How data-driven experimentation helped Electrolux boost service repair page completion rate by +4%

The company

Electrolux Group is a global leader in home appliances with more than 80 brands, including AEG, Frigidaire, and Zanussi. With a global workforce of 45,000 employees, the company generated SEK 134 billion in sales in 2023.

At its core, Electrolux focuses on creating exceptional customer experiences. They have been actively fostering a culture of experimentation that balances meeting consumer needs and achieving business results by understanding what customers want and optimizing based on their feedback. 

Their experimentation strategy consists of 3 foundational blocks: objective, research, and analysis. This approach has enabled Electrolux to sell around 60 million products annually across more than 150 markets.

The challenge

At the heart of Electrolux’s experimentation strategy is Contentsquare’s Experience Intelligence Platform, which enables Rebeiro Jeyapaul, Web Analytics & Insights Manager at Electrolux, and his team to collect both quantitative and qualitative data on customer behavior, sentiment, and intent. Recently, Rebeiro and his team decided to take a closer look at the performance of their repair service page. This page allows customers to book repair services online when their products break down. To improve leads and conversions, the team sought to gain a clear and in-depth understanding of how users were experiencing and interacting with the page.

Their analysis of the customer journey revealed a 35% abandonment rate at one of the page’s critical first steps: the product search. Determined to understand what was driving customers away, the team turned to Contentsquare to uncover actionable insights.

The solution

To pinpoint the cause of the drop-off and increase leads, Rebeiro’s team used 2 Contentsquare capabilities to discover friction points and better understand user interaction on the page. 

Here’s how they did it:

1. Identify key pain points in the funnel

Using Session Replay, Rebeiro and his team analyzed how users interacted with the repair service page, focusing specifically on the product search step. This step requires users to find their specific product—such as a washing machine or refrigerator model—before they can proceed with booking a repair. 

Watching back user sessions, the team identified a recurring issue: users were struggling to find the correct product option for their appliance during the search step. They discovered the search tool was unintuitive, inaccurate and provided results that weren’t relevant to the search queries used. 

An anonymous user, who the team referred to as "Lisa", encountered this frustration but persisted by refining her search criteria until she found the product option that matched her appliance. However, not every user was like Lisa. Most customers abandoned the page and didn't return after one failed attempt. 

2. Understand user experience on the page

The team used Heatmaps to visually analyze how users interacted with the repair service page and identify areas of frustration.

The heatmap analysis uncovered that users were struggling with booking an appointment. By analyzing the data, the team gained key insights, such as

• Click distribution: highlighting the areas on the page where users clicked most frequently

• Attractiveness rate: identifying which elements users found most relevant for their task

• Exposure rate: measuring the percentage of users who interacted with these elements

These metrics provided the team with a clearer understanding of how users were navigating the page, which sections drew the most interaction, and where improvements were needed to reduce frustration and enhance the booking experience. 

"We looked at attractiveness rate and exposure rate in heatmaps to try to understand the experience users were having and where they were interacting on the page. Based on this data, we identified which areas were working well and which areas were not."

Rebeiro Jeyapaul

Web Analytics & Insights Manager

3. A/B test the new design

Based on the insights gathered, the team developed a new design and ran an A/B test, using the original page as the control and the redesigned page as the variant. 

Within just 3 weeks, they observed positive results. However, instead of rushing to implement the design, they paused to analyze which aspects of the redesign were working well and why.

Using Contentsquare’s Heatmaps, they confirmed that users were interacting with key areas on the variant page that were critical to progressing their journey—further indicating that the new design was meeting its objectives. Armed with this validation, the team made a few small adjustments and eventually launched the final version of the page.

"You need to go back to the data and try to understand what’s working well. From the insights in Contentsquare, we could see that the users were interacting in the areas that were most important to them, which is also where we wanted them to be interacting."

Rebeiro Jeyapaul

Web Analytics & Insights Manager

The results

This iterative, data-driven approach saw vast improvements to the service repair page. The team noticed, after just 3 weeks, a

• -8% decrease in abandonment rate 

• +4% increase in completion rate 

Optimizing key steps in the user journey, such as improving the product search functionality and appointment booking flow, led to a smoother, more intuitive user experience and higher engagement on Electrolux’s repair service page. 

What’s next

Electrolux has built a culture that prioritizes learning, experimentation, and continuous improvement. By focusing on data-driven decisions rather than assumptions, the company ensures that customer experience remains at the core of all experiments and changes made.

In 2022, Electrolux partnered with Contentsquare and Optimizely to run 204 tests, achieving a 20% success rate with 42 driving the most results. By 2023, although the number of tests decreased to 171, the success rate improved significantly to 27%, with 47 delivering impactful outcomes.

Purchase path conversion rates also increased by +28.6% while lead conversion rates grew by +70% from Q1 to Q4 of the same year. These results are clear indicators of how their iterative, data-driven strategy is driving better outcomes.

Rebeiro Jeyapaul recently presented at Contentsquare’s CXC Stockholm event, speaking about how Electrolux built a foundation for successful experimentation.

You can watch his session to learn more about their process.

Bonus: Key takeaways

3 steps for brands looking to build an experimentation culture like Electrolux

To get similar, or even better, results from your experiments and prioritize ideas that resonate most with users, Rebeiro recommends these steps:

1. Start with an objective 

Rebeiro and his team initially overlooked this critical step in their experimentation process—they started with research instead of first defining clear objectives. Over time, they realized that having a clear direction was essential for success.

To implement this, it’s crucial to define measurable success metrics at all levels. Typically, companies operate with objectives structured across 3 levels:

• Level 1: business KPIs—high-level goals set by the leadership team that align with overall company strategy.

• Level 2: functional KPIs—team-specific objectives that support the broader business goals.

• Level 3: operational KPIs—day-to-day performance metrics tied to individual activities.

Defining these performance indicators upfront to understand what your organization is trying to achieve is important because you can later tie back your experimentation to the respective KPIs. 

KPIs work like a growth tree: your operational KPIs support your functional KPIs, and the functional KPIs further support your business KPIs. 

For example, in the ecommerce industry, sales revenue is often the primary business KPI, with leadership emphasizing its importance. To make this actionable, you need to break down this high-level objective into specific, measurable key results across all levels. This approach ensures that all experimentation efforts tie back to overall business goals.

2. Do extensive research

Companies often have conflicting opinions about what to experiment on first. To solve this and capture the right data for their experiments, Rebeiro and his team follow a 3D, triangulated approach.

1. Quantitative data: using tools like Session Replay and Google Marketing Platform (which includes Google Analytics 360 Suite) to gather measurable metrics and understand user behavior.

2. Qualitative data: leveraging tools like Heatmaps and Google Search Console to analyze how users arrive at the site and interact with it.

3. User feedback: collecting direct input through tools like Qualtrics XM or Contentsquare’s VoC capability to understand customer frustrations and identify areas for optimization.

This method ensures that decisions are based on robust data, rather than assumptions or opinions, leading to more focused and successful experimentation.

3. Analyze your data 

While the insights you find during research might all seem valuable, prioritization is key, especially when you have a limited budget. 

To manage this, Rebeiro and his team sit together and use a scale of importance versus relevance to evaluate and prioritize objectives for each experiment. They ensure that only 1 or 2 objectives are selected—having too many makes it difficult to measure success effectively.

Here are other tips to keep in mind while building your own experimentation culture:

• Validate insights with data. Always back up assumptions or findings with reliable data to ensure your decisions are grounded in facts, not opinions.

• Iterate constantly. Use each experiment as a stepping stone for further improvement, refining your approach based on what the data reveals.

View experimentation as a continuous cycle. Experimentation doesn’t end with one successful result. It’s an ongoing process of learning, testing, and optimizing to adapt to changing user behaviors and business needs.