DIAGNOSTIC ANALYTICS

WHAT IS DIAGNOSTIC ANALYTICS

Diagnostic analytics is a type of advanced analytics in which data or content is examined to answer the question, “Why did it happen?” Drill-down, data discovery, data mining, and correlations are some of the approaches used. Diagnostic analytics is a method of analyzing data to determine the causes of trends and correlations between variables. It may be considered a natural next step after identifying trends with descriptive analytics. Manual diagnostic analysis, algorithms, and statistical software can all be used (such as Microsoft Excel). 

There are several concepts to grasp before diving into diagnostic analytics: hypothesis testing, the distinction between correlation and causation, and diagnostic regression analysis.

1. Hypothesis Testing

The statistical process of demonstrating or disproving an assertion is known as hypothesis testing. A hypothesis to test can help to steer and concentrate your diagnostic analysis.

Future-oriented hypotheses (for example, “If we update our company’s logo, more individuals in North America will buy our goods.”) might help with predictive or prescriptive analytics. When using diagnostic analytics, assumptions are historically oriented (for example, “I believe our product’s recent price increase caused this month’s sales decline”). The hypothesis directs your investigation and serves as a reminder of what you want to prove or disprove.

2. Causation vs. Correlation

It’s crucial to know the difference between correlation and causation when looking into relationships between variables. The directional motions of two or more variables are connected when they are correlated. As the two variables are positively linked, it indicates that when one increases or decreases, so does the other. If two variables are negatively linked, one rises while the other falls.

The key to diagnosing data is to understand that just because two variables are linked does not mean one caused the other.

3. Analysis of Diagnostic Regression

Some correlations between variables are obvious, while others require more investigation, such as regression analysis, which can be used to determine the relationship between two variables (single linear regression) or three or more variables (multiple linear regression). A mathematical equation is used to express the connection, which translates to the slope of a line that best matches the variables’ relationship.

“Regression allows us to gain insights into the structure of that relationship and provides measures of how well the data fits that relationship,” says Harvard. Business School Professor Jan Hammond, who teaches Business Analytics, one of three courses that comprise the Credential of Readiness (CORe) program, says,  “Such insights can be tremendously useful for assessing past trends and forecasting.”

DIAGNOSTIC ANALYTICS IN ACTION EXAMPLES

1. Evaluating Market Demand

One use of diagnostic analytics is to determine the causes of product demand.

Consider the meal kit subscription service HelloFresh. Millions of data points, including geographic location, stated personal data, meal-type, taste preferences, and usual order cadence and timing, are collected from users around the world.

2. Customer Behavior Explanation

Diagnostic analytics is the key to understanding why consumers do what they do for organizations that collect customer data. These findings may be utilized to enhance goods and user experience (UX), reposition brand messages, and assure ”function.”

Examine the significance of client retention to a subscription-based business, using the HelloFresh example as an example. Because retaining customers is less expensive than acquiring new ones, HelloFresh uses diagnostic analytics to identify why customers are leaving to discontinue subscriptions.

3. Recognizing Technology Issues

Running tests to establish the source of a technological issue is one example of diagnostic analytics that necessitates the use of a software program or proprietary algorithm. This is commonly referred to as “running diagnostics,” and you may have done it previously while suffering from computer problems.

4. Enhancing Company Culture

Diagnostic analytics may also be used to improve business culture. Employees’ physical and psychological safety, topics of interest, and the traits and talents that make someone successful and happy are all things that human resource departments can collect data on. Many of these insights came from internal, anonymous surveys and exit interviews, which were used to identify factors that influenced workers’ decisions to stay or leave.

Because of its ability to infer correlations and causality between two variables, diagnostic analytics is a valuable asset in decision-making. Analytics gives a full, A thorough view of a situation, assisting business executives in making sound decisions.