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Minimizing data difference, maximizing data quality

  • Web analytics
Client
hugo-boss
International retailer

Textile industry

Tools we used
Key project parameters:
  • Identification of 33 possible scenarios for missing data
  • Elimination of the biggest sources of error
  • Solution development for effective attribution in the current data infrastructure
Table of contents

The e-commerce platform HUGO BOSS Webstore was already equipped with a CRM system and a data warehouse (DWH). But its fast growth in terms of both revenue and geographic reach placed extremely high demands on the technologies – this called for an update.

While the DWH is for executing the actual business (logistics, transactions, etc.) only, Google Analytics is used to answer the basic Web Analytics questions like sessions, marketing touchpoints, onsite user behavior or attribution. Both systems track the same e-commerce transactions, yet there were deltas between the data they produced.

Differences in numbers can not only lead to false interpretations of KPIs, but also cause confusion for employees – they don’t know which system to trust and which numbers to rely on when planning actions like new marketing campaigns. As the importance of the Webstore was clearly increasing, HUGO BOSS approached FELD M for support in determining exactly why there were deltas between the systems and what could be done to minimize them.

Understanding the data: Deep dive into Google Analytics and DWH

We identified the data warehouse as the single source of truth for HUGO BOSS for all transactional data. The goal of the project was to find an explanation for the problem of missing data and, ideally, to suggest solutions to solve it. If possible, the results should include actual numbers regarding the amount of lost data. The data discrepancies mainly affected the online marketing functions (SEA, SEO, affiliate, display, social), but others like CRM, e-mail, IT and BI were also involved.

In an initial workshop, we were able to identify 33 scenarios that could cause data differences between the two systems. Within these scenarios were topics like “Intelligent Tracking Prevention” (ITP) from Safari, the correspondent “Enhanced Tracking Prevention” (ETP) from Firefox, duplicated transactions, fraud detection and cancellations, adblockers and page loading speed. After defining these scenarios, we began deep dives into each.

Based on the wide range of experience within the FELD M team, we had a head start on the project. Most of the problem scenarios we looked at were familiar from other Web Analytics setups. In addition, the FELD M specialists were able to carry out Advanced Analytics in BigQuery and technical evaluations swiftly.

For ITP, for example, we worked on a complex evaluation of the whole customer journey and were able to provide HUGO BOSS with a deeper understanding of the data. We explained the increasing volume of direct traffic and the reasons for the shorter costumer journey.

Improved e-commerce data quality for more precise marketing attribution

With this clear picture of data and how differences arose between the in-house data warehouse and Google Analytics, HUGO BOSS can now implement the recommended solutions to keep the data quality high – a key asset in marketing attribution and a high ROI on marketing. Although not all data gaps could be closed, with the detailed explanations from FELD M, HUGO BOSS is now aware of the reasons and how data are affected.

 

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