In the B2B SaaS space, the time between campaigns and performance can be months because they have long sales cycles. Hence, establishing the value of a campaign to the C-suite, effectively answering probing questions, and achieving more SaaS marketing budget is challenging.
Advanced marketing analytics helps CMOs establish the efficacy of these campaigns over this long time span. Thus, the Boards get an accurate picture of the ROI and the overall marketing performance.
CMOs chasing revenue goals cannot afford to ignore advanced SaaS marketing analytics. A recent Gartner report shares that CMOs who embrace scaled judgment are 11% more likely to report revenue growth. By scaled judgment, we mean a marketer’s ability to leverage data-driven insights that can help the business identify and act on key market signals.
Thus, in this age when data is drowning organizations, CMOs need to lean into the breadth and complexity of marketing data to unlock high-value impact for the business and drive revenue.
In this B2B marketing analytics implementation guide, we will take a deep dive into advanced marketing data analytics and how CMOs can use it to their advantage.
What Is Advanced Marketing Analytics?
Advanced marketing analytics uses sophisticated tools and techniques to unearth hidden marketing insights from data. These insights are used to predict trends, gather behavioral forecasts, point out campaign performance issues, and recommend relevant next-best actions (NBAs).
Marketers can leverage advanced marketing data analytics to fetch more value from campaigns and improve ROI. Since advanced marketing analytics deploys machine learning models, data science, statistical techniques, and predictive analytics, it allows marketers to automate and optimize their SaaS marketing decisions and processes.
Compared to basic marketing analytics, advanced marketing analytics SaaS helps marketers by closely approximating what happens in the real world. It enables them to achieve trustworthy perspectives and actionable SaaS marketing insights that produce meaningful actions. For a CMO, this means ensuring all campaigns generate greater revenue impact.
Types of Advanced Marketing Analytics
Advanced marketing analytics can be broken down into 3 primary types based on how they can help marketers. Each gathers and analyzes data to improve marketing effectiveness; however, they achieve different goals.
- Descriptive Analytics
Descriptive analytics uses data to understand past SaaS marketing performance while offering context for what’s happening in the present.
For instance, to understand how a specific piece of content is performing on your blog, your marketing analyst will have to look at its page views, average time on page, number of social shares, and other web analytics for the 1st month. These B2B SaaS marketing metrics will be compared to the 1st month of a similar post published in the past.
Descriptive analytics will look at the differences that could explain the performance of these 2 posts. For instance, your current post may be getting more views compared to the past one because of a paid campaign.
Thus, descriptive analytics focuses on identifying problems, anomalies, or discrepancies. Its goal may not be to identify future trends but it can form a basis for predictions, especially in industries with cyclical patterns.
That being said, if you need to predict future trends and patterns, count on predictive SaaS marketing analytics.
- Predictive Analytics
Predictive marketing analytics crunches large volumes of data and uses statistical techniques like data mining, predictive modeling, and machine learning algorithms to make accurate predictions on future trends and outcomes. These predictions get increasingly accurate over time.
For marketers, SaaS predictive analytics can help your business grow in the following ways -
- Customer Segmentation
To segment customers based on their behavior, demographics, firmographics, and interests, marketers can experiment with cluster models to find certain patterns. These can help in arriving at customer segments relevant to the business.
- Customer Acquisition
The segmentation can be used to create identification models that can identify and target prospects resembling your ICP. This works just like Facebook’s Lookalike Audiences where you can upload a list of the emails of your ideal customers and the platform will target ads to similar people.
- Lead Scoring
Predictive lead scoring uses past customer data to rank prospects in order of their likelihood to convert. With this insight, marketers can trigger tailored brand messages or prioritize their outreach efforts to gain maximum conversions.
- Content Recommendations
Based on audience behavior and patterns, marketers can use collaborative filtering to obtain content and ad recommendations.
- Personalize Experiences
With customer segmentation, lead scoring, and content recommendations marketers can boost the effectiveness and relevance of their marketing activities to get maximum ROI on their efforts.
No wonder, organizations have ramped up their investment in SaaS predictive analytics solutions.
In fact, a McKinsey report reveals that a staggering 64% of B2B firms will increase their investments in SaaS predictive analytics.
- Prescriptive Analytics
Prescriptive analytics takes past SaaS marketing data into account when recommending impactful actions for the future. In that way, like SaaS predictive analytics, prescriptive analytics aims to predict the future but focuses on actions. It uses advanced techniques like graph analysis, simulation, complex event processing, neural networks, recommendation engines, heuristics, and machine learning to decide on the next-best actions based on the predictions.
Say, predictive marketing analytics suggests that your landing page will get an influx of a new set of visitors based on a targeted paid campaign. Prescriptive analytics will help you determine how to take the best advantage of it. Using it, you will be able to pick the most suitable products and deals to boost revenue during this period.
Notice how Revlitix, our revenue marketing platform offers recommendations and next-best actions (NBAs) to marketers after stitching data from various platforms.
When used properly, prescriptive analytics can help businesses make informed decisions based on data and probability-weighted projections. Thus, nothing relies on instinct.
Here’s a summary of the questions the three types of advanced marketing analytics answer.
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Advanced Marketing Analytics versus Basic B2B SaaS Marketing Analytics
The basic B2B marketing data analytics comprises calculations and monitoring metrics like conversion rates, cost per acquisition, and cost per click, among others. These B2B SaaS marketing metrics are combined with segmented traffic sources and customer types to visualize the relevant data.
Though this style of analytics allows businesses to obtain useful insights, it cannot be used to answer complex questions that involve predicting future trends and basing critical marketing decisions on them. They need statistical techniques and ML models, which is only possible with advanced B2B marketing analytics.
Advanced marketing analytics uses mathematical recipes applied to marketing data to spot patterns and the relationships between them. It focuses on predicting the future as opposed to looking into the past to summarize information. Using these advanced techniques, the B2B SaaS marketing team can improve operational efficiency, boost customer satisfaction, scale revenue, and optimize ROI.
Here are a few limitations of basic B2B marketing analytics for SaaS.
- Inaccurate Estimation of Marketing Needs
Since they do not rely on advanced analytical techniques, basic marketing analytics can under or over-estimate the need for a product, customer behavior, or competitor performance. This may cause the team to make erroneous decisions.
- Missed Market Opportunities
A thorough market analysis should point to potential opportunities. However, basic marketing analysis isn’t comprehensive enough and doesn’t have sufficient context, leading to missed opportunities.
- Incorrect Data Interpretation
Data interpretation is a complex process. Without MI models, this process can seem effort-intensive and time-consuming. Thus, your team will refer to inaccurate conclusions.
Here’s a quick overview summarizing the key differences between basic versus advanced marketing analytics SaaS.
How to Apply Advanced Marketing Analytics
- Data Normalization
The results of your advanced analytics deployment are only as good as the data you supply. Hence, it’s important to prepare a strong data foundation through a robust automated data and reporting pipeline. This will help your team optimize resources, eliminate human error, and improve data quality.
When data is gathered, it comes from various sources and is diverse in terms of quality. You can get the best from advanced analytics SaaS techniques when there’s a large volume of granular data. Hence, businesses should clean up their existing data and prepare a framework for the advanced analytics model.
Here is a chart that explains the evolution of advanced B2B marketing analytics and how Revlitix helps businesses accelerate their transition with advanced analytics.
- Setting Up Conversion and Revenue KPIs
Most SaaS marketing teams track conversions using metrics like sales (point of sales) or subscriptions (when a visitor becomes a client). But SaaS customer journeys are long and complex; hence, sales or subscriptions aren’t the only conversion events to be considered.
For instance, if we refer to the top of the funnel stage, opening a promotional email could be a conversion for that stage.
Regardless of the conversion event you consider, make sure you monitor them against concrete goals based on industry standards and the business’s performance in the past. Historical data like past performance or user behavior can help identify behaviors associated with future conversions.
So, once you identify prospects with such behavior signals, you can prioritize them and unearth the factors that have the strongest impact on conversions.
Each metric you track has several NBAs (next-best actions) attached to it. As shared earlier, a revenue marketing tool like Revlitix can define and allow you to create and execute recommendations based on past business performance. The platform is powered by ML algorithms that generate actionable insights within seconds.
Thus, if your email marketing campaign is live and recipients must open the email, you could set a recommendation to measure the open rate, the clickthrough rate, and social shares among others.
- Integrating Data across Platforms
Though most marketing teams are aware that integrating data is key to improving performance, they shy away from this step. Marketing campaigns are often spread across multiple channels, each having its independent analytics tool. Hence, getting all the data together poses serious data engineering challenges.
In the absence of data integration, it’s easy for marketers to get myopic on the channel they are responsible for. For instance, a social marketer will only look at social metrics. But that’s not the only marketing touchpoint. A SaaS prospect is probably checking emails, looking at Twitter posts, and reading blog posts simultaneously.
So, when marketing data isn’t integrated, there’s no way a marketer can learn about a customer’s journey. Revlitix can handle this data integration for you. It links popular marketing tools, getting all the data that feeds advanced marketing analytics. Thus, your team gets a holistic view of marketing data, empowering them to forecast accurately, understand customer behavior, and make informed marketing decisions.
- Funnel Tracking
Tracking prospects by funnel or funnel analytics is a critical part of a firm’s advanced marketing analytics strategy. Marketing funnels allow marketers to monitor important milestones in the customer journey and point out the areas that need improvement.
Say, your email marketing campaign generates many newsletter subscribers but low click-throughs. This means you need to improve the content quality of your newsletter.
Funnel analytics allows you to see how user segments move through the funnel, allowing you to focus on segments that need to be prioritized in terms of personalization.
- Determining CLV
Investing efforts and time in the wrong set of prospects can be fatal to your marketing budget. By setting relevant KPIs and tracking users through the funnel, you can build a list of qualified prospects. However, you need to filter out high-value prospects to achieve revenue goals.
Also, the average annual churn in SaaS is 32-50%. This means the industry experiences an average of around 50% retention rate. So, a considerable number of customers do not come back while a small percentage stay loyal in the long term, adding the most value to the business.
Determining the CLV allows you to predict the expective value a customer will add to your business over their lifetime with the company. Comparing CLV versus CAC will ensure you don’t go overboard with your acquisition spend for low-value prospects. Thus, you can optimize your acquisition channels and spend on reactivating or retargeting prospects who may be high-value.
Customer lifetime predictive value models identify high-value customers and based on past data they predict how much they will spend on your services. Such predictions determine the firm’s advertising and targeting SaaS marketing strategies for those customers, thereby maximizing ROI.
Cluster analysis uses mathematical models to segment customers and prospects into manageable subsets. These homogeneous groups or personas can then be easily targeted for enhanced personalization. For instance, they can be targeted with relevant offers and incentives personalized to their needs and preferences. These clusters help in improved customer modeling and predictive marketing analytics.
Why cluster analytics is one of the top advanced B2B marketing analytics methods?
Let’s take a step back and think of how customer segmentation would be done manually. You are probably used to web analytics tools like Google Analytics where traffic and user segments need to be manually defined. This approach is great for smaller user dimensions.
When it comes to scaling this to a high number of user attributes, advanced analytics, and machine learning help. For instance, clustering can help segment customers based on their browsing activity, demographics, monetary value, and offline behavior.
Similarly, product clustering can be performed based on the product attributes like when a product was purchased, who purchased it, or which store it was purchased from.
- Anomaly Detection
Most marketing campaigns produce high volumes of data with multiple B2B SaaS marketing metrics and an ever-increasing dataset to explore. For instance, they have a huge variety of ad groups and keywords, each with metrics like conversions, ROI, spending, and more.
These numbers are tough to monitor if the change is beyond the bounds of natural variation or if there’s an unexpected change in data patterns. These deviations are caused by incidents in the real world.
Every marketer needs to be conscious of these changes and know the root cause to take corrective action for application performance, manage product quality issues, and improve user experience.
Anomaly detection is a statistical technique to spot anomalies or outliers in time-series data for a specific metric. It could be a technical glitch or a potential opportunity, like a change in customer behavior. It deploys machine learning and statistical methods to warn marketers about sudden and beyond-normal deviations in metrics like page views, daily active users, cost per lead, revenue, traffic, churn rate, or conversion rate.
Revlitix offers a tailored KPI reporting feature where you can set your parameters and move to other priority tasks that need your attention. The platform’s Smart Alerts will detect anomalies in campaigns, channels, keywords, and more and alert you before it’s too late to act.
Forecasting involves making educated predictions of a target market’s future performance to optimize marketing activity and budget, reduce churn, act proactively, and have better control of inventory. Marketers use forecasting to predict online traffic, conversion, revenue, and other metrics based on historical data.
Mostly every BI and ERP system talk about its forecasting capabilities, yet, they fall short of delivering a business’s potential. Forecasting needs to be based on advanced analytical methods. That’s when businesses can leverage it to drive operational efficiency, revenue growth, and customer satisfaction.
Investing in forecasting can help enterprises get a solid grip on data, get an accurate view of opportunities, and make faster decisions. If used effectively, forecasting can help businesses adjust their future campaigns and revenue goals.
Ready to apply advanced analytics and transform your SaaS?
Key Challenges in Implementing Advanced Marketing Analytics
Here are a few key challenges in implementing advanced marketing analytics SaaS.
1. Navigating the Transition
According to experts, consistent growth and evolution of a SaaS product require firms to invest in advanced technology architecture that scales.
Marketers accustomed to traditional BI tools might not be flexible enough to adopt analytics SaaS tools. They might feel skeptical when shifting from traditional marketing practices to data-driven decision-making.
Besides, managing client’s expectations during this transition can be demanding.
2. Overcoming Data Silos and Errors
Collecting and compiling clean, high-quality data from multiple sources is challenging. In fact, several studies reveal that manual data wrangling (cleaning, structuring, and organizing data) is a major stumbling block for marketers.
It may comprise duplication errors and inconsistencies that can negatively impact the analysis process.
For instance, you might observe duplicate data entries in your website analytics data because of a technical glitch or other reasons. This can lead to inflated KPIs, making it challenging to gauge your distinct marketing channel performances.
Besides, multiple marketing platforms can have their own data format and API. This can make it difficult to build a centralized data ecosystem.
3. Tailoring B2B SaaS Analytics for Unique Business Needs
Every SaaS firm has unique business needs. Finding and deploying an advanced marketing solution with customization capabilities and flexibility to adapt to these unique needs can pose a huge challenge.
The data analytics SaaS software may lack features that offer a customized view of KPIs based on their job role.
For instance, the CMO might want to view KPIs like conversion rate, customer acquisition cost (CAC), customer lifetime value (CLV), etc.
On the other hand, the marketing analyst might want to analyze ROI, click-through rate (CTR), etc.
The customization limitation can hinder their ability to gain actionable SaaS marketing insights tailored to their specific goals.
4. Enhancing Anomaly Detection in Real-time
Your team may struggle to identify performance anomalies and potential issues with traditional BI tools. This can lead to missed opportunities and negatively impact your ROI. What’s more? This can lead to a poor customer experience and an increased customer churn rate.
For this, it’s crucial to deploy robust analytics for SaaS with advanced AI and ML capabilities.
5. Addressing Budget Issues
Implementing advanced SaaS analytics tools often requires a significant investment.
This can be a challenge for SaaS companies, given that sales and marketing expenses roughly account for 50% of their revenue, particularly in high-growth businesses.
Besides, the market is already flooded with a plethora of data analytics SaaS tools claiming to be the go-to solution for marketing needs.
Finding the right B2B SaaS analytics solution equipped with state-of-the-art features that too without breaking the bank can be overwhelming, especially for SaaS firms in their early stages.
Implementing the right advanced marketing analytics is pivotal to addressing these challenges and enhancing marketing effectiveness.
Don’t let challenges hold back your SaaS marketing analytics journey.
Advanced Marketing Tools You Can Count on
Turning data into actionable insights that can power marketing initiatives and drive revenue is what advanced analytics is all about. But it takes a relevant tech stack to leverage advanced B2B marketing analytics.
For long, marketers have counted on BI tools and data science models to discover insights or make predictions. In fact, marketers who have worked with these tools their entire careers are often unwilling to switch. However, if they move to advanced tools that sit atop these conventional ones, they will acquire the skills and self-sufficiency required to build advanced analytics apps using AI/ML techniques.
Further, businesses are experiencing long lead times for building their advanced marketing solutions. This can negatively impact marketing effectiveness and revenue. Hence, marketers need a tool that will help them be self-sufficient enough to apply advanced analytics quickly.
Revlitix is one such advanced analytics tool that offers a visual and intuitive platform that’s perfect for technical marketers. The revenue analytics software connects data with insights and turns them into actions.
The revenueOS meets every marketer’s needs - it detects performance anomalies before it's too late, collaborates with the team, builds out-of-the-box, impressive customized dashboards, and uses custom playbooks to reduce time to action.
The platform is powered by our proprietary ML models and reinforcement learning algorithms. It takes care of all the technical marketing needs while allowing marketers to stay creative.
From tracking crucial KPIs to delivering actionable insights, Revlitix streamlines the marketing analytics process, fostering revenue growth.
The alerts feature in Revlitix offers prompt notifications for anomalies before it’s too late.
This can help marketers -
- Automate monitoring of the KPIs that matter the most
- Get notified on platforms like Slack, MS Teams, or emails
- Assign alerts to the specific team member in real-time
The outcome? Task prioritization, zero risk of human errors, and positive business outcomes.
More than that, budget is often a key concern for marketers when adopting B2B SaaS analytics tools. With Revltix, there’s no need to fret!
Revlitix is a cost-efficient advanced analytics marketing tool, making it ideal for a wider range of SaaS businesses, ensuring that even small or mid-sized enterprises can benefit from data-driven marketing strategies.
Before We Conclude: Bonus!
Tips for CMOs Wanting to Adopt Data-Driven Decision-Making through Advanced Marketing Analytics
- Tailor Your Marketing Tech Stack to Your Revenue Goals
Advanced marketing analytics comprises the automation of routine tasks like data processing and software integration into the workflow. Manually, your team will spend a lot of time instead of focusing on the analysis or other creative aspects of marketing.
And, manual work brings in insights granularity and the risk of human error.
Hence, it’s important to review your marketing goals and tailor your tech stack accordingly. You need to prioritize data-driven decision-making through a marketing analytics stack that can uncover the hidden value of data and measure performance from your experience designs in real-time.
Opt for advanced analytics marketing solutions that help demonstrate the correlation between your SaaS marketing strategies and the bottom line. Plus, they should speed up the feedback loop by integrating directly with your reporting and analytics platforms.
- When deciding on which tools to invest in, consider the ones that connect analytics to actions.
- Use a flexible analytics stack that covers all operations, thereby getting rid of silos.
- Pick Analytics Marketing Tools That Are Easy to Integrate
Marketing data often comes from the website; however, you will need data on the backend processes too. The tech stack you invest in should be easy to integrate, allowing you to consolidate multiple data sets into a single source of information.
Revlitix handles quick data transfer and easily integrates with various systems. Thus, it saves your team the time and effort involved in the complex overhaul of data-driven technology. Thus, a technical marketer can gain access to marketing insights with a few clicks.
For instance, this comprehensive B2B SaaS analytics tool can seamlessly integrate with platforms like-
Consolidating your advanced SaaS marketing analytics efforts into a single platform like Revlitix will help you streamline your delivery and analysis environment and keep you ahead of the shifting SaaS landscape.
- Don’t Shy Away from Experimenting
As your business scales, customers will evolve. This calls for marketers to consistently revisit advanced marketing solutions and review their effectiveness. Look at factors such as price sensitivity, customer interactions, value perceptions, and others when experimenting with your marketing analytics implementation.
Revlitix’s advanced data analytics for SaaS can empower your marketing team to gauge the ever-evolving customer needs and fine-tune strategies.
SaaS marketing teams can hugely benefit from implementing advanced SaaS marketing analytics throughout their entire operations. It helps firms automate and optimize their marketing efforts, thereby supporting them in achieving their revenue goals.
We are sure that this B2B marketing analytics implementation guide will help you leverage advanced marketing analytics to automate and optimize your marketing processes for stronger outcomes.
Frequently Asked Questions
- What is advanced marketing analytics?
Advanced marketing analytics uses sophisticated techniques like predictive modeling, modern statistical methods, data mining, and machine learning to dig our uncovered insights from large volumes of data. It helps technical marketers make accurate predictions, thereby recommending the most appropriate plan of action.
The practices and technologies used can efficiently analyze data to discover correlations and relationships and predict outcomes of scenarios. When used effectively, advanced marketing analytics can reveal new customer behavior patterns, upcoming market trends, campaign performance issues, and more.
- Why is advanced analytics important in SaaS?
The SaaS domain is rife with technological advancements and boasts immense growth potential. Thus, it has become a crowded space with hundreds of players fighting to grab market share.
In such a cut-throat scenario, the companies aiming to stay ahead need to leverage data to analyze the landscape and carve a niche for themselves (and that too quickly!). \
Thus, they need advanced marketing analytics to crunch enormous amounts of data and perform complex maneuvers. Yet, it has to be straightforward enough for non-technical users and agile enough to allow end users to customize their reports and derive the insights they need.
Advanced marketing analytics will do all this plus keep the SaaS CAC down, give them a clear glimpse of the future, and sharpen their competitive edge.
- What insights can be obtained from advanced data analytics SaaS tools?
Advanced data analytics SaaS tools offer a wide range of insights, such as in-depth customer behavior analysis, predictive trends, and crucial campaign performance KPIs.
They can help marketers differentiate high-value customers, prioritize prospects, provide accurate lead scoring, and offer actionable recommendations.
Besides, these advanced marketing tools for SaaS allow marketers to detect anomalies by sending real-time alerts and notifications. This can significantly save from adverse scenarios that might negatively impact the business revenue in the long term.
- What are the types of advanced marketing analytics?
The most popular types of advanced marketing analytics are -
- Descriptive analytics - It uses data to understand past marketing performance and offers context for what’s happening at present. It is usually characterized by conventional BI and visualizations like pie charts and bar charts.
- Predictive analytics - It uses predictive modeling and deep learning techniques to examine historical data and predict future trends and outcomes. It’s a powerful tool to forecast what will happen next under specific business scenarios.
- Prescriptive analytics - It is a powerful advanced marketing analytics tool technique that prescribes the best ways to achieve specific business objectives. It goes a step beyond SaaS predictive analytics to suggest the NBA (next best actions) for achieving a range of potential business outcomes.
- What are the key benefits of marketing analytics SaaS?
Here are the key benefits of analytics for SaaS.
- Uncover Valuable Insights: Marketing analytics SaaS platforms allow marketers to unveil valuable SaaS marketing insights from your data.
- Make Data-Driven Decisions: With analytics SaaS, marketers can optimize strategies and make data-driven decisions for the best business outcomes.
- Leverage SaaS Predictive Analytics: Advanced marketing for SaaS with predictive analytics capabilities helps marketers foresee customer and market trends, thus boosting overall performance.
- Utilize Customization Feature: The right marketing analytics SaaS allows marketers and distinct stakeholders to customize dashboards according to their unique needs, as mentioned in this B2B marketing analytics implementation guide.
- Save Resources, Time, and Budget: Advanced marketing analytics tools help streamline data compilation and analysis, saving resources, time, and budget.
- How can Revlitix help in my advanced marketing analytics journey?
Automating routine data processing and software integration are the basic requirements for the successful implementation of SaaS advanced marketing analytics. If done manually, this technical process can take a lot of time and effort, leaving no time for analysis.
Further, stitching data from different platforms, crunching multiple dashboards to find the campaign ROI, or monitoring hundreds of KPIs across multiple channels is effort intensive.
Revlitix makes lives easier for marketers as it completely takes care of their technical needs.
- It functions as your virtual analyst by analyzing thousands of KPIs across every channel and millions of data combinations each day. It then surfaces insights that have a positive impact on revenue.
- It automates ad hoc analysis that marketers perform from the CMO to every department and channel.
- It eliminates the hours spent looking through reports and brings down the cost of building dashboards.
- It builds custom and multi-channel dashboards within a few clicks, literally.
- It spots anomalies and recommends the next-best actions tied to each marketing metric.
- It executes recommendations with one-click optimizations.
- It connects data to task management automatically, unlike most task management tools in which every task is manually created by different people.
- And more!