For marketers, analytics play an important role in extracting meaningful insights from data that comes from a wide range of sources. “Analytics” is an umbrella term with multiple analytics practices underneath. Which type you should employ depends on the question you’re trying to answer. Here’s what you need to know.
Descriptive analytics: Understand the past
Descriptive analytics examine historical data to help marketers understand what has happened in the past. They provide insights into patterns, trends, and key performance indicators (KPIs) within a given dataset.
Examples of descriptive analytics include:
- Summarizing sales data to identify monthly trends
- Analyzing website traffic to determine peak hours over a period of time
Predictive analytics: Forecast future outcomes
Often a follow-up to descriptive analytics, predictive analytics leverage statistical algorithms to forecast future outcomes based on historical data. This helps organizations anticipate future trends, behaviors and events, which enables proactive decision-making.
Common predictive analytics use cases include:
- Predicting customer churn
- Forecasting sales for the upcoming quarter
- Estimating equipment maintenance needs
Prescriptive analytics: Recommend optimal actions
Prescriptive analytics go beyond predicting future outcomes by suggesting optimal actions to take to achieve your desired results. They guide marketing decision-makers in selecting the best course of action to maximize positive outcomes or minimize negative ones.
Prescriptive analytics could be used to:
- Recommend marketing strategies that increase customer engagement
- Suggest price adjustments for maximum profits
Artificial intelligence (AI): Mimic human decision makers and actions
The use of AI in marketing is a big topic, and many consider AI to be a system that uses data to make decisions and act on them, not simply a specific type of analytics. We've included it here because it's so valuable to the analytics process in making your analysis better, faster, or stronger.
AI refers to the development of computer systems capable of performing tasks that typically require human intelligence, including learning, reasoning, problem-solving, and decision-making. AI enhances analytics by automating processes, uncovering complex patterns, and making data-driven predictions.
The practical applications of AI are almost endless, but a few examples related to analytics include:
- Using machine learning algorithms to identify fraudulent transactions
- Using generative AI to score emotional progression in customer interactions
How can you apply analytics to your business goals?
OneMagnify can help you determine where, when, and what type of analytics are best suited to help you take your marketing to the next level. Learn more about our analytics expertise, then contact our experts to take the first step.
To learn more about analytics and AI for marketing, check out OneMagnify's on-demand webinar AI Foundations for Marketers. This comprehensive webinar is designed to equip marketers with a foundational understanding of artificial intelligence and how AI tools can be applied in their field. In today's age of AI hype, it's important to gain a foundational understanding to harness AI's true potential and impact.
