Software as a service (SaaS) applications have become a boon for enterprises looking to maximize network agility while minimizing costs. They offer app developers on-demand scalability and faster time-to-benefit for new features and software updates. 

SaaS takes advantage of cloud computing infrastructure and economies of scale to provide clients a more streamlined approach to adopting, using and paying for software.

However, SaaS architectures can easily overwhelm DevOps teams with data aggregation, sorting and analysis tasks. Given the volume of SaaS apps on the market (more than 30,000 SaaS developers were operating in 2023) and the volume of data a single app can generate (with each enterprise businesses using roughly 470 SaaS apps), SaaS leaves businesses with loads of structured and unstructured data to parse.

That’s why today’s application analytics platforms rely on artificial intelligence (AI) and machine learning (ML) technology to sift through big data, provide valuable business insights and deliver superior data observability.

What is application analytics?

Broadly speaking, application analytics refers to the process of collecting application data and performing real-time analysis of SaaS, mobile, desktop and web application performance and usage data.

App analytics include:

• App usage analytics, which show app usage patterns (such as daily and monthly active users, most- and least-used features and geographical distribution of downloads).

• App performance analytics, which show how apps are performing across the network (with metrics such as response times and failure rates) and identify the cause and location of app, server or network problems.

• App cost and revenue analytics, which track app revenue—such as annual recurring revenue and customer lifetime value (the total profit a business can expect to make from a single customer for the duration the business relationship)—and expenditures such as customer acquisition cost (the costs associated with acquiring a new customer).

Using sophisticated data visualization tools, many of which are powered by AI, app analytics services empower businesses to better understand IT operations, helping teams make smarter decisions, faster.

AI in SaaS analytics

Most industries have had to reckon with AI proliferation and AI-driven business practices to some extent.

Roughly 42% of enterprise-scale organizations (more than 1,000 employees) have used AI for business purposes, with nearly 60% of enterprises already using AI to accelerate tech investment. And by 2026, more than 80% of companies will have deployed AI) )AI-enabled apps in their IT environments (up from only 5% in 2023).

SaaS app development and management is no different.

SaaS offers businesses cloud-native app capabilities, but AI and ML turn the data generated by SaaS apps into actionable insights. Modern SaaS analytics solutions can seamlessly integrate with AI models to predict user behavior and automate data sorting and analysis; and ML algorithms enable SaaS apps to learn and improve over time.

Using comprehensive, AI-driven SaaS analytics, businesses can make data-driven decisions about feature enhancements, UI/UX improvements and marketing strategies to maximize user engagement and meet—or exceed—business goals. 

SaaS app analytics use cases

While effective for some organizations, traditional SaaS data analysis methods (such as relying solely on human data analysts to aggregate data points) sometimes fall short in handling the massive quantities of data SaaS apps produce. They may also struggle to fully leverage the predictive capabilities of app analytics.

The introduction of AI and ML technologies, however, can provide more nuanced observability and more effective decision automation. AI- and ML-generated SaaS analytics enhance:

1. Data insights and reporting

Application analytics help businesses monitor key performance indicators (KPIs)—such as error rates, response time, resource utilization, user retention and dependency rates, among other key metrics—to identify performance issues and bottlenecks and create a smoother user experience. AI and ML algorithms enhance these features by processing unique app data more efficiently.

AI technologies can also reveal and visualize data patterns to help with feature development.

If, for instance, a development team wants to understand which app features most significantly impact retention, it might use AI-driven natural language processing (NLP) to analyze unstructured data. NLP protocols will auto-categorize user-generated content (such as customer reviews and support tickets), summarize the data and offer insights into the features that keep customers returning to the app. AI can even use NLP to suggest new tests, algorithms, lines of code or entirely new app functions to increase retention.

With AI and ML algorithms, SaaS developers also get granular observability into app analytics. AI-powered analytics programs can create real-time, fully customizable dashboards that provide up-to-the-minute insights into KPIs. And most machine learning tools will automatically generate summaries of complex data, making it easier for executives and other decision-makers to understand reports without needing to review the raw data themselves.

2. Predictive analytics.

Predictive analytics forecast future events based on historical data; AI and ML models—such as regression analysis, neural networks and decision trees—enhance the accuracy of these predictions. An e-commerce app, for example, can predict which products will be popular during the holidays by analyzing historical purchase data from previous holiday seasons.

Most SaaS analytics tools—including Google Analytics, Microsoft Azure and IBM® Instana®—offer predictive analytics features that enable developers to anticipate both market and user behavior trends  and shift their business strategy accordingly. 

Predictive analytics are equally valuable for user insights.

AI and ML features enable SaaS analytics software to run complex analyses of user interactions within the app (click patterns, navigation paths, feature usage and session duration, among other metrics), which ultimately helps teams anticipate user behavior.

For instance, if a company wants to implement churn prediction protocols to identify at-risk users, they can use AI functions to analyze activity reduction and negative feedback patterns, two user engagement metrics that often precede churn. After the program identifies at-risk users, machine learning algorithms can suggest personalized interventions to re-engage them (a subscription service might offer discounted or exclusive content to users showing signs of disengagement).

Diving deeper into user behavior data also helps businesses proactively identify app usability issues. And during unexpected disruptions (such as those caused by a natural disaster), AI and SaaS analytics provide real-time data visibility that keeps businesses running—or even improving—in challenging times. 

3. Personalization and user experience optimization.

Machine learning technologies are often integral to providing a personalized customer experience in SaaS applications.

Using customer preferences (preferred themes, layouts and functions), historical trends and user interaction data, ML models in SaaS can dynamically tailor the content that users see based on real-time data. In other words, AI-powered SaaS apps can automatically implement adaptive interface design to keep users engaged with personalized recommendations and content experiences.

News apps, for instance, can highlight articles similar to the ones a user has previously read and liked. An online learning platform can recommend courses or onboarding steps based on a user’s learning history and preferences. And notification systems can send targeted messages to each user at the time they’re likeliest to engage, making the overall experience more relevant and enjoyable.

At the application level, AI can analyze user journey data to understand the typical navigation paths users take through the app and streamline navigation for the entire user base.

4. Conversion rate optimization and marketing.

AI analytics tools offer businesses the opportunity to optimize conversion rates, whether through form submissions, purchases, sign-ups or subscriptions.

AI-based analytics programs can automate funnel analyses (which identify where in the conversion funnel users drop off), A/B tests (where developers test multiple design elements, features or conversion paths to see which performs better) and call-to-action button optimization to increase conversions.

Data insights from AI and ML also help improve product marketing and increase overall app profitability, both vital components to maintaining SaaS applications.

Companies can use AI to automate tedious marketing tasks (such as lead generation and ad targeting), maximizing both advertising ROI and conversation rates. And with ML features, developers can track user activity to more accurately segment and sell products to the user base (with conversion incentives, for instance). 

5. Pricing optimization.

Managing IT infrastructure can be an expensive undertaking, especially for an enterprise running a large network of cloud-native applications. AI and ML features help minimize cloud expenditures (and cloud waste) by automating SaaS process responsibilities and streamlining workflows.

Using AI-generated predictive analytics and real-time financial observability tools, teams can anticipate resource usage fluctuations and allocate network resources accordingly. SaaS analytics also enable decision-makers to identify underutilized or problematic assets, preventing over- and under-spending and freeing up capital for app innovations and improvements.

Maximize the value of SaaS analytics data with IBM Instana Observability

AI-powered application analytics give developers an advantage in today’s fast-paced, hyper-dynamic SaaS landscape, and with IBM Instana, businesses can get an industry-leading, real-time, full-stack observability solution.

Instana is more than a traditional app performance management (APM) solution. It provides automated, democratized observability with AI, making it accessible to anyone across DevOps, SRE, platform engineering, ITOps and development. Instana gives companies the data that they want—with the context that they need—to take intelligent action and maximize the potential of SaaS app analytics.