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The Ultimate Guide to Marketing Analytics for SaaS Companies

Ninad Pathak
May 3, 2023
February 27, 2024
Table of Contents

Marketing analytics has become an integral part of any successful marketing strategy.

The industry is expected to grow at a CAGR of 14.8% between 2023 and 2028. Meanwhile, businesses of all sizes must spend the time to upgrade their in-house tech and employee skill-set to stay ahead of the curve.

But with the industry peppering buzzwords and jargon, marketing analytics can seem more complicated than it actually is.

In this guide, we’ll work to make the complex simpler. We’ll discuss what marketing analytics is, why it is necessary, which teams and people in a company should look into it, common limitations and pitfalls, and more. Let’s get started.

What is Marketing Analytics?

Marketing analytics refers to the analysis of data to gain insights into the performance of marketing campaigns and activities. It relies on the number of leads generated, conversion rates, customer acquisition cost (CAC), and customer lifetime value (LTV). Analyzing these metrics can help marketers and founders pave the way for marketing campaigns.

For instance, marketing analytics can help businesses understand which marketing channels or strategies are generating the most leads or conversions, and which ones may need improvement. It can also help identify trends and patterns in customer behavior, preferences, and buying habits, enabling businesses to make more informed decisions about their marketing efforts. 

This is done through the use of tools like Google Analytics, Looker Studio, Factors.ai, and others for data visualization, statistical analysis, and predictive modeling. 

The data is analyzed from various sources, such as website traffic, social media platforms, email campaigns, and Customer Relationship Management (CRM) systems. 

The Benefits of Marketing Analytics

Marketing analytics offers multiple benefits in a B2B context. It helps businesses with interpreting data and making the most accurate and informed decisions. But that’s just one aspect of it. Let’s take a look at some other benefits of marketing analytics:

Improves Targeting

Marketing analytics enables you to identify the most relevant and receptive audience for your product or service. Then combining demographic, psychographic, and behavioral metrics, you can create a detailed customer persona that represents your ideal target audience. This data-driven approach allows you to tailor your messaging, offers, and content to resonate with these individuals, ultimately leading to higher conversion rates.


For instance, suppose your marketing analytics reveals that your most engaged customers are millennial entrepreneurs in the tech industry. In that case, you can fine-tune your marketing campaigns by focusing on the channels they frequent—such as LinkedIn and Twitter. Then begin creating content around industry trends, tips for startup growth, or case studies of successful tech entrepreneurs while subtly pitching your product or services.

Reduces Cost of Acquisition

The cost of acquiring a new customer can often exceed the expenses related to retaining an existing one. The average customer acquisition cost (CAC) including all SaaS industries is $704. Marketing analytics helps businesses identify the most cost-effective channels and methods for customer acquisition by studying metrics like cost-per-click, cost-per-lead, and return on ad spend.

customer acquisition cost

Let’s take an example here. Say your marketing analytics data shows that LinkedIn ads are generating a lower cost per acquisition (CPA) and higher conversion rate compared to Facebook ads. In this case, you can shift a larger portion of your ad budget to LinkedIn, driving down your overall cost of acquisition.

Improves the Customer Experience

86% of buyers are willing to pay more for brands that offer great customer experience. And SaaS companies doing $1 billion annually can expect to earn an additional $1 billion with just 3 years of investing in customer experience.

Analyzing customer feedback data can help SaaS marketers identify areas of improvement in their products or services. This leads to a better overall experience for customers. Marketing analytics can also help companies understand customer touchpoints, identify gaps in their communication strategies, and improve customer support.

Helps with Customer Retention

As time goes by, fewer and fewer customers stick around. This can be clearly demonstrated with the retention curve.

user retention curve

However, spending money to retain existing customers is often more cost-effective than acquiring new ones. 

Existing customers are 50% more likely to buy a product and spend 31% more on average than new customers. Marketing analytics can help businesses identify patterns and trends in customer behavior that may indicate potential churn or dissatisfaction. 

For instance, if your marketing analytics reveals that customers who don't engage with your email campaigns are more likely to churn, you can modify your campaigns to launch a re-engagement strategy. You can target these at-risk customers with personalized content and offers to reignite their interest and loyalty. Sometimes, special pricing or preferential rates go a long way for customers.

Unveils Upselling Opportunities

Upselling is a powerful strategy in the B2B SaaS world. An average SaaS business generates 16% of its annual contract value (ACV) from upselling to customers. Companies doing $40 to $75 million annually can attribute twice as much revenue to upselling. With marketing analytics, you identify existing customers who are most likely to benefit from and be receptive to upgrading their current plans or purchasing additional products or services.

For instance, with marketing analytics data you may notice a few high-usage customers that could benefit from improved speed or better service offerings in your higher plans. Personalizing an upsell for those businesses and accounts can help you better improve your bottom lines in the long run. 

What’s the Difference Between Product Analytics and Marketing Analytics?

Product analytics and marketing analytics are two important types of analytics that businesses need to use for making informed decisions. Unfortunately, these terms are used interchangeably and can cause confusion. Let’s look at the differences between product analytics and marketing analytics. 

Difference Between Product Analytics and Marketing Analytics

Product analytics analyzes the product experience. This type of analytics is generally performed on the data of users already using the product. It focuses on understanding how users interact with a product, including feature adoption, engagement, and product usage. With product analytics, the goal is to drive product-led growth by identifying areas for improvement, prioritizing feature development, and optimizing user experience. 

Marketing analytics, on the other hand, helps identify how effective your marketing campaigns are. It helps identify how easily customers can find your product or website, what channels are working best for your marketing, and how many users are converting into paid users through the journey. Metrics for marketing analytics include lead generation, conversion rates, customer acquisition cost (CAC), and customer lifetime value (LTV).

For B2B businesses, both product and marketing analytics are important for driving growth. Product analytics helps businesses optimize product offerings and user experience. Marketing analytics helps companies improve their marketing strategies, target their audience more effectively, and allocate resources better.

Are Marketing Analytics and Marketing Attribution the Same Thing?

Marketing analytics and marketing attribution are complementary, yet distinct, disciplines within the realm of digital marketing. While both are essential for evaluating and optimizing marketing efforts, they serve different purposes and bring unique insights to the table. In this article, we will delve into the nuances of each concept, highlighting their primary focus, use cases, metrics, tools, and more.

Marketing Analytics vs Marketing Attribution - Quick Comparison

Marketing Analytics
Marketing Attribution
Primary Focus Evaluating overall marketing effectiveness  Measuring the impact of touchpoints on conversions or other outcomes 
 Use Cases  Identify high-performing channels, calculating CAC & LTV, evaluating ROI, tracking customer engagement, and informing marketing strategy  Evaluating individual channel effectiveness, identifying influential touchpoints, optimizing spend, tailoring messaging, and informing marketing strategies
 Metrics  CAC, LTV, Churn Rate, Retention Rate, ROI  Measured using models such as Single-Touch Attribution, Multi-Touch Attribution, Position-Based Attribution, Custom Attribution
 Tools  Google Analytics, Adobe Analytics, Mixpanel, Tableau, HubSpot  Factors.ai, Adobe Marketo, Dream data,

Marketing Analytics

Marketing analytics encompasses the measurement, management, and analysis of marketing performance data to optimize marketing effectiveness and maximize return on investment (ROI). Its primary focus is to provide a comprehensive understanding of marketing efforts, from the impact of individual campaigns to the overall health of a marketing strategy.

Use Cases:

  1. Identifying high-performing marketing channels
  2. Calculating customer acquisition cost (CAC) and customer lifetime value (LTV)
  3. Evaluating marketing ROI
  4. Tracking customer engagement and retention
  5. Analyzing data to inform marketing strategy and decision-making


Here are some of the marketing analytics metrics that you must keep a watch on. 

  1. Customer acquisition cost (CAC): This metric helps you identify the cost of acquiring a new customer. It accounts for all the marketing and promotional costs involved in reaching and converting the customer. 
  2. Lifetime value (LTV): Once a customer is converted, how long do they remain a paying customer? To find this value, note the length of time that each customer has stayed with your business and the total revenue generated by each. Then, multiply the average revenue per customer by the average length of time (lifespan). For example, if you have 100 customers paying, on average, $300 per month for 6 months, your customer lifetime value will be $300 x 6 = $1800. 
  3. Churn Rate: How many of your customers are dropping off after they turn into paying customers? In fact, you can even count the churn rate for your free users as this can help you start digging deeper into the reasons why they don’t sign up for one of your paid plans. 
  4. Retention Rate: This is the opposite of the churn rate. Here we check how many customers stick around for a specific period of time and more. For example, you may want to consider a customer as a retained one if they continue to pay for your SaaS for longer than 3 consecutive months. 
  5. Return on investment: This metric is a must for businesses, no matter the industry. In the case of B2B SaaS businesses, this metric lets you understand how much money you are making based on the marketing spend.


  • Google Analytics: This is the most popular tool for marketing analytics—majorly because it’s free of cost. Google Analytics provides a wealth of information about traffic, user behavior, and much more. 
  • Factors: Factors is a powerful marketing analytics platform that specializes in B2B visitor identification, account analytics, and more. It integrates with your existing marketing stack and helps businesses reveal anonymous website visitors, decode customer journeys, and drive marketing ROI.
  • Adobe Analytics: Adobe Analytics has a range of tools for tracking customer behavior, segmenting users, and identifying trends to improve overall marketing performance.
  • Mixpanel: Mixpanel is more focused on product analytics. It enables businesses to monitor user interactions within web and mobile applications.
  • Tableau: Tableau helps marketers transform raw marketing data into interactive visualizations that are easy to understand and share. 
  • HubSpot: HubSpot provides businesses with a holistic view of their performance and the tools necessary to optimize their strategies. From tracking website traffic and user behavior to measuring the effectiveness of email campaigns and social media outreach, it enables companies to make smarter marketing decisions and achieve better results.

Marketing Attribution

Marketing Attribution is the process of determining the value and impact of various touchpoints along the customer journey, helping marketers understand which channels, campaigns, and tactics are most effective in driving conversions and other desired outcomes. Attribution primarily focuses on assigning credit to specific marketing efforts that contribute to a conversion, sale, or other predefined goals.

Use Cases:

  1. Evaluating the effectiveness of individual marketing channels and campaigns
  2. Identifying the most influential touchpoints in the customer journey
  3. Optimizing marketing spend and resource allocation
  4. Informing marketing strategy and tactics based on attribution insights
  5. Tailoring messaging and targeting to maximize conversion potential

Attribution Models:

There are several attribution models available that help businesses assess the impact of their marketing efforts. Here is a breakdown of some of the most common types:

  1. Single-Touch Attribution: Assigns credit to a single touchpoint, either the first touch or the last touch. Common single-touch models include First-Touch Attribution, Last-Touch Attribution, and Last Non-Direct Touch Attribution.
  2. Multi-Touch Attribution: Distributes credit across multiple touchpoints throughout the customer journey. Examples of multi-touch models are Linear Attribution, U-Shaped Attribution, and Time Decay Attribution.
  3. Position-Based Attribution: This attribution model allocates credits based on the position of a touchpoint in the customer journey. For instance, it gives 40% of the credit to both the first and last ad interactions and corresponding keywords. The remaining 20% is spread across the other ad interactions on the user journey.
  4. Custom Attribution: Utilizes data and machine learning algorithms to create custom attribution models tailored to the unique needs of a specific business or industry. This approach allows for a more accurate representation of the customer journey and the impact of various marketing channels and tactics.


  1. Factors: Factors is an AI-driven marketing attribution platform that provides detailed insights into the impact of each touchpoint on your customer journey. With its advanced modeling capabilities, Factors helps businesses make informed decisions on marketing spend and resource allocation.
  2. Adobe Marketo Measure: A part of the Adobe Marketo suite, Measure offers powerful marketing attribution and revenue planning features. By connecting marketing and sales data, Measure enables businesses to accurately attribute revenue to specific marketing efforts.
  3. Dreamdata: Dreamdata is a B2B revenue attribution platform that allows you to track and analyze the performance of your marketing efforts across all channels. By unifying data from various sources, Dreamdata provides a holistic view of your customer journey and helps optimize your marketing strategy.
  4. Ruler Analytics: Ruler Analytics is a marketing attribution platform that connects closed revenue data with the customer journey. It provides insights into which marketing channels are driving conversions, allowing businesses to optimize their efforts and improve ROI.

Who Should Be Thinking About Marketing Analytics at SaaS Companies?

Marketing analytics helps businesses measure the effectiveness of their marketing campaigns, optimize strategies, and make data-backed decisions to drive growth. But who should be thinking about marketing analytics at SaaS companies? Generally, it is CMOs, marketing executives, and CROs who stay updated with this information.

operational model

But, there's more to the story.

While it's essential for top-level executives and decision-makers to understand marketing metrics, it's not practical for everyone involved to monitor every single metric. Requiring the marketing team to constantly share their progress is counter-intuitive to progress as well. That’s where dashboards help teams stay in sync and keep tabs on what’s happening within marketing. 

Dashboards offer a streamlined and efficient way to stay on top of the effectiveness of marketing strategies. They present data in a visually appealing and easy-to-understand format, allowing for quick insights and adjustments when needed. Here are a few benefits of dashboards. 

  • Enhanced collaboration: A well-designed dashboard helps teams across the board to stay updated with the changes in marketing. It also reduces the communication required to get data from individual teams as everyone can directly pull data from the dashboards. This increases transparency between teams.
  • Increased accountability: Because of the increased transparency, teams, and individuals have accountability for the tasks assigned to them. This allows for greater productivity across teams and helps things progress faster.
  • Customization: Dashboards are designed to be completely customizable. You do not need to showcase the same verbose data to everyone involved. Dashboards can help you summarize and visualize the required data based on who you want to demonstrate or showcase the data to. 

If you want to implement a marketing dashboard within your company, we have an in-depth article on CMO dashboards. This will guide you through the process of designing a dashboard that serves the unique needs of your SaaS app development company

Addressing Common Marketing Analytics Challenges

As businesses continue to evolve and adapt to the digital era, marketing analytics has become a vital aspect of marketing success. However, there are several common challenges that marketers face when trying to harness the power of data analysis.

Siloed data

The importance of seamless data integration cannot be overstated. With various marketing tools and platforms being used simultaneously, data can become fragmented and difficult to consolidate. Apart from that, customer data can get siloed at each of the touchpoints as well.


data silos

This siloed data can lead to an incomplete view of your marketing efforts and hinder your ability to make data-driven decisions. Marketing attribution tools like Factors combine data from multiple platforms and create a unified timeline that helps you view all the touchpoints of your customers in a single place. 

Not knowing what to track

Identifying the right metrics to track is essential for marketing success. Focusing on the wrong metrics or not tracking them at all can lead to misguided decision-making. In fact, tracking a metric incorrectly can lead to worse outcomes than not tracking it at all. 

For example, if you incorrectly attribute the success of your conversions to your paid ads, you may end up spending more on the wrong channel and wasting your marketing efforts.

Incorrect understanding of metrics

Metrics should be analyzed in tandem with other data to gain meaningful insights. For example, if the cost of acquisition in one quarter is dramatically lower than that of the previous two quarters, it could be the direct result of pausing a poor-performing marketing campaign. 

But only looking at the CAC may lead to incorrect conclusions—CAC is going lower and thus your profits are going up. 

Lack of automation and limited visualizations

Manual data analysis can be time-consuming, and error-prone, and ultimately limit your ability to make the most of it. Automating data collection and analysis processes is essential for maximizing efficiency and accuracy. Also, visualizing data through intuitive dashboards and reports enables easier understanding for everyone involved.

Factors address these common challenges by offering a powerful marketing analytics platform featuring seamless integrations, automation, and advanced visualization capabilities. Comprehensive marketing analytics and attribution solutions like Factors help overcome these challenges and ultimately achieve better results from your marketing efforts.

Top Marketing Analytics Use Cases for SaaS Companies

Marketing analytics can provide valuable insights and drive growth in various aspects of a SaaS company’s marketing efforts. The three key use cases for marketing analytics include acquisition and conversion tracking, campaign performance analysis, and customer segmentation and personalization. Here’s what each of them means:

1. Acquisition and Conversion Tracking

Acquisition and conversion tracking allows SaaS businesses to monitor how efficiently their marketing efforts are attracting and converting customers. For example, a SaaS company offering project management software might use acquisition and conversion tracking to analyze the sources of their website traffic. This could include social media platforms, search engines, and email marketing campaigns. They can then determine which channels are most effective in driving new users to sign up for their software. If the analysis reveals that content and SEO are generating a high number of sign-ups, the company can allocate more resources to that platform to capitalize on its success.

2. Campaign Performance Analysis

Campaign performance analysis evaluates the effectiveness of marketing campaigns and helps improve the campaigns through data. This involves monitoring click-through rates, conversion rates, and cost per acquisition, to determine the overall success of each campaign.

Imagine a SaaS company specializing in human resources software launches a Google Ads campaign targeting HR managers. 

Campaign performance analysis can be used here to evaluate the effectiveness of the ads. If the data indicates that one type of ad is outperforming the others, the company can optimize the campaign by allocating a higher budget to the more successful ad. This constant analysis and improvement can lead to more efficient marketing campaigns and higher ROI.

3. Customer Segmentation and Personalization

Customer segmentation and personalization involve grouping customers based on shared characteristics and tailoring marketing messages and offers to meet their specific needs.

For instance, a SaaS company that provides email marketing software could segment its customers into groups based on their industry, company size, or the frequency of their email campaigns.

They could then create content and offers targeted at each segment. A small e-commerce business might receive tips on how to maximize its email open rates during the holiday season. A large enterprise client, on the other hand, might receive a case study showcasing how their email marketing success has successfully scaled email campaigns for similar organizations.

Grow your business with streamlined marketing analytics

Marketing analytics is a powerful tool for driving growth and improving marketing performance in B2B companies. Focusing on key use cases, such as acquisition and conversion tracking, campaign performance analysis, and customer segmentation and personalization, will enable you to make data-driven decisions and optimize your marketing efforts. 

As a result, you'll attract more customers, improve customer satisfaction, and ultimately, increase your company's revenue. Don't miss out on the potential that marketing analytics can unlock for your SaaS company—start harnessing the power of data with Factors.ai today.


Why is marketing analytics important?

Marketing analytics makes marketing effective. It offers deep insights into the preferences and behavior of your customers. It allows you to shape the marketing campaigns in accordance with your customer’s expectations and helps you make data-backed decisions rather than relying on intuition and experience. 

What are the three types of analytics that can be used in marketing?

Descriptive analytics, predictive analytics, and prescriptive analytics. 

  • Descriptive analytics analyzes historical data to understand past performance. 
  • Predictive analytics uses data and statistical algorithms to forecast future outcomes
  • Prescriptive analytics provides recommendations on how to optimize marketing efforts based on data-driven insights.

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