We have created custom marketing analytics solutions for many B2B companies including DS Smith, Teleperformance and Autodesk. Here is the approach that we used for B2B marketing analytics implementation for our clients:
We mostly use this approach for planning a custom B2B marketing analytics implementation. However, it can also be used as a selection criteria for a third-party marketing analytics software.
Below are core data sources for b2b marketing analytics:
| Category | Data Sources | Purpose |
| CRM Analytics | Salesforce, HubSpot | Analysing Lead Generation and Nurturing |
| Email Marketing Analytics | Mailchimp, Brevo, Klaviyo | Analysing the effectiveness of outbound marketing |
| Website Analytics | Google Analytics, Hotjar | Analysing the user journey and optimising the website for conversion rates |
| PPC Analytics | Google Ads, Bing Ads, Facebook Ads, Pinterest Ads, etc | Analyse cost per lead, find the most effective lead generation channels |
| SEO Analytics | Google Business Profile, Google Search Console, SEMRush, Ahrefs | Analyse the effectiveness of organic search efforts |
Below, we will dive into every analytics type in more detail by exploring the main KPIs and giving examples of the successful implementation.
Lead generation is the main task of a marketing department in B2B, and it is essential to analyse. The main data source for this analysis is the company CRM, which contains data on every lead, where they come from, and the associated deals. This data can be used to create CRM dashboards that analyse the movement of a lead through the sales funnel.
The main KPIs to analyse your lead generation activities are:
An MQL is a lead who has shown interest in your brand and has been targeted by your marketing efforts. These leads have reacted to our marketing campaigns, but they are not sales-ready yet. They may not have sufficiently demonstrated interest, OR they have not agreed to meet with a sales rep.
Please keep in mind that MQLs must have the right job title (or role) and the right company size for the region (minimum company size differs by region). If this rule is not fulfilled, the lead must not be categorised as MQL.
A Sales Qualified Lead represents the lead that has been nurtured and influenced by the marketing team and, as a consequence of that process, is Sales Ready. These leads may have or may not have met with the BDs, but once it is categorised as an SQL, the person should be passed to the BD. SQL scenarios include:
These are people to whom a sales rep has spoken and sees a potential sales opportunity. Just because a BD has spoken to a person, it does NOT automatically qualify them as a SAL.
After the sales appointment, one of three things will happen:
Analysing conversion rates throughout the funnel helps evaluate the lead quality. High quality MQLs will have higher conversion rates to becoming SQLs. It is very common to visualise the marketing funnel as on the screenshot below and include conversion rates from one step to another.
It is in the interests of every organisation to generate leads that are highly motivated to convert. The way you measure this is by tracking pipeline velocity, which is the number of days it takes your leads to progress from any step in the marketing/sales funnel to the next one.
Analysing your pipeline velocity helps to predict when the deals are expected to close. This is therefore an important step towards predictable revenue as it helps businesses to predict the new business revenue in the future months.
Apart from calculating your metrics for the entire organisation, it is important to break them down in meaningful ways when you create a custom dashboard. Number of leads and conversions should be broken down by lead sources, countries and industries so that the marketing team knows what efforts are working to generate leads that convert.
Importantly, the metrics above should be combined rather than looked at in isolation. For example, you can combine the pipeline velocity, conversion rates and the deal value to calculate the expected revenue. Combining your lead-generating metrics this way helps to create a deeper analysis of your lead generation process.
Analysing CRM data helps to measure the impact of the marketing organisation on the sales pipeline.
The remainder of this article will be focused on how to measure the effectiveness of individual marketing channels.
The primary KPI for measuring the effectiveness of email marketing is the number of positive replies which essentially qualify as leads. However, the idea of email marketing analytics is to find ways to optimise the email marketing efforts to generate more positive replies.
There are essentially 2 things that can be optimised:
A/B testing is the most popular technique where you clone your sequence and change one attribute (such as audience or subject line). You keep both of your sequences running and compare the results over time and then turn off the one that performs worse.
Most email marketing tools have in-built capabilities for A/B testing, but they sometimes have limitations. In this case, it is useful to export your email marketing data and analyse it in Power BI, Tableau or other third-party tool.
The key metrics for A/B testing your campaigns are:
1. #Sent Emails – receiving 10 replies per 100 emails is a very good result. However, receiving 10 replies per 5,000 emails shows that the results are very disproportionate to the amount of effort it takes to achieve them.
2. Open rate – if the open rate is 50%, it means that half of the work that the team does is for nothing, since half the targeted people don’t read the emails.
3. Click rate –email recipients who click on your site show more interest than others. It is sometimes a good idea to keep targeting them in your marketing communications.
4. #Replies – since this one is our primary objective, it needs no explanation.
The screenshot above demonstrates how to compare your email marketing sequences to each other. As for analysing the audience that you target, this comes down to analysing the company profile and personal profile of your email recipients.
For example, below is an example of the Power BI HubSpot dashboard that we created for our client. It analyses the company profile that they are targeting now and they can filter to deal stage = “Won” to find which companies are buying from them.
A key marketing goal is to convert website traffic into leads by collecting their contact details. The main marketer tools for driving website visitors to submit their contact details is changing the page structure and internal linking of the website pages. This is exactly what the website analytics is about.
There are 3 essential tools for website analytics:
| Software | Purpose | Best for |
| Google Analytics | Captures the statistics on what pages users visit and actions that they perform | Websites of all size |
| Heatmap software (e.g. Hotjar) | A/B Testing software (e.g. Google Optimise) | Websites with low traffic |
| A/B Testing software (e.g. Google Optimize) | Enables marketers to change one thing on the page (e.g. a colour of a button) and evaluate the impact of the change | Websites with high traffic |
Google Analytics is great for understanding which pages users go through before they convert. For example, we use the path exploration reports in our GA4 to see that most visitors that convert do this on the “contact us page”. We can also see which informational pages help us the most to drive website traffic towards the “contact us” page.
If your website has a defined user journey for converting visitors into paying customers, you can also build a website funnel analysis. This helps to identify at which step of the website funnel your website visitors drop off.
For example, the gif below shows a dashboard that we built for our client who has 2 online stores in Gainesville and Tampa. The dashboard analyses the conversion of users from visiting a home page to picking a location, viewing the pricing page, leaving their email, adding products to card, completing checkout and purchasing the product.
The main objective of PPC advertising in B2B environment are:
PPC Analytics helps to compare the performance of every PPC marketing source based on these metrics. This helps to plan allocating the PPC budget and ensure that more budget is allocated to marketing sources with the highest return on investment.
Depending on the PPC channel, it is also possible to analyse the keywords that generate most conversions with the lowest cost per conversion. This is important to ensure that the top keywords are targeted consistently across all the search-based PPC channels.
It is worth saying, though that not all the PPC channels/campaigns are conversion-oriented. For example many of our clients use Facebook to generate awareness and Google to generate conversions. It is important that the awareness campaigns are measured using different KPIs as compared to lead generation campaigns.
For example, consider our LinkedIn Ads Power BI template below. The awareness section focuses on cost per 1000 impressions and cost per click since the main goal of awareness campaigns is to get more eyeballs on the brand. Once a user switches to the lead gen section of it, the KPIs change to cost per lead and number of leads.
If you are using SEO platforms like SEMRush or Ahrefs, they likely already provide you with some SEO reports. For many companies this is enough, and Ahrefs/SEMRush do a fantastic job teaching you how to use them on their blogs. If you are looking get started with SEO analytics, we would highly recommend to begin with their educational content.
Some need to take their SEO analytics one step further by creating custom SEO dashboards. There are several cases when companies may want to do this:
3. Sometimes these SEO tools have limitations in their reporting. For example, they offer some surface-level competitor reporting, but if you want to analyse how the visibility changes over time, you may need to build custom reports. For example, we have built the SEMRush report in Looker Studio below to help our client see how their brand performs vs competitors over time.
In our Business Intelligence consultancy, we mainly use Power BI, Tableau and Looker Studio for producing custom marketing analytics reports. Comparing these 3 is outside of the scope of this article so we will only share quite advice here.
Power BI excels in seamless integration with Microsoft ecosystems and internal reporting. If you are planning to only share your reports within your organisation, we would recommend Power BI as your reporting tool.
Looker Studio is great for analysing data from Google systems like Google Analytics 4, Google Search Console, Google Ads, etc. It is also a better choice if you want to share your marketing analytics reports with people outside of your organisation, such as your investors
Tableau is best if you need advance data visualisation or you want to analyse the data from your Salesforce account.
If you want to learn more, you can read our guides about Tableau vs Power BI and Looker Studio vs Power BI.
Identify your business objectives and find KPIs to measure your progress towards them. Use our BI implementation planning process if you want to be very structured :
Use SMART goals. For example, “Increase webinar-attendee-to-MQL conversion by 20% in Q3 using LinkedIn retargeting”.
Integrate siloed data into a single platform (e.g., Power BI) to create a 360° customer view. For instance, combining CRM data with web analytics reveals which content drives lead generation.
Tools like Power BI, Tableau, and Looker Studio can be enhanced with AI-driven automation to analyse multi-touch attribution, segment audiences dynamically, and personalise campaigns at scale.
Deploy predictive analytics to optimise lead scoring, prioritising accounts based on firmographics, engagement patterns, and historical conversion data. Implement real-time dashboards to monitor KPIs like pipeline velocity, CAC, and CLTV, enabling agile adjustments to campaigns.
Pair these tools with A/B testing frameworks to refine messaging and channel strategies
Build dashboards that highlight:
B2B marketing analytics is no longer optional, it’s the backbone of competitive strategy. By implementing a robust analytics framework, companies like HubSpot and Salesforce have reduced customer acquisition costs by 30% while doubling lead quality. Start small: focus on integrating one key data source, then scale with AI and predictive models. Remember, the goal isn’t just to collect data but to transform it into actionable stories that drive boardroom decisions.
Ready to accelerate your B2B Marketing analytics journey?
