Context & Challenge: helping new drivers get their first successful ride

At BlaBlaCar, new drivers (called "Newbie Drivers") struggled to secure their first passengers compared to experienced drivers. When publishing their first ride, they had a significantly lower Request Rate (percentage of rides receiving at least one passenger request), making them much more likely to churn early if they failed to get a booking.

This was a critical issue because:

• Drivers who didn’t get a request were less likely to publish again, leading to lost supply.

• Fewer successful new drivers meant lost revenue opportunities for BlaBlaCar.

  
  

To measure new driver success, we tracked three key metrics:

1. Request Rate – Probability of receiving at least one request after publishing a ride.

2. Accept Rate – Probability of accepting a passenger’s request.

3. Success Rate – The overall likelihood of a trip happening, combining Request Rate and Accept Rate.

My Role: Leading the experiment design, implementation and next steps

As the Growth Manager in charge of this experiment, my responsibilities included:

• Defining the experiment scope and specifications: I ran multiple business case simulations to estimate the revenue impact of an uplift in Request Rate and determine the threshold needed for a revenue-positive outcome.

• Collaborating with a Data Scientist to design the A/B test protocol: We ensured an unbiased test structure by segmenting drivers properly (e.g., balancing test and control groups across regions to avoid geographic biases).

• Analyzing the results and scaling recommendations: Post-experiment, we analyzed the impact, presented findings, and proposed the next steps for scaling and expanding beyond pricing.

Hypothesis: Could lower recommended prices increase the request rate, and then, overall trip success?

Newbie Drivers faced multiple disadvantages compared to more experimented drivers: no reviews, no past ratings, sometimes incomplete profiles, and suboptimal trip details. However, one key factor stood out: pricing.

BlaBlaCar used a standardized price recommendation based on distance, applied equally to all drivers, for many years. However, internal data showed that new drivers were more likely to follow the recommended price than experienced drivers and that lower pricing often led to more requests.

This led to our hypothesis:

If we recommend a significant lower price for Newbie Drivers, they would set a more competitive price, increasing their chances of receiving a request, and then being succesful —without removing their control over pricing.

The approach: A/B testing a 25% lower recommended price

To isolate the impact of pricing, we ran a controlled A/B test over two weeks in France:

• Control Group: Standard recommended pricing.

• Test Group: Recommended price reduced by ~25%.

To ensure a clean and unbiased test, we designed a robust methodology:

✔ Balanced segmentation: We matched test and control groups across similar driver profiles, routes, and regions to eliminate geographical or behavioral biases.

✔ Precise measurement: We focused on Request Rate, Accept Rate, and overall Success Rate to fully understand the experiment's impact on the entire activation journey.

Results: A 15-20% increase in new driver requests

• 📈 +15-20% request rate: Newbie Drivers in the test group received significantly more passenger requests, which was the main objective.

• 📉 -5-6% accept rate: Slightly lower acceptance rate, due to lower prices offered, making the ride slightly less interesting money-wise. It was an identified damage control that we needed to mitigate.

• ✅ Overall success rate increased: Despite the slight decrease in accept rate, the net effect was positive, leading to significantly more first successful rides for new drivers.

Impact & next steps: Laying the groundwork for future new driver's activation strategies

Beyond validating the impact of pricing, this experiment paved the way for broader Newbie Driver activation improvements. With the pricing lever confirmed, we identified additional areas for optimization, such as:

• 🚀 Profile education – Encouraging new drivers to upload a profile picture and optimize trip details.

• 📊 Best practices for listing rides – Recommending better departure times and posting in advance.

• 💡 Expanding product roadmap initiatives – These learnings helped prioritize future experiments aimed at increasing Newbie Driver success rates through a mix of pricing, education, and user guidance.

This pricing experiment helped establish a scalable framework for improving new driver activation on BlaBlaCar.