Data and Analytics for B2B SaaS Marketing — What to Measure and What to Ignore

Your Marketing Data Is Telling You Something. Are You Listening?

Most B2B companies are sitting on data they never properly read, from campaigns that were never properly designed, tracking outcomes nobody can act on. That is not a technology problem. It is a strategic one. And before you add another dashboard or another analytics platform to the pile, you need to understand what you are actually trying to measure — and why.

This article is not a general primer on analytics. It is about something more specific: how to use Open Graph metadata, Google Analytics Tag Manager, and a deliberate content classification system to tell you, with precision, whether the content your business depends on is actually reaching anyone.

Table of Contents

1. Digital Selling and Data Analytics for SaaS and PaaS Marketing
2. Measuring Campaign Performance
3. Changing Your Primary Key Indicators
4. Optimising Your Content Strategy
5. Targeting the Right Audience
6. Using Predictive Analytics
7. Do Not Use Marketing Automation
8. Continuously Improving and Adapting
9. What to Do Next

1. Digital Selling and Data Analytics for SaaS and PaaS Marketing

Data-driven marketing starts with knowing your customers — not in the vague, persona-document sense, but in the practical sense of understanding what they read, what they search for, where they drop off, and what actually moves them toward a conversation with you. That requires analysing real customer data: their preferences, their problems, and the patterns in how they buy.

We know that 83% of B2B buyers define their purchase requirements before speaking to sales. They are researching digitally, forming opinions, and building shortlists long before your BDRs know they exist. If your content is not positioned where those buyers are looking, and tagged so your analytics can tell you whether it is working, you are invisible at the moment it matters most.

The tools that give you that visibility include:

• Google Analytics and Google Tag Manager: Track user behaviour on your website — page views, time on page, scroll depth, video engagement, and conversion events.
• Google Data Studio: Google renamed this tool to Looker Studio in 2022, then reversed that decision in April 2026 and brought the Data Studio name back. Whatever you call it, this is where you combine your data sources, build dashboards, and automate alerts when metrics move outside expected ranges.
• CRM systems: The record of every customer interaction — preferences, purchase history, and communication history — that your analytics should connect back to.
• Market research: Surveys, interviews, and direct conversations that tell you what your data cannot. Never skip this.

2. Measuring Campaign Performance

If you cannot measure it, you cannot change it. That sounds obvious, but the number of B2B businesses running campaigns with no clear KPI framework — or worse, tracking vanity metrics that never connect to revenue — is staggering. Read our B2B Performance Marketing article to understand why most of what passes for campaign measurement in the B2B space is either broken or irrelevant to actual growth.

The KPIs worth tracking are:

• Click-through rates (CTRs)
• Conversion rates
• Cost per lead (CPL)
• Return on investment (ROI)

Use SEO and search monitoring tools alongside Google Analytics to track your search engine visibility and understand which pages and content types are pulling organic traffic. The goal is not traffic for its own sake. The goal is to know which content is bringing the right people closer to a decision, and which content is being ignored entirely.

3. Changing Your Primary Key Indicators

Your marketing team uses Google every day. Google Analytics (GA4), Google Tag Manager, Google Data Studio, and Google Search Console are all standard kit. But standard kit used without a proper framework produces standard noise. The question is not whether you have the tools. The question is what data you are choosing to look at, and whether it tells your team anything they can act on.

One of the most practical things we recommend is adding Open Graph metadata to your content so you can classify and track it by category. The categories we use are: Primary, Secondary, General, Products, Services, and How to Buy. This gives your analytics an entirely different layer of intelligence — not just which pages were visited, but what type of content was consumed and whether that aligns with your commercial priorities.

Here is the content structure we call Social 444, which combines content classification and content type:

Primary Content — Educates

• Thought Leadership
• Business Case
• Whitepapers
• Professional Publications

Secondary Content — Reaffirms

• Social Proof
• Case Studies
• Infographics
• Podcast, Video, Live

General — Communicates

• Blogs, New Hires
• CSR, Awards, PR
• Guest Activities

Product Content

• What your product offering is
• How it works
• How to install it
• Best Practices
• Training
• FAQs

How to Buy

• Explaining how you organise project management
• How professional services get involved
• How to complete your order forms

Once the Open Graph metadata labels are configured within your website CMS, you can set up Google Tag Manager to fire a trigger whenever one of those tagged articles is viewed. As you can see from the image below, all our content is appropriately tagged.

You can layer on additional Tag Manager triggers — scroll depth, video views, and how long a video was watched. All of this goes toward building a precise picture of what people actually do when they land on your site, not just that they arrived.

When it comes to reporting, GA4 will annotate your reports with the classification labels — Primary, Secondary, and so on — so you can see at a glance which category of content is performing and which is not. If Primary content or Product content is receiving no traffic, you know immediately whether the problem is with your promotional adverts, your metadata, your labelling, or the content itself. If it is no good, edit it and change it.

Beyond Open Graph tags, use UTM codes to identify which specific adverts are being clicked when you post on social media. When you combine UTM data with GA4 and content classification, your analytics will show you exactly which adverts are driving traffic to which type of content. In a world where privacy restrictions make individual-level tracking increasingly difficult and politically uncomfortable, this is the right approach. You want to know what content is being read and which adverts are working — not who the individual browser is.

For a deeper look at how this all connects, browse our Analytics articles where we cover the practical configuration in detail.

4. Optimising Your Content Strategy

Your analytics should be the engine of your content decisions. Look at which topics are pulling traffic, which formats are generating the longest engagement, and which pieces of content are being read by people who go on to make contact. That tells you far more than any content calendar built on gut feel.

One rule I apply without exception: every piece of content should be reviewed by a salesperson before it is published. Marketers write for marketers. Salespeople know what questions prospects actually ask, what objections come up, and what language resonates in a real conversation. Get them in the room before you hit publish.

For visual content — images, graphics, and video thumbnails — the AI image generation landscape has developed rapidly. The main tools worth evaluating for B2B commercial use are Midjourney, Adobe Firefly, DALL-E (via ChatGPT), and Canva AI. Adobe Firefly is trained exclusively on licensed and public domain content, which gives it a clean copyright position for commercial use — a genuine consideration if your legal team reviews assets. Midjourney produces more artistically striking output but faces unresolved IP litigation. Many teams use both: Midjourney for concept exploration, Firefly for final production. For video generation, Adobe Firefly now includes video capabilities through its partnership with Runway, making it a more complete production tool than it was even a year ago.

5. Targeting the Right Audience

Data and analytics let you segment your audience by behaviour, by content consumed, by the adverts they clicked, and by the stage of research they appear to be in. That segmentation is what makes your marketing feel relevant rather than random.

We use our Social 444 methodology to structure targeted campaigns across social channels. The principle is straightforward: match the content type to where the prospect is in their research process. Primary content for early-stage awareness. Secondary content to reaffirm the decision. Product and How to Buy content for those close to committing. When your analytics are properly configured with Open Graph tags and UTM codes, you can see whether this is actually working — and adjust if it is not.

6. Using Predictive Analytics

Predictive analytics uses historical data, patterns, and machine learning to make informed projections about what is likely to happen next. Applied to B2B marketing, this means forecasting which accounts are moving toward a buying decision, identifying where churn risk is building, and allocating budget toward the activities most likely to produce a result.

The important caveat: predictive analytics is only as good as the model you feed it. If your underlying content strategy is misaligned with how buyers actually research and decide — and in most B2B businesses, it is — then predictive tools will give you faster answers to the wrong questions. Fix the model first. Then use the tools to run it at scale.

AI tools like ChatGPT, Claude, and Gemini can assist with pattern analysis and content performance interpretation, but they are tools that execute your strategy, not replace it. If the strategy is broken, AI makes it fail faster.

7. Do Not Use Marketing Automation

Marketing automation was sold to B2Bs on the promise of efficiency — automate the repetitive tasks, free up your team for strategic work, and let the platform nurture leads through the funnel. That promise has not delivered for B2B, and the data makes it clear why.

Only 3% of B2B website visitors fill out forms. That figure has been consistent across industries, company sizes, and markets for years. Buyers know exactly what happens after they submit a form: a barrage of follow-up calls from BDRs. The whole automation infrastructure — pay-per-click to a landing page, landing page to a gated form, form to a nurture sequence — is built on a trigger that most B2B buyers refuse to pull.

Paying for pay-per-click to drive traffic to a landing page, and then expecting a business buyer to fill out a form, signals to that person that they are about to be chased by salespeople. For consumer brands, this can work. For B2B, it consistently fails. The automation platform just makes the failure more expensive and harder to diagnose.

The alternative is not abandoning digital marketing. It is building an open-access model where your content does the educating and your analytics tell you which content is working — without requiring the prospect to identify themselves.

8. Continuously Improving and Adapting

The point of all this data is not to produce a quarterly report. It is to create a continuous feedback loop. Review your data regularly. Track your KPIs against the content classifications. See what is moving and what is not. When something is not working — an advert type, a content category, a particular format — change it. Do not wait for a strategy review cycle to act.

Stay close to what is changing in your market. B2B buyer behaviour has shifted substantially. Buyers are increasingly using AI tools including ChatGPT, Claude, and Gemini to research vendors before your website even enters the picture. That means your content needs to be findable, clear, and genuinely useful — not keyword-stuffed or gated behind a form. The businesses that adapt their content and analytics strategy to match how buyers actually behave will pull ahead. The ones that keep running the same playbook will keep getting the same results.

9. What to Do Next

The analytics and data work described in this article only pays off when it sits inside a coherent go-to-market model. Without that, you are measuring the performance of a broken system more accurately. The things worth doing — and doing properly — are:

• Understanding your customers and how they research
• Measuring campaign performance against the right indicators
• Classifying your content with Open Graph metadata so your analytics can read it
• Optimising your content based on what the data shows, not what you assume
• Targeting the right audience with content matched to their stage of research
• Using predictive analytics inside a sound strategic model
• Ditching marketing automation and building an open-access content model instead
• Reviewing and adapting your approach continuously, not annually

None of this is complicated in principle. What makes it hard is that most B2B businesses have been running the wrong model for years — demand generation, gated content, automated nurturing — and the analytics they have built reflect that model. Changing the analytics without changing the model is rearranging the measurement. You need to change both.