Crisis-Driven Retail Transformation: Israel’s Largest FMCG N

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During the COVID period, Israel’s largest FMCG retail chain faced a severe structural shock. Movement restrictions sharply reduced physical shopping, while a large share of FMCG customers—especially older and habit-driven buyers—were not yet comfortable buying groceries and household goods through a standard e-commerce interface.

This was not a typical digital growth initiative. It was an emergency transformation problem: demand still existed, but the habitual buying environment had collapsed.

Management set a clear task: find a way to restore sales to the highest achievable level as quickly as possible, moving as close as possible to the pre-COVID baseline. My responsibility was to design the project system, define the solution architecture, assemble the required specialists around the problem, and prepare the transformation logic for approval and execution. The approved solution was built as a staged migration model aligned with customer psychology: first reduce behavioral resistance, then introduce personalization and predictive commerce once trust and usage patterns were established.

Across the broader restriction period, sales were down by approximately 60–65%, with sharper declines at the most extreme points.

The core problem was deeper than traffic loss:

· customers still needed essential goods;

· many were not ready to shop in a purely digital way;

· standard catalog navigation did not replicate how FMCG purchases are usually made;

In practice, the challenge was to move a traditionally offline audience into online purchasing quickly, at scale, and without forcing a sudden behavioral break.

My role was to architect the transformation process: define the solution logic, design the project structure, assemble the required cross-functional resources, coordinate execution after approval, and report progress and outcomes to management.

My contribution sat at the intersection of anti-crisis sales transformation, product strategy, CX design, and digital commerce architecture.

 The core working group included more than 10 people across:

· psychology;

· UX/UI;

· analytics;

· CRM and marketing;

· external ML support.

This project was an emergency transformation program focused on anti-crisis sales recovery. The market context required rapid launch, live observation, and iterative refinement in production.

The first stage lasted 5 months.

Rather than forcing customers directly into a standard e-commerce catalog, we introduced a transitional interface designed to mimic the familiar logic of physical shopping. The experience was built around virtual shelf navigation inspired by real planogram principles. It was not a 3D store simulation, but it preserved enough of the visual and behavioral logic of in-store selection to feel intuitive.

This was the key strategic choice.

The problem was not only digital readiness. It was behavioral mismatch. Many customers were used to choosing products by visual recognition, shelf context, and habitual store movement. A standard catalog stripped away that decision environment. The temporary interface restored it in a digital form.

The solution worked because it did not ask users to change their behavior all at once. It created a laminar transition:

· familiar visual product structure;

· lower cognitive friction;

· easier basket-building for routine purchases;

· reduced resistance among older and less digitally adapted customer groups.

This was supported by detailed UX refinement based on customer feedback, shopping psychology, visual hierarchy, and navigation patterns.

The interface was introduced through a multi-channel customer activation effort, including:

· social media;

· email and printable guidance;

· inserts in delivered orders;

· promotional messaging inside the digital storefront.

Customers could choose which interface to use, and the system preserved their behavior and preferences over time.

Stage 1 Outcome

· The solution reached approximately 112,000 users.

· Around 32,000 new usersengaged with the digital shopping path.

· It created a practical migration layer at a moment when competitors were slower to adapt.

The importance of this stage was that it created a psychologically acceptable entry point into online buying for customers who otherwise might not have transitioned at all.

Once users had crossed the behavioral barrier, the second stage shifted the model from transitional UX to personalized digital commerce.

The company had a strong structural advantage: a high-coverage loyalty ecosystem. More than 80% of customers had identifiable purchase history linked to loyalty cards. That made it possible to move beyond catalog shopping and into behavioral prediction.

Historical data made it possible to identify patterns such as:

· purchase intervals;

· seasonal shifts;

· price and promotion sensitivity;

· household consumption habits;

· likely family composition changes;

· category relationships and adjacent needs.

This created the basis for a predictive basket model.

Predictive Basket

When a returning user entered the website or app, the system could pre-fill a substantial part of the expected order based on prior behavior and detected patterns.

Users often kept most of what the system had already selected for them.

In many repeat sessions, customers retained roughly 82% to 94% of pre-filled basket items, only removing a small portion or adding several missing products before checkout.

That turned shopping from a manual search process into a partially automated approval flow.

Explainable Recommendations

The next important step was that the system moved beyond routine essentials and began surfacing adjacent recommendations with clearer reasoning.

Instead of acting like a black box, the product increasingly explained why a suggestion might be relevant—based on known purchasing behavior, recurring patterns, category preferences, or situational context.

This mattered because it reduced resistance to automation. Customers were not being forced into a machine-led purchase path; they were being onboarded into it gradually, with visible logic and increasing trust.

Migration to the Final Model

The move from the temporary interface to the final personalized catalog was gradual.

For approximately 3 months, users could still choose between interfaces while the company communicated the transition in advance and monitored reactions.

That migration was highly successful:

· approximately 92% of usersmoved from the temporary shopping interface to the final personalized catalog experience.

 The core measurable outcomes of the program were these:

· Online sales grew by 300%+ over the broader transformation period.

· Median session time decreased from 8–12 minutes to 2–3 minutes.

· Average basket value increased by 16–18%.

· Migration from the temporary interface to the final catalog reached ~92%.

These are the headline results.

There were also additional directional outcomes that reinforced the business case:

· during the strictest restriction period, online accounted for roughly 75–80% of sales mix;

· after restrictions were lifted, online remained the dominant channel at around 60%, indicating durable behavioral change rather than temporary emergency usage;

· cross-category penetration increased by roughly 30% after recommendations became more visible and better integrated into the purchase journey.

 The result came from the way the solution matched the reality of the moment:

• the crisis was real and immediate;
• customer behavior could not be changed through abstract digital optimization alone;
• the business needed a bridge rather than a forced jump;
• personalization became materially more effective after behavioral migration had been solved first.

The sequence mattered:

1. recreate enough of the familiar shopping logic to reduce resistance;
2. migrate users into digital behavior without cultural shock;
3. then use loyalty data and predictive logic to make online buying materially easier than offline buying.

That was the basis of the commercial result and the distinctiveness of the solution.

Beyond immediate sales recovery, the program helped establish online commerce as a structurally stronger business model inside the retail system.

As digital demand stabilized, the company gained greater flexibility to optimize parts of its retail network and rely more on broader-assortment, more centralized formats where appropriate.

Customer feedback also suggested a deeper shift in shopping behavior. For some users, weekly purchasing became a much lighter and more routine process, with far less time spent on travel, search, and repeated low-value decisions.