Understanding Key Drivers Analysis: Definition, How to Use It, Benefits, and More

What Is Key Driver Analysis?

Key Driver Analysis is a critical technique used across the insight world, focusing on identifying and understanding the factors which are likely to impact selected key business outcomes.

Whether you are aiming to increase customer satisfaction, boost product usability, or optimize user experience, KDA (Key Driver Analysis) provides the roadmap for discovering what drives these goals.

At its core, the process involves utilizing statistical techniques such as particular forms of regression analysis to evaluate the relationships between survey data variables (as well as potentially other sources) and identifying the variables which are measured to have the most influence on important outcomes.

KDA is a key tool in the world of market research as it enables businesses to make strategic decisions based on robust data-driven insights, ensuring resources are targeted to the areas of greatest impact.

Consider a company evaluating factors affecting employee satisfaction. KDA can reveal whether salary, work conditions, management effectiveness, or career development opportunities hold more sway over employee morale.

Understanding these key drivers helps companies foster a more satisfied, productive workplace, which is critical in today’s competitive business environment.

What Are Key Drivers?

In the context of KDA, key drivers are the independent variables which significantly affect critical business metrics or outcomes.

These drivers can vary widely across different industries and specific company goals but include aspects like experiential factors, product features, customer service quality, and pricing strategies.

The role of key drivers is to provide actionable insights that help businesses enhance performance outcomes. For instance, in a retail setting, key drivers might include understanding how store layout and customer service quality influence overall sales performance.

By adapting KDA to monitor these drivers continuously, businesses can not only respond to changes effectively but also anticipate shifts in consumer behavior or even market conditions.

As key drivers are likely to change over time, there is often a need to refresh models once new data has been collected. What works today might not hold tomorrow as market dynamics, consumer preferences, and competitive landscapes evolve. Therefore, regular updates and monitoring are essential to maintain a competitive edge.

Selecting Outcome and Predictor Variables

In KDA, outcome/dependent variables are the metrics or end results that a business aims to influence, while predictor/independent variables (or key drivers) are the factors believed to influence these outcomes. Selecting the right outcome and predictor variables is crucial for effective analysis.

Outcome Variables: These could be direct business metrics such as NPS (Net Promotor Score), customer loyalty indices, or employee satisfaction, or even brand consideration, depending on the specific objectives of the KDA.

Predictor Variables: These are potential influencers on the outcome variables and can include a wide range of factors from pricing strategies, product features, marketing activities, and more. The selection of appropriate predictor variables is critical as it directly impacts the validity and effectiveness of the KDA.

The relationship between outcome and predictor variables is typically explored through statistical methods such as special forms of regression analysis, which help validate whether the selected predictors have a significant impact on the outcomes.

Proper selection and validation of these variables ensure that the key drivers identified are truly capable of influencing business performance metrics.

By thoughtfully selecting and analyzing these variables, businesses can craft strategies that effectively target the most impactful areas, leading to significant improvements in overall performance and customer satisfaction.

Analytical Techniques in Key Driver Analysis

Key Driver Analysis can be performed by various statistical techniques, with some being more up to the task than others.

Correlation Analysis & Linear Regression Analysis.

Whilst many sources will recommend the use of correlation analysis and linear regression analysis for KDA, we would advise against their use for this approach. Typically, survey data contains variables which are very highly correlated with each other, this correlation, known as multicollinearity, can cause models to be overly flat (in the case of correlation analysis) or to be unable to assign the correct level of importance (in the case of linear regression). These failures tend to limit the usefulness of the results of these models when faced with highly co-linear data sets (of which survey data almost always is!)

Instead, we suggest you use one of the following three methods for your KDA.

Average Over Orderings

Average over orderings (AOO) techniques remove the damaging impact of multicollinearity on KDA results by a brute force method. To do this, a range of different regression models are run, with each of the predictor variables being entered in such a way that every unique ordering of variables takes place. Each predictor variable then has its average impact across all the models calculated, giving us the average contribution over its ordering.

Whilst this method can help with the problems inherent in highly correlated data, it can be particularly computationally intensive, as each additional predictor variable added to the model increases the number of potential models to be run exponentially.

Johnson’s Relative Weights

Johnson (2000) sought to find a method to uncover this relative impact through algebraic means, rather than the brute force method of AOO. His result, which he called epsilon, is a solution for dividing up the overlap in variance that works for any number of predictors.

Its results correlate remarkably highly with that of AOO methods, whilst not requiring anywhere near the same level of computation to run the models. 

Random Forest Analysis

Brieman (2001) developed the Random Forest method, which rather than building a single model, builds a “forest” of models, with each tree utilizing a random subset of respondents and each branch of that tree utilizing a random subset of predictor variables. Due to these randomizations, the multicollinearity present in the data becomes de-correlated. With this de-correlated data, the “increase in node purity” metric can be used to measure relative importance for your KDA.

Actionable Insights from Key Drivers Analysis

Once the statistical analysis is complete, the next crucial step in Key Driver Analysis is interpreting the results to make data-driven decisions that can enhance business performance. This phase involves visualizing the data through charts and matrices to better understand the impact of different drivers and deriving actionable insights from these observations.

The ultimate goal of KDA is to provide actionable insights to stakeholders:

• Prioritization of Efforts: By understanding which drivers are most influential, businesses can allocate their efforts and resources more effectively.

• Strategy Development: Insights from KDA can inform the development of targeted strategies that specifically address the most impactful areas.

Example: If data analysis shows that ease of use is a primary driver for software product satisfaction, a company might focus on simplifying its user interface to enhance customer satisfaction.

By effectively interpreting the results of Key Driver Analysis, organizations can not only improve existing processes and outcomes but also innovate new strategies to stay ahead in competitive markets.

Practical Examples of Key Drivers Analysis in Action

Key Driver Analysis is not just theoretical but a practical tool that has been applied across various industries with notable success. Here are some examples of how KDA can be strategically utilized:

Customer Satisfaction Studies

For businesses in the service industry, customer satisfaction is pivotal. By implementing KDA, a hotel chain can identify key factors such as room cleanliness, check-in process efficiency, and staff responsiveness that most affect guest satisfaction. Targeted improvements in these areas can significantly enhance overall customer experience and loyalty.

Enhancing Customer Loyalty and Retention

In the retail sector, understanding what drives customer loyalty can help stores tailor their marketing and service strategies. For example, if price and product variety are identified as key drivers for a fashion retailer, then competitive pricing and broadening the product range can be effective strategies for increasing customer retention.

Influencing Sales Performance

Consider a consumer electronics company that uses KDA to find out that product features and after-sales support are major drivers of sales performance. By focusing on enhancing these areas, the company can not only improve sales but also customer satisfaction with the product.

Tailoring Marketing Strategies

KDA can guide companies in crafting marketing strategies that resonate more effectively with their target audience. By understanding the key drivers of consumer decision-making, companies can create more impactful marketing campaigns that directly address those drivers.

Conclusion

Key Drivers Analysis is a powerful tool for any organization aiming to thrive in today’s data-driven market landscape. By understanding and leveraging the key factors that influence critical business outcomes, companies can not only improve current performance but also strategically plan for future growth and success.

We encourage businesses to integrate Key Driver Analysis into their strategic planning processes to uncover hidden opportunities for enhancement and to drive significant performance improvements across all areas of operation.

By embracing the insights provided by KDA, companies can make more informed, impactful decisions that not only meet but exceed customer expectations, fostering a cycle of continuous improvement and innovation.

To find out more tips and tricks, please watch our KDA webinar.