Inside 7S Analytics SLA Control Tower: Fabian Heinrich on Zalando* marketplace challenges

Why is marketplace compliance so hard to manage? Fabian Heinrich shares how 7S Analytics helps online shops detect SLA issues before it's too late, and why early insights can make or break marketplace performance.

*Zalando is not a partner company. The brand is only mentioned to describe one use case of our marketplace tool.

1. What is the 7S Analytics SLA Control Tower, and what specific problems does it solve for online shops?

Fabian Heinrich: The 7S Analytics SLA Control Tower is a dedicated module within the broader 7S Analytics platform, which is designed to evaluate shipping performance. In general, 7S Analytics harmonizes and standardizes shipping data from various carriers, making it possible to consistently track and compare key shipping KPIs such as the performance of individual delivery providers.

The SLA Control Tower specifically monitors whether service level agreements (SLAs) are being met. The goal of the SLA Control Tower is to give online shops near real-time insights into their shipping performance. It tracks the entire process from order placement to final delivery / first delivery attempt, including all key milestones along the way. The tool is designed to help identify risks at an early stage, allowing timely action to prevent low-visibility phases on marketplaces and safeguard planned revenue.

2. Where do you see online retailers struggling most when it comes to SLA compliance and marketplace performance (e.g. on Zalando*)?

Fabian Heinrich:  Online retailers face significant challenges with SLA compliance and marketplace performance especially when they rely on their own logistics set-up. This becomes even more complex when shipping across borders within Europe, where meeting the strict delivery timeframes required by platforms like Zalando* can be difficult. A typical example: Zalando* defines clear timeframes for key metrics such as DoT (Delivery on Target – from order to delivery) and RoT (Reimbursement on Target - from customer return to refund). These two KPIs frequently present issues for merchants. 

3. What are the consequences of SLA violations on Zalando* that many shops underestimate, and how does 7S Analytics SLA Control Tower help prevent revenue loss?

Fabian Heinrich: Failing to meet Zalando’s strict SLA standards can lead to serious penalties, such as temporary delisting or low visibility on the platform. During such periods, we’ve seen sellers lose up to 40% of their revenue simply because their products are no longer prominently displayed.

This is exactly where the 7S Analytics SLA Control Tower comes in. We've fully reverse-engineered Zalando’s* complex SLA logic and achieved a forecasting accuracy of over 99%. This gives online retailers near real-time visibility into their SLA performance, along with a precise prediction of where they will stand in seven days based on Zalando’s* own reporting. With this insight, they can take corrective action early and avoid costly consequences before they occur.

"The 7S Analytics SLA Control Tower reverse-engineered Zalando’s* complex SLA logic and achieves a forecasting accuracy of over 99%."

4. 7S Analytics SLA Control Tower is about detecting issues before it’s too late. Can you explain what that looks like in practice? Maybe even share a real-world example?

Fabian Heinrich: In practice, the 7S Analytics SLA Control Tower is all about enabling teams to move from reactive “firefighting” to proactive, data-driven decision-making. To illustrate how this works, let me share two real examples of use cases across the supply chain.

In the first case, the brand faced a fulfillment challenge where orders were at risk of missing the pickup cut-off time. The Control Tower immediately flagged the issue, it identified that certain shipments were spending too much time in the pick-and-pack phase. Thanks to pre-defined thresholds, the system automatically triggered an escalation to the fulfillment provider, prompting them to prioritize the affected shipments and apply hypercare where needed.

In a second example, the Control Tower uncovered a structural issue within the returns process. There was limited transparency around reimbursement-related items, which impacted compliance and customer trust. By analyzing SLA deviations, the Control Tower helped surface inefficiencies, such as infrequent return pickups and non-optimal routing. As a result, the brand restructured the return flow with direct-to-warehouse linehauls, optimized internal processes, and enabled immediate reimbursement for individual items. This effectively eliminated periods of low visibility in the return cycle.

Beyond fulfillment and returns, this scalable and proactive approach is also being used in areas like peak season delivery optimization, carrier performance tracking, and linehaul management.

5. Finally, what’s your personal tip for online retailers looking to level up their Zalando* logistics with data? Where should they start?

Fabian Heinrich: In my view, the key is simple: transparency, transparency and action!

It’s all about staying one step ahead, understanding what might happen and identifying early on what can be done to mitigate potential delays. Of course, not every issue can be avoided, and there won’t always be an immediate solution. But even from "negative" data, valuable insights can be drawn, helping to optimize processes for the next cycle and build greater operational resilience. Marketplaces (especially Zalando*) are critical sales channels for many retailers. So why not start right there and do everything possible to ensure maximum performance? Those who consistently analyze and improve their logistics performance not only reduce risk, but also gain a competitive edge.

Thanks for the interview, dear Fabian!

Want to learn more? Check out our dedicated 7S Analytics webpage!