The term “data analytics” is the latest buzz in the streets of marketing, and for good reason—analytics are central to precise decision-making and strategy formation today. So, if your marketing department isn’t leveraging data, your strategies may not be as effective as you think.
Sure, you may have had some success here and there depending on your marketing instinct or popular strategies. If your strategy isn’t backed by data, however, there’s no guarantee of being able to replicate that success. To do data the right way, you’ll need to understand the two most common types used in marketing—predictive vs prescriptive analytics.
This guide will explore the differences between these two main types of data analytics, how to use them in different case scenarios, and the role Artificial Intelligence (AI) plays in analytics.
What Are the Four Types of Analytics?
While this guide will primarily focus on predictive vs prescriptive analytics, there are other types of data analytics that can help inform your strategy as well.
Descriptive: Business Intelligence
The easiest way to explain descriptive analytics is that it answers the question: “What happened?” This type of analytics is usually the first step a company takes to create future marketing strategies because it looks at past or historical data.
Descriptive analytics uses raw data from past performances to pull trends that can help inform future events and how to handle them. The most common method that descriptive analytics use is data visualization, where you represent data using visual tools, such as graphs, charts, or maps.
For instance, descriptive analytics can help answer the following questions:
- What were the sales trends for our products over the past year?
- Which marketing campaigns generated the highest engagement levels on social media platforms last quarter?
- What demographic segments make up the majority of our customer base?
Diagnostic: The Reasons Why
Diagnostic analytics should be the next step after descriptive analytics, but many organizations skip this step and go straight to “What will happen next?” However, to predict what will happen in the future and create effective strategies, you must understand the reason(s) why certain performances occurred.
As the term suggests, diagnostic analytics seeks to find out why an outcome happened the way it did. Like descriptive analytics, diagnostic uses historical data but focuses on the “why” rather than the “what.”
This type of analytics can help answer questions such as:
- Why did sales for our new product decline in the last quarter?
- Why are millennials the biggest share of our customer base for our new product?
- Why did social media channels have higher conversion rates than other marketing channels?
Predictive: Trend Forecasting
Seeing into the future is a superpower every marketer wants to have, and that is exactly what predictive analytics helps you to do. Predictive analytics are a type of advanced analytics that answer the question: “What will happen?” They leverage trends and insights pulled from descriptive and diagnostic analytics and predict future outcomes.
Predictive analytics rely on several statistical techniques, such as machine learning (ML), game theory, and modeling to determine the probability of a given outcome. While predictive analytics models may provide a “peek” into the future, the predicted outcomes are not guaranteed. They simply offer a probability that a certain outcome will occur based on the data provided.
Prescriptive: Detailed Roadmaps
Prescriptive analytics help answer the question: “What will you do?” Having an idea of what will likely happen in the future is great, but do you intend to do something about it? Prescriptive analytics provides actionable recommendations and potential implications in response to insights gathered from predictive analytics.
These analytics combine insights from predictive analytics with optimization techniques to enhance decision-making and the overall efficiency of marketing strategies.
Predictive vs Prescriptive Analytics: Key Differences
Now that you understand the primary difference between predictive vs prescriptive analytics, let’s take a look at a side-by-side comparison of each:
| Predictive Analytics | Prescriptive Analytics | |
| Purpose | To forecast future outcomes based on historical data | To recommend actions to achieve desired outcomes |
| Focus | “What is likely to happen?” | “What should we do?” |
| Techniques Used | Statistical modeling, machine learning, regression analysis, time series | Optimization algorithms, simulation, scenario analysis, decision analysis |
| Data Utilization | Uses historical and current data to predict future events | Uses predictive insights and additional data to suggest optimal actions |
| Complexity | Generally less complex, focused on analysis | More complex, integrating predictive models with optimization techniques |
| Value Proposition | Improves forecasting accuracy and risk assessment | Enhances decision-making quality and operational efficiency |
5 Examples of Predictive Analytics in Marketing
Predictive analytics can be useful in many business scenarios, but it’s especially important for these five key marketing techniques.
1. Audience Segmentation
Your audience has one essential thing in common: your business. However, that doesn’t mean they’ll have the same traits and behaviors. That’s why segmentation is important—it helps you create several groups within your existing and potential customer base to align your marketing strategies with their needs.
By using predictive analytics and machine learning, you can segment your audiences faster and more accurately instead of making “informed guesses.” Predictive analytics leverage ML algorithms that use large data sets to spot similar characteristics in your audiences’ data and group them according to pre-set segments. Additionally, these analytics can help identify where new customers may fit best for creating better personalized campaigns.
2. Accurate Lead Scoring
Without lead scoring, your marketing team may end up spending too much resources on individuals who may not have any interest in your products or services. It’s critical for helping you figure out which of your potential customers are more likely to convert into buying customers.
Predictive analytics can be especially helpful in lead scoring in several ways. For one, by analyzing past data of each lead, such as customer behaviors and interactions with your brand, predictive analytics can help you determine if they will convert. Secondly, if a lead falls into a segment within your customer base, predictive analytics can help you assign points or values to your leads. To do this, you can use past data from the segmented audiences on how fast they converted.
3. Look-Alike Modeling for Customer Acquisition
Your organization should always be on the hunt for new customers to drive revenue growth. However, identifying and acquiring new customers isn’t always easy, and it can eat up a lot of your team’s limited time.
With predictive analytics, you can make this process more efficient and effective. This technique uses machine learning to analyze your existing customer data and define your ideal customer profile and their characteristics. When you apply the same patterns to a larger data set of prospects, you can identify individuals who share the same characteristics as your existing customers. This enables you to create more targeted campaigns that attract and retain new customers.
4. Customer Retention Through Proactive Churn Management
Some amount of customer churn is inevitable for several reasons, including pricing issues, poor customer experience, high competition, etc. But you shouldn’t accept this as the status quo. Efforts channeled toward customer retention should be just as focused as those channeled to customer acquisition.
One way to manage your churn rate is through predictive analytics, analyzing historical data to identify patterns or behaviors related to churn. With these insights, you can predict or identify when customers begin to lose interest in your products or services and implement strategies that “revive” this interest.
For instance, if data indicates that a customer’s purchase frequency is declining, predictive analytics can flag this as a risk of churn. You can then take proactive measures, such as sending personalized offers, discounts, or reminders to re-engage them.
5. Ad Recommendations and Demand Forecasting
While fluctuations in demand from customers are normal, how you react to them determines your organization’s resilience and ability to meet its customers’ needs. Predictive analytics plays a crucial role in this process by enabling you to anticipate your customers’ future needs.
By analyzing how demand for your products and services has been in the past, you can predict how future demand will look like. From these insights, you can create effective strategies that help sustain or increase demand in the future, which is essential for growing revenue.
Additionally, predictive analytics can help you forecast demand by analyzing certain data points such as browsing history, demographic behavior, individual customer behaviors, and seasonal changes. All these combined (and sometimes individually) can help you predict how demand will change in the future and how to prepare for it.
5 Marketing Use Cases of Prescriptive Analytics
These prescriptive analytics examples provide a better understanding of just how useful analytics can be in your marketing activities.
1. Product Enhancements Through Feature Selection
Just because there’s proven demand for a product or service doesn’t mean it has reached its full potential. To attract new customers and retain existing ones, you need to continually improve your product line.
With prescriptive analytics, you can ensure that your products continue meeting your customers’ changing needs while maintaining a competitive edge. For instance, based on the insights you gather from predictive analysis on what future customer needs might look like, prescriptive analytics can recommend adjustments to your product to ensure it maintains demand.
Prescriptive analytics can also be helpful during the development of a new product. In such cases, you can collect data from surveys of your target audience or test beta versions of the product before launching the product. The data you collect from these activities can help inform the features you should include and those you shouldn’t to ensure customers have a great experience.
2. Optimal Pricing Strategies and Conversion Optimization
To help curb churn rates and low conversion rates, you can incorporate optimized pricing in your marketing strategies. This is because pricing is one of the main causes of customer churn and the lack of customer conversion.
Prescriptive analytics can help address these problems by providing actionable insights and their potential outcomes. It starts with prescriptive analytics models simulating different pricing scenarios and how different customers react to them. Based on these reactions, the model recommends the best price for each customer segment. This strategy can be especially useful in e-commerce, where you can adjust your prices in real time as customers browse for products on your site.
3. Organic Content Creation and Personalization
Organic content is a vital aspect of today’s marketing strategies because audiences value personalized experience. In fact, research indicates that personalized experiences are more likely to convert customers. That means your marketing strategy should incorporate personalization and organic content to increase conversion rates. Prescriptive analytics is a great way to streamline the personalization process.
A great example of how this works is TikTok’s For You Page (FYP). TikTok’s algorithm gathers its users’ data on engagement history, such as liked videos, videos watched until the end, and the niche of videos you most interact with. Based on this data, the FYP will recommend similar content or videos, which is what gets users hooked on the platform for hours.
4. Email Marketing Campaign Automation
Email marketing automation is an excellent example of prescriptive analytics in use.
Prescriptive AI collects data from your customer base and segments them into different categories, then sends them personalized emails. Once delivered, any interactions your customers have with the emails, like opening or clicking on links, trigger another segmentation, and the automation sends more niche messages. Prescriptive analytics also help you determine the best time to send emails to customers by analyzing past engagement data.
For instance, if you run an e-commerce site, you can use prescriptive analytics to automate your holiday email campaign. With prescriptive modeling, you can analyze your customers’ purchase history to help segment them into various groups, such as gift buyers, last-minute shoppers, and self-gifters. The model can then create personalized messages for each segment with optimized subject lines, images, and call-to-action buttons that guarantee conversion.
5. Campaign Budget Allocation
Prescriptive analytics doesn’t only focus on creating better strategies—you can also use it to optimize your budgets for maximum marketing ROI. This works by analyzing past budgeting data against ROI and recommending the most optimum budget allocation for high ROI. Prescriptive analytics also recommends simulating different budget allocation scenarios to see which ones are likely to yield the best results.
The Role of AI in Prescriptive and Predictive Analytics
Artificial intelligence plays a crucial role in both prescriptive and predictive analytics by enhancing their efficiency, effectiveness, and accuracy in different ways. These include:
- Automation: The most significant role that AI plays in data analytics is automating repetitive tasks that require a lot of human resources and time. These tasks include integrating and analyzing data, extracting actionable insights from data, and recommending the best course of action.
- Advanced Data Processing and Integration: AI algorithms can integrate large volumes of data from different sources with high accuracy rates and faster speeds compared to manual processes. After integration, these AI systems then leverage ML models to identify trends and correlations that are vital in enhancing predictive analytics.
- Scenario Analysis: Before AI models in prescriptive analytics can provide actionable recommendations, they evaluate numerous scenarios and outcomes. This is vital to ensure that your marketing department only implements the best and most effective course of action for your unique situation.
- Adaptive Strategies: Customer data, such as purchasing behavior, is ever-changing. For marketing strategies to work, they must constantly adapt to these changes. Prescriptive AI models make this possible by dynamically adjusting strategies as conditions and data change.
- Model Training and Improvement: AI facilitates continuous training and improvement of predictive and prescriptive analytics models as new marketing data becomes available. It leverages techniques such as reinforcement learning, which allows analytics systems to adapt and learn based on feedback from their analysis.
- Scalability: The more data you use for analytics, the more accurate your predictions and recommendations become. However, depending on manual methods can be extremely time-consuming and error-prone. AI enables marketing departments to work with large amounts of data comfortably at any given time without significant resource investments.
Optimize Your Marketing Efforts With an AI Data-Driven Solution
Data-driven decisions are the key to maintaining a competitive edge in an increasingly crowded and evolving landscape. But to leverage data-driven insights and various analytics types, you need advanced solutions and expert guidance that enhance accuracy, speed, scalability, and adaptability. AUDIENCEX Intelligence (AXi) is one such solution, an advanced suite of privacy-safe, AI-powered data science and performance advertising tools that are leveraged effectively by our dedicated teams.
AXi’s AI predictive analytics and behavioral data science, powered by our AXi Predictor tool, enable our clients to understand their audiences better, which is crucial for creating effective campaigns. These models use data science to help in the visualization of your existing customer base, gather behavioral insights, and build out custom predictive audiences that are most likely to convert, delivering a higher ROI and maximizing the results of any campaign.
AXi also offers the Optimizer tool, with custom bidding algorithms and real-time autonomous optimization to get the most out of any ad spend throughout the lifecycle of a campaign, maximizing results and minimizing costs. And, to ensure full transparency into campaign performance across every channel, AXi Explorer provides insight into every touchpoint.
The AXi suite is powered by log-level and historical data access, in addition to the largest fully consented and opted-in customer data set in the industry. This ensures that you are well ahead of evolving privacy regulations and achieving fully cookieless performance.
With the power of these tools and predictive data, our expert teams gather prescriptive insights that are effectively deployed throughout your campaigns from end to end. This data informs our full service teams, in every department from creative to strategy and media, to develop data-driven campaigns that drive tangible results for any goal.
If you’d like to learn more about our AI-powered solutions, seamless media access, tech-enabled creative, holistic strategy, privacy-safe data science, and how all of our services come together to drive true performance, reach out today. We’ll connect you with an expert member of our team who can help you navigate your challenges and goals, and find out how our services and solutions can work for you.