Product People Drives Data Model Transformation for Deeper Catalog Exploration

The Client

Our Client is a rapidly growing B2B e-commerce platform that provides professional equipment for businesses across Europe. Their product catalog serves industries such as catering, manufacturing, and workshops.

They have experienced consistent revenue growth, driven by increasing demand for high-quality equipment at competitive prices, and supported by a strong distribution network.

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The Mission: Interim Senior Product Manager for Product Information Management and Shopping Experience

Our Client was transitioning from an output-driven approach to a more customer-centric mindset.

To drive growth, the company focused on improving product discovery by adding features like alternatives, search filters, and better navigation to encourage deeper exploration of the product catalog.

For e-commerce businesses, deeper catalog exploration is strongly linked to higher conversions—the more users explore the catalog, the greater the likelihood they find a product that fits their buying criteria. Historically, at our Client, users who viewed two or more products had a conversion rate approximately twice as high as those who viewed only one.

This insight shaped our key result: increase the percentage of customers who see more than one product from 12% to 25%.

To enable these user-facing improvements, the underlying product data model in the product information management system (PIM) had to be redesigned. And that is where Product People came into the picture.

The current team had only one dedicated product manager looking at the shopping experience and product information management system. There were two dedicated engineering teams — one focused on the shopping experience and one focused on the backend product information management system — collaborating with the product team.

Our Client sought an interim senior product manager to set the strategic foundation, linking the data model with customer-facing features, before bringing on a full-time Product Manager.

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Our Main Quest: Drive Deeper Catalog Exploration by Transforming the Data Model and Enabling Customer-Centric Features

Launch: Onboarded Fast

To deliver meaningful outcomes, we first had to understand the current situation and surrounding context. We did this by:

1. Engaging in OKR Planning Early
2. By getting involved in the OKR planning process from the start, we gained a clear understanding of the company’s goals and strategy, as well as how our scope of work aligned with broader business objectives.
3. Mapping the Digital Technology Landscape
4. Since our scope of work was closely tied to several key tools—such as Akeneo (Product Information Management), CommerceTools (a headless commerce platform), and Bloomreach (CRM platform)—we invested time in understanding how these systems worked together to enable the customer experience.
5. Training on Akeneo
6. To develop a comprehensive data model transformation strategy, we first conducted an in-depth study of Akeneo, mastering its key concepts and understanding its role in managing product information effectively.

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Solved for the Client: Data Model Transformation

Problem:

A significant proportion of user traffic came from Google Shopping, but 9 out of 10 users viewed only one product before leaving the site. To encourage users to explore more products—and ultimately increase conversions—one key tactic was to present them with alternative products. The more users explored the catalog, the higher the likelihood of conversion.

However, the underlying data model was not equipped to support the required features. The data lacked standardization across the catalog and did not align with the definitions provided by Akeneo, the Product Information Management (PIM) system used by our Client.

📚 Akeneo: a Product Information Management system that helps e-commerce businesses keep every product detail (like descriptions, images, and pricing) accurate in one place. This makes it easy to share the same information across online stores, marketplaces, and other channels.

Solution:

The key steps for achieving the data model transformation were:

1. Educational Workshop on Akeneo PIM
2. Data Model Transformation Roadmap
3. Template for Attribute Mapping
4. Migration Strategy
5. Playbook

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Educational Workshop on Akeneo PIM

We conducted an Educational Workshop on Akeneo PIM with the Category Management and Engineering teams to:

1. Align on Company Goals: Explain how improved data modeling directly impacts the desired outcomes.
2. Introduce Key PIM Concepts: Provide a clear understanding of the core principles of a Product Information Management system.
3. Highlight Current Challenges: Discuss the problems stemming from the existing implementation to build a shared understanding of the need for transformation.

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Data Model Transformation Roadmap

The Educational Workshop served as a crucial first step in building awareness for the category management team, but a clear roadmap was needed to guide the execution of the data transformation.

To address this, we developed a high-level data model transformation roadmap outlining the key milestones, dependencies, and responsibilities required to achieve the desired outcomes.

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Within each milestone, we further identified the breakdown of necessary steps and owners.

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Template for Attribute Mapping

The next step was to collaborate with Engineering and Category Management to create a template for mapping the existing data model to the desired new model. This mapping would be consumed in later stages for data migration.

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Migration Strategy

A migration strategy was essential to transition the existing data into the desired structure. To achieve this, we collaborated closely with Engineering to develop a custom migration tool. This tool significantly reduced the risk of errors associated with manual migration while also optimizing for efficiency and time.

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Playbook

The transformation of the data model was not a one-time effort but an ongoing process across the catalog, requiring a significant investment of time and resources. To mitigate risks and minimize the time required, a repeatable, scalable, and safe process was essential.

We created a Playbook for the Category Management team, detailing all the key steps with clear and actionable instructions.

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Solved for the Client: Development-Ready Delivery Roadmap

Problem:

The data model transformation alone wasn’t enough to achieve deeper catalogue exploration—it only served as a foundation for the new user-facing features. However, this presented a chicken-and-egg challenge: we needed to define the features to scope the data model changes while also accounting for technical feasibility and category management constraints within both the current and evolving data model.

Solution:

To create a development-ready delivery roadmap, we adopted a parallel approach to align the data foundation with the feature roadmap. This was achieved through:

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1. Brainstorming Features

A combination of competitive research, experimentation, and effort estimations led to a matrix of potential solutions.

2. Calculating Projected Impact

Based on the experimentation results, we developed a business case for each of the potential features brainstormed.

3. Preparing a Now-Next-Later Roadmap

The combination of projected impact, expected effort, and dependencies led to the creation of a Now-Next-Later roadmap.

4. Creating a Release Sequence Plan

Once the high-level roadmap was established, we broke the features into smaller releases and planned their sequence of work, considering technical dependencies, data dependencies, and the capacities of the Engineering and Category Management teams.

This approach would also allow us to gather user feedback after each release, enabling us to refine the roadmap and adjust priorities based on real-world insights.

5. Writing Development-Ready Functional Requirements

For the planned releases, we created Product Requirement Documents (PRDs) and worked with Engineering and UX to align the scope of work, functional requirements, tracking needs, and next steps.

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Solved for the Client: Key Performance Indicator (KPI) Tree

Problem:

The current product lacked tracking for key events, making it impossible to measure the impact of the proposed and built features. Without visibility into user behavior, there was no way to assess how these changes influenced business outcomes.

Solution:

We developed a KPI Tree rooted in the ultimate metrics we aimed to influence—such as conversions—and branched it into leading metrics within our locus of control.

This was accomplished through close collaboration with the Engineering and Business Intelligence teams to:

1. Identify the tracking requirements.
2. Implement the necessary tracking mechanisms.
3. Design an approach to present and consume actionable insights effectively.

The KPI Tree is not static but evolves over time. Our approach was to use the initial KPI Tree to track and measure the success of the initiatives we launched, while also analyzing whether any key information was missing for future decision-making. This would allow us to iteratively improve the KPI Tree to better align with evolving business needs and priorities.

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Discovery Mini-Missions: Our Side Quests

In addition to our Main Quest, we also did the following Side Quests

• Build vs Buy analysis for the CRM tool
  • The current CRM tool was being used for product recommendations. We conducted an analysis to evaluate the cost incurred versus the value generated and compared this with the option of building baseline recommendation functionality in-house.
• Playbook for Complementary Products Mapping
  • While our primary focus was on presenting Alternative Products, a parallel initiative led by another PM aimed to surface Complementary Products to users.
  • We collaborated with the PM to create a playbook for mapping complementary products in Akeneo PIM, enabling features that supported cross-selling of complementary items.

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Mission Achievements: Delivered Outcomes

💡 Defined a scalable process for mapping and migrating attributes from the existing data model to the desired structure. Successfully mapped 12+ product families out of the top 40 in the product catalog.

💡 Crafted a development-ready delivery roadmap with a collective projected impact of +4% on the North Star Metric: the percentage of users that see two or more products.

💡 Created a KPI Tree to ensure all the necessary tracking was in place for measuring the impact of launched features.