When it comes to making informed marketing optimizations, there's no argument about the critical role that data plays. Accurate data analysis is the foundation of effective marketing decision-making and strategic planning.
However, working towards data-driven decision-making is often easier said than done.
One of the first steps you need to take in the pursuit of becoming more data-driven is fully understanding the different types of data you have and the different insights you’re able to draw from it.
One of the most significant distinctions in data types is between structured and unstructured data.
Structured data is the kind of data that fits neatly into tables and spreadsheets and is easy to analyze. Unstructured data is more free-form, including everything from social media posts to video content. It's richer and more complex but also more challenging to analyze.
In the context of marketing performance analysis, structured data is most commonly used. It's easier to manipulate and analyze, providing clear insights into campaign performance and market trends.
However, within structured data, there are additional subsets of numerical data: discrete and continuous data. Understanding the fundamental differences between the two is important to ensure accurate analysis and data visualization.
In this article, we’re going to provide a clear overview of the differences between the two types of data, their application in marketing, and how you can ensure that both your discrete data and continuous data remain accurate and valid during data integration.
What is discrete data?
Discrete data is countable, finite, and usually comes in the form of whole numbers.
To help illustrate this with an example, imagine you’re counting the number of clicks on a social media ad. This is discrete data.
These clicks are discrete because they can only be whole numbers. You can have 1000, 1100, or 1200 clicks, but not 1000.25.
Other examples within a marketing context could be the number of products sold, the number of customers visiting a store, or the number of employees in a company.
Each of these examples involves countable values that cannot be meaningfully broken down into fractions or smaller parts.
What are examples of discrete data in marketing?
Within online marketing, discrete data is commonly used for measuring performance, trends, and the effectiveness of campaigns.
Some common examples might include:
- Sales performance
Looking at the number of sales by day is a good example of discrete data, as each transaction is a separate, countable event. You can't have half a transaction — it's always a whole number.
- Website visits
Similar to sales transactions, website visits are distinct and countable. You either have a visit, or you don't; there's no such thing as a fraction of a visit.
- Social media engagements
These are discrete because each like or reaction is a singular event. You can count the number of likes on a post, but you can't have 0.25 of an engagement.
- Email campaign click-throughs
Each click on a link in your email campaign is a discrete event, as you count these in whole numbers. There's no half-click - a recipient either clicks on a link or they don't.
What is continuous data?
Continuous data includes values that can be measured on a continuous scale and can take any value within a specified range.
The key difference when compared to discrete data, is that continuous data can assume any numeric value, including fractions and decimals.
This makes continuous data effective for more nuanced and detailed measurements.
For example, if you're measuring time spent on a website, the data can be very specific, such as 2 mins 33 seconds instead of being rounded to the nearest whole number.
The nature of continuous data means that it can be meaningfully split into smaller parts, making it ideal for analysis where changes can be small and you need a high degree of precision.
What are examples of continuous data in marketing?
Continuous data can provide valuable insights in a marketing context due to its precision and ability to capture subtle variations.
Some common examples might include:
- Website visit duration
It wouldn’t be useful to report on the website visit duration in whole numbers like “1 minute” or “2 minutes”. Doing so would mean that you could potentially miss important trends and patterns in visitor engagement on your website.
Rather than simply rounding up to the nearest minute, continuous data allows us to measure the exact website visit duration, such as 1 minute and 37 seconds.
So, if you notice that the average visit duration is gradually decreasing from 1 minute 37 seconds to 1 minute 35 seconds and 1 minute 33 seconds, it could help you identify that there’s something on your site that you need to address.
- Time spent watching a video ad
Similar to website visit duration, specifying the exact amount of time the average viewer spends watching a video ad is important.
You wouldn’t get the precise insight you needed by rounding it to the nearest minute — it's about capturing the precise duration down to the second to monitor trends in viewer interest and engagement accurately.
Discrete Vs. continuous data: The differences summarized
Understanding the differences between discrete vs continuous data is important in marketing data analysis, as the two types of data have unique characteristics and should typically be used and visualized in different ways.
To clarify the distinctions between the two types of data, we’ve put together a handy comparative table that outlines their characteristics.
|
Feature |
Discrete Data |
Continuous Data |
|
Definition |
Values that can only be distinct, separate, whole numbers. |
Values that can take any number within a range, including decimals or fractions. |
|
Examples in Marketing |
Number of website visits, sales transactions, social media likes, email campaign click-throughs. |
Time spent on a website, duration of video engagement. |
Mapping and validating your discrete and continuous data
The majority of modern marketing departments are managing data across a number of different marketing channels and platforms.
To help streamline the data analysis process and ensure that everyone in the business is working from consistent and standardized data, many businesses are looking to data integration to help deliver a single source of truth.
To quickly summarize the role of a data integration platform, it helps bring each of your disparate data streams into a single, standardized, centralized database, which can help businesses access and analyze their data more effectively.
One of the challenges in effective data integration is ensuring that your data is loaded into the centralized data destination in the correct structure and format.
For example, you’d want to make sure that your average website visit duration retains the granularity you need and isn’t rounded to the nearest whole number.
This is where choosing a data integration platform with robust data mapping, data transformation, and data validation features becomes important.
Data mapping and data enrichment ensure that the fields in each of your data sources are correctly mapped to the correct field in your data destination in the agreed format and structure to make it optimal for analysis and decision-making.
Data validation is equally important for verifying the accuracy and quality of your data integration, identifying anomalies like missing or duplicate entries. This ensures your integrated data is reliable and accurate so teams can confidently make informed decisions.
When you’re looking for a data integration solution for your business, it’s important to consider its functionality to maintain the quality and integrity of your data. This is critical if you’re going to ensure the teams across your business have the right data for making accurate, informed marketing decisions with confidence.
Adverity: helping you consolidate all your business data
Making the transition to become more data-driven in your marketing decision-making can sometimes feel daunting.
But with the right tools and technologies, it doesn’t need to be.
Adverity is a market-leading data integration platform specifically designed to handle the data needs of marketing teams.
With more than 600 data connectors, you’re able to connect to, transform, and integrate all your marketing data - whether it's structured or unstructured, discrete or continuous.
Intuitive data mapping functionality and robust data validation tools ensure that all data formats remain consistent, accurate, and aligned with your unique business needs.
To experience how Adverity can improve your data management, book a demo or try our platform free for 14 days.
