We all know that customer experience data has some unique properties that make it difficult to analyze. That is why today we will talk about how driver analysis can help us to get to know our customers better.
As specialists in customer experience research, we spend a lot of time analyzing customer data. Depending on your needs, this analysis ranges from the simplest to the most complex. So, what better way to learn more about this term and its importance for customer research .
What is driver analysis?
Driver analysis is a term that describes a set of related techniques that can be used to help organizations understand which elements of the customer experience have the most impact on critical outcomes, such as overall satisfaction, recommendation, or NPS , and loyalty behaviors, such as retention.
The idea is to understand the impact that different aspects of the customer experience have on some outcome variable. Often times that result is overall satisfaction or recommendation intent, but there’s no reason why it can’t be just any variable that interests you, like withholding or spend quota. driver analysis
- What is important to customers?
- What makes the difference?
- What if we changed the predictor (perhaps improving satisfaction or reducing wait times)?
Importance of conducting a driver analysis
It is important to identify and understand the drivers of key business results, such as customer satisfaction or loyalty, to improve processes and maximize performance and profitability.
For example: If you want to understand “How satisfied are your employees with their work?”, You would need a key factor and all related metrics to find the analysis for this specific aspect.
Therefore, the driver analysis can be used to answer these types of questions. This will help you find the percentage of agreement, neutral and disagreement related to the selected question.
Best practices for driver analysis
Here are some of the best practices for this type of analysis:
- Start with correlation . It’s straightforward, unaffected by lack of data, and makes fewer assumptions than more complex techniques. It will give you good guidance on what the drivers are, and frankly, it’s probably the best option for most organizations. driver analysis
- Combine it with declared importance . This will help you to fully understand how your customers see each aspect of the experience at this time, and you will have a tool that will allow you to control the continuous evolution of customer needs.
- Use dimension reduction techniques . Especially if your questionnaire is long, it makes sense to use dimension reduction techniques to look for patterns and groups. This can help you understand how customers think.
- If you want to know what is making the difference in your outcome variable right now, relative importance techniques are the best way to break it down.
- If your questionnaire lends itself to being divided into bundles of related questions , or if you want to investigate more sophisticated causal modeling techniques, then partial least squares trajectory modeling is the best technique for customer data.
Be prepared for some hard work and make sure you have a good sample size, but if you can make it work, you are running one of the most robust analyzes that can be done with customer data.
- Finally, whichever path you take, remember that some of the most interesting links you investigate may be non-linear .
Apply a survey to obtain data for your analysis
A driver analysis investigates the relationships between potential drivers and customer behavior, such as the likelihood of a positive recommendation, overall satisfaction, or the propensity to buy a product.
For this, the data collected in a questionnaire is usually used, in which the demographic data of the client is asked , their level of satisfaction with various aspects of your company’s services (for example, if the value for money or if the department customer service was helpful), as well as their likelihood of recommending your company to other people.
The correlations between the scores for the behavior of the customer of interest (likelihood of recommendation) and those of the potential drivers can then be calculated to see if there is evidence of a relationship between them.
If there is a positive correlation between satisfaction with customer service and the likelihood of recommending the company to others, for example, satisfaction with customer service is said to drive recommendations in a positive direction. Drivers can also be associated with changing customer behavior in a negative direction. With a driver analysis, statistical models can be used to quantify relationships between multiple variables. This can help you understand what drives customer behavior and, ultimately, how to improve your performance.