Dev Summit 2026

Most retail models fail by confusing correlation with causality. High-value customers might create more projects, but does creating a project actually increase their value? This presentation showcases the Adeo XP Award-winning "Drivers of LTV" project, which isolated the true incremental impact of driver—from life events to digital interactions—to build a high-precision business simulator.

In an era of infinite output, where AI can generate endless solutions in seconds, the role of the human leader shifts from "creator" to "framer." I want to explore why the abundance of AI-driven production makes the quality of the initial input—the problem framing—the only true competitive advantage and the skill of the future combined with strategic alignment among product teams.
Product teams success will heavily rely on radical team alignment with shared outcomes. When the speed of outputs accelerates, the direction and strategy we are heading towards needs to stay clear and precise.
Let's move beyond "more" and master "better" with intentional input and unified outcomes and visions.

This keynote will explore how Zalando positions itself as a tech company, with AI at the core of its strategy.
It will highlight how AI is leveraged to elevate customer experience, driving differentiation through personalization, inspiration, and seamless journeys.

## Context

Leroy Merlin is more than a home improvement retailer; it is a **one-stop shop that empowers people with end-to-end solutions to enhance their homes and their lives**. Leroy Merlin Brazil is a benchmark within the group when it comes to service sales.

However, throughout our digital journey, services had historically been treated as a **complement to product purchases**, confined to checkout flows or hidden behind low-visibility pages.

This structural constraint reduced offer visibility, limited the sale of standalone services, and prevented us from fully leveraging one of our **core strategic assets**.

In this presentation, we will share how we **redesigned and rebuilt the digital service journey** to transform installation services into **searchable, discoverable, and scalable digital products**.

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## Redesigning the Digital Service Journey

The initiative was grounded in a **robust Discovery phase**, including:

- Deep dives with the **Instala operational team**
- Interviews with **store floor staff at the Tietê store (São Paulo, Brazil)**
- **Behavioral analysis using Microsoft Clarity**
- **A/B testing experiments**

Based on these insights, we redesigned the user journey to make services **visible and accessible across the entire digital funnel**.

### Key Experience Changes

**Smart Search**
Relevant installation services are now suggested **directly within the search bar**, allowing users to discover services earlier in their journey.

**Listing Pages (PLP)**
Dedicated tabs allow users to **toggle between "Products" and "Services"**, enabling easier discovery of standalone services.

**Product Detail Page (PDP)**
A new **service component** was introduced on product pages, including:

- Transparent service pricing
- Warranty information
- A **toggle switch** enabling users to add installation services **directly to the cart together with the product**

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## Preliminary Results

Early results already demonstrate the impact of integrating services directly into the product discovery journey.

The new experience generated a **242% increase in sessions that add services to the cart**, growing from an average of **40 to 137 daily visits** with service attachment.

We estimate this increase represents an **additional annual revenue of approximately R$613,000 from services attached to product purchases**.

Service discoverability also improved significantly. In just **five days after launch**, visits to **service pages increased by 17%**, demonstrating the impact of making services more visible within the search experience.

These early results reinforce our core hypothesis: **when services are made visible and integrated into the decision journey, they unlock new value within digital retail ecosystems.**

We call ghost stockout the situation in which the stock is marked as available on digital stock management inventories, but not physically available in the store.

We present an unsupervised algorithm for detecting ghost stockouts using only daily sales data. We model each SKU's daily sales outcome as a time dependent Bernoulli process with a parameter representing the probability of selling one product on a given day, denoted as p₀(t). A ghost stockout manifests as an anomalous zero sales streak, whose probability

P(N,t) = ∏ from i=1 to N of (1 - p₀(tᵢ))

serves as a detection signal. In a ghost free environment this signal is uniformly distributed across SKUs; in the presence of ghost stockouts, the empirical distribution exhibits a relative abundance of low P(N,t) values far exceeding the theoretical uniform baseline, a signature that is both theoretically predicted and experimentally confirmed.

We estimate p₀(t) via an adaptive moving average procedure that self calibrates its window size to maintain a bounded relative estimation error across the full range of SKU sale rates and seasonal profiles. Crucially, the entire pipeline, including p₀(t) estimation, error bounding and signal computation, was implemented in pure SQL on BigQuery, eliminating the need for dedicated ML infrastructure and dramatically reducing deployment cost.

Validated across two retail stores over one month (3,220 physical inventory checks), the method achieved an overall true positive rate of 15.5%, rising to 17.8% on self service departments. In the top performing department store pairs the TPR reached 35%, demonstrating that when applied to structurally well defined availability contexts, this fully unsupervised algorithm delivers detection performance competitive with supervised approaches.