Data Analytics, The Heart Of Online Marketing Analytics

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Key Takeaways

• Data analytics is the practice of collecting, organising and analysing large volumes of data to find patterns and make marketing decisions on evidence rather than instinct.
• It comes in four types: descriptive (what happened), diagnostic (why), predictive (what will happen) and prescriptive (what to do next).
• A campaign analysis runs in seven steps: set the goal, choose the data, clean it, analyse it, report it, adjust the approach, and keep measuring.
• The toolset scales with the team: Google Analytics for the website, Hotjar for behaviour, HubSpot or Salesforce for customers, Excel or Google Sheets for basics, and Python for large or predictive work.
• Thailand’s PDPA applies to every dataset, so consent and security sit inside the process, not after it.

Whatever the era, the thing that matters most in marketing is marketing data. Online marketing is more than creating content and running ads. It needs effective data analysis, what everyone calls data analytics, fed back into the marketing approach, so decisions rest on real data and lead to more conversions and better results. That is what data analytics in marketing is for.

What Is Data Analytics?

Data analytics is the practice of taking large volumes of data from many sources (big data), then analysing and organising it with statistical tools and techniques to find the relationships inside it. Data analytics supports brand planning, marketing decisions and other work, such as improving ROI or finding the customer segments most likely to buy a product or service.

In an age when information arrives in huge volumes from every direction, data analytics has become the heart of online marketing. It solves problems and supports decisions with precision, and it reduces the risk of personal bias and gut feeling in decision-making.

Why Data Analytics Matters To Online Marketing

• A deep understanding of the audience. Demographics, behaviour and needs can all be analysed, so the marketing approach fits the audience more closely.
• Real-time adjustment. Ads, email and the website can be tracked as they run, with no need to wait for a campaign to end before assessing it.
• Effective measurement. Click rates, website visits and shares can all be checked.
• The right platform for the right customers. Customers are found in the right place at the right time.

What Are The Types Of Data Analytics?

Data analytics today divides into four types, each with a different purpose and emphasis.

1. Descriptive Analytics

Descriptive analytics gives an overview of past results, such as a revenue report or the performance of past campaigns. The data shows what worked well and what needs improving.

For example, take the results report for an email marketing campaign sent with a festive promotion. If the open rate is high but the click-through rate is low, the content or the offer may not be appealing enough to the recipients.

2. Diagnostic Analytics

Diagnostic analytics answers “why did this happen?” by analysing the causes and the relationships between factors, to inform decisions or improve the campaign.

For example, if revenue fell last month, diagnostic analytics helps find the reason: a change in pricing, advertising that missed the audience, or a slow-loading website that pushed customers off the product page early.

3. Predictive Analytics

Predictive analytics uses historical data and statistical models to forecast what may happen next, whether next quarter’s revenue or the likelihood that customers will buy again.

For example, last year’s purchase data for the same period can predict which customers are likely to return, using a statistical model or machine learning, so the marketing team can plan a specific push for that group.

4. Prescriptive Analytics

Prescriptive analytics uses the analysis to recommend the next move, for the best result from the campaign.

For example, once the analysis shows that certain customer groups tend to buy certain products during festivals, prescriptive analytics can recommend a plan, such as adding those products to a special campaign to bring customers back more often.

The 7 Data Analytics Steps For A Marketing Campaign

Data analytics is central to a successful campaign, because it is what makes decisions fast and accurate. It runs in seven steps.

Set The Goal And Objectives

First, know what result the campaign is for: more website visits, more conversions or better customer retention. That sets the direction for data collection and analysis.

Decide Which Data To Collect

Once the goal is set, decide which data will support the analysis, give a clear view of the campaign and make decisions better. For example:

• Customer data, such as gender, age, buying behaviour and location
• Website data, such as visitor numbers, time on page and the most popular pages
• Revenue data, such as revenue, conversion rate and customer lifetime value
• Social media data, such as engagement, shares, likes and comments

Organise The Data

Next, organise and clean the data. It has to be accurate, so remove duplicates and apply one standard throughout, such as a single currency, and a consistent definition of website visitors as sessions (visits) and users (individual people).

Analyse The Data

Choose the right tools for the analysis, such as pivot tables, statistical analysis or data visualisation, to see the relationships and the marketing trends. Google Analytics, for example, shows website visit data that explains user behaviour and the effect of a campaign.

Report The Results

After the analysis, report the results systematically and clearly, using diagrams, charts, graphs or a dashboard, so everyone sees the same picture.

Adjust The Marketing Approach To The Data

Then refine the marketing approach, such as redefining the audience or moving budget to the segment that performs best, to lift results.

Track And Measure Continuously

The heart of data analytics in a marketing campaign is continuous tracking and measurement, so the approach keeps improving.

What Are Data Analytics Tools, And Which Are Popular?

Data analytics tools are the software used to collect and analyse complex data, so decisions are accurate and effective. These are the tools most often used in marketing campaigns.

Google Analytics, For Website Visits

Used to track and analyse website visits, visitor numbers, where visits come from and how users behave on the site.

Strengths

• Detailed, end-to-end data, such as the most visited pages, bounce rate and time on site
• Conversion funnel analysis to assess a campaign’s performance
• Connects to other tools, such as Google Ads and Search Console

Who is it for? Online marketers and brand owners who want to track website and campaign performance.

Hotjar, For User Behaviour

Used to track user behaviour on the website, through heatmaps, session recordings and surveys.

Strengths

• Heatmaps show where users click or scroll on a page
• Session recordings show behaviour in each session
• Insight into the real user experience

Who is it for? Marketers who want to understand how users interact with the website and improve its UX and UI.

HubSpot And Salesforce, For Customer Relationships

Used to track and manage customer data, and to analyse the revenue and campaign data that drives conversions.

Strengths

• Connect to every online marketing platform, such as email, social media and web pages
• Track leads, conversions and customer communication
• Support integrated marketing

Who is it for? Teams that want to track customer conversions and manage relationships across every channel.

Excel And Google Sheets, For The Basics

Used for managing data, building pivot tables and running statistical calculations.

Strengths

• Easy to use and familiar to most people
• Basic analysis and charts
• Advanced calculation functions and data organisation

Who is it for? New marketers, or teams working with basic to mid-level data who want a tool that is easy to use and share.

Python (Pandas, NumPy, Matplotlib), For Large Datasets

Used for complex analysis, such as big data, data mining and machine learning for prediction.

Strengths

• Handles large, complex datasets well
• Builds predictive models with machine learning, such as linear regression and decision trees
• Functions for charts and clear data display

Who is it for? Marketing teams or organisations with large datasets that need deep analysis or trend prediction.

Want A Data-Driven Marketing Plan? Talk To Primal

Brands that use data-driven marketing make precise decisions and keep improving their results. If you are looking for a partner that understands how to build a marketing approach on data, Primal is a digital marketing agency ready to make your marketing data-rich and effective, with an AI SEO service that puts the same discipline behind your search visibility.

Fill in the form for a consultation and a data-driven plan from our online marketing team.

References

1. HubSpot, “How to blend web analytics and digital marketing analytics to grow better”, retrieved 12 October 2026, https://blog.hubspot.com/marketing/marketing-analytics

Frequently Asked Questions About Data Analytics (FAQs)

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