How Maiia scale its User feedback Strategy with Screeb?

At Cegedim Santé, a recognized player in medical computing, Alexandre Dandler, the UX/UI manager, faced major challenges in restructuring feedback collection for their flagship product, Maiia. This case study explores the problems and impacts related to inefficient feedback collection on a product like Maiia. It also details how Alexandre and his team transformed their approach to feedback collection by placing data at the heart of product decisions. This new method now allows for a better understanding of the needs of both professional and individual users, and helps to better prioritize the product roadmap.

Summary of Maiia's User Feedback Strategy

Challenges at Maiia:

1. Difficulty Measuring Results: It was challenging to objectively analyze feedback due to biases introduced by anecdotal evidence from the sales team.
2. Inefficient Feedback Collection: The initial reliance on emails for feedback collection did not effectively engage users, most of whom use the app.
3. Insufficient Feedback Volume: The company's focus on intensive qualitative feedback methods did not allow for the scalable collection of extensive insights.

Implemented Solutions:

• Adoption of Screeb: Transitioning to an in-app customer feedback platform significantly improved the quantity and quality of feedback collected.
• Integrated Feedback Loop Strategy: Using Screeb for in-app surveys and precise data segmentation, the team facilitated informed, data-driven decisions.

Notable Impacts:

• Support for Product Decisions: The new tools enabled the collection of rapid and accurate feedback, effectively guiding development choices.
• Optimization of the Product Roadmap: By leveraging detailed user feedback, the team could prioritize development efforts more strategically, thus meeting the most critical user needs.

The Context of User Feedback at Maiia

Cegedim Santé, a leader in medical computing for over two decades, offers innovative digital solutions for health professionals and their patients, with Maiia as its flagship product. Alexandre Dandler, serving as UX/UI manager, is tasked with optimizing this platform to facilitate its use and enhance the experience for both professionals and individuals.

With the support of a twelve-member team, Alexandre has implemented:

• Processes to improve collaboration between product and development teams;
• UX tools designed to capture user needs more precisely;

Before Alexandre's arrival, Cegedim Santé already understood the importance of collecting user feedback to refine their understanding of needs and, thus, build and prioritize a product roadmap aligned with Maiia's growth.

However, Alexandre quickly identified several major challenges related to the existing feedback collection system:

1. A feedback collection system exclusively by email, while the majority of traffic is generated via the application.
2. An insufficient volume of user feedback, limiting the ability to make significant improvements.
3. Difficulty for him and his team to effectively measure the outcomes of initiatives taken.

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First Problem: Inefficient Feedback Collection Tool

The current tool, which relies solely on email, fails to effectively target Maiia's main audience, who predominantly use the web and mobile app. This method leads to several notable problems:

• Inadequate Targeting: difficulty in segmenting users based on their profile or context of use.
• Inappropriate Collection Channel: email surveys reach a low response rate given that most users prefer the app.
• Insufficient Data Collection to clearly identify user friction points.
• Inappropriate Choices in the Product Roadmap, not addressing the real needs of users.
• Frustrations of the Sales Teams confronted with user dissatisfaction, often expressed over the phone.

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Tip:

To maximize the effectiveness of your feedback collection, it is essential to know the preferred mode of interaction of your users.

If a web or mobile app is at the core of your service, seriously consider an integrated feedback tool. In-app feedback rates are significantly higher than those obtained by email.

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To resolve this issue, Alexandre identifies several solutions:

• Finely segment user feedback to ensure relevant analysis.
• Ensure a match between the surveyed individuals and the questions asked to improve the quality of responses.

Maiia’s Problem: Inability to Measure Feedback Results

The second major issue faced by Alexandre’s team was their inability to reliably measure the results of user feedback, whether positive or negative. These were regularly influenced by the subjective impressions of salespeople, which biased the analysis.

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A Concrete Example at Maiia Illustrating the Impact of a Misinterpretation of User Feedback

A concrete example illustrates the impact of this problem on the organization and crisis management of the company:

In response to a crisis period, Maiia decides to launch a new feature on a Wednesday.

The next day, several salespeople expressed their discontent concerning the ergonomics of this novelty. Under pressure, the Product team decided to withdraw the feature, reverting to the previous version.

But what about the end users?

Without the ability to quickly and effectively measure the returns of the end users, the team backtracked and removed a feature that could have been appreciated by them.

Data-Driven Decision Making

💡 Second Tip:

When working with multiple departments with varied profiles, it is crucial to base your decisions on factual data rather than on subjective opinions.

Opt for a tool that allows you to analyze your user feedback and extract key insights simply, quickly, and effectively!

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To resolve this issue, Alexandre will implement:

• The ability to objectively measure user feedback, thus avoiding reliance solely on the opinions of internal teams.

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The Impact of Screeb on this IssueFollowing the implementation of Screeb, Maiia faced a new crisis situation similar to the previous one.

This time, however, the Product team decided to react differently by launching an urgent Customer Satisfaction Survey (CSAT) among users to base their decisions on objective data rather than subjective perceptions.

The results of this survey revealed that the dissatisfaction, as expressed by the salespeople and some users, was not as pronounced as it appeared:

1. The CSAT scores were not particularly positive, but they were similar to those obtained during the previous campaign;
2. The negative feedback reported by the salespeople constituted only a small percentage of the overall feedback.

Thanks to these insights, the Product team was able to make an informed decision, based not on a limited group of users but on the majority of feedback.

Third Challenge: Insufficient Quantity of Feedback Collected

To ensure high-quality feedback, Maiia was already using targeted feedback collection methods:

• Focus Groups: This method generates data on user reactions and facilitates the emergence of various viewpoints.
• Individual Interviews: These allow for the collection of detailed feedback on the user experience through open-ended questions.
• Shadowing: This method reveals natural behaviors and unexpected difficulties, providing valuable insights into the actual user experience.

However, these approaches proved insufficient for collecting user feedback on a large scale. Combined with traditional tools for collecting feedback via email, these methods were slow and consumed a lot of time and energy for often limited results.

To extract key quantified insights and effectively prioritize the Product roadmap, Alexandre needed to collect a greater number of feedbacks. For this, he implemented a dedicated CSAT schedule.

💡 Tip:

Like Alexandre, it is essential to mix different feedback collection strategies: combine the depth of individual interviews with the breadth of multiple-choice surveys to enrich the data.

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Example of a Dedicated CSAT Schedule:

The Solution: Moving from Traditional Feedback Collection to a Large-Scale Approach

Following these three challenges, Alexandre decided to transform the way of collecting user feedback at Maiia and sought a solution that meets these criteria:

• In-App feedback solution, suitable for web and mobile applications
• Segment the collection of feedback according to the different Products of Cegedim Santé and the target;
• A solution that places data analysis at the core of the product with measurement of quantitative and qualitative results
• A solution that allows for managing different types of data collection
• A solution that can collect user feedback on a large scale and centralize it

Maiia’s Final Choice for Screeb

After evaluating various platforms based on price, features, and user interface, Alexandre and his team chose Screeb for several reasons:

• Optimal performance on the collection of In-App web and mobile feedback;
• Intuitive and playful UX product interface;
• Competitive pricing, often cheaper for comparable features;

Screeb enables Alexandre to quickly collect quantified and qualified feedback on the same product. Integrated with ProductBoard, which centralizes all insights and user feedback, Alexandre has developed an effective feedback loop strategy:

1. Creation of quantitative in-app surveys with Screeb;
2. Segmentation of user feedback according to their satisfaction and needs;
3. Synchronization with ProductBoard and the CRM;
4. Planning of individual interviews via Screeb to better understand user needs;

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Examples of Surveys Conducted at Maiia:

Impact No. 1: A Crucial Tool for Product Decision-Making

To illustrate this impact, here's an example narrated by Alexandre:

“During a busy period, we had to quickly develop a feature without conducting Product Discovery, relying on our existing knowledge.

Faced with two competing proposals, we did not know which was the most appropriate.

We were thus able to quickly target and survey users on their preferred option via Screeb, and decide on the best version to develop.”

Impact No. 2: Better Choice in Prioritizing the Product Roadmap

By collecting a significant amount of user feedback and easily extracting key insights, Screeb now allows Maiia to prioritize its product roadmap more effectively, based on facts rather than opinions.

Conclusion

Alexandre Dandler's story reveals the importance of good feedback collection, and thus the choice of a tool suited to your target. By moving from traditional and ineffective feedback methods to a dynamic, data-driven approach, Cegedim Santé has not only improved its product development cycle but has also strengthened its commitment to meeting the evolving needs of healthcare professionals and their patients.

Adopting a feedback collection strategy is not merely an option but a necessity to remain relevant and responsive in a competitive market. As we move forward, the ability to respond to user feedback will continue to define tomorrow's leaders, particularly in sectors as critical as healthcare.

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