Marketing teams require more than just data and analytics tools to implement effective marketing strategies. They must also understand the type of data they collect and how to analyze it to gain meaningful insights. This requires going back to basics and understanding ordinal data, which is one of the key marketing data types. In this article, we will explore ordinal data and how it informs data-driven marketing decisions.
What is Ordinal Data?
Ordinal data is quantitative data in which variables are organized into ordered categories, such as a ranking from 1 to 10. However, the variables lack a clear interval between them, and values in ordinal data don’t always have an even distribution. Examples of ordinal data are the level of customer satisfaction, which could be Very Satisfied, Satisfied, Neutral, Dissatisfied, or Very Dissatisfied. Using ordinal data, you can calculate the frequency, distribution, mode, median, and range of variables.
Types of Data
There are various other data types, such as nominal, interval, and ratio data. Nominal data is a classification of data whose variables have a finite set of values and categories that aren’t ordered, while interval data is a type of data where the interval between two values isn’t constant. Ratio data does not provide any information about the values it represents, and this information must be obtained from other sources referenced by the ratio data. It is often used in the analysis of financial information but can also be applied to other types of data.
Ordinal Data Examples and How to Collect Them
Ordinal data occurs in different formats, and here are a few examples and how to synchronize them with your business strategy to improve your data management efforts.
1. Interest Level: Market research involves analyzing both qualitative and quantitative data to understand customer needs, their buying preferences, and what motivates them to buy from you. For example, if you host conferences regularly, surveys can help you know how well you did and whether your attendees want to attend the conference again. Interest-level data ranges from not interested, slightly interested, neutral, to very interested.
2. Education Level: This type of ordinal data provides insights into your target audience’s proficiency level. Education-level data comes in handy when using analytics in your recruitment process to help you evaluate the job applications of potential candidates.
3. Socioeconomic Status: Understanding the socioeconomic status of your target audience helps create and refine your customer segments based on their demographic and psychographic profiles.
4. Satisfaction Level: The satisfaction level reflects how content your customers are with different brand interactions, such as your customer onboarding process or how well you resolve customer issues. Satisfaction level data helps you gauge customer service and sales handling satisfaction to identify areas for improvement.
5. Comparison: This involves comparing two or more data points to learn what characteristics are similar, which ones are different, and the degree to which they’re different or similar. For example, you may want to compare revenue performance from 2021 to 2022.
To collect ordinal data, you can use surveys or Likert scales using survey software. You must run surveys with questions that rank answers using an implicit or explicit scale.
Conclusion
In conclusion, marketing teams need more than just data and analytics tools to ensure effective marketing strategies. Ordinal data is one of the key marketing data types that marketing teams need to understand, and it helps in making data-driven marketing decisions. Collecting ordinal data requires running surveys with questions that rank answers using an implicit or explicit scale.
