Customer experience data can be difficult to analyze because of its unique characteristics. That’s why today, we’re going to discuss how driver analysis can help you understand your customers better.
You probably spend a lot of time analyzing customer data in customer experience research. Depending on your needs, this analysis can range from simple to complex. Let’s explore what driver analysis is and why it’s so important in understanding customer behavior.
What is Driver Analysis?
Driver analysis is a set of techniques that can help you understand which parts of the customer experience have the biggest impact on key outcomes, like overall satisfaction, recommendations, or loyalty behaviors such as customer retention.
The goal is to see how different aspects of the customer experience influence these outcomes. While the focus is often on overall satisfaction or the likelihood of recommending your company, you can apply it to any area you’re interested in, like retention or how much customers spend.
This analysis helps you answer three main questions:
- What do your customers care about the most?
- What really makes a difference in their experience?
- What if you changed something, like improving satisfaction or reducing wait times?
Driver analysis is particularly helpful for understanding the reasons behind customer satisfaction scores like NPS, CSAT, or CES. When planning your service team’s strategy, this analysis highlights the exact factors that impact a customer’s decision to rate their experience higher or lower. This way, you can pinpoint specific areas that need improvement to boost customer satisfaction.
Additionally, it allows you to prioritize what to focus on and when, ensuring your efforts are directed where they matter most.
Importance of Performing a Driver Analysis
Understanding what influences key business outcomes, like customer satisfaction or employee loyalty, is crucial for improving processes and boosting performance and profits.
For example, if you want to know how satisfied your employees are with their jobs, you must look at key drivers and related metrics to analyze this area. Driver analysis can help you answer these kinds of questions by showing you the percentages of employees who agree, are neutral, or disagree with the question you’re exploring.
Conducting a driver analysis is crucial for understanding the key business metrics influencing your desired outcomes. Here’s why it matters:
- Better Decision-Making: Driver analysis helps you pinpoint the most important factors, enabling you to make smarter decisions that align with your goals.
- Efficient Resource Use: By identifying what really drives success, you can allocate your resources more effectively, focusing on areas that deliver the best results and minimizing waste.
- Improved Customer Experience: Knowing what impacts customer satisfaction and loyalty allows you to enhance their experience and reduce the chances of losing them.
- Competitive Edge: Gaining insights into critical success factors can give you an advantage over competitors who may not be focusing on these key areas.
- Proactive Problem Solving: With driver analysis, you can anticipate potential issues and address them before they become bigger problems.
- Targeted Marketing: Understanding what influences customer behavior helps you craft more effective and focused marketing campaigns.
- Boosted Employee Engagement: By identifying what affects employee satisfaction and motivation, you can improve engagement and performance.
- Strategic Planning Based on Data: Driver analysis ensures your strategies are grounded in the factors that truly drive success, making it more likely that you’ll reach your goals.
Types of Driver Analysis
- Regression Analysis: This method is used to find out how one thing relates to another. For example, understanding how different marketing activities drive sales.
- Correlation Analysis: It shows how strong the connection is between two things. While it doesn’t mean one causes the other, it helps figure out which things are worth looking into more.
- Root Cause Analysis: This focuses on finding out why something happened rather than just what contributed to it.
- Key Driver Analysis (KDA): Often used in customer satisfaction surveys, it identifies which factors most influence an overall satisfaction score. It is also known as relative importance analysis. For example, understanding what aspects of a service drive customer loyalty.
- Conjoint Analysis: This method helps understand how much people value different product or service features, showing what trade-offs they’re willing to make.
- Machine Learning Models: Techniques like decision trees or neural networks can reveal the factors that influence complex outcomes, showing what has the biggest impact.
Best Practices for Driver Analysis
Here are some best practices for this type of analysis:
- Start with correlation.
It’s easy to use, isn’t affected by missing data, and requires fewer assumptions than more complex methods. It will give you a good idea of the key factors, and for most organizations, it’s likely the best choice.
- Combine this with stated importance.
This approach will help you understand how customers currently view different aspects of their experience. Plus, it gives you a tool to track how customer needs change over time.
- Use dimension reduction techniques.
If your questionnaire is long, these techniques can help identify patterns and groupings in the data, giving you insight into how customers think.
- Apply relative importance techniques.
If you want to know what’s currently impacting your outcome variable, this method is the best way to break it down.
- Try partial least squares path modeling.
If your questionnaire can be divided into related question bundles or if you want to explore advanced causal modeling, this technique is ideal for analyzing customer data.
Using a Survey to Gather Data for Driver Analysis
Driver analysis helps you understand what influences your customers’ behaviors, like whether they’ll recommend your company, how satisfied they are, or how likely they are to buy your products.
To conduct a driver analysis, you first need to collect data, which is usually done through a survey. Here’s how to do it in a straightforward way:
1. Create the Survey
Start by designing a survey that will help you gather the customer feedback data needed for your driver analysis.
- Include Demographic Questions: Ask for basic information like age, gender, location, and income. This will help you see if certain groups of customers have different experiences or behaviors.
- Ask About Satisfaction: Include questions about how satisfied customers are with different parts of your business, like the value for money, product quality, and customer service. Use a rating scale so customers can rate their experience.
- Measure Key Behaviors: Ask questions to gauge the likelihood of customers recommending your company to others, making another purchase, or trying out a new service.
2. Collect Responses
Distribute the survey to your customers and gather their responses. Make sure to reach enough people so your survey data is reliable. Offering small incentives, like a discount or a chance to win a gift card, can help increase participation.
3. Analyze the Data
Look for relationships between customer satisfaction scores and behaviors like recommending your company. For example, see if there’s a link between customer satisfaction with your service and their likelihood of recommending you.
4. Understand the Relationships
The next step is to look at the data to see how different factors are related.
- Positive Links: If you find that higher satisfaction with customer service correlates with a higher likelihood of recommending your company, this suggests that good customer service is a key driver for positive word-of-mouth.
- Negative Links: On the other hand, if a factor is negatively linked to a behavior, it means that increasing satisfaction in that area could reduce the likelihood of a negative outcome.
5. Use Advanced Analysis
To explore the issue further, you can use statistical methods like regression analysis to measure how different factors influence customer behavior. This will help you identify the most impactful areas for improvement.
Improving your performance can be tough when you’re unsure where to focus your efforts and investments. Driver analysis can show you what really matters to your customers and help you compare how different factors are performing. With this insight, you can make better decisions on what to improve or maintain.
Ready to begin your survey?
