Data analysis is the driving force behind informed decision-making today; however, it is not just about acquiring data and it also focuses on how you use it. How do you make the most of the valuable data you have collected? How are decisions informed by the results of your analysis? What is the relationship between descriptive and predictive analytics? We will answer these questions below.
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Descriptive vs. Predictive Analytics
Descriptive analysis helps you understand what happened while predictive analysis enables you to determine what should happen next. That is the fundamental difference between the two.
Descriptive Analytics Overview
The descriptive analytics field focuses on what you can learn from your company’s sales results, customer behavior patterns, responses to marketing strategies, and more.
It paints a picture of how it enables decision-makers to identify patterns of success and missed opportunities. This information can be used to troubleshoot problem areas of a company’s performance and build 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 confident choices will affect performance. Predictive analysis is constantly refreshing itself. If new data agrees with or contradicts historical patterns, analysts factor this new information in when modeling new predictions. Thus, it consistently becomes more accurate.
How Descriptive and Predictive Analytics Work Together
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 their predictions’ accuracy.
Let’s illustrate with an example referenced below:
Examples of Descriptive and Predictive Analytics
Descriptive Analytics Example
Let’s assume Company A runs three marketing campaigns — TV advertising, social campaigns, and email marketing.
- The email campaign costs $1,000 and generates $4,000 in purchases.
- The TV campaign costs $5,000 and generates $10,000 in purchases.
- The social media campaign costs $500 and generates $1,200 in purchases.
Descriptive analytics helps paint the following picture for Company A:
- The email campaign created a net profit of $3,000, resulting in 4x the ROI.
- The TV campaign created a net profit of $5,000, resulting in 2x the ROI.
- The social media campaign created a net profit of $700, resulting in 1.4x the ROI.
Stakeholders can then understand which types of campaigns boost profits the most (in this case: TV followed by email) and which campaigns they can afford to run and still generate a profit if the marketing budget is tight (in this case: social and email). This helps make strategic business decisions that provide clear returns on investment.
Prescriptive Analytics Example
Regression Analysis: Use regression models to analyze the relationship between campaign costs and generated revenue. This can help predict how much revenue a new campaign might generate based on the amount spent.
Time Series Forecasting: Analyze historical data over time to identify patterns and trends. For example, if email campaigns consistently perform better during certain months, this can be factored into future campaign planning.
Machine Learning: Implement machine learning algorithms to evaluate various campaign parameters (such as timing, platform, and messaging) and predict outcomes based on past performance.
Implementation: Predict Future Revenue: Based on the historical ROI of each campaign, predict future revenues for new campaigns. For instance, if a new email campaign is budgeted at $1,200, the predictive model could estimate a revenue generation of around $4,800 (assuming the same 4x ROI).
Optimize Campaign Budgeting: Use the model to allocate budgets more effectively. For instance, if the analysis suggests that increasing the social media budget by 20% could improve its ROI based on user engagement data, you can adjust budgets accordingly. Scenario Analysis: Create scenarios to understand the potential impacts of different strategies, such as increasing the social media budget or running a combined campaign across channels.
Discover the Uses of Descriptive and Predictive Analytics
Descriptive and predictive analytics integration is essential for any forward-thinking business. It is the difference between taking a shot in the dark and knowing the next step before taking 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.
