Data analysis is the driving force behind informed decision-making today. But it’s not just about acquiring data — it’s how you use it. Difference Between Descriptive and Predictive Analytics are so important to define.
How do you make the most of the valuable data you’ve collected? How are decisions informed by the results of your analysis? And what is the relationship between descriptive and predictive analytics?
We’ll answer these questions below.
Table of Contents
Descriptive vs. Predictive Analytics
The fundamental difference between descriptive and predictive analytics is this:
Descriptive analysis helps you understand what has happened; predictive analysis helps you know what should happen next.
Descriptive Analytics Overview
This field of analytics focuses on what you can learn from your company’s sales results, patterns of customer behavior, responses to marketing strategies, and more.
It paints a picture of how cause has led to effect. This lets decision-makers identify patterns of success and missed opportunities alike.
This information can be used for troubleshooting problem areas of a company’s performance and building on success stories.
Predictive Analytics Overview
Where descriptive analysis focuses on what is already known, predictive analysis extrapolates trends and patterns to help decision-makers understand how certain choices will affect performance.
Predictive analysis is constantly refreshing itself. If new data agrees with or contradicts historical patterns, analysts will factor this new information in when modeling new predictions. It consistently becomes more accurate.
How Descriptive and Predictive Analytics Work Together
So, how do you describe the relationship between descriptive and predictive analytics?
The description informs prediction. This is a virtuous cycle that smart analysts constantly refine to improve the accuracy of predictions.
Let’s illustrate with some examples.
Examples of Descriptive and Predictive Analytics
Descriptive Analytics Example
Say a company runs three types of marketing campaigns — TV advertising, social campaigns, and email marketing.
- The email campaign costs $1000 and generates $10000 in purchases.
- The TV campaign costs $5000 and generates $15000 in purchases.
- The social campaign costs $400 and generates $800 in purchases.
Descriptive analytics paints the following picture.
- The email campaign created a net profit of $9000, or a 1000% ROI.
- The TV campaign created a net profit of $10000, or a 300% ROI.
- The social campaign created a net profit of $400, or a 200% ROI.
Stakeholders can then understand which types of campaigns boost profits the most (TV followed by email), but also which campaigns they can afford to run and still generate a profit if the marketing budget is tight (social and email).
Prescriptive Analytics Example
Historical data might suggest that customers are more interested in a certain type of product (e.g. dehumidifiers) at a specific time of year (e.g. late Fall).
Thus, we can comfortably predict that a new product (e.g. a dehumidifier with additional air purifying capabilities) serving the same purpose will perform best if it’s launched at that time of year.
Discover the Uses of Descriptive and Predictive Analytics
Descriptive and predictive analytics integration is essential for any forward-thinking business. It’s the difference between a shot in the dark and knowing the next step before you take it.
Want to find out how your company can utilize the relationship between descriptive and predictive analytics?Schedule a consultation with an engineer from dbSeer now to start your journey.
