A Guideline to Other Analytics Tools for Marketing 2

Adglobeclients LLC

Google Analytics, Search Console, and Looker Studio cover the basic layer of website analytics, SEO performance, and reporting. But in many marketing systems, this is not enough.

If a business wants to understand user behavior more deeply, evaluate product quality, connect marketing with sales, analyze the full customer journey, and find weak points in the funnel, it needs additional analytics tools.

Below is a practical guideline to the main categories of non-Google analytics tools and how to use them.

1. Product Analytics: Mixpanel and Amplitude

Product analytics tools help teams understand how users interact with a product after they sign up, log in, or start using a platform.

While standard web analytics usually answers the question “where did the user come from and what did they do on the website?”, product analytics goes deeper. It helps answer questions like:

how users move through the product; where they get stuck; which features they use most often; why they return; why they leave; what actions lead to retention or payment.

Mixpanel and Amplitude are two of the most popular tools in this category.

They are especially useful for SaaS products, mobile apps, marketplaces, online platforms, and services with personal accounts or complex user journeys.

For example, instead of only measuring how many users registered, a company can analyze how many of them completed the first useful action, returned after seven days, used a key feature, upgraded to a paid plan, or became active long-term users.

Product analytics is useful when the goal is not just to attract traffic, but to understand how users actually experience the product.

2. Behavioral Analytics: Hotjar, Microsoft Clarity, FullStory

Behavioral analytics tools help marketers and product teams see how users interact with pages visually.

These tools usually include heatmaps, session recordings, click tracking, scroll maps, and sometimes user feedback forms.

Hotjar, Microsoft Clarity, and FullStory are common examples.

They help answer questions that numbers alone often cannot explain:

do users see the main call-to-action; where do they click; which parts of the page do they ignore; how far do they scroll; where do they hesitate; why do they abandon a form, cart, or registration flow.

For example, GA4 may show that a landing page has traffic but low conversion. Behavioral analytics can show that users do not scroll far enough to see the offer, click on non-clickable elements, or leave the page when they reach a confusing form.

Hotjar and Clarity are good options for website and landing page analysis. FullStory is often used by larger teams that need deeper session analysis, product debugging, and UX research.

Behavioral analytics is especially useful when a business already has traffic, but users are not converting as expected.

3. End-to-End Analytics: Roistat, Calltouch, Alytics, OWOX

End-to-end analytics connects marketing costs with real business results.

Standard web analytics can show leads. But in many businesses, a lead is not the final goal. The real question is: which channel brought revenue, profit, qualified clients, or repeat purchases?

End-to-end analytics tools combine data from advertising platforms, websites, call tracking, CRM systems, and sales data.

This allows businesses to track not only cost per lead, but also:

cost per sale; revenue by channel; ROMI; lead quality; sales conversion rate; profitability of campaigns; which ads bring real customers, not just cheap requests.

For example, one campaign may generate leads at a low cost, but most of them never buy. Another campaign may generate more expensive leads, but those leads turn into paying clients. Without end-to-end analytics, the first campaign may look better. With proper analytics, the second one may be the real growth driver.

This category is especially important for companies with several advertising channels, phone calls, offline sales, long sales cycles, or CRM-based lead processing.

4. CRM Analytics: HubSpot, Salesforce, Pipedrive, amoCRM

CRM analytics shows what happens after a user becomes a lead.

Marketing can bring traffic and requests, but the sales team still has to process them. If this part is not measured, the business may keep blaming advertising while the real problem is slow response time, weak follow-up, poor qualification, or unclear sales stages.

CRM analytics helps track:

number of leads by channel; conversion from lead to deal; speed of first response; deal stages; reasons for lost deals; average deal size; sales cycle length; revenue by source; manager performance.

This is especially important for B2B, education, real estate, medical services, consulting, agencies, and any business where the purchase does not happen instantly on the website.

For example, if leads from paid search convert into deals faster than leads from social media, the marketing team can adjust budgets. If leads are good but deals are lost at the proposal stage, the problem may be in the offer, pricing, or sales communication.

CRM analytics connects marketing with real sales work.

5. BI Tools: Power BI, Tableau, Metabase

BI tools are used when data becomes too complex for standard reports.

Power BI, Tableau, and Metabase help collect data from different sources: advertising accounts, CRM systems, websites, financial tools, databases, spreadsheets, and internal platforms.

These tools are useful when a company needs not just marketing reports, but a broader business analytics system.

BI dashboards can show:

marketing spend; sales performance; revenue; profit; customer segments; regional performance; product performance; team KPIs; forecasting and trends.

BI tools are especially useful for companies with several products, markets, teams, or sales channels.

For example, a company can combine advertising data, CRM data, and financial data to understand not only which campaign generated leads, but which campaign created profitable customers.

This is where analytics moves from marketing reporting to management decision-making.

6. Privacy-Focused Analytics: Matomo, Plausible, Fathom

Privacy-focused analytics tools are designed for businesses that care about data ownership, compliance, and simpler tracking.

Matomo, Plausible, and Fathom are popular examples.

They are often used by companies that want cleaner analytics, less dependency on large platforms, and more control over user data.

These tools are useful when:

privacy is a priority; the business operates in markets with strict data rules; the team wants simple website analytics; the company wants to reduce cookie-related complexity; there is no need for heavy enterprise-level tracking.

Plausible and Fathom are usually chosen by smaller teams that want a clean and simple interface. Matomo is often chosen by companies that need more control, including the option to host analytics on their own infrastructure.

Privacy-focused analytics may not always replace advanced marketing analytics, but it can be a strong solution for websites that need clear, ethical, and lightweight tracking.

How to Choose the Right Analytics Tools

A good analytics stack should be built around business questions, not around the number of tools.

You do not need to connect everything at once. Too many tools can create confusion instead of clarity.

For a service-based website, the basic stack may include web analytics, CRM analytics, call tracking, and a simple dashboard.

For e-commerce, it is useful to combine website analytics, advertising analytics, sales data, behavioral analytics, and product/category performance reports.

For SaaS or mobile apps, product analytics is essential. Tools like Mixpanel or Amplitude help measure activation, retention, user cohorts, feature usage, and subscription behavior.

For businesses with large ad budgets, end-to-end analytics is important because it shows which campaigns bring revenue, not just leads.

For companies with complex reporting needs, BI tools help connect marketing, sales, finance, and product data in one place.

Main Principle

Analytics tools should not be installed just because they are popular.

Every tool must answer a specific business question.

Not:

“Let’s add another analytics platform.”

But:

“What decision will we make better after connecting this tool?”

Good analytics helps a business understand where users come from, how they behave, where they drop off, which channels bring real clients, and what needs to be improved to grow profitably.

The goal is not to collect more dashboards. The goal is to make marketing, sales, and product decisions more accurate.

When analytics is built correctly, it stops being just a reporting system and becomes a growth system.

A Guideline to Other Analytics Tool