4 Common mistakes in Survey Dashboards

4 Common mistakes in Survey Dashboards

Summary & Webinar screencast

In this article, we’ll explore the realm of research dashboards and tell you how to guard against common mistakes in creating them. Dive into four major missteps that often hinder effective dashboard design. Learn how to deal with information overload, choose the right metrics, optimize user experience and harness the power of context. Plus, don’t miss the screencast of the webinar available below (please note that the content of the screencast is in German language):


In today’s data-driven world, survey dashboards have become indispensable tools for extracting valuable insights. However, it is easy to fall into the trap of overwhelming users with an abundance of information, leading to information overload. To create effective survey dashboards, it is essential to prevent information overload and condense the data to its most valuable essence. This chapter explores strategies to avoid information overload and emphasizes the importance of building a clear and concise index.

First mistake: Information overload

To make the material a bit more professional and real-data oriented, we conducted a small social research beforehand asking which of the mistakes in Creating a dashboard you consider the most serious. Information overload took the first place. And all in all, no wonder because it’s all too easy to fall into the trap of overwhelming users with an avalanche of information.

So how do we avoid this most serious mistake? At this point we need to focus on the proper visualization of the data we want to present on such a dashboard. And we can do this with the help of column charts and also stacked bar charts.

Linkedin poll about common mistakes in survey dashboard Information overload

Column and Stacked Bar Charts Guidelines

Stacked bar charts provide a powerful way to showcase the composition of different survey responses within a single chart. Column charts are a popular choice for visualizing survey data due to their simplicity and effectiveness in presenting comparisons. To maximize effectiveness of these two visualization data methods and avoid overwhelming users, consider the following guidelines:

Clear labeling: Ensure that each column is labeled accurately and meaningfully, allowing users to interpret the data at a glance. Utilize descriptive titles and concise captions to guide their understanding.

Proper scaling: Avoid distorting the data by selecting appropriate scales for the axes. Scaling should be consistent, making it easier to compare values across different columns.

Column chart made in Survalyzer

Color coding: Implementing a coherent and consistent color scheme throughout the dashboard is vital when working with stacked bar charts. Applying a consistent color code allows users to identify and track specific categories across various stacked bar charts seamlessly. This improves comprehension and aids in drawing connections and insights between different survey responses.

Highlighting key insights: Stacked bar charts can contain a wealth of information, and it is essential to guide users towards the most critical findings. Utilize visual cues, such as annotations or emphasis, to highlight significant insights or trends within the stacked bar charts. These cues draw attention to specific data points, enabling users to quickly grasp the most essential information and make informed decisions based on the survey results.

Stacked bar with coloring

Ordering the Results

Ordering the results is a crucial aspect when dealing with information overload in survey dashboards. Let’s consider an example using a column chart. By strategically ordering the results, such as arranging them in descending order based on a specific metric, you can immediately highlight the most significant data points. This not only simplifies the visual interpretation but also allows users to identify key patterns or trends effortlessly.

The benefits of ordering the results in a column chart are twofold. First, it enables users to quickly identify the highest and lowest values, providing immediate insights into the data distribution. This can be particularly useful when comparing different survey responses or tracking performance over time. Second, an ordered column chart reduces cognitive load by presenting the information in a structured manner. Users can focus their attention on the most relevant data points without getting overwhelmed by unnecessary details, leading to more efficient decision-making.

Ordering the results inside the column chart

Condensing to the Max: Building an Index

Condensing the data involves distilling the key findings into concise and digestible formats. Instead of bombarding users with extensive tables, charts, and graphs, prioritize the most significant metrics and insights. Displaying a few well-designed visualizations that effectively convey the core message is more impactful than overwhelming users with a myriad of data points.

Building an index is another valuable strategy for managing information overload. An index provides users with a clear structure and navigation system, allowing them to quickly locate the specific information they need. It acts as a roadmap, guiding users through the survey dashboard and ensuring they can access relevant insights efficiently.

Building index from Survalyzer Survey Results

Second mistake: Wrong Key Metrics

In the pursuit of creating impactful survey dashboards, it is crucial to choose the right key metrics that align with your organizational goals and provide meaningful insights. Selecting the wrong key metrics can lead to misleading interpretations, misdirected efforts, and an incomplete understanding of your customers’ experiences. Therefore, it is essential to navigate the vast landscape of available metrics and identify the ones that truly reflect the core aspects of your business.

To accurately reflect business performance and customer sentiment, leveraging AI for key driver analysis is highly effective. Survalyzer’s guide on AI-based Key Driver Analysis highlights how AI can identify the most relevant metrics, ensuring your dashboard focuses on what truly matters.

Building index from Survalyzer Survey Results

Common KPIs

  • Net Promoter Score (NPS) – is metric for gauging customer loyalty and advocacy. By asking respondents a simple question – “On a scale of 0 to 10, how likely are you to recommend our product/service to a friend or colleague?” – organizations can categorize customers into promoters, passives, and detractors.
  • Customer Satisfaction (CSAT) – delves into the realm of customer contentment and fulfillment with your products or services. It allows organizations to gauge the overall sentiment and perceived quality of their offerings through specific survey questions tailored to capture customer satisfaction levels.
  • Customer Effort Score (CES) – places the spotlight on the ease or difficulty customers encounter when interacting with your organization. It measures the effort required by customers to resolve issues, seek information, or make a purchase. By identifying pain points in the customer journey, organizations can optimize processes and streamline customer interactions.

Net promoter Score (NPS) When to use it?

NPS is particularly effective in gauging the emotional connection customers have with your brand, shedding light on their loyalty and likelihood to promote your offerings. By categorizing respondents into promoters, passives, and detractors, you can identify brand advocates, detect areas for improvement, and even predict future business growth.

However, it’s important to note that NPS may not be suitable for all scenarios. In low-touch interaction touchpoints or transactional contexts where customers have limited exposure to your brand or product, NPS might not provide the desired level of insights. In these cases, the customer experience may be too brief or focused on a single transaction, making it challenging to capture an accurate representation of their overall sentiment.

Measuring brand perception
Measuring customer loyalty
Competitor comparison
Low touch interaction touchpoint
Transactional context

Customer Satisfaction (CSAT): When to use it?

When considering the use of Customer Satisfaction (CSAT) as a key metric, one important factor to consider is whether there is a more suitable domain-specific Key Performance Indicator (KPI) available. In certain domains or industries, there might be specialized metrics that provide more accurate and targeted insights into specific aspects of the business.

On the other hand we can point a rule “In dubio pro CSAT”, which is a Latin phrase that translates to “In doubt, favor Customer Satisfaction.” It is a principle that emphasizes prioritizing customer satisfaction when faced with uncertainties or conflicting decisions. It suggests that, when unsure about the best course of action, it is generally preferable to prioritize customer satisfaction as a guiding factor. This principle underscores the importance of putting the customer’s experience and satisfaction at the forefront of business decisions.

For a deeper look into how to measure CSAT effectively, Survalyzer’s article on Measuring Customer Satisfaction offers great tips. It explains how to use simple survey questions, collect timely feedback, and analyze results to improve customer experience. These insights can help make sure your focus on customer satisfaction is both smart and effective.

Satisfaction with product or service
Transactional context
Small purchases
In dubio pro CSAT
Competitor analysis
Suitable domain KPI available

Customer Effort Score (CES): When to use it?

When it comes to utilizing the Customer Effort Score (CES) as a key metric in survey dashboards, there are certain scenarios where its application proves to be highly valuable. CES is particularly useful in situations where businesses aim to measure the level of effort customers expend when engaging with their products, services, or support systems.

However, it is crucial to recognize that CES may not be the most appropriate metric in all circumstances. For instance when the buying process is complex or in service industries like healthcare.

Technical Helpdesk
Onboarding Experience
Online Purchase Experience
Online DIY Experience
Complex Buying Process

Third mistake: Ignore User Experience

This chapter explores the third common mistake in survey dashboards: ignoring the user experience. We will delve into the importance of employing appropriate visualizations to optimize data presentation, minimize cognitive load, and promote understanding. By understanding the right visualization techniques, you can transform your survey dashboard into an intuitive and engaging platform that captures attention and facilitates data-driven decision-making.

Among the most common mistakes we can encounter in this area are misaligned coloring, too many push notifications, lack of interactivity and lack of export capabilities.

Integrating AI for text analytics can significantly enhance user experience in dashboards. Survalyzer discusses the benefits of using ChatGPT to analyze open-ended survey responses, which simplifies the process and provides deeper insights, making the dashboard more interactive and user-friendly. Learn more about this integration in our article on ChatGPT for analysing open responses.

Misaligned coloring

Inconsistency or inappropriate use of colors can confuse users and make it challenging to interpret the data accurately. Consistency in color coding and adhering to established conventions can enhance the user experience and facilitate better comprehension.

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No push notifications

While push notifications can be a valuable means of keeping users informed and engaged, bombarding them with excessive notifications can lead to annoyance and disrupt their workflow. Rather than overwhelming users with a barrage of push notifications, a more balanced approach is to leverage email notifications.

By sending periodic email updates summarizing important survey insights, users can stay informed without feeling overwhelmed. Email notifications allow users to digest information at their own pace and provide a more organized and structured approach to delivering updates.

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No interactivity

A static survey dashboard fails to engage users and limits their exploration of the data. Incorporating interactive elements, such as drill-down options, filters, and tooltips, empowers users to dig deeper into the insights and derive more value from the dashboard.

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No export capabilities

Restricting users’ ability to export data from the survey dashboard can impede their ability to further analyze or share findings. Enabling export functionalities, such as downloadable reports or data exports in various formats, ensures flexibility and promotes collaboration.

By addressing these aspects and prioritizing user experience, you can create a survey dashboard that not only presents data effectively but also fosters user engagement and satisfaction. Taking into account considerations like appropriate color usage, interactive features, and export capabilities, you can enhance the overall usability and impact of your survey dashboard.

Export report feature inside Survalyzer Dashboard Chart

Fourth mistake: missing context of key metrics

In the realm of survey dashboards, it is imperative not only to select the right key metrics but also to present them within their proper context. Unfortunately, the significance of context is often overlooked, and it remains one of the most prevalent yet underappreciated mistakes made in dashboard design. To shed light on this crucial aspect, we embarked on a journey of exploration.

We conducted a thorough examination and even reached out to professionals like you through a LinkedIn poll, seeking their opinions on the most common mistakes. Surprisingly, the missing context of key metrics emerged as a diamond in the rough, hidden amidst the choices. In this final chapter, we aim to unmask the gravity of this mistake and underscore the necessity of contextualizing key metrics in survey dashboards.

Export report feature inside Survalyzer Dashboard Chart

Unveiling the Power of Context

To further emphasize the importance of context in survey dashboards, let’s examine a real-life example. At the images below you can see a results from customer satisfaction (CSAT) survey that asks respondents to rate their personal bank advisor across various aspects such as competence, clear communication, understanding of financial needs, timely responses, and investment performance.

Export report feature inside Survalyzer Dashboard Chart

In this example, we witness firsthand how the inclusion of context in survey dashboards can elevate the understanding and interpretation of key metrics. The second chart, with its two series, provides a more holistic view, enabling stakeholders to make informed judgments based on a broader frame of reference. By contrasting Christian’s performance against the bank average, it becomes evident whether he excels in certain aspects or if there is room for improvement.

Export report feature inside Survalyzer Dashboard Chart

By presenting key metrics within a broader frame of reference, such as benchmarking against averages or comparing performance across different segments, we can unlock the true potential of our data. It is crucial to remember that a single data point lacks significance without the proper context to interpret and evaluate its meaning.


In this comprehensive exploration of survey dashboard best practices, we’ve delved into the key factors that can make or break your data analysis endeavors. From taming information overload and mastering chart guidelines to selecting the right key metrics and prioritizing user experience, we’ve paved the way for transformative insights.

Don’t overlook the nuances of color alignment, interactivity, and export capabilities—the details that bring your dashboards to life. And remember, context is key; avoid the pitfall of missing critical context for your key metrics. As you absorb these invaluable lessons, take your newfound knowledge and apply it with confidence.

Christian Hyka

Managing Partner

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