By Rick Kieser, Ascribe CEO

The customer satisfaction survey has become an epidemic. Whether you are buying a product, eating at a restaurant, or enjoying some other experience, it won't be long before you receive an email asking you to complete a survey about the experience. As sociologist Anne Karpf writes in The Guardian, "So many organizations now want our feedback that if we acceded to them all, it would turn into a full-time job – unpaid, of course. … The result is that I'm suffering from feedback fatigue and have decided to go on a feedback strike." She is certainly not the only consumer who feels this way!

Quality of Feedback: A Tale of Two Surveys

With consumers having such negative perceptions and experiences with customer satisfaction surveys, you have to wonder about the quality of the feedback going back to the business. I recently took my family to Disneyland. As usual for Disney, most of the experience was stellar. After our visit, I had two ideas I wanted to share. 1) The staff was outstanding, knowledgeable, and helpful, and 2) They should not upcharge Genie+ customers for lightning lanes on select rides. As I expected, less than 24 hours after leaving, I received the usual invitation to give my feedback about our experience at Disneyland. Given my profession, I was looking forward to this! I clicked on the link and started the survey.

Ten minutes later, I had completed less than 20% of the questionnaire, it was a compilation of closed and open end questions with no end in sight. I was done. I aborted the survey. Even worse, in my ten minutes invested, I did not find an opportunity to provide the two pieces of feedback I wanted to share!

Now, compare that to the survey sent by a hotel I visited. It was only two questions long. The first question asked me to rate my experience on a 10-point scale. The second was an open-ended question: "Please tell us about your experience." Again, I wanted to share two thoughts: 1) The hotel restaurant was spectacular, with a beach view and great food. 2) We had to wait over 20 minutes before a server came to help us. As you can imagine, I was happy to complete that survey! Three minutes, DONE.

Which survey do you think gave better information about my thoughts and feelings? The hotel survey, of course, because it let guests tell them what they wanted to share about their visit in their own words.

Customer Satisfaction Surveys that Customers Like

Now, there may be internal or political reasons that make it difficult to change from a rating scale-based survey to one that is primarily open-ended. However, if we want more insightful feedback and customers who are happy to give it, we need to respect the customer’s time and move beyond lengthy surveys with many frustrating questions. We need short and sweet surveys that allow the respondent to express their thoughts clearly and quickly their way.

One of the traditional complaints about using open-ended responses over scaled responses is that open-ended responses are too wordy, too complicated, and too expensive to code and analyze quickly. That is no longer true, as we have the technology today to interpret these results efficiently and cost-effectively. Because of this, we need to get our surveys aligned with what is possible in data analysis solutions now, or we risk alienating our survey respondents to the point where they will no longer volunteer to answer questionnaires and we risk eroding their view of the brand or service.

The best solution is to create questionnaires with a few closed-end questions and one open-ended question: "Tell us about your experience." Yes, just one open-ended question. The technology can separate and analyze the responses. A few closed-end questions are needed to filter for data analysis, such as satisfaction rating, demographics, and so forth. But you can replace all the open-ended questions (e.g., What did you like? What did you dislike? Why did you give that rating?) with just one question.

Open-End Analysis in Just Minutes

The latest and best technologies can take even the most wordy, rambling, and detailed responses and analyze them in minutes. When you are thinking about collected customer opinions, social reviews are the epitome of vehicles through which customers express how they are really feeling in their own words. Here's an example of over 1,500 reviews scraped from the internet from recent London Eye visitors, all unstructured, open-ended comments. As you may know, the London Eye or Millennium Wheel, on the South bank of the Thames, is the most popular paid tourist attraction in the U.K., with over 3 million visitors annually. Here is an example of one person's review.

In spite of some rather lengthy reviews, within a matter of minutes we were able to identify and quantify the dominant themes from these 1,500 reviews using Ascribe's CX Inspector with Theme Extractor. We also created a cross tab identifying differences in responses based on who else was along for the experience: family, couples, friends, or solo. If coded manually, this data set would have taken a market research firm two days to analyze, at significant cost. With CX Inspector the results were ready within 30 minutes.

Here is another example of what is possible with today's technology. We analyzed 1,500 customer reviews with 145,000 words on a local ice cream shop in just over 20 minutes using Ascribe's CX Inspector. Again, the key themes were immediately identified, and using sentiment analysis, we could quickly understand customer likes and dislikes. It looks like the ice cream is delicious, and some staff are friendly and provide a positive experience, but some people indicate the experience is marred by poor service and expensive prices! This store owner would be able to quickly understand what they need to address to improve customer satisfaction.

As a final example, here are results of 2,500 customer surveys for a sports arena. In addition to a seven-point rating question, the survey included a follow-up open-ended question: "Why did you rate your experience 1 to 7?" The responses, which included a total of 58,000 words, were analyzed in 20 minutes with CX Inspector to reveal that while the arena delivers a great experience with terrific staff, concession lines and parking are key drivers of dissatisfaction. Again, the arena management can quickly understand what they need to work on to improve the visitor experience.

Find Out What Customers Really Think

Customer satisfaction surveys are ubiquitous, but the traditional approach of lengthy questionnaires may not be the best way to understand what customers are truly thinking if they get impatient answering the questions or are not willing to finish the survey. With new technology capable of coding and analyzing open-ends so easily, quickly, and cost-effectively, there is no need to have burdensome customer satisfaction surveys with a battery of close-ended and open-ended questions. By allowing customers to express themselves in their own words quickly, brands can better understand the customer experience and what matters most to them, while building customer loyalty through an improved survey experience. You will get better and richer customer feedback. And the best and only open-end question you need to ask is, "Tell us about your experience."

Embracing open-ended questions in your customer satisfaction surveys lets you alleviate feedback fatigue and invite genuine insights. The advent of generative AI-driven text analytics tools like Ascribe's CX Inspector with Theme Extractor allows brands to delve deeper into open-ended feedback quickly and easily. Customers will reward brands willing to ditch the traditional satisfaction survey in favor of an open-ended approach with more meaningful and actionable customer feedback. 

Increase your customers' satisfaction by simplifying your surveys! Contact Ascribe today to discuss your needs, and we will find the best solution for you!  

By Rick Kieser, Ascribe CEO

Market Research has been around for about 100 years. Some might say we are set in our ways, but in reality the industry has been rapidly innovating in response to marketing changes, technology, and other challenges. We've come far from door-to-door interviewers and Mad Men-esque focus groups! And we are now experiencing one of the most fundamental shifts in our industry and specifically in text analysis in decades.

An Erupting Volcano

In the market research landscape, our traditional data analysis approach resembles a long-dormant volcano getting ready to erupt. For years, many have focused on the top of the volcano, the visible summit—the structured data that is easy to access and straightforward to analyze from surveys. However, a repository of unstructured data remains largely untouched beneath the surface. These text comments hold valuable insights that have been challenging to access and analyze. While difficult to explore, this unstructured data contains incredible potential if it could be unleased.

As we all know, the landscape is changing rapidly. Loaded with open-end responses and unstructured data from surveys, social media, and the internet, the volcano is exploding. According to Gartner, unstructured data constitutes 80% to 90% of all new enterprise data and is growing rapidly. Just as molten lava transforms the surrounding landscape, this eruption of unstructured data, which is now overwhelming for many, will similarly reshape our approach to understanding consumers. Harnessing the insights from this eruption has been one of the most significant disruptions in the evolution of market research since we began.

Many businesses are unprepared for this shift. Only about 10% of unstructured data is currently stored, and even less is analyzed, according to the International Data Corporation. Historically, analyzing unstructured data was a time-consuming process, akin to decoding hieroglyphs. Few marketing people had the time, resources, or motivation for such an effort.

Is your company experiencing this volcanic eruption of open-end comments coming from customers, partners, and employees? Are you ready to dig below the easy-to-analyze closed-end data to get beneath the surface to find true insights, thoughts, desires, and intentions? A fast, cost-effective, and proven analysis approach is the key to unlocking the potential of unstructured data.

Enter Generative AI, heralding the next evolution of Market Research. Leveraging Gen AI with text analytics to analyze this massive amount of unstructured data provides access to the insights hiding underneath the volcano. It's a transformation from the elementary knowledge of the known structured data landscape to a new universe of depth and clarity of consumer understanding. While much of the uncovered insights will be used to answer everyday business questions, if analyzed properly, the data has the reliability and strength to guide even the most critical strategic decisions for your business.  

‍

Gen AI's Rapid Evolution Creates Challenges

Gen AI is advancing at warp speed, generating constant evolution across the MR industry. And with that evolution comes challenges. Remember that less than a year ago, the Market Research industry was vilifying Gen AI as the industry's demise! The MR Industry is now embracing Gen AI to unleash the value of the huge mass of previously-neglected verbatims and unstructured feedback. Many new, young firms are jumping in to sell their Gen AI solutions to marketers, and to the less experienced, their solutions appear magical at first glance. As new versions of Gen AI are being rapidly released, those platform developers are having difficulties keeping up with providing human-like theme-based insights that are quantifiable and verifiable to the unstructured data analysis. Some of the challenges created by the more recent Gen AI releases include:

• Gen AI analysis creates a plethora of results – too many ideas and codes, making it challenging for brand marketers to sort out the most important ideas.
• Gen AI is now creating complex codes, combining two ideas into one. For example, "too salty and spicy," combines two ideas about how a food tastes. However, this result makes it difficult to determine how big an issue "salty" is and how big an issue "spicy" is. For ideas to be actionable, we need concise, singular ideas, each of which is quantified.
• Summaries created by Gen-AI are not precise enough for use in decision-making. Now, of course, marketers are enamored with the summarizing capabilities of Gen AI. If you have purchased anything online lately, you can quickly find a consumer ratings summary created by Gen AI. However, these summaries are often too broad to be meaningful, and it is always challenging to identify the underlying data the summary was sourced from. The summaries miss important insights and may not represent information in the right proportions. Finally, it is well recognized that Gen AI may also make things up (now referred to as hallucinations or mirages) depending on how the tool was trained.

Ascribe Leverages Gen AI to Develop Innovative Solutions

Ascribe has been innovating to solve these Gen-AI-created issues with the latest release of one of our most impactful techonology developments – Theme Extractor. Helping Ascribe stay one step ahead of the industry, Theme Extractor is included in all of Ascribe's solutions, including CX Inspector for text analytics and Coder, a verbatim analysis platform for market research companies,. Ascribe has a 25-year history as the original verbatim analytics platform for the MR Industry, building state-of-the-art open-end analysis solutions. We have processed more than 4 billion unstructured comments, exponentially more than all other providers' experience in processing customer feedback combined. Our developers have deep experience with Gen AI and are uniquely equipped to build solutions that meet the needs of our partners.

‍
Theme Extractor Extracts Superior Well-Developed Ideas from Open-End Comments

The initial version of Theme Extractor leveraged Generative AI to transform the results of open-end analysis from single-word codes to descriptive, meaningful codes that articulate the essence of the idea, a huge advancement for the market research industry. Note the example below from customer satisfaction results for a retailer; whereas before an idea might be a single-word topic such as "items", with Theme Extractor, the idea becomes "have popular items in stock." Similarly, a code of "employees" becomes "more employees would be helpful." As you can see, Theme Extractor extracts much more detailed information from the customer responses, giving you a deeper understanding of the consumers' thoughts and feelings.  

‍

‍

The most recent release of Theme Extractor has improved the accuracy of the results and addresses the issues of too many codes and complex codes being created by the latest versions of Gen AI. Theme Extractor creates concise ideas focused on one theme, correcting the tendency of Gen AI to combine ideas into complex topics. As such, in a mascara product study, the complex code "Lengthens and separates lashes" Theme Extractor separates into two themes, "Lengthens lashes" and "Separates lashes." Separating ideas is important in the analysis to make the results useful for decision-making. This is a vital detail that can easily be overlooked in a sales demonstration but is critical to the experienced brand marketer or market researcher in real life.

Also, Theme Extractor reduces the overlap between codes, thereby reducing the number of themes. In the same mascara study, the "High price" and "Too expensive" codes are more likely to be one combined idea, resulting in less overlap and more effective analysis. Finally, it is important to note that during analysis with Theme Extractor, the user can suggest the number of codes to classify the results into, which further puts the power of AI under the user's control.

Another important Ascribe innovation is the ability to quantify the emotions and empathy Gen AI identifies around a topic. Emotion and empathy can be insightful, but if they are unquantified, they are insufficient to be helpful to brand marketers. The magnitude of those emotions (or topics) and how they link to satisfaction, dissatisfaction, loyalty, and other customer states must be quantified to use the insights identified. For example, an unquantified analysis of emotion might yield two ideas, "Love the service" and "Wish the front desk staff were friendlier", from which you would conclude that "Love the Service" is more important as it is a stronger emotion. However, if the results indicated that only one person said, "I love the service," and 100 people said, "I wish the front desk staff were friendlier," the latter becomes more important. The ability to understand the emotion must be combined with frequency and quantification to get a useful insight, giving brand marketers the understanding they need for actionability and decision-making.

‍

Enabling Human Guidance of Gen AI Produces Powerful Customized Results

Other innovations in the most recent Theme Extractor upgrade enable human control to guide and oversee the analysis and output of the results. Many users, especially experienced market researchers, want a deliverable similar to what they have produced in the past, for example, to enable tracking against previous studies or to use language relevant to the business. Ascribe's platforms enable manual input, giving you control over the automation in real-time and letting you adjust the results to meet your business needs. Some of the manual changes available in the latest Theme Extractor:

• Turn Gen AI on or off.
• Provide context for the data for more accurate results.
• Easily edit your results by renaming, combining, drag and drop manipulation, etc.
• Train a codebook and save it for future use; great for trackers.
• Set the number of codes and levels of nets appropriate for your needs.
• Connect the theme to the original responses (drill down.)
  

Ascribe's innovative Theme Extractor enables market researchers to analyze a dataset with open-end comments in minutes or hours, adjusting the amount of manual editing to control costs, timing, and end results.

‍

Ascribe Innovations for 2024

Being the original verbatim analytics platform, we at Ascribe are continually exploring ways to harness advanced technologies, including Gen AI, to develop superior open-end analysis solutions that are easy to manage and maximize efficiencies in meeting your business insight needs. We are building APIs to enable direct access to our system for real-time integration with other platforms. We are developing a Gen AI tool to summarize results from the analysis accurately and will also provide updated charting and visualization capabilities to meet your analysis and reporting needs.  

‍

Choosing the Right Gen AI Approach

Using Gen AI will be critical to tap into the value delivered through successful analysis of unstructured data. It is essential that you choose the right Gen AI approach for you. If you seek a partner to help you get value from your text analysis, choose that partner carefully. Look for a trusted partner with years of experience in MR, extensive experience with Gen AI, and live training and support services to help you when needed. When evaluating the platform, request a live demo using your data to see firsthand if the results meet your business needs. Finally, ensure the interface is user-friendly and integrates easily with your current operating processes and management tools.

Recently, we have had discussions with companies attempting to build their own analysis platforms. While their initial solution might work for one specific use case, it becomes difficult and expensive when they need to scale it and make it repeatable, and even more so as they will need to continually absorb and respond to the rapid advancements in Gen AI. AI expertise is scarce and expensive, so building your own platform quickly becomes a costly and time-consuming strategic commitment for your company. We welcome the opportunity to discuss your business needs and share how Ascribe can meet those needs with speed and cost efficiencies.

‍

Gen AI Is No Longer A Question - It's a Necessity

As you work to harness the explosive insight power of open-ends flooding your company from disparate sources, you must decide how to implement Gen AI in text analysis so that it becomes more effective, faster, and less costly. Your decision about the partner you choose to help you with this could pay huge dividends in the future. After all, it's no longer a question of if you should implement Gen AI; it's only a question of how.

Remember to look beyond the demo to your partner's expertise in Gen AI, text analysis, and the MR industry. Their experience in the insights industry will ensure that they are not only keeping up with the rapidly evolving status of Gen AI but also how to translate that evolution into products and innovations that can best serve you and your business. Curious to see it in action? Request a demo today.

Faire en sorte que les gens répondent à vos sondages peut être un véritable défi — même si vos questions sont bien conçues. L'un des moyens les plus efficaces d'augmenter le taux de participation, c'est d'offrir des incitatifs pour les sondages.

Dans cet article, nous abordons :

• Ce que sont les incitatifs pour les sondages
• Pourquoi et quand les utiliser
• Les types d'incitatifs les plus courants
• Leurs avantages et inconvénients
• Des conseils pour les utiliser sans nuire à la qualité des données

‍

Qu'est-ce qu'un incitatif pour sondage ?

Un incitatif est une récompense offerte aux répondants en échange de leur participation à un sondage. L'objectif est d'augmenter le taux de réponse en reconnaissant la valeur du temps des participants.

Les incitatifs peuvent être :

• Directs (ex. : cartes-cadeaux, codes promo, échantillons gratuits)
• Indirects (ex. : dons faits à une cause au nom du répondant)
• Garantis (chaque répondant reçoit une récompense)
• Conditionnels (ex. : tirage au sort parmi les participants)

Choisis et remis stratégiquement, les incitatifs peuvent aider à recueillir plus de réponses de la bonne audience, tout en renforçant la relation avec les participants.

‍

Est-ce que les incitatifs fonctionnent vraiment ?

Oui — ils augmentent de façon significative le taux de réponse.

Les données le prouvent :

• Même de petites récompenses prépayées (ex. : 5 $) peuvent doubler les taux de complétion.
• Un coupon de 2 $ pour un film a déjà permis d’augmenter les réponses de plus de 300 %.

Cela dit, les incitatifs ne se contentent pas de motiver la participation : ils peuvent aussi influencer qui répond, et comment. D'où l’importance d'une mise en place rigoureuse.

‍

Quand utiliser un incitatif ?

Tous les sondages n'en ont pas besoin. Par exemple :

• Les formulaires de retour rapide ou les sondages NPS obtiennent souvent un bon taux de réponse sans récompense.
• Les bases clients engagées sont souvent déjà motivées à répondre.

Les incitatifs sont utiles quand :

• Vous ciblez un public peu engagé
• Le sondage est long ou exigeant
• Vous demandez une présence physique (ex. : entrevue en personne)
• Vous ciblez un groupe difficile à recruter

‍

Comment bien utiliser un incitatif pour un sondage

1. Déterminez si c'est nécessaire Si vos taux de réponse sont déjà bons, il est inutile d'ajouter des incitatifs. Réservez-les pour les sondages longs ou plus exigeants.
2. Choisissez le bon type Tenez compte de votre audience, de votre budget, et de la valeur des données que vous cherchez à recueillir.
3. Réfléchissez au bon moment Remettre l'incitatif avant ou après la participation ? Le moment peut influencer la motivation et les coûts.
4. Assurez une livraison fluide Les récompenses numériques (ex. : cartes-cadeaux électroniques) sont simples à distribuer. Prévoyez un processus clair et professionnel.
5. Offrez une valeur cohérente Des récompenses trop faibles n’attirent pas, et trop généreuses peuvent biaiser les réponses. Trouvez un équilibre entre motivation et intégrité des données.

‍

8 types d'incitatifs populaires pour les sondages

1. Incitatifs monétaires Cartes-cadeaux, transferts, chèques. Simple, efficace, très motivant. Idéal pour : panels B2C, sondages longs
2. Tirages ou concours Moins coûteux mais biais possible. Nécessite des mentions légales. Idéal pour : campagnes de notoriété, publics larges
3. Produits gratuits ou essais Échantillons, accès temporaire à un service. Crée un lien avec la marque. Idéal pour : tests produits B2B/B2C
4. Dons caritatifs Une récompense altruiste qui attire des profils motivés par la cause. Idéal pour : études sociales ou liées au secteur non lucratif
5. Récompenses par points Points cumulables à chaque sondage. Favorise la fidélité. Idéal pour : panels communautaires sur le long terme
6. Contenu exclusif Accès à des livres blancs, rapports, webinaires. Idéal pour : publics professionnels (B2B)
7. Codes promo ou rabais Pour vos propres produits ou ceux d’un partenaire. Peut stimuler l’achat. Idéal pour : e-commerce, marques DTC
8. Incitatifs en partenariat Co-branding avec une autre entreprise. Moins coûteux, audience partagée. Idéal pour : études communes

‍

Avantages des incitatifs

• Augmente la participation, surtout pour les sondages longs
• Améliore l’engagement et le taux de suivi
• Montre du respect pour le temps des répondants

‍

Risques à surveiller

• Risque de réponses biaisées ou peu sincères
• Représentation faussée si l’incitatif attire un certain profil
• Coûts logistiques ou financiers élevés
• Surutilisation = perte de valeur perçue

Pour limiter les risques : validez votre échantillon, utilisez des questions de contrôle, et ne laissez pas l’incitatif prendre le dessus sur l’objectif de l’étude.

‍

En conclusion

Utilisés stratégiquement, les incitatifs peuvent vraiment faire la différence. Il faut simplement que leur valeur soit proportionnelle à l’effort demandé et aux insights recherchés.

Bien pensés, ils augmentent la participation tout en montrant du respect pour le temps et l’opinion de vos répondants — la base de toute étude de qualité.

‍

Survey coding is an invaluable tool for researchers aiming to analyze the open-ended responses in surveys. This method involves categorizing and labeling textual data from responses to questions that allow participants to express themselves freely, beyond the constraints of predefined choices.

In this post, we'll explore what survey coding is, why it's essential, and how it can transform unstructured open-ended responses into actionable, quantitative data. We'll walk you through the process of creating a comprehensive codebook, discuss the best practices for ensuring consistency and accuracy, and highlight some of the common challenges you might encounter along the way.

What Is Survey Coding?

Survey coding of open-ended responses involves organizing and categorizing textual data gathered from survey questions to make it easier to analyze. Here's a detailed breakdown of the process:

• Collection of Responses: In surveys, alongside multiple-choice questions, there are often open-ended questions where respondents can provide their answers in their own words.
• Initial Review: The responses are first reviewed to understand the range of answers provided and the different ways respondents interpret the question.
• Development of Codebook: A codebook is created which defines categories or themes that the responses can be sorted into. This involves identifying common themes, patterns, or recurring phrases within the responses.
• Coding the Responses: Each response is read and assigned one or more codes based on its content. This coding process can be done manually by researchers or with the aid of text analysis software which can help to automate some parts of the process.
• Refinement of Codes: As coding progresses, some codes might be split, combined, or refined to better capture the nuances of the responses. This is an iterative process that may require going back to previously coded responses and reassigning them under the new scheme.
• Analysis: Once coding is completed, the coded data can be analyzed quantitatively (e.g., calculating the frequency of each code) or qualitatively (e.g., examining the context around certain codes to understand deeper meanings).
• Reporting: The results are then compiled into a report, providing insights such as common themes, unusual opinions, or general sentiment about the surveyed topics.

Survey coding is essential for effectively using open-ended responses, as it transforms qualitative text into quantifiable data, allowing for a more structured analysis that can complement the statistical findings from closed-ended questions.

Benefits Of Quality Survey Coding

Survey coding, especially of open-ended responses, offers several important benefits that enhance the value of survey data for research, decision-making, and strategy development. Here are some key advantages:

1. Rich Insights: Open-ended responses can provide depth and context that closed-ended questions might miss. Coding these responses helps in extracting these nuanced insights systematically, allowing for a more comprehensive understanding of participants' opinions and experiences.
2. Quantifiable Data from Qualitative Responses: By categorizing qualitative responses into predefined codes, researchers can quantify this data. This quantification makes it easier to perform statistical analysis, such as identifying trends or comparing subgroups within the data.
3. Identification of Themes and Patterns: Coding helps in identifying common themes and patterns that may not be immediately apparent. This can be especially useful in exploratory research where the range of possible responses is not well known beforehand.
4. Enhanced Data Management: Coded data are easier to manage, store, and retrieve. Researchers can quickly access and analyze large volumes of data without needing to sift through each individual response repeatedly.
5. Improved Reliability and Consistency: A well-defined coding scheme ensures that data is processed consistently, reducing the variability introduced by different researchers’ interpretations. This enhances the reliability of the data, making the findings more robust.
6. Facilitates Comparison and Tracking Over Time: Coded data can be compared across different groups or tracked over time more easily than raw textual data. This is particularly useful for longitudinal studies or when comparing responses across different demographics.
7. Supports Mixed-Methods Research: Coding allows for the integration of qualitative data into predominantly quantitative studies, supporting mixed-methods approaches that can provide both breadth and depth in research findings.
8. Feedback for Future Surveys: Insights derived from coded responses can inform the development of future surveys, such as by helping to refine questions, adjust response options, or identify new areas of interest that require exploration.

Overall, survey coding is a powerful tool that transforms text data into highly-precise structured, actionable information, providing a deeper understanding of the research subject and enhancing the impact of the findings.

When Do You Use Survey Coding?

Survey coding is used in several specific situations during research and data analysis, particularly when dealing with qualitative data from surveys. Here are some common scenarios where survey coding is especially useful:

1. Analyzing Open-Ended Survey Responses: Whenever surveys include open-ended questions where respondents can write their answers freely, coding is used to organize these textual responses into quantifiable categories. This allows for systematic analysis alongside the quantitative data from closed-ended questions.
2. Exploratory Research: In early stages of research, where the aim is to understand broad themes and sentiments about a topic, coding helps identify and categorize these themes from survey responses. This is useful for shaping further research or developing hypotheses.
3. Market Research: Companies often use survey coding to analyze customer feedback on products, services, or experiences. Coding helps identify common complaints, suggestions, or praises, guiding business improvements and product development.
4. Academic Studies: Researchers in fields like sociology, psychology, and health often use survey coding to analyze data collected through questionnaires. It helps them understand patterns, relationships, and influences among variables based on participants’ textual responses.
5. Customer Satisfaction and Feedback Analysis: To gauge customer satisfaction and gather actionable feedback, businesses code responses from satisfaction surveys. This can inform customer service policies, product improvements, and overall business strategies.
6. Policy and Public Opinion Research: In policy-making and public opinion surveys, coding is used to categorize responses to open-ended questions about laws, regulations, or political issues. This helps in understanding public sentiment and informing policy decisions.
7. Longitudinal Studies: In studies that track changes over time, coding allows researchers to consistently categorize responses across different time points. This is crucial for accurately measuring how opinions, behaviors, or experiences change.
8. Content Analysis: Coding is used in content analysis where the content of text data—such as responses to an open question about media usage or preferences—is categorized into defined codes to analyze trends and patterns.
9. Qualitative Data Integration: In research, where both quantitative and qualitative data are collected, coding qualitative responses allows for integration with quantitative data, providing a richer, more comprehensive analysis.

In all these scenarios, survey coding is an effective solution for transforming unstructured comments into structured data that can be analyzed statistically.

Survey Coding Best Practices

Adhering to best practices in survey coding ensures that the data derived from open-ended responses is reliable, consistent, and useful for analysis. Here are some key best practices to follow when coding survey responses:

1. Develop a Comprehensive Codebook: Start by creating a detailed codebook that clearly defines each code, including descriptions and examples. This serves as a guideline for coders to apply the codes consistently. It should also include rules on how to handle ambiguous or unclear responses.
2. Train Coders Thoroughly: Ensure that all coders are thoroughly trained on the codebook and understand the objectives of the coding process. Regular training sessions can help maintain consistency, especially as the codebook might evolve over the course of a project.
3. Ensure Inter-Coder Reliability: Use multiple coders for the same set of responses initially to check for inter-coder reliability, which is the level of agreement among different coders. This helps identify any ambiguities in the codebook and ensures that the coding is reliable and consistent.
4. Use Pilot Testing: Before full-scale coding, conduct a pilot test with a sample of responses. This helps in refining the codebook by identifying new themes or issues that weren’t initially apparent. Adjust the codebook based on the findings.
5. Iterative Process: Be prepared to revisit and revise the codes as you process the responses. As you dive deeper into the data, new themes might emerge or existing codes might need refinement.
6. Maintain Coding Consistency: Regularly review the coding work to ensure consistency over time, especially for large projects or long-term studies. This might involve periodic retraining sessions or recalibrations of the coding rules.
7. Automate When Appropriate: Consider using software tools for coding if the volume of data is large. Many tools offer features like text parsing, pattern recognition, and preliminary coding suggestions, which can increase efficiency. However, human oversight is crucial to handle nuances and context that the software might miss.
8. Document All Processes: Keep detailed records of all coding decisions, changes to the codebook, and any issues encountered during the coding process. This documentation is vital for the credibility and replicability of the research.
9. Analyze Coded Data Critically: When analyzing the coded data, be critical of the codes themselves and the potential for bias or error. Analysis should consider not just the frequency of codes but also their context and the interrelations between different themes.
10. Ensure Ethical Standards: Respect the confidentiality and anonymity of survey respondents, especially when handling sensitive information. Ensure that all data handling and coding practices comply with ethical guidelines and legal requirements.

By following these best practices, you can maximize the accuracy and utility of the coding process, thereby enhancing the quality of data derived from open-ended survey responses.

Differences in Using Survey Coding vs Text Analysis To Analyze Open-End Survey Responses

Survey coding and text analytics are both methods used to process and analyze text data, but they have different focuses and methodologies. Understanding their distinctions can help in choosing the right approach for a given research need.

Survey Coding

Survey coding primarily deals with categorizing and tagging open-ended responses collected from surveys. It involves interpreting responses based on a predefined set of categories or themes that researchers develop to capture the essence of the text data.

Methodology:

• Manual or Semi-Automated: Coding can be done manually by researchers or semi-automatically using software that assists in categorizing responses.
• Developing a Codebook: Researchers create a codebook that defines each category or code. This includes descriptions of what type of response fits each category.
• Application: Codes are applied to each response to summarize and categorize the data, making it easier to analyze statistically.

Survey Coding Use Cases

It is commonly used in market research, social science research, customer feedback analysis, and anywhere qualitative data needs to be quantitatively analyzed.

Text Analytics

Text analytics involves a broader set of techniques designed to extract information and insights from text data. It uses algorithms and natural language processing (NLP) techniques to uncover patterns and insights within large volumes of text.

Methodology:

• Automated Tools: Text analytics is typically performed using software and algorithms that can process large datasets more efficiently.
• Techniques: This includes sentiment analysis, keyword extraction, topic modeling, and more. These techniques automatically identify and quantify various elements within the text without needing a predefined codebook.
• Natural Language Processing (NLP): Text analytics heavily relies on NLP to understand the grammar, structure, and even the sentiment of the text.

Text Analytics Use Cases

Text analytics is used in a wide array of applications like business intelligence, market analysis, customer service improvements, and sentiment analysis across various types of text sources like social media, customer reviews, and news articles.

Key Differences Between Survey Coding and Text Analytics

• Scope: Survey coding is more specific in scope, focusing on categorizing survey responses into predefined themes. Text analytics is broader, applying various computational techniques to extract insights from text responses.
• Automation: Survey coding can be manual or semi-automated, while text analytics is highly automated, leveraging complex algorithms and machine learning.
• Purpose: Coding is primarily about simplifying and structuring text for analysis, often in academic or formal research contexts. Text analytics is about discovering patterns and insights in text data, used across many industries for various business and research purposes.

In essence, while both methods aim to derive meaningful information from text, they do so in different ways and are suited to different types of analysis and data volumes.

FAQs

What is survey coding?

Survey coding is the process of categorizing and labeling open-ended responses collected from surveys. This process involves defining a set of codes, which are thematic or categorical labels, and applying them to the responses to organize the data into meaningful groups. This makes it easier to analyze qualitative data quantitatively.

Why is coding important in survey research?

Coding is essential in survey research because it transforms raw, open end comments into structured, analyzable form. This allows researchers to perform statistical analysis, identify trends, and draw significant conclusions from the data. Coding also ensures that data interpretation is systematic and consistent, improving the reliability of the research findings.

What are the differences between manual and automated coding?

Manual coding involves researchers applying codes to survey responses by hand, which can be time-consuming but allows for nuanced understanding. Automated coding uses software to apply predefined codes to text data. While faster and more consistent, it may not handle nuances as effectively as a human coder. The choice between manual and automated coding depends on the project's scale, complexity, and available resources.

Contact the Survey Coding Experts at Ascribe

Survey coding is an essential practice for transforming unstructured, open-ended responses into structured, actionable data. If you are seeking survey coding capabilities, Ascribe, with over 25 years of experience and having processed over 6 billion responses for the top global market research firms and corporations, offers cutting-edge open end analysis solutions. Ascribe Coder is the leading coding survey platform designed for high efficiency and precision, and CX Inspector is the premier text analytics solution equipped with advanced tools to decipher and illuminate the underlying sentiments and insights in textual data.

For a deeper dive into how Coder and CX Inspector can transform your data analysis process and significantly enhance your research outcomes, we invite you to schedule a demo and let us show you what we can do using your own dataset.

La recherche de terrain est une méthode de recherche qualitative qui consiste à observer et analyser les sujets dans leur environnement naturel. Contrairement aux expériences contrôlées ou aux tests en laboratoire, elle vise à capturer des comportements, des interactions et des contextes réels — permettant ainsi aux professionnels des études de mieux comprendre comment les individus ou les organisations fonctionnent au quotidien.

Qu’il s’agisse d’étudier des individus, des groupes ou des dynamiques sociales, la recherche de terrain permet des observations directes et contextualisées, offrant des insights riches et nuancés.

‍

Pourquoi mener une recherche de terrain ?

La recherche de terrain permet de découvrir des informations difficiles — voire impossibles — à obtenir avec des méthodes classiques comme les sondages ou les expériences. Elle permet notamment de :

• Étudier des comportements dans leur contexte naturel
  
  
• Comprendre les contextes sociaux ou culturels qui influencent ces comportements
  
  
• Explorer de nouveaux sujets lorsque les données existantes sont limitées ou indisponibles
  
  

Elle est particulièrement utile lorsque :

• Le sujet est complexe ou sensible
  
  
• L’environnement influence fortement les comportements
  
  
• L’objectif est d’obtenir des insights exploratoires plutôt qu’une validation à grande échelle
  
  

Avec les bons outils — comme un logiciel de gestion des interventions terrain ou une application de sondage mobile offline — la recherche de terrain peut être plus fluide, structurée et évolutive.

‍

Méthodes courantes en recherche de terrain

Voici cinq méthodes couramment utilisées en recherche de terrain. Chacune permet de collecter et interpréter les données de manière différente selon les objectifs poursuivis.

1. Entretiens qualitatifs

Entretiens individuels basés sur des questions ouvertes qui encouragent les participants à s’exprimer librement.
Idéal pour : comprendre les perspectives, motivations et expériences personnelles.

2. Observation directe

Observation des sujets dans leur environnement naturel, sans interaction ni influence du chercheur.
Idéal pour : capturer des comportements en temps réel avec un minimum d’interférence.

3. Observation participante

Le chercheur intègre le milieu observé et participe aux activités tout en les analysant de l’intérieur.
Idéal pour : les études immersives où la confiance et la perspective d’initié sont essentielles.

4. Ethnographie

Étude approfondie d’un groupe ou d’une culture, axée sur les interactions dans un cadre social donné. Elle implique souvent des observations prolongées.
Idéal pour : comprendre les normes sociales, les dynamiques communautaires et les influences culturelles.

5. Études de cas

Analyse détaillée d’un événement, d’un individu, d’un groupe ou d’une organisation dans son contexte réel.
Idéal pour : explorer en profondeur des phénomènes complexes, sans objectif de généralisation.

‍

Avantages de la recherche de terrain

• Forte validité externe : les données étant recueillies dans des environnements réels, les résultats sont souvent plus généralisables.
  
  
• Données riches et contextuelles : la recherche de terrain fournit des informations détaillées que les sondages structurés ne permettent pas toujours de capter.
  
  
• Contexte social et découvertes spontanées : elle révèle des dynamiques ou comportements sociaux que les participants ne verbalisent pas forcément.
  
  
• Base empirique pour des hypothèses : elle permet de valider ou affiner des hypothèses à partir d’observations concrètes.
  
  

‍

Limites de la recherche de terrain

• Chronophage : elle prend souvent plus de temps à planifier, exécuter et analyser.
  
  
• Coûteuse : les déplacements, le personnel, le matériel et la logistique peuvent représenter des coûts importants.
  
  
• Biais du chercheur : dans les approches immersives, il peut être difficile de garder une objectivité totale.
  
  
• Échantillons limités : les tailles d’échantillon sont souvent faibles, ce qui limite la portée statistique des résultats.
  
  

‍

Recherche de terrain vs autres méthodes qualitatives

Contrairement aux entretiens ou sondages menés dans des environnements contrôlés ou à distance, la recherche de terrain se distingue par la présence du chercheur directement sur le terrain. Il ne s’agit pas seulement de recueillir des opinions, mais d’observer des comportements, des routines et des dynamiques sociales au moment où elles se produisent.

La recherche de terrain complète donc les autres approches en apportant du contexte, de la profondeur et une compréhension directe.

‍

En conclusion

La recherche de terrain est l’un des moyens les plus puissants pour comprendre comment les gens agissent et interagissent dans la vie réelle. Elle permet d’accéder à des insights que les environnements contrôlés ou les sondages standards ne peuvent pas fournir. En revanche, elle exige plus de temps, de ressources et de préparation.

En choisissant la méthode adaptée à votre objectif et en vous appuyant sur des outils modernes, vous pouvez mieux comprendre votre audience, votre communauté ou un environnement social donné — et transformer ces connaissances en décisions plus éclairées et percutantes.

Choisir la bonne méthode d’étude est essentiel pour obtenir des insights fiables et exploitables. Que vous testiez une hypothèse, cherchiez à comprendre un comportement client ou exploriez une nouvelle idée, votre approche aura un impact direct sur la qualité des données recueillies — et sur la facilité avec laquelle vous pourrez les analyser.

Les deux grandes approches en matière d’études sont qualitative et quantitative. Bien que complémentaires, elles répondent à des objectifs et des contextes différents. Dans cet article, nous allons comparer les deux, expliquer leurs méthodes et techniques d’analyse, et vous aider à choisir celle qui convient le mieux à votre prochain projet.

‍

Qu’est-ce que la recherche quantitative ?

La recherche quantitative repose sur des données mesurables : elle utilise des chiffres, des statistiques et des questions structurées pour repérer des tendances, tester des relations ou valider des hypothèses. Puisqu’elle produit des résultats chiffrés, elle est souvent présentée sous forme de graphiques, tableaux ou tableaux de bord.

Exemples courants :

• Questions fermées dans un sondage
• Sondages d’opinion et échelles d’évaluation
  
  
• Comptages d’observation
  
  
• Données expérimentales
  
  

Idéal quand vous avez besoin de validation à grande échelle ou de comparaisons entre segments.

‍

Qu’est-ce que la recherche qualitative ?

La recherche qualitative s’appuie sur des informations non chiffrées (textes, paroles) pour explorer des opinions, des perceptions, des motivations ou des comportements. Elle donne aux répondants la liberté de s’exprimer, ce qui la rend idéale pour découvrir des insights profonds ou des points de vue inédits.

Exemples courants :

• Questions ouvertes dans un sondage
• Entretiens individuels
  
  
• Groupes de discussion
  
  
• Observation ethnographique
  
  
• Analyse de texte, vidéo ou audio
  
  

Idéal pour explorer des thèmes, formuler des hypothèses ou comprendre le « pourquoi » derrière un comportement.

‍

Principales différences entre recherche qualitative et quantitative

‍

Vue d’ensemble des méthodes

Méthodes quantitatives :

• Questions fermées : choix de réponses prédéfinis pour une analyse facile
  
  
• Expériences : test de relations de cause à effet
  
  
• Observations : mesure de comportements observables
  
  
• Sondages / échelles de notation
  
  
• Sondages téléphoniques / en ligne structurés
  
  

‍

Méthodes qualitatives :

• Questions ouvertes : permettent une expression libre
  
  
• Entretiens : conversations individuelles approfondies
  
  
• Groupes de discussion : échanges modérés entre plusieurs participants
  
  
• Ethnographie : observation dans un contexte réel
  
  
• Analyse documentaire : étude de contenu écrit
  
  

‍

Comment les données sont analysées

Analyse quantitative :

• Utilise des analyses statistiques pour repérer tendances, relations et écarts
  
  
• Résultats présentés sous forme de tableaux, graphiques, tableaux de bord
  
  
• Techniques courantes : statistiques descriptives, régressions, tests inférentiels
  
  

Analyse qualitative :

• Organisation et interprétation de réponses ouvertes
  
  
• Recours à l’analyse thématique, analyse de discours, nuages de mots
  
  
• Des outils comme Ascribe permettent d’automatiser la codification et l’analyse de sentiments sur de grands volumes de verbatims
  
  

‍

Avantages et limites

Recherche quantitative

Avantages :

• Résultats fiables et généralisables
  
  
• Analyse facilitée grâce à des outils structurés
  
  
• Idéal pour valider des hypothèses ou suivre des indicateurs
  
  

Limites :

• Ne capte pas les nuances ou les émotions
  
  
• Limité par les options de réponse prédéfinies
  
  
• Nécessite un échantillon important

‍

Recherche qualitative

Avantages :

• Permet une compréhension en profondeur
  
  
• Flexibilité dans la collecte des données
  
  
• Favorise l’identification de problèmes ou la formulation d’hypothèses
  
  

Limites :

• Analyse plus longue et complexe
  
  
• Plus coûteuse en temps et en ressources
  
  
• Échantillons souvent trop petits pour une généralisation
  
  

‍

Comment choisir la bonne approche ?

Le choix dépend de vos objectifs :

• Vous voulez tester ou valider une hypothèse ? → Choisissez le quantitatif
  
  
• Vous voulez explorer un sujet ou comprendre un comportement ? → Choisissez le qualitatif
  
  
• Vous avez besoin des deux ? → Optez pour une approche mixte
  
  

N’oubliez pas de prendre en compte vos ressources, délais et capacités d’analyse. La meilleure méthode, c’est celle qui vous aide à obtenir les insights dont vous avez besoin — clairement, efficacement et avec fiabilité.

En matière d’enquêtes téléphoniques (CATI), le taux de réponse ne dépend pas seulement des questions posées — mais aussi de la manière dont vous vous adressez aux répondants. Et cela commence par la confiance.

À une époque où la méfiance envers la collecte de données grandit, la confiance est devenue un facteur décisif : c’est elle qui détermine si la personne va répondre… ou raccrocher.

Selon l’étude GRBN Global Trust Survey 2024, 33 % des répondants dans le monde déclarent faire confiance aux sociétés d’études, contre 26 % qui n’y font pas confiance, pour un indice de confiance net (Net Trust Index) de +7. Un progrès modeste, mais qui montre que la méfiance reste présente.

Voici 5 bonnes pratiques pour instaurer cette confiance — du premier appel jusqu’au remerciement final.

‍

1. Écouter d’abord — puis proposer un rappel

De nombreux enquêteurs sont formés à dérouler rapidement l’introduction pour entrer dans le questionnaire. Mais les appels réussis commencent souvent par l’inverse : écouter.

Le répondant est-il occupé ? Semble-t-il hésitant ? Est-il disposé à continuer ?

Sinon, proposez un rappel clair et respectueux — et assurez-vous que votre solution CATI le permet. Qu’il s’agisse d’un rappel ferme (bonne personne, mauvais moment) ou d’un rappel souple (profil incertain), offrir une certaine flexibilité au répondant renforce la relation de confiance.

🔎 Astuce : optez pour une solution CATI avec gestion flexible des rappels, programmation par plage horaire, et messages personnalisables.

‍

2. Poser les questions sensibles au bon moment

Beaucoup d’enquêtes démarrent par des questions sociodémographiques. Pourtant, poser des questions personnelles trop tôt — sur l’âge, les revenus ou la religion, par exemple — peut décourager la participation avant même d’aborder le cœur du sujet.

À la place :

• Introduisez les questions sensibles plus tard dans l’entretien
• Commencez par des questions pertinentes pour l’expérience du répondant
• Utilisez la logique de routage pour ne poser que les questions nécessaires

Même une enquête partiellement complétée peut être exploitée via des pondérations, sans compromettre l’intégrité des données.

‍

3. Favoriser le dialogue, pas l’interrogatoire

Une enquête téléphonique doit ressembler à une conversation naturelle — pas à une lecture de script.

Formez vos enquêteurs à :

• Adopter un ton adapté à leur interlocuteur
• Parler avec clarté et empathie
• Utiliser des relances ouvertes quand c’est pertinent

Les superviseurs doivent surveiller la qualité des appels, non seulement pour des raisons de conformité, mais aussi pour évaluer le ton et la relation établie. Votre solution CATI doit permettre un suivi en temps réel, avec rapports sur la productivité, les points d’abandon et le ressenti du répondant.

🧠 Un enquêteur motivé et bien formé est souvent le principal facteur de succès — et de complétion.

‍

4. Réattribuer les rappels aux enquêteurs les plus performants

Tous les rappels ne se valent pas. Lorsqu’un suivi est nécessaire, confiez-le à vos enquêteurs les plus expérimentés ou ayant les meilleurs taux de conversion — surtout si le premier contact était hésitant.

Ces profils sont plus aptes à :
✔️ Créer un lien rapidement
✔️ Repositionner la valeur de l’étude
✔️ Donner une dimension humaine à l’échange

⚠️ À noter : de nombreux répondants refusent pour des raisons de temps — mais un bon enquêteur peut souvent les requalifier… s’ils souhaitent vraiment s’exprimer.

‍

5. Doter votre équipe des bons outils

Même le meilleur enquêteur ne peut rien sans une technologie fiable et adaptée.

Pour instaurer la confiance, votre plateforme CATI doit inclure :
📅 Gestion des rappels par tranche horaire ou par quart
🗣️ Attribution des appels par langue, localisation ou expérience
📊 Gestion de quotas complexes
🌐 Intégration fluide avec des enquêtes web (mode mixte)
📞 Système d’appel sur site ou infonuagique, avec support SVI

Une plateforme performante réduit les frictions et permet à vos enquêteurs de se concentrer sur l’essentiel : le répondant.

‍

En résumé

La confiance ne se simule pas — et ne se force pas. Elle se construit, appel après appel, par l’écoute, la flexibilité et le respect mutuel dès les premiers instants.

Le résultat ? De meilleurs taux de réponse, une qualité de données renforcée, et des relations durables avec ceux qui rendent vos études possibles.

‍

Besoin d’une solution qui aide vos équipes à gagner la confiance des répondants — et à améliorer les complétions ?
Réservez une démo pour découvrir comment Voxco soutient les professionnels des études grâce à des outils puissants de productivité, de supervision qualité et de gestion des appels en temps réel.

‍

‍

La satisfaction des employés joue un rôle essentiel dans la réussite globale d’une organisation. Des employés heureux et motivés restent plus longtemps, sont plus performants et représentent mieux votre entreprise — auprès des clients comme des futurs talents. Mais comment savoir s’ils sont réellement satisfaits ?

C’est là qu’interviennent les sondages de satisfaction des employés. Bien conçus, ils fournissent des insights honnêtes et exploitables sur ce que pense votre personnel — et sur ce qui pourrait freiner leur engagement.

Dans cet article, nous aborderons :

• Ce qu’est un sondage sur la satisfaction des employés
  
  
• Pourquoi il est essentiel d’en mener régulièrement
  
  
• 10 questions clés à inclure dans votre prochain sondage
  
  

‍

Qu’est-ce qu’un sondage sur la satisfaction des employés ?

Un sondage de satisfaction des employés est un questionnaire structuré permettant d’évaluer ce que les employés pensent de leur travail, de l’environnement de travail, de la direction, de la rémunération, des perspectives d’évolution, etc.

Ces sondages vont au-delà de simples notations : ils permettent de savoir si les employés se sentent alignés avec les objectifs de l’entreprise, s’ils sont motivés et s’ils se sentent soutenus dans leur rôle.

‍

Pourquoi mener des sondages sur la satisfaction des employés ?

Les organisations qui sollicitent régulièrement les retours de leurs employés sont mieux placées pour renforcer le moral, réduire le taux de rotation et favoriser une culture d’entreprise saine. Voici comment ces sondages peuvent vous aider :

1. Identifier les lacunes en compétences et les besoins en formation

Les sondages permettent de repérer les domaines où les employés se sentent peu préparés ou insuffisamment soutenus, ce qui aide les RH à concevoir des programmes adaptés.

2. Encourager des retours honnêtes et anonymes

Lorsqu’ils sont conçus pour garantir la confidentialité, ces sondages offrent un espace sûr pour exprimer des opinions sincères, y compris sur des sujets sensibles comme la reconnaissance ou le leadership.

3. Améliorer la rétention et réduire le turnover

Comprendre et traiter les causes du désengagement permet de limiter les départs coûteux. Les sondages agissent comme un indicateur précoce de l’épuisement professionnel ou du manque d’alignement.

‍

10 questions efficaces pour un sondage de satisfaction des employés

Ce que vous demandez compte. Voici 10 questions éprouvées pour recueillir des données pertinentes et passer à l’action :

1. Avez-vous des opportunités pour apprendre et développer de nouvelles compétences ?

Évalue si les employés ont l’impression de progresser ou s’ils cherchent leur développement ailleurs.

2. Votre responsable vous soutient-il dans la réalisation efficace de votre travail ?

Un bon encadrement est essentiel à la performance. Cette question révèle si les employés se sentent accompagnés ou livrés à eux-mêmes.

3. Entretenez-vous des relations positives avec vos collègues ?

Les dynamiques d’équipe sont fondamentales. Des tensions internes peuvent avoir un impact majeur sur la satisfaction.

4. Vous sentez-vous rémunéré de façon équitable pour votre rôle ?

La rémunération n’est qu’un aspect, mais elle reste un facteur clé. Cette question met en lumière les éventuelles frustrations liées à la paie.

5. Recommanderiez-vous cette organisation comme un bon endroit où travailler ?

Cette question de type eNPS (Employee Net Promoter Score) donne une vue d’ensemble de la satisfaction et de la fidélité.

6. Pensez-vous que vos opinions sont prises en compte par la direction ?

L’engagement est plus fort lorsque les employés se sentent écoutés. Cela permet de tester la solidité des boucles de feedback.

7. Comment évalueriez-vous votre équilibre vie professionnelle / vie personnelle ?

Stress, surcharge ou manque de flexibilité émergent souvent ici. Cela permet de mieux comprendre le bien-être global.

8. Où vous voyez-vous dans cette organisation dans un avenir proche ?

Cette question donne une idée de l’engagement à long terme et de la perception des opportunités d’évolution.

9. Vous sentez-vous reconnu pour votre travail et vos contributions ?

Le manque de reconnaissance est une cause fréquente de désengagement. Cela permet d’évaluer la qualité des signes de reconnaissance.

10. Avez-vous une compréhension claire des objectifs et de la vision de l’entreprise ?

La clarté favorise l’alignement. Cette question vérifie si les employés savent vers quoi ils travaillent.

‍

En résumé

Les sondages de satisfaction des employés sont bien plus qu’un outil de feedback : ils constituent une base concrète pour construire une organisation plus alignée et performante. En posant les bonnes questions, vous pouvez détecter les points faibles, renforcer l’engagement et réduire le turnover.

Mais n’oubliez pas : collecter les données n’est que la première étape. Ce qui compte vraiment, c’est ce que vous en faites. Ce sont les actions concrètes, tirées des insights, qui font toute la différence.

‍

What is Systematic Sampling?

Systematic sampling is a type of probability sampling method used in research to select individuals from a target population at regular intervals. Unlike non-probability sampling, where not every individual has an equal chance of being chosen, systematic sampling ensures that each member of the population has a known and equal probability of selection. The process involves choosing a random starting point and then selecting every kᵗʰ individual from a structured list, where k is the sampling interval determined by dividing the population size by the desired sample size. This method offers a simple, efficient way to create representative samples—especially when working with large populations and well-defined sampling frames.
‍

How to Implement Systematic Sampling in Your Research

Systematic sampling can be implemented in just two main steps:

1. Calculate the sampling interval
   Divide the total population size (N) by the desired sample size (n) to determine the sampling interval (i). If the result is a decimal, round it to the nearest whole number.
   
   
2. Select a random starting point
   Choose a random starting point (r) between 1 and the sampling interval (i). From there, select every i-th element in the population list until the desired sample size is reached.
   
   

Before proceeding, it’s crucial to ensure that the sampling frame is not arranged in a cyclical or repetitive pattern. If it is, using a fixed interval may introduce bias.

Researchers often use survey platforms or social research tools with built-in sampling capabilities to streamline this process. For instance, Voxco’s survey platform offers advanced features that allow users to easily generate systematic samples through its panel management tools.

‍

Example of Systematic Sampling

Let’s say a researcher wants to select a sample of 25 individuals from a population of 1,000:

• Population size (N) = 1,000
  
  
• Sample size (n) = 25
  
  
• Sampling interval (i) = N / n = 1,000 / 25 = 40
  
  

This means the researcher will select every 40th individual from the list.

Next, a random starting point (r) must be chosen between 1 and 40. Suppose the researcher picks 17. The sample will then include the 17th person, the 57th, the 97th, and so on, continuing in 40-unit intervals until 25 participants are selected.

‍

Types of Systematic Sampling

There are three primary types of systematic sampling methods:

• Systematic Random Sampling
  The most common form, where a random start is followed by selection at fixed intervals.
  
• Linear Systematic Sampling
  In this method, the list is treated linearly. Once the end is reached, the sampling stops—even if the desired sample size isn’t met.‍
• Circular Systematic Sampling
  The population list is treated as a continuous loop. After reaching the end, the count continues from the beginning until the sample size is completed.

‍

1. Systematic Random Sampling

This is the most common and straightforward type. Here's how it works:

• Calculate the sampling interval using the formula: i = N / n
  
  
• Choose a random starting point (r) between 1 and i
  
  
• From that point onward, select every i-th element until the desired sample size is reached
  
  

2. Linear Systematic Sampling

In this method, the population list is treated as a linear sequence. Once the end of the list is reached, sampling stops—even if the full sample size hasn’t been met. Steps include:

• Create a sequential list of the population
  
  
• Determine your desired sample size (n) and compute the skip interval: k = N / n
  
  
• Pick a random starting number (r) between 1 and k
  
  
• Add k repeatedly to r to select the remaining units

‍

3. Circular Systematic Sampling

Here, the list is treated as circular, allowing the sampling to continue from the beginning if the end of the list is reached before the full sample is drawn:

• Calculate the interval: k = N / n
  
  
• Select a random starting point (r) between 1 and N
  
  

Move forward in k steps, looping back to the start of the list as needed, until n units are selected

‍

When Should You Use Systematic Sampling?

Systematic sampling is especially useful in the following research scenarios:

• When the population list is already randomized: If the sampling frame is randomly ordered, systematic sampling provides a quick and unbiased way to select a representative sample.
  
  
• When the population is large and well-defined: It's ideal for large-scale surveys where listing and selecting every individual manually would be time-consuming. The method simplifies the process without compromising accuracy.
  
  
• When resources or time are limited: Systematic sampling requires less effort than simple random sampling while still maintaining the principles of probability sampling, making it efficient for researchers with tight deadlines or limited staff.
  
  
• When you're using a structured list (like customer databases or employee rosters): As long as the list isn’t organized in a cyclical pattern, systematic sampling is a great choice for drawing samples from such structured data.
  
  
• When consistent intervals are meaningful or necessary: If your research benefits from evenly spaced sampling (e.g., time-based studies or product quality checks), systematic sampling can provide consistency in selection.

‍

Advantages of Systematic Sampling

• Simple to implement when a complete and ordered sampling frame is available
  
  
• Easy to understand and execute, even for researchers with limited statistical training
  
  
• Efficient and organized, especially compared to more complex sampling methods like stratified sampling
  
  
• Minimizes bias when the list is randomly ordered, ensuring a fair and representative sample

‍

Disadvantages of Systematic Sampling

• Risk of systematic bias if the population list is ordered in a repeating or cyclical pattern, which may align with the sampling interval and distort results
  
  
• Potential for data manipulation, as researchers could intentionally choose intervals or starting points that skew results
  
  
• Lower randomness compared to methods like simple random sampling, which can increase the risk of selecting similar types of units repeatedly
  
  

‍

Conclusion

Systematic sampling offers a practical, efficient, and widely-used approach for drawing representative samples—particularly when dealing with large populations and organized sampling frames. While it comes with a few limitations, especially regarding potential bias in non-random lists, its simplicity and speed make it a valuable tool in both academic and commercial research. When paired with the right tools, like Voxco’s survey platform, systematic sampling can help streamline the research process and ensure reliable results.

‍

Text mining, also known as text data mining or text analytics, refers to the process of deriving high-quality information from text. Leveraging techniques and tools from both AI (artificial intelligence) and NLP, text mining involves the discovery of patterns, trends, and insights in text data. Text mining is widely used in various fields, including marketing, business intelligence, healthcare, finance, to make sense of large amounts of unstructured text and derive actionable insights. 

How Text Mining Applications Benefit Your Company

Text mining can provide numerous benefits to a company across various departments and functions. Here are some of the key ways it can add value:

1. Customer Insights and Sentiment Analysis
2. Market Research and Competitive Analysis
3. Improving Customer Service
4. Enhancing Product Development
5. Boosting Marketing Efforts
6. Human Resources and Employee Insights
7. Knowledge Management
8. Operational Efficiency

By leveraging text mining, companies can unlock valuable insights from unstructured text data, leading to improved decision-making, enhanced customer experiences, and increased operational efficiency.

What Are the Main Steps in the Text Mining Process?

Text mining typically includes the following tasks:

1. Information Retrieval: Extracting relevant information from large text collections, such as documents, emails, web pages, and social media posts.
2. Natural Language Processing (NLP): Using computational techniques to analyze and understand human language. NLP includes tasks like tokenization, part-of-speech tagging, named entity recognition, and sentiment analysis.
3. Text Categorization: Automatically classifying text into predefined categories or topics. This can be used for organizing documents, spam detection, and more.
4. Text Clustering: Grouping similar documents or text segments together based on their content. This helps in identifying themes and patterns within large text datasets.
5. Sentiment Analysis: Determining the sentiment expressed in a piece of text, such as positive, negative, or neutral. This is commonly used in social media monitoring and customer feedback analysis.
6. Topic Modeling: Discovering abstract topics within a collection of documents. Techniques like Latent Dirichlet Allocation (LDA) are commonly used for this purpose.
7. Information Extraction: Extracting specific pieces of information, such as names, dates, and relationships, from unstructured text.
8. Summarization: Creating concise summaries of large texts to highlight the most important points.
9. Text Visualization: Using graphical representations to help understand and interpret text data, such as word clouds and topic maps.

Text Mining Examples in Marketing

There are many use cases available for text mining. If you were in marketing, for example, here are some of the most common use cases you might consider.

• Learning about positive, negative, and neutral reactions from your audience: Sentiment analysis is an excellent tool for marketers as it allows you to quickly see what the reception is to the topic that you’re studying. When you have a good understanding of your audience’s reactions, you can tailor your marketing based on that information.
• Categorizing survey responses: Group survey responses into broad topics or get granular with it, depending on your needs. You can focus on the areas that are most important for a particular campaign. Recurring themes may require closer examination, so you can conduct more studies that focus specifically in those areas to get more information.
• Translating and scoring survey results: Are you working with more than one language on your survey responses? You don’t need to translate that as a separate step before it goes into your text mining application. Simply choose a software that supports the languages you see the most and it can automate the process.
• Gauging interest in a new concept: Even when you do your best at developing a concept that should appeal to your audience, sometimes the latest project just falls flat. You can start to troubleshoot why that happened by using text mining and open ended survey questions to see what your audience is thinking about the latest products, services, and company moves. By gauging the interest in a new concept before you move forward with the project, you can handle development much more cost-effectively. This helps you avoid particularly high-profile failures, as a small study may end up with respondents that are more on-board with the concept than a more representative sample of your audience.
• Understanding the customer experience: Do you know why your customers feel the way that they do about your customer experience? It’s not enough to know if they are happy or not. You need to know the why behind it if you want to excel at marketing. Text mining gives you the why so that you can continually improve the experience and the marketing tools that support it.
• Discovering your customer satisfaction ratings and the meaning behind them: Your audience gives you a lot of feedback on whether they’re happy or not, you just need a way to analyze it. Use text mining to look through customer service records to identify customers who may be open to purchasing again, those that are upset with the company and need attention, and others that may need a push to move away from being ambivalent in either direction.
• Tracking the success of new products and services: You want to know how well your new products and services are doing now, not weeks or months from now. Automating the analysis through a text mining tool means that you can get near a real-time understanding of how well a product launch is going.
• Finding new business opportunities: Open ended survey responses allow you to find replies that are outside of the norm. Sometimes your customers have adopted a product or service for a use case that never came up in research studies. Expanding horizontally or vertically may be possible based on this data, which can offer an excellent approach to building your business.
• Using customer service data for marketing strategies: Your customer service data is a marketing goldmine, but it’s often overlooked due to the logistical challenges of processing the information. Text mining eliminates these concerns and allows you to find out more about your customers, what they like, dislike, and how to keep them loyal and happy.
• Providing hard data for reports and presentations: If you need a way to make your case to upper management, having powerful visualizations in helpful reports and presentations is one way to make it happen. Text mining creates structure out of unstructured data, so you’re able to use it in this fashion. Customizable dashboards are another way to easily access the data in a form that’s user-friendly for most marketers. When you can easily work with the data, that makes it more accessible to power all types of marketing efforts.
• Improving the value of social media comments: People are more than happy to comment on social media posts, but harnessing that data is hard if you’re doing it manually and have a relatively active page. Text mining makes this process more efficient and allows you to leverage such a large and frequently updated data set. Consistently looking at your social media comments is also a good way to stay ahead of any public relations problems you may encounter. You can execute your crisis communications plan as soon as you start seeing negative comments pop up.
• Creating performance benchmarks for marketing campaigns: Get more benchmarking metrics for your marketing campaigns so you can study how customer sentiment changes over time, the ways they react to new campaigns, and isolating the characteristics that lead to a successful marketing effort.
• Powering Voice of the Customer programs: Voice of the Customer programs are greatly improved when you have a cost-effective and productive way of working with audience feedback.

Whether you’re using text mining for a one-off study or an ongoing series, your team will benefit from its implementation. It takes some time to fine-tune the results for your use cases, but once you get it dialed in, you’re going to wonder how you ever did without it.

Choose Ascribe For Your Text Analysis Needs

Ascribe has two advanced text analytics solutions to meet your business needs. CX Inspector is a text analysis solution that quickly unlocks actionable insights from large data sets with unstructured or open end responses and creates charts to visualize the results. Coder, another text analytics solution, is the leading verbatim coding platform designed to improve the efficiency of coding. Contact us for more information or request a demo with your data.