Motivi’s in-store sales had historically outstripped online sales.
Could personalised assortments incentivise customers to visit stores and buy more frequently? Motivi implemented responsive assortment using Evo Assort and Evo Plan, differentiating by store and over time.
The result: an exceptional V-shaped recovery after the end of Covid closures, with an additional €8.8m impact opportunity to be captured. Freed of traditional assortment constraints, sales of slow-moving products could quickly rebound once transferred.
Auto-assortment: V-shaped sales rebound,
without adding discounts or markdowns
Motivi Case Study
Context: Motivi in 2020
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: Miroglio 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.
The challenge: 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 lifecycles could motivate customers to visit stores and buy more often.
Fear of missing out is a crucial driver of customer behaviour in the fashion industry today. Personalised 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 cannibalising existing sales.
-Nicoletta Greco, Motivi CPO
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 was 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.
The solution: responsive store assortments with simple rules
Motivi partnered with Evo to implement Evo Assort and Evo Plan for more dynamic assortments. Prescriptive Artificial Intelligence allowing 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.
Evo implemented a responsive assortment system: personalised product assortments for each store, differing over time to reduce the shelf life of slow-moving products.
This approach relied on:
1. Tracking company and market data
Evo combined 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 behaviour of 22% of the Italian population.
2. Estimating sales potential
The Evo system used carefully calibrated attribute-matching to estimate the sales potential of new products. The algorithm filters this through Motivi’s visual display rules, which were translated into simple system inputs.
3. Moving inventory dynamically
Evo Assort 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 2 regions during a systematic, 4-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.
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.
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 recognised this and carefully followed our existing display requirements so that dynamic assortments had minimal impact on staff.
Long-term results: €8.8 million impact opportunity
After the successful pilot, Greco desgined 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 assortment was 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.
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