Qualtrics Stats IQ – Regression Analysis
Objective: This blog post refers about the feature available in Qualtrics called Stats IQ. Through which we can perform Regression analysis and use cases of it in Customer and Employee Experience Management.
Instead of job satisfaction and organizational dedication, which are shown to influence organizational outcomes, employee engagement has become a hot topic in the workplace. The federal government now assesses its agencies on how effectively they enforce engagement-friendly conditions on an annual basis. (Burnet, 2019) Federal agencies are also rated as “Best Places to Work” on an annual basis based on their combined score on three survey items considered to represent job/organizational satisfaction.
Individual factors that influence employee engagement are referred to as “drivers of employee engagement.” There are several organizational characteristics that, when combined, are critical for promoting high levels of employee engagement. As a result, it is critical to recognize the key individual factor(s) that can inspire workers to perform their functions effectively and efficiently in order to drive them to achieve high levels of engagement and dedication to their job and organizational roles.
While the Net Promoter Score® is a tried-and-true measure for measuring customer loyalty, knowing what motivates customers is often the more valuable insight. Regression analysis assists companies in determining what matters to their consumers or employees and where their efforts should be focused.
Regression analysis is a collection of statistical methods for connecting a set of independent variables in data to a single dependent variable, or the desired outcome. We can use regression to figure out how important each independent variable is and how it affects the dependent variable.
Surveys are an excellent way to gather information about customer/employees opinions and satisfaction levels. They also inquire about how much a customer agrees with a comment like “The company is attentive to my needs” or “The company is trustworthy.” When a survey contains a CSAT or NPS® query, statistical tools use regression to link the survey responses to the satisfaction level measurement.
Numbers called coefficients are assigned to each independent variable as one of the regression’s outputs. The regression coefficients are used to determine which questions, or company characteristics, have the greatest effect on customer satisfaction. These coefficients are frequently plotted against the average output of each attribute to construct a key driver map. Independent variables are grouped into meaningful, actionable target areas for the company in key driver maps. This information can be used by stakeholders to determine the aspects of the customer experience to emphasize, track, or enhance.
Models can be created using regression analysis to better explain why customers defect. A predictive model assigns probabilities based on past customer data, indicating how likely potential customers are to leave. Given that consumers become more important the longer they remain with a brand, this can assist companies in identifying customers who are at risk of defecting and working to maintain them proactively.
There is no one-size-fits-all approach to determining what drives the customer experience. Surveys reveal what people are thinking about their experiences, but businesses are still unsure if those experiences influence broader business goals. Regression analysis evaluates and rates the variables that influence overall customer satisfaction, resulting in a roadmap for your customer experience plan.
Assume you’re an HR expert who wants to know if an employee’s age has a significant impact on their maturity or not. The value of experience and capability in determining remuneration or the significance of IQ (Intelligence Quotient) vs. EQ (Emotional Quotient) in problem-solving abilities or the impact of a sedentary lifestyle at work on employee output. All these are common occurrences in the workplace. However, they have a massive influence. How do you, as an HR specialist, figure out which variables have the most influence on employee productivity? The solution is regression analysis. It aids in the explanation of a relationship involving two or more variables.
Some of the possible use cases and advantages were Qualtrics Stats-IQ feature – Regression Analysis will be helpful are listed below.
- In a credit card business, regression analysis aids in the understanding of various variables such as a customer’s probability of credit default, predicted consumer conduct, credit balance prediction, and so on, and based on these findings, the company introduces clear EMI options thus mitigating risky customer default.
- Assume a business that sells sports equipment needs to know if the money spent on promoting and branding their products has yielded a significant return.
- Based on age, income, gender, ethnicity, state of residence, previous purchase, and other factors, businesses can use logistic regression to predict whether customers in a specific demographic would buy their product or buy from their competitors.
- We may estimate a maximum offer for a specific television commercial slot based on the number of companies that have seen that slot.
- A car insurance provider could use forecast claims to Insured Declared Value ratio to create a suggested premium table using linear regression. The risk can be calculated based on the car’s characteristics, the driver’s details, or demographics. The findings of such an investigation could help guide important business decisions.
- Regression analysis is used in pharmaceutical industries to evaluate quantitative stability data for the retest time or shelf life estimate. The essence of the relationship between an attribute and time decides whether data should be converted for linear or non-linear regression analysis in this approach.
- Improve operational efficiency: before introducing a new product line, companies conduct market surveys in order to better understand the effect of different factors on the product’s manufacturing, packaging, delivery, and consumption.
- Regression analysis aids businesses in making more informed strategic workforce choices by converting a large amount of raw data into actionable information.
- Knowing how to analyze survey findings using regression analysis makes it simple to provide factual support to management so that they can make educated decisions and avoid errors caused by intuition.
Conclusion: I hope this blog post helped in knowing about Qualtrics – StatsIQ product information and various use cases of it. I also encourage to ask questions or provide feedback related to this and ready to clarify them.
Burnet, S. (2019). Employee Engagement and Burnout: A Quantitative Study of their Correlations with Job/Organizational Satisfaction. Dissertations. 144. Retrieved from https://digital.sandiego.edu/dissertations/144?utm_source=digital.sandiego.edu%2Fdissertations%2F144&utm_medium=PDF&utm_campaign=PDFCoverPages