How to analyze marketing data (from my own experience)

Data. Metrics. Analysis.

What could be more boring and complicated than that? 

My thoughts exactly a couple of years ago. When I had just started my marketing journey as a marketing assistant and then social media manager, all I cared about was likes, comments, the number of followers, and shares. That can only get you so far.

For a long time, I was scared of these numbers and couldn’t understand what I should do with all of that information. Now, after working with various projects and analyzing campaigns with multimillion-dollar budgets, I can say that I feel much more confident with numbers and their interpretation. 

At least I try to be. 😂

In this article, I want to talk about metrics that matter for marketing strategies, how to collect them properly, and, most importantly, how to analyze this data and make decisions based on it. I will give some particular examples, so you can picture the whole campaign scenario and understand the topic better. 

Let’s go!

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1. Why do marketing analytics matter?
2. Basics of data analysis
3. Advertising metrics and analysis
4. Marketing analytics for B2B companies
5. How to turn analytics into a strategy

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Why do marketing analytics matter?

I think it’s obvious to every marketer that without collecting and analyzing data, you cannot run any successful campaign. It’s a process of learning, failing, analyzing, improving, testing, and then eventually coming to something that works.

There is a big difference between TRACKING data and UNDERSTANDING data.

‍One of the mistakes that I had in the past was collecting everything but analyzing nothing. I was just staring at this data and thinking, “So what?” 

You cannot succeed with an approach like that. 

Marketing revolves around numbers. If you know what data you need, how to collect it, and what to do with it, your chances of running a successful campaign increase drastically. 

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Basics of data analysis

I like to think about marketing data analysis from a certain perspective, which is

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That means that you need to analyze the metrics you have, get insights out of this analysis, and make decisions based on those insights. 

Sounds simple enough, but we all know that it’s easier said than done. 

Here are a couple of things that I consider important before analyzing data:

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1. Understanding the difference between qualitative and quantitative data.

Quantitative data is something that is represented in numbers, like conversion rate (10%), click-through rate (2.5%), open rate (25%), cost per lead ($30), etc. 

The purpose of this data is to measure performance, compare results over time, and draw conclusions on whether the campaign was successful or not. 

These statistics can be collected from tools like Google Analytics, HubSpot, Meta Ads, LinkedIn Analytics, or CRM dashboards. 

Qualitative data is descriptive and contextual. It tells you why something happened or how your customers feel. 

For example: customer feedback (good or bad), comments and reviews on social media channels, client interviews or surveys, etc. 

It serves the purpose of understanding consumer behavior, motivations, emotions, and reasons behind the numbers (quantitative data).

There are multiple ways you can collect this kind of data. It can be through surveys, interviews, polls, comment analysis, or different tools that help you gather mentions of your brand across the whole internet. 

Both qualitative and quantitative data are important to understand the whole picture. I think you should try to use qualitative data to explain the quantitative data. 

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2. Being careful with vanity metrics.

Vanity metrics represent the data that might look impressive, but don’t actually show real business impact or drive decision-making.

Let’s take metrics from social media analytics like likes, comments, and shares. I’m pretty sure if you are doing social media marketing, you track those, which might be important, but you need to be very careful. 

10K likes ≠ 10K customers.

If the goal of your campaign is to bring traffic to the website or sell X amount of items, you should concentrate on different metrics entirely. You would need to track clicks, conversions, the number of items sold, etc. 

Open rate in email marketing might fall in this category, too, if you track it on its own. High opens don’t mean people clicked, read, or converted. 

Number of website visitors. This metric also seems to be important, but it doesn’t tell you if people engaged with your content, if they followed your newsletter, or if they reached out for more information. 

You always need to analyze deeper and have a context. Always return to the question: What is the goal of this campaign? 

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3. One unsuccessful campaign doesn’t mean that the whole strategy is wrong. 

You should do as many tests as you can in order to see what part of your campaign is not working well. It could be the creative itself, your CTA, the channel of distribution, or the landing page that you lead people to. 

There are so many variations and options of what might actually not perform as well as expected. So try to break it down as much as you can and see what needs improvement or replacement. 

Now I want to share some particular examples with you. It will be broken down by different categories:

• Standard advertising measurements, because almost every business runs advertising on different channels.
• Metrics and their analysis in a B2B environment, since that’s where I have the most experience. That would include agencies, SaaS, and other types of B2B companies. 

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Advertising metrics and analysis

Before you come up with the list of metrics that you need, you have to define the goal of the campaign. 

Duuh. I know that probably sounds obvious for most people, but it’s never bad to remind everybody of those things. 

Here is how it can look. 

After you have your goal, you will understand what metrics can showcase if you have achieved this goal or not. 

Example:

Let’s imagine that the goal of our marketing campaign is to make people sign up for a free consultation. We chose that the channel of distribution will be Meta Ads. Based on that, we need to build a marketing funnel and define our key metrics. 

Our funnel.

Our key metrics that are based on that funnel.

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I know it’s impossible to predict every single scenario, but let’s talk about what data you might see while running a similar campaign and how you could interpret that data. 

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For an ad itself: 

- Low reach → audience might be too small → try expanding targeting

- Low CTR (<1%) → ad doesn’t resonate → test new visuals or headline

- High reach + low CTR → ad is not relevant for the audience and/or has a weak hook/CTA

- High CTR + high CPC → good ad, limited reach → try to test audience size

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For the landing page:

- High clicks but high bounce → probably message mismatch between ad and page

- Low scroll depth → content too long or not catchy, or CTA too low on the page

- High CTR but low landing-page view rate → could be technical or page-load speed issues

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For conversions:

- Low CTR + high CVR (conversion rate) → narrow but highly qualified audience → scale budget

- CPL (cost per lead) increasing → ad fatigue, audience exhaustion, or tracking issue

- If leads don’t show up → communication gap → send reminders or add calendar invites

- If consultations don’t convert → evaluate consultation process or targeting accuracy

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Those are just some of the reasons that might be causing the issue and some of the solutions that could help. Every campaign is different, and what I mentioned above might not be the right choice in your particular case.

You should explore and test on your own and find what’s going to work best for you. 

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Marketing analytics for B2B companies

For the B2B area, the buying cycles and metrics are different (compared to B2C). Of course, everything we talked about before applies here (if you run a Meta ad), but there are new metrics that come in. 

The sales cycle can last weeks or months, so it’s important to track data carefully to avoid losing anything. 

Before we dive into anything deeper, I want to share some acronyms that not everybody knows about, because they are some of the main metrics when it comes to B2B.

- MQL (Marketing Qualified Lead): Leads that match your ICP (ideal customer profile).

- SQL (Sales Qualified Lead): Leads that are interested in getting to know your product better.

Usually, in B2B, the conversion funnel would look like that.

Now let’s get to metrics.

We can break them down by categories for easier use. 

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Cost metrics:

- CAC (Customer Acquisition Cost): (Total marketing + sales spend) ÷ new customers

- CPL (Cost per Lead): Ad spend ÷ total leads

- ROI / ROAS: Revenue generated ÷ ad spend

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Content performance (if you use a website or social media channels for lead generation):

- Top converting pages (could be blog or service pages; check in GA4 and Search Console)
- Engagement per channel (LinkedIn, email, ads)

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Example: 

Let’s imagine that we are a web development company. 

We make different kinds of landing pages, websites, and designs. Our goal for the marketing campaign will be to get five clients to buy a full service from us. The main channel of distribution will be LinkedIn Ads, supported by email and LinkedIn outreach.

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Our funnel.

Our key metrics that are based on that funnel.

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Let’s move on to the actual data now and how you might interpret it. 

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For LinkedIn Ads:

- Low impressions → audience too narrow → try to expand targeting or increase budget

- High impressions + low CTR (<0.7-1%) → message isn’t connecting → change headline or offer

- High CTR + high CPC → engaging ad but expensive audience → test new targeting segments or creative

- High engagement + low conversions → awareness good, offer weak → test different offers (free audit instead of demo)

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For the landing page:

- High ad clicks but low form submissions → weak CTA or unclear offer → simplify form or improve copy

- Low time on page → content doesn’t hold attention → add testimonials, visuals, or value props higher on the page

- High bounce rate (>65%) → mismatch between ad message and landing page → align tone and benefits

- Low scroll depth → CTA placement issue → move the form or contact button higher up

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For the consultation/demo stage:

- Leads book but don’t show up → reminder gap → send automated calendar invite and/or follow-up message

- Leads attend but don’t convert → weak consultation structure → improve pitch

- Leads engage but go silent → need stronger nurturing content → add success stories

- High lead volume but low close rate → leads not qualified → experiment with  targeting

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For the final purchase stage:

- High deal volume, low revenue → too many low-ticket projects → narrow offers to full-service packages

- Long decision time → could be client uncertainty → try introducing limited-time discounts or stronger social proof

- CAC increasing over time → ad fatigue or sales inefficiency → refresh creatives or audit follow-up process

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Note, those are just some of the possible reasons and solutions for B2B campaigns. Your case is unique and might be completely different. 

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How to turn analytics into a strategy

After everything we talked about in this article, it is obvious that there is no one-size-fits-all solution. Every marketing campaign is special with its own goals, metrics, approaches, and solutions. 

There are some rules, though, that I think everyone who wants to increase their campaign efficiency should follow. 

1. Do your reporting regularly, ideally on the same day and at a similar time. 

2. Try to visualize data in a way that will be easier for you to understand and analyze.

3. Always set benchmarks and goals that you want to achieve in every campaign. 

4. Be aware of your business needs and capabilities so you can adjust your strategies accordingly. 

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Closing thoughts

From my point of view, no matter how much you read and how many videos you watch about metrics, analytics, and their analysis, the best way to learn is through PRACTICE. At least, that’s how it’s always been for me. 

Don’t be afraid to test, fail, learn, and improve as you go. It will get better, and this whole campaign analysis won’t feel that overwhelming anymore. 

Learn new marketing analytics tools, try to visualize your data differently, and see if any AI tools can be of any help to you. It is much easier now with all that variety out there. 

I hope this article helped you at least a bit. 

Good luck with your marketing campaigns!

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