Data analytics—often called business analytics by organizations—is the process of using data to answer questions, identify trends, and extract insights. These insights can be valuable to organizations because they help drive decision-making and strategy formulation.
There are four key types of data analytics:
- Descriptive, which answers the question, “What happened?”
- Diagnostic, which answers the question, “Why did this happen?”
- Predictive, which answers the question, “What might happen in the future?”
- Prescriptive, which answers the question, “What should we do next?”
Each analytics type serves a specific purpose and can be used in tandem with the others to gain a full picture of the story data tells.
Diagnostic analytics provides crucial information about why a trend or relationship occurred and is useful for professionals aiming to support their decisions with data. Here’s an introduction to diagnostic analytics and key considerations for using it at your organization.
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Diagnostic analytics is the process of using data to determine the causes of trends and correlations between variables. It can be viewed as a logical next step after using descriptive analytics to identify trends. Diagnostic analysis can be done manually, using an algorithm, or with statistical software (such as Microsoft Excel).
There several concepts to understand before diving into diagnostic analytics: hypothesis testing, the difference between correlation and causation, and diagnostic regression analysis.
Hypothesis Testing
Hypothesis testing is the statistical process of proving or disproving an assumption. Having a hypothesis to test can guide and focus your diagnostic analysis.
Hypotheses can be future-oriented (for example, “If we change our company’s logo, more people in North America will buy our product.”), but these aid predictive or prescriptive analytics. When conducting diagnostic analytics, hypotheses are historically-oriented (for example, “I predict this month’s decline in sales was caused by our product’s recent price increase.”). The hypothesis directs your analysis and serves as a reminder of what you’re aiming to prove or disprove.
Correlation vs. Causation
When exploring relationships between variables, it’s important to be aware of the distinction between correlation and causation. If two or more variables are correlated, their directional movements are related. If two variables are positively correlated, it means that as one goes up or down, so does the other. Alternatively, if two variables are negatively correlated, one variable goes up while the other goes down.
The key in diagnostic analytics is remembering that just because two variables are correlated, it doesn’t necessarily mean one caused the other to occur.
If your organization is able to dedicate resources to running controlled experiments, you may be able to determine causation between variables. While determining causation is ideal, correlation can still offer the insight needed to make sense of your data and use it to make impactful decisions.
Diagnostic Regression Analysis
Some relationships between variables are easily discerned, but others require more in-depth analysis, such as regression analysis, which can be used to determine the relationship between two variables (single linear regression) or three or more variables (multiple regression). The relationship is expressed by a mathematical equation that translates to the slope of a line that best fits the variables’ relationship.
“Regression allows us to gain insights into the structure of that relationship and provides measures of how well the data fit that relationship,” says Harvard Business School Professor Jan Hammond, who teaches the online course Business Analytics, one of the three courses that make up the Credential of Readiness (CORe) program. “Such insights can prove extremely valuable for analyzing historical trends and developing forecasts.”
When regression analysis is used to explain the relationships between variables in a historical context, that’s an example of diagnostic analytics. The regression can then be used to develop forecasts for the future, which is an example of predictive analytics.
Diagnostic analytics can be leveraged to understand why something happened and the relationships between related factors. With the basics under your belt, consider these four examples of diagnostic analytics in action and how they can apply to your company.
4 Examples of Diagnostic Analytics in Action
1. Examining Market Demand
One use case of diagnostic analytics is determining the reasons behind product demand.
For example, take meal kit subscription company HelloFresh. The company gathers millions of data points from global users, including information about geographic location, disclosed demographic data, meal type, flavor preferences, and typical order cadence and timing.
HelloFresh’s team uses this data to identify relationships between trends in customer attributes and behavior. As a hypothetical example, imagine the HelloFresh team identifies a spike in fish-based recipe orders. After conducting diagnostic analysis, they find that the attributes most highly correlated with ordering fish recipes are identifying as female and living in the northeastern United States.
From there, the team could conduct market research with that specific demographic to learn more about the demand for fish recipes. Was it caused by a recent scientific study touting the health benefits of fish for women? Perhaps people who live in the northeastern United States have a refined palate for seafood because they live relatively close to the Atlantic Ocean. Their reasoning could provide impactful insights to HelloFresh.
Dipping into the other types of analytics, the team could also consider whether the trend is expected to continue (predictive analytics) and if it’s worth the effort and money to create more fish-based recipes to cater to this audience’s preference (prescriptive analytics).
2. Explaining Customer Behavior
For companies that collect customer data, diagnostic analytics is the key to understanding why customers do what they do. These insights can be used to improve products and user experience (UX), reposition brand messaging, and ensure product-audience fit.
Continuing with the HelloFresh example, consider the value of customer retention to the company, which operates on a subscription model. Keeping customers is more cost-effective than obtaining new ones, so the HelloFresh uses diagnostic analytics to determine why departing customers choose to cancel subscriptions.
During the cancellation process, departing customers must provide their reason for canceling. Options range from “doesn’t fit my budget” to “doesn’t fit my schedule or dietary needs,” and there’s also an option to write in an answer. By gathering this data, HelloFresh can analyze the most cited reasons for losing customers among specific regions and demographics and use diagnostic analytics to answer the question, “Why are people cancelling their subscriptions?”
These insights can help improve HelloFresh’s product and user experience to avoid losing more customers to those reasons.
3. Identifying Technology Issues
One example of diagnostic analytics that requires using a software program or proprietary algorithm is running tests to determine the cause of a technology issue. This is often referred to as “running diagnostics” and may be something you’ve done before when experiencing computer difficulty.
Some of these algorithms are constantly at work in the background of your machine, while others need to be initiated by a human. One type of diagnostic test you may be familiar with is solution-based diagnostics, which detects and flags symptoms of known issues and conducts a scan to determine the root cause. This can allow you to address the issue and escalate it if the cause is serious.
4. Improving Company Culture
Diagnostic analytics can also be leveraged to improve internal company culture. Human resource departments can gather information about employees’ sense of physical and psychological safety, issues they care about, and qualities and skills that make someone successful and happy. Many of these insights come from running internal, anonymous surveys and conducting exit interviews to identify factors that contributed to employees’ desire to stay or leave.
Gathering information about employees’ thoughts and feelings allows you to analyze the data and determine how areas like company culture and benefits could be improved. This can include anything from wishing the company made more corporate social responsibility (CSR) contributions to feeling discriminated against at work. In these cases, the data presents a case for allocating more resources to CSR and diversity, equity, inclusion, and belonging efforts.
Insights from surveys and interviews can also enable hiring managers to determine which qualities and skills make someone successful at your company or on your specific team, and thus help attract and hire better candidates for open roles.
Diagnostic analytics can help boost employee happiness, safety, and retention, as well as lead to more effective hiring processes.
Answering Big Questions with Data
Diagnostic analytics can enable you to get to the “why” behind data trends. With a deeper understanding of your data—whether it be about customers, employees, or technology issues—you can feel empowered to make data-driven decisions.
To boost your analytics skills, consider taking an online course, such as Business Analytics. Ask questions of datasets, learn to run single linear and multiple regressions, and hear from real-world business professionals who’ve used data analysis to impact their organizations.
Do you want to become a data-driven professional? Explore our eight-week Business Analytics course and our three-course Credential of Readiness (CORe) program to deepen your analytical skills and apply them to real-world business problems.
