Businesses are under greater pressure to meet customer needs. Our 2023 State of Customer Experience (CX) survey found that 80% of customers believe the experience is as important as products and services, with two-thirds expecting brands to understand their unique needs.
Yet, how are businesses actually performing against these expectations? Over half of surveyed customers felt that companies failed to provide accurate information, and 66% felt they were treated as just a number. This disconnect highlights the importance of customer feedback, especially in asking the right questions to gain valuable insights.
However, sending out a survey is only the beginning. Analysing survey results goes beyond simply collecting data. Only by interpreting these results and turning them into quantifiable insights can you understand how customers truly feel. A well-structured survey is essential for achieving this.
Many businesses struggle with the question, “How can you turn responses into valuable insights?” This comprehensive guide will help you understand how to go about analysing survey results effectively and turning those results into actionable insights.
How to Analyse Survey Results
You’ve put your survey into the field and received plenty of responses. Now it’s time to let the magic happen and start analysing survey results. Along with forming a picture of how your customers feel about your brand and customer service activities, you can measure progress against your CX objectives and use the results to inform actionable insights that drive improvements.
In the past, this would have involved time-consuming manual data entry and analysis, often making results outdated by the time they were reviewed. In today’s data-driven world, speed is essential, which is why more businesses are turning to automated analytical algorithms for analysing survey results and gaining targeted insights.
Let’s explore this process further.
Define Clear Objectives and Survey Goals
Before you begin analysing your data, you need to define your objectives for the survey. This involves understanding what insights you want to gain from your customers.
For example, are you aiming to measure customer satisfaction? Do you want insights into customer loyalty, or are you tracking retention rates? Are there specific actions you want to implement to improve lead generation or sales conversions?
Whatever the goal, setting clear objectives ensures that your survey meets its intended purpose. Here are a few examples of objectives you might track with your survey:
- Increase your customer satisfaction (CSAT) score by 0.5 points within three months.
- Shift your Net Promoter Score (NPS) from negative to positive in six months.
- Improve customer retention by 20% over the next year.
- Increase lead generation by 20% within a year.
- Boost sales conversions by 25% in the next six months.
Your survey goals will help track progress against these objectives, leading to valuable insights and quick wins that support your CX aims.
This process is at the heart of how we, at Netigate, approach this:
Clean and Organise Data
Cleaning and organising data. Okay, this might be one of the most labour-intensive parts of analysing survey results. This step involves collecting responses and organising them into relevant sections for clarity and accuracy.
Even with a well-designed survey, some responses may be misleading, contradictory, or incomplete, potentially skewing your results. The data cleaning process allows you to remove any inconsistent answers from your sample.
But to make it short, here are the key steps for cleaning and organising your data:
- Remove inconsistencies, such as spelling mistakes, unfinished responses, and poorly formatted answers that could affect results.
- Group similar responses to help identify patterns and trends.
- Look for outliers in numerical data that are inconsistent with other responses or implausible.
- Decide whether to include partially completed surveys based on your survey’s objectives.
- Filter out responses that do not provide coherent or relevant answers.
Cleaning and organising data ensures it’s ready for accurate analysis, helping you achieve reliable insights.
The Analysis Phase
The analysis phase is where you begin uncovering insights. By collating responses from each question, you can assess key performance indicators (KPIs) and measure your progress against customer experience (CX) objectives. Here are some of the KPIs you might track:
- Net Promoter Score (NPS): NPS helps you understand how likely customers are to recommend your brand. This is one of the most common CX metrics for measuring loyalty. Customers respond to, “How likely are you to recommend [Your Company] to friends, family, or colleagues?” with a score from 0 to 10. Scores are categorised as follows: detractors (0-6), passives (7-8), and promoters (9-10).
- Customer Satisfaction (CSAT): CSAT gauges customer satisfaction with your products, services, or overall experience. Typically, customers answer, “How satisfied were you with your experience with [Your Company]?” on a scale of 1 to 5. CSAT scores are calculated as an average of all responses, with higher scores indicating greater satisfaction.
- Employee Net Promoter Score (eNPS): Similar to NPS, eNPS measures employee engagement and loyalty to your brand, helping you assess whether employees act as brand ambassadors and what actions might increase their engagement.
Find out more about how you can analyse employee experience with the power of AI
These metrics are straightforward to calculate and provide a high-level view of your progress on CX goals. By analysing survey results through these key metrics, you gain measurable insights into customer loyalty, satisfaction, and employee engagement.
Extracting Insights from Open-Ended Feedback
While quantitative metrics like NPS and CSAT offer a high-level view of performance, extracting insights from open-ended feedback can reveal deeper, hidden value in your CX activities.
Adding open-text feedback fields in your survey allows customers to share more detailed feedback behind their ratings. For instance, a follow-up question like “What could we improve on in the future?” provides the context and reasoning behind customer scores.
Manually going through each response can be time-consuming, especially when feedback is extensive. This is where automated analysis tools, such as Netigate, along with advanced AI technologies like sentiment analysis and natural language processing (NLP), add significant value. They help interpret customer emotions and topics, providing richer insights that would otherwise require extensive manual work.
By automating this process, you can turn open-ended feedback into actionable insights quickly, improving your ability to respond to customer needs effectively.
Industry Benchmarking
To put it simply, using benchmarks to help you in analysing survey results gives you an idea of how your business compares to the competition.
Establishing a baseline allows you to set a reference point for essential metrics like CSAT and NPS. This helps measure improvements in customer satisfaction and loyalty as you roll out new CX initiatives, giving you a clear view of the impact of your efforts.
There are two main types of benchmarking:
- Internal Benchmarking: Compare performance across departments, teams, or branches within your business.
- External Benchmarking: Measure your company’s performance against industry standards and competitors.
Benchmarking helps identify strengths and weaknesses, highlighting what you’re doing well and areas that require improvement. These insights allow you to set realistic, measurable targets for continuous improvement.
Read more about custom CX benchmarks to maximise business success
Visualising and Distributing Results
Once you’ve finished analysing survey results and know how they stack up against competitors and industry standards, it’s good to practice presenting them in a clear, visual format for your audience.
Stakeholders, especially those invested in CX initiatives, prefer results presented in a way that’s easy to interpret at a glance rather than sifting through lengthy text. For example, if you’re tracking CSAT over time, a chart showing score trends provides a clear view of how recent changes have affected customer satisfaction.
Considering the best way to distribute results is also important. Think about your audience’s needs—options might include regular reports, a real-time CX dashboard, or monthly newsletters to keep everyone informed and engaged.
Effective visualisation and distribution ensure that insights are easily understood and acted upon.
Actionable Steps
You’ve now analysed your survey results. Great! But we’re not done! It’s time to develop strategies based on your findings to achieve your objectives. These strategies should lead to specific actions that drive improvements in customer experience.
For example, you may decide to refine your marketing messages, implement AI-powered chatbots for quicker customer support, reduce response times for customer queries, or introduce initiatives to increase customer loyalty.
Each action should be directly tied to insights from your survey results and linked to a CX objective. Assign each activity to a responsible person or team, with a clear timeline and milestones to ensure accountability and focus.
Monitoring for Continuous Improvement
There’s always room for improvement, and regular monitoring of your activities is critical to achieving your objectives and driving continuous improvements. Each activity you identify should feed into a KPI that tracks progress toward a CX objective.
This involves regularly tracking metrics, distributing follow-up surveys, and keeping the customer feedback loop open. If you’re not progressing toward meeting your objectives, don’t be afraid to change your strategies.
However, if you have met your objectives, take the time to review them and understand what the next step is to help take your customer satisfaction or loyalty to the next level.
Methods for Analysing Survey Results
Earlier, we discussed the steps involved in how to analyse survey results. Now, let’s dive into the specific methods used in the analysis process to gain deeper insights from your data.
In the past, data analysis involved time-consuming, manual processes, requiring teams to collate responses and analyse them manually. Today, advanced data analysis techniques and automated tools like Netigate’s Customer Experience platform have made this process much faster and more efficient.
Real-time Analysis
With real-time analysis, quick, easy-to-read snapshots of key metrics are available on demand. This enables an ongoing review of KPIs and allows you to track the impact of your CX initiatives over time, making it easier to adjust strategies based on current data.
For example, Fibrus, a UK-based broadband provider to rural areas, partnered with Netigate to increase its NPS and TrustPilot rating, which was as low as 1.7 at one point.
The biggest value it has delivered is that it quantifies the impact, it helps us drive a business case around a specific action, which is essential when you are growing as fast as we are.
Stephen Riley
After eight months of using Netigate to inform its CX activities, Fibrus saw a massive improvement in several metrics, including:
- Contacts per customer reduced by 30%
- Trustpilot score increased from 1.7 to 3.9
- NPS increased from -28 to +56
This improvement was driven by real-time analytics and regular monitoring of their KPIs, which helped them understand the impact of their new CX activities and meet their objectives.
Sentiment analysis
AI tools, such as natural language processing (NLP) and sentiment analysis, interpret open-ended feedback to understand the emotions behind your customers’ responses.
This helps you understand whether they’re happy, frustrated, or angry about their interactions with your brand. This offers additional layers and context to the results and insights you receive, allowing you to create more impactful actions.
Text analysis
Although similar to sentiment analysis in that it reviews open-ended text responses, text analysis identifies common words, phrases, and topics that crop up in your customer’s feedback.
This allows you to find common themes in the feedback you may have been unaware of without allowing customers to add free-text responses. This added level of feedback will enable you to create more targeted actions to help resolve issues before they become full-blown problems and continue doing the things you’re being recognised as successful for.
Segment analysis
Understanding the difference in feedback between different customer demographics is another way to increase the depth of analysis and uncover hidden gems.
For example, you’ve released a new product and want to measure customer satisfaction. By analysing the results by age range, gender, or another demographic, you can understand whether the new product resonates better with one sector of your customer base over another.
Final Thoughts
Focusing on customer experience has never been more critical for businesses of all sizes. Regularly connecting with your customers is essential, and customer surveys are one of the best ways to create these touchpoints, enabling you to monitor loyalty, satisfaction, and other key indicators of your company’s performance.
The methods for analysing survey results have evolved significantly in recent years with advancements in AI-powered survey analytics tools, such as Netigate. Alongside simplifying the creation and distribution of surveys, Netigate provides real-time insights through intuitive, automated survey result visualisations.
By leveraging these tools to analyse customer survey responses efficiently, you can respond quickly to your customers’ needs and reach your CX goals faster.
If you’d like to learn more about how Netigate can help your business make the most of survey analysis and insights, download our free e-book, “7 Tips for Smarter, Faster Survey Analysis,” and start your free 30-day trial today to jumpstart your journey toward seamless survey analysis and impactful CX insights.
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