In an environment where every decision matters, data analytics has become a strategic asset for companies of all sizes. But simply having information isn’t enough: you have to know how to interpret it correctly.
There are different types of data analysis , each with a distinct focus, applicable to various stages of a business. In this article, we explain the four main types, with practical examples and how they can help you make better decisions in areas such as marketing, loyalty programs, and contact centers.
Table of Contents
Why is it important to know the types of data analysis?
Because each type answers a different question . Understanding the purpose of each one allows you to apply the appropriate method according to your objective.
| Type of analysis | Question that answers |
| Descriptive | What happened? |
| Diagnosis | Why did it happen? |
| Predictive | What could happen? |
| Prescriptive | What should I do about it? |
Types of data analysis
1. Descriptive analysis: the starting point
This type of analysis summarizes and organizes data to show what has already happened. It is the first step in any data-driven strategy.
Practical example in loyalty:
Review how many users redeemed points in the last quarter or which product was redeemed the most.
Common tools: dashboards, pivot tables, charts, performance reports.
✅ Ideal for monitoring KPIs, evaluating results, and detecting initial patterns.
2. Diagnostic analysis: looking for causes
It goes a step beyond the descriptive. It doesn’t just say what happened, but why it happened . It’s based on correlations, comparisons, and patterns of behavior.
Practical example in call centers:
Discovering that the increase in complaints is due to changes in the service process or a poorly communicated promotion.
Common tools: cohort analysis, segmentation, cross-tabulations.
✅ It is useful for detecting bottlenecks or root causes of business problems.
3. Predictive analytics: anticipating the future
It uses historical data and statistical models to predict future behaviors or trends .
Practical example in loyalty programs:
Predict which users are most likely to drop out of the program and send them a personalized retention campaign.
Common tools: machine learning algorithms, scoring, regressions, clustering.
✅ Powerful for planning preventative actions and making proactive decisions.
4. Prescriptive analysis: the guide to action
This is the most advanced: it not only predicts what might happen, but also recommends what to do to achieve the best result .
Practical example in CRM:
The system suggests sending a specific promotion to a segment with high purchase intent, on the channel they use most and at the most effective time.
Common tools: artificial intelligence, automation, scenario simulations.
✅ Key to making automated decisions, optimizing resources and maximizing results.
Which one do you need for your company?
Not all businesses need to jump straight to prescriptive analytics. The important thing is to start with the basics (descriptive and diagnostic) and evolve as your database grows and your decisions require more precision.
At LMS we combine these levels of analysis to design truly effective loyalty, CRM and contact center strategies.
Do you want to take your data analysis to the next level?
At Loyalty Marketing Services , we design customized solutions to transform data into actions that build loyalty, drive sales, and connect with your customers or employees.
👉 Would you like to know how to apply this in your company? Schedule a meeting here.
Frequently Asked Questions
What is the most common type of analysis?
Descriptive analysis is the most widely used, as it summarizes information and shows the current state of the business.
Do I need advanced software to do predictive analytics?
Yes. For predictive and prescriptive analytics, it’s ideal to have tools like smart CRMs, AI platforms, or machine learning algorithms.
How long does it take to implement an analytics system in my company?
It depends on the desired level. A basic system can be ready in weeks; an advanced one can take months, but with exponential results.
Does LMS offer these services?
Yes. We implement data analytics as an integral part of loyalty programs, contact centers, and customer retention strategies. We adapt to your technological infrastructure and objectives.
Article written by: Daniel Velasco Rallo
Strategy & Loyalty – Loyalty Marketing Services
