What is a Key Driver Analysis?
Key Driver Analysis (KDA) is a statistical method used to determine which factors have the most significant impact on a particular outcome. It can be used to determine the relationship between independent variables (possible drivers) and a dependent variable (the target outcome), such as customer satisfaction or loyalty.
Consider a hotel manager aiming to boost bookings. By employing Key Driver Analysis, they can pinpoint how pricing, guest recommendations, and cleanliness each play a role in a guest’s decision to book a room. This insight allows for strategic resource allocation to enhance these key drivers and ultimately increase bookings.
The Major Components of Key Driver Analysis
Key Driver Analysis goes beyond measuring customer satisfaction, providing broader business insights. The new Survalyzer feature also integrates AI, following our ChatGPT integration, and comprises essential elements for robust data interpretation. Our feature include the following two components:
- Independent Factors: A key strength of Key Driver Analysis is its ability to break down the various factors that drive results. Whether it’s the quality of a product, the efficiency of service, or the competitivenessof pricing, KDA lays bare how each factor contributes to the ultimate goal.
- Dependent Factor (Target Value): At the heart of Key Driver Analysis is the dependent factor — our main point of interest. It could be the rate of customer retention, the frequency of repeat purchases, or any other metric indicative of success.
In essence, KDA acts like a business compass, guiding you towards what needs improvement. It could be about increasing a hotel chain’s booking rates or boosting a retail brand’s customer satisfaction.
Key Driver Analysis Use Cases
Using Key Driver Analysis, it is possible to understand how the underlying factors affect the following metrics:
- CSAT (Customer Satisfaction): by measuring things that affect customer happiness, like product quality or service speed.
- CES (Customer effort score): by revealing which parts of the customer journey, like website navigation or support interactions, need to be simplified for better experiences.
- NPS (Net promoter score): by helping understand what turns customers into brand advocates, focusing on factors like brand trust or value perception.
Overall Key Driver Analysis pinpoints critical areas, so you know how to enhance customer loyalty, increase positive word-of-mouth, and improve customer retention, ultimately driving business growth.
How to read Key Drive Analysis Chart?
To conduct a Key Driver Analysis, start by surveying customers about potential drivers (like cleanliness, customer service, price) and their overall satisfaction, often measured by previously mentioned NPS, CSAT, or CES. After collecting data, analyse it using multiple linear regression (a way to understand how multiple factors affect a particular outcome) to understand how each factor correlates with customer satisfaction. The results are plotted in a quadrant chart, where:
- Key Weaknesses (Top Left Quadrant): These factors are crucial for determining the outcome metric but currently have low performance scores.
Meaning: Improvement in these areas is vital because they significantly affect customer satisfaction or loyalty. Focusing efforts here can lead to substantial gains in the overall outcome metric.
- Key Strengths (Top Right Quadrant): These are areas where the organization is performing well and are also highly influential in determining the outcome metric.
Meaning: Maintaining and possibly enhancing these strengths ensures sustained positive impact on the outcome metric.
- Unimportant Weaknesses (Bottom Left Quadrant): These factors have low performance scores but don’t significantly impact the outcome metric.
Meaning: While improvements can be made, they should be lower priority as they won’t substantially influence overall satisfaction or loyalty.
- Unimportant Strengths (Bottom Right Quadrant): These factors are performing well but don’t have a significant impact on the outcome metric.
Meaning: Resources might be better allocated elsewhere, as improvements here won’t greatly enhance the key outcome metric.
Integration in Professional Analytics
Picture yourself as a data detective, and Survalyzer Professional Analytics is your magnifying glass. This product isn’t just about numbers and charts; it’s about telling a story hidden in your data. By using its various modules, you’ll be able to conduct Key Driver Analysis in a simple and intuitive way, as follows:
- Tables: Think of this as your data playground. Here, you can sort, shuffle, and organize information just like you would with puzzle pieces, finding patterns and connections that reveal the bigger picture. It’s about making sense of the raw data, turning it into a narrative that speaks to you.
- Charts: Ever tried to explain a complex idea and wished you just had a drawing to do it? That’s what the Charts module does. It transforms rows of data into colorful, engaging visuals. Whether you’re a fan of bar graphs or pie charts, this module helps you see the story behind the numbers.
- Pages (Summary): This is where everything comes together. Like the final chapter of a mystery novel, the Pages module synthesizes all your findings into a coherent, accessible dashboard. It’s like stepping back to admire a completed jigsaw puzzle – everything makes sense now. If you would like to know the most important rules and how to avoid the most common mistakes when creating survey dashboards take a look at our comprehensive guide on the subject.
Example of dashboard with Key Driver Analysis
As we transition from exploring the dynamic modules of Survalyzer Professional Analytics, it becomes clear how each component contributes to transforming raw data into a narrative. This storytelling approach is illustrated in a sample dashboard with conducted Key Driver Analysis:
From the chart, we can observe the following:
- Price is plotted highest on the importance axis, indicating that it’s considered the most crucial factor by users when it comes to their satisfaction with the CRM software. Its relative performance is also high, suggesting that users are generally pleased with the pricing structure of the product.
- Appearance is another key driver with high importance and performance, showing that users find the visual aspect of the software to be both influential and satisfactory.
- Quality and Support fall into the quadrant indicating lower importance and performance. This suggests that while these areas might not be the primary drivers of user satisfaction, there is room for improvement that could potentially increase their overall impact on customer satisfaction.
- Ease of Use and Functionality are seen in the quadrant with low importance but higher performance. This could imply that while the software performs well in these areas, they are not top priorities for the user base. It’s an interesting insight, as typically one might expect functionality to be a key concern.
In our exploration of Key Driver Analysis, we uncovered how it can improve customer satisfaction and other vital business metrics. The powerful visualization tools found in Survalyzer Professional Analytics will help you come up with informed decisions and understand what drives your vital business metrics.