Auodoc Case Study: How Mid-Funnel Optimization Drove GMV Growth

Autodoc is one of Europe’s largest online automotive parts retailers, operating across multiple EU markets through both web and mobile platforms.

Niche: Automotive parts and accessories

Business model: E-commerce marketplace

Platforms: iOS and Android

To increase GMV through the acquisition of new payable users.

1. Autodoc already had a large user base. Incremental acquisition becomes significantly harder at this stage.
2. Creatives should be relevant to the user's needs at this moment. There is no impulse behavior with auto parts.
3. There is a high level of competition. Marketplaces, individual brands, service providers, and offline stores compete for the same demand.

1. Avoiding the Cold Start

• Strengthening of the technical setup.

The client shared key internal events via MMP postbacks across all traffic sources, including organic. This provided stronger signals to ad platforms and accelerated algorithm learning.

We alsoaligned all partners around a single sourceof truth for attribution to eliminate discrepancies in performance evaluation.

Result:faster optimization cycles and reduced non-target spend.

• Understanding User Behavior in Context

We built creatives based onoperational product data(products with consistent demand, first basket behavior, seasonal shifts, high return categories), not assumptions.

This ensured that creative messaging reflected real user intent rather than generic automotive themes.

2. Testing Before Scaling

We ran structured creative and bidding tests to identify combinations capable of reaching both CPI and purchase targets.

3. Build the working strategy

At this stage, we had strict expectations around both CPI and purchase conversion, which required maintaining efficiency without sacrificing scale.

Optimizing purely for installs increased volume but reduced purchase quality. Optimizing purely for purchase narrowed the audience and increased CPI.

So, we decided to focus on mid-funnel events such as:

• registration

• car brand or model input

• add to cart

• add to wishlist

Optimizing toward a strong mid-funnel signal allowed us to balance cost and intent.

This became the foundation for scalable growth.

4. Controlled Scaling

Once stable patterns emerged, we moved todisciplined scaling.

We avoided spontaneous or frequent budget spikes that could disrupt algorithm learning. Instead, wepatientlyexpanded placements and structures that demonstrated consistent efficiency.

+1000 new users per day

Year over year:

+15% growth in new users

+11% growth in purchases

+9% growth in GMV Stable AOV

Growth in purchases was intentionally slightly lower than user growth. We prioritized purchasing power and overall revenue impact over cheap scale.

GMV growth confirms that scaling was not only about increasing volume but also about improving the quality of acquired users and their contribution to revenue.

The model delivered sustainable expansion without eroding core business metrics.

In mature apps, systematic growth is possible without losing quality.

In our case, this means a deliberate, data-driven search for the balanced (mid-event) optimization, with a clear understanding of the audience and its behavior over time.

Focusing on mid-funnel signals allowed us not only to scale efficiently, but also to drive real business impact reflected in GMV growth.

This requires time and creativity, but stable growth is not an easy task and it is worth the effort.

Looking to increase GMV and scale user acquisition efficiently?

If you’re exploring growth opportunities for your app or e-commerce product, feel free to reach out to us at clients@mobihunter.co.