KQI vs. KPI: What's the Difference?
Updated June 24, 2022
KQIs and KPIs are two metrics used to profile and measure data and goals. You can evaluate them separately or together to understand how your business performs and where you can find success. Understanding how these two metrics relate to each other can help you decide which to use in any situation and how to get the most accurate reports and results. In this article, we discuss what KQIs and KPIs are and review their similarities and differences in data evaluation and usage.
What is a KQI?
KQI stands for key quality indicator. This metric measures the quality of excellence of a task or project. To find quality, you compare two or more items against each other that are similar to see which has the best results. You rarely measure quality with just one number, like some other metrics. This may make it more complex to gather and analyze data. Some resources used to collect qualitative data may include stories, thoughts, feedback or other noncomputational reporting methods.
What is a KPI?
KPI stands for key performance indicator. This is a measurable value that can tell you how effectively you're meeting business goals. You can set KPIs based on the data with which you're working. You can have high-level KPIs, which focus on the overall performance of a company, or low-level KPIs that focus on the processes within one department. An example of a low-level KPI could appear in a marketing department where advertisers try to generate more traffic to an e-commerce website.
KPIs can be qualitative, like KQIs, or quantitative, measured in tangible numbers. Examples of quantitative KPIs include:
Service response time
Number of resolved helpdesk tickets
Customer lifetime value
What are KRIs?
KRIs stand for key risk indicators. These measurements can tell you how risky an activity may be for your business. These metrics differ slightly from KPIs and KQIs because they look at situations you may try to prevent rather than ones you try to achieve. They're often used in reports and discussions that involve KPIs and KQIs.
Why are KPIs and KQIs important?
KPIs and KQIs are important in business because they help you understand how different aspects of your work, process and team relate to each other. Some benefits of tracking both methods include:
Both KQIs and KPIs can give you data about how well your business is performing or providing quality to customers. The data can show proof that your methods work. It can also give you evidence to prove that changing strategy could be a better choice for the future of the company. Constant monitoring and evaluation can help you understand the health of your business and keep it operating at top performance.
The data you collect to set and review KQIs and KPIs can help you learn about unknown or unrecognized opportunities in your industry. This could provide the opportunity for you to enter a niche market. It may also show you new solutions, resources or design methods that could provide additional benefits in the future.
KPIs and KQIs can help you identify if your business strategies work. If they do, you can apply the principles from the successful practices to new ventures. If they're not working, you can see where and how to make adjustments to help the team meet its goals.
Setting and monitoring KPIs and KQIs can help you recognize patterns in your company over time. They can help you see if you have cycles or seasonal processes that you implement throughout the year. They can also tell you if there are lulls within a certain process that you can update and improve. For example, the data may show that six months after hiring employees may benefit from refresher training or education on company safety policies.
Similarities of KQIs vs. KPIs
Departments and teams often mention KPIs and KQIs in the same reports and context. This may be because they're similar in areas such as:
You can apply the principles of KPIs and KQIs in a variety of industries. They can help you decide about different aspects of your business, products and services. Some industries where you may use these metrics include:
KPIs and KQIs can benefit either the business or the consumer, depending on the tracked factors and data collected. For example, tracking the number of people that visit your website can be either business- or customer-centric depending on the campaign. If you're using that KPI because you just started a new promotion that gives people a discount on products, that could be a business-focused metric to look at conversions. However, if you just did a website makeover for accessibility and user-friendliness, that KQI may be customer-centric because the focus is user experience.
Differences between KQIs vs. KPIs
In business, sometimes people use the terms KPI and KQI interchangeably, but they have distinct differences in areas such as:
Amount of data
You can measure both KPIs and KQIs as single or multiple metrics. However, you may be more likely to measure KPIs with just one metric. For example, if you're looking to see if your website traffic increased after a promotion, the number you look at is the visitor count. However, you can measure KQIs in groups to understand if a particular activity meets quality standards. When looking at the quality of a product or process, you may account for:
Longitudinal data that shouldn't change, like questionnaires and raw data
Data you shouldn't clean, or that should be unaltered and not manipulated
Data derived from other data
Datasets that correlate with each other
Data with defined value ranges, or start and end parameters
Data validation to make sure it's in the desired, preferred or correct format for analysis
Data with time and duration constraints
These factors can show you a larger picture of the KQI outcome. You can also pick a few of them to compare to get a more specified and accurate measure of quality.
Because of the amount of data used for analysis, it may be easier to set, understand and review KPIs. To return to the example of website traffic, you may only concern yourself if that number goes up, goes down or stays the same. You may not look to find out the exact cause of why it did or didn't change. Your data may not be specific enough to give that information.
In contrast, KQIs require a better understanding of statistics. You may encounter terms such as:
Confidence intervals: These are the expectations of how your data looks when you plot it on a graph. Often you expect most of the results to be in the middle to form a bell curve.
Statistical significance: This number is a percentage or threshold that tells you if the data collected differs significantly from your expected outcome. It can tell you if you need to run another test or trial to get more accurate data.
Normality test: This procedure allows you to test data and determine whether your test or process followed a normal curve or had influence from a positive or negative even that changed the visual graph.
With KQIs, because you have a larger pool of data and more considering factors, you can learn why a certain situation occurred. You can look more closely at the data to draw a conclusion about the quality of your project and why you got a certain outcome.
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