Algorithmic Pricing & Pricing Platforms for the European "Amazon of Fashion" as Intern Product Managers

The Client: European "Amazon of Fashion"

Founded in 2008, our client is a German multi-national eCommerce company with headquartered in Berlin, Germany, it also has tech hubs in Dublin, Dortmund, and Helsinki.

The company follows a platform approach, offering fashion and lifestyle products to customers in 23 European markets.

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The Mission: Interim Product Manager

This was our fourth mission for our client. We joined in early April 2022, to be Interim Product Managers on the Pricing and Commercial teams. Our main goal was to enhance and optimize their pricing platform and support the team’s continued success.

The company has a Lounge Designer Outlet that offers customers up to 75 % discount compared to the retail recommended price on high-quality brands. Moreover, this Lounge has several teams, of which, we interacted with the Pricing Team, Algorithmic Data Products Team, the Pricing Analytics Team, and the Product Team. The main pricing ambitions of the Lounge revolve around determining the products to be discounted which depends on Stock Levels (overstock from fashion stores + directly from brands) and Themes: Seasons, Holidays, Events, and Weather.

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Our Main Mission

Launch: Onboarded Very Fast

We onboarded quickly and made sure to gain knowledge about the company’s goals, vision, and pain points. This was made possible by our ability to conduct in-depth stakeholder interviews, which allowed us to swiftly comprehend the demands and difficulties that the pricing team was facing. We designed various solutions for the Pricing Platform after conducting interviews to get insight into the direction we should go in.

As the solution design started to take shape, we discovered when we initially joined the team that the Lounge platform of the company needed a dedicated product manager and a point of contact for stakeholders for this pricing platform. Our swift onboarding helped us to establish ourselves in no time.

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Explore and Conquer: Solved for the Client

Discovery for the Pricing Platform

The Lounge proposition aimed to provide exciting fashion moments through a distinct and ever-changing product offering. This is especially relevant for the core individualist customer portrait, the ‘treasure hunters’, who are looking for unique articles within the framework of deals.

We successfully designed the discovery process by finding the opportunities, conducting stakeholder interviews, and then iteratively redesigning the price class categorization for demand forecasting and steering. This involved a series of interviews with various stakeholders, documenting results and deriving insights from the interviews.

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Introduced Incremental Development Approach

We were able to introduce an incremental development approach where each increment was 8 weeks. A new feature was first proposed by the product team in the form of a Narrative Document. Once the Narrative had all the open questions answered, a work package for that narrative was documented and shared with the solution driver from the development team. Development and Product teams then decide upon the work packages that would go into increments before starting to develop those work packages.

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Initiative 1 - Lounge Pricing Scheduling and Orchestration

Problem analysis

The Orchestrator was responsible for managing price offers generated from different price sources. As a key driver during the solution discovery, we outlined and worked on the following problems:

1. The functionality of the orchestrator didn’t incorporate multiple price sources. It was important to have multiple price sources in order to facilitate analysis.
2. It didn’t provide traceability for different price sources.
3. There was no support for different price sources for the purpose of A/B testing.
4. It didn’t have a uniform data schema for two of the main teams to conduct pricing analytics through dashboards.

What we did

In this delivery-driven process, we took into account the price offers from different data sources, separated price sources based on which prices will be published, proposed the application of calculation rules on them, and separated the prices that will remain unpublished. After that, we designed a solution to materialize the published prices to export them to Simple Price Service (SPS).

The solution was broken down into 7 work packages for the developers to work on after finding the customer journey touchpoints during the discovery process. The solution included:

1. Materialization of the desired prices.
2. Introduction of a component to take ADP prices for A/B testing.
3. Separate tables for comparison of different data sources.
4. Make sure that the data schema for each queue is similar so that the data can be easily compared.

Initiative 2 - Freshness for Price Class

Problem analysis

Freshness is a term used to define how new article content is on the Lounge platform. However, the existing logic had faults in reflecting this stock freshness in our pricing to guarantee that the overall offering is exciting for the end customer.

1. Supplier Stock Classification: Current classification included inconsistent labeling of certain articles as new which could lead to potential revenue losses due to the higher profit margin classification and, thus lower discount rates.
2. Online Visibility: Current classification did not consider pricing or demand relevant input parameters, potentially jeopardizing the article’s initial profit when the articles were first going online on the platform.
3. Repeat Articles: Incorrect categorization of certain articles that have never been online could lead to potential revenue losses since the freshness of the articles hasn’t been factored into their pricing.

What we did

Mainly driven by the early stage discovery of Freshness, we formulated our hypotheses through extensive research and documentation, we have run several workshops to drive alignment and mutual understanding of the pricing definitions across the Lounge. Furthermore, for the areas that required deep-dive analytical research, we have aligned with the Pricing Analytics Team to initiate data support and analytics. The proposed solution underlined the following topics:

1. Reclassification of article classes specially designed for the pricing team to accommodate the pricing steering needs.
2. Incorporation of all the relevant pricing parameters that are missing in the current classification as well as minimizing the drawbacks of the current article classes.
3. Improved focus on steerability through increased flexibility of the margin constraints application to different article classes.

Freshness Discovery was a sub-initiative of the Price Class PRFAQ that explored the full scope of the stakeholder needs to define new article classes.

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Mission Achievements: Delivered Outcomes

💡 Created and aligned pricing definitions across our client’s Lounge platform through Discovery Workshops.

💡 Designed Work Packages to ensure the implementation of the New Version of the Orchestrator by the Pricing Platform Team.

💡 Developed PRFAQs and Narrative Documents to answer open questions that came up during the discovery process.