Maximizing the Value of SaaS Application Analytics with AI

Applications of AI

Software as a Service (SaaS) applications have become a huge benefit to businesses looking to maximize their network agility while minimizing costs. SaaS applications offer app developers on-demand scalability, allowing them to quickly benefit from new features and software updates.

SaaS leverages cloud computing infrastructure and economies of scale to give clients a more streamlined approach to deploying, using, and paying for software.

However, SaaS architectures can overload DevOps teams with data aggregation, sorting, and analysis tasks: Given the amount of SaaS apps on the market (more than 30,000 SaaS developers were active in 2023) and the amount of data a single app can generate (each enterprise uses about 470 SaaS apps), SaaS leaves companies with a lot of structured and unstructured data to parse.

That's why today's application analytics platforms leverage artificial intelligence (AI) and machine learning (ML) technologies to sift through big data, provide valuable business insights, and enable greater data observability.

What is Application Analytics?

Broadly defined, application analytics refers to the process of collecting application data and analyzing performance and usage data for SaaS, mobile, desktop, and web applications in real time.

App analytics include:

  • App Usage AnalyticsShows app usage patterns (number of daily and monthly active users, most and least used features, geographic distribution of downloads, etc.).
  • App performance analysis, It shows you how your apps are performing across your network, including metrics such as response times and failure rates, and pinpoints the cause and location of any app, server, or network issues.
  • App cost and revenue analysis, Track your app's revenue (such as annual recurring revenue and customer lifetime value (the total profit a company can expect from a single customer over the life of their business relationship)) and expenses, such as customer acquisition costs (the costs associated with acquiring a new customer).

Using advanced AI-powered data visualization tools, App Analytics Services helps businesses gain a deeper understanding of their IT operations and empowers teams to make faster, smarter decisions.

AI in SaaS Analytics

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

Approximately 42% of enterprise-scale organizations (1,000+ employees) are using AI for business purposes, approximately 60% of companies are already using AI to accelerate their technology investments, and by 2026, more than 80% of companies will have AI-enabled apps deployed in their IT environments (up from just 5% in 2023).

The same goes for developing and managing SaaS apps.

While SaaS provides businesses with cloud-native app capabilities, 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 the sorting and analysis of data, while ML algorithms enable SaaS apps to learn and improve over time.

Comprehensive AI-driven SaaS analytics enable companies to make data-driven decisions about feature enhancements, UI/UX improvements, and marketing strategies to maximize user engagement and achieve (or exceed) their business goals.

SaaS App Analytics Use Cases

Traditional SaaS data analysis methods (such as relying solely on human data analysts to aggregate data points) may be effective for some organizations, but they may not be able to handle the vast amounts of data that SaaS apps generate, and may not be able to fully leverage the predictive capabilities of app analytics.

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

1. Data insights and reporting

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

AI technology can also uncover and visualize data patterns to aid in feature development.

For example, if your development team wants to understand which app features have the biggest impact on retention, they can use AI-driven natural language processing (NLP) to analyze unstructured data. NLP protocols automatically categorize user-generated content (such as customer reviews and support tickets) and summarize the data to provide insights into features that bring customers back to your app. AI can also use NLP to suggest new tests, algorithms, lines of code, or entirely new app features to increase retention.

AI and ML algorithms also enable SaaS developers to provide detailed oversight of app analytics. AI-powered analytics programs can create real-time, fully customizable dashboards that provide up-to-date insights into KPIs. Most machine learning tools also automatically generate summaries of complex data, making it easier for executives and other decision makers to understand the reports without having to review the raw data themselves.

2. Predictive analytics.

Predictive analytics predicts future events based on past data. AI and ML models (e.g. regression analysis, Neural Networks and Decision trees can be used to improve prediction accuracy: for example, an e-commerce app can predict which products will be popular during the holiday season by analyzing historical purchase data from past holiday seasons.

Most SaaS analytics tools, such as Google Analytics, Microsoft Azure, and IBM® Instana®, offer predictive analytics capabilities that enable developers to forecast trends in the market and user behavior and modify business strategies accordingly.

Predictive analytics is equally valuable for user insights.

AI and ML capabilities enable SaaS analytics software to perform complex analysis of user interactions within an app (click patterns, navigation paths, feature usage, session duration, etc.), ultimately helping teams predict user behavior.

For example, if a company implements a churn prediction protocol to identify users at risk of churn, it can use AI capabilities to analyze reduced activity and negative feedback patterns, two user engagement indicators that often precede churn. After the program identifies users at risk of churn, machine learning algorithms can suggest personalized interventions to re-engage those users (a subscription service might offer discounted or exclusive content to users who show signs of churn).

Deeper analysis of user behavior data allows businesses to proactively identify app usability issues, and in the event of an unexpected disruption (such as a natural disaster), AI and SaaS analytics provide real-time data visibility to help businesses continue, or even improve, their business during challenging times.

3. Personalization and user experience optimization.

Machine learning technology is often an essential component of delivering personalized customer experiences in SaaS applications.

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

For example, a news app can highlight articles similar to 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 a notification system can send targeted messages to each user at times when they're most likely to engage, making the overall experience more relevant and enjoyable.

At the application level, AI can analyze user journey data to understand the common navigation paths users take within an 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 through form submissions, purchases, sign-ups, subscriptions, and more.

AI-based analytics programs can automate funnel analysis (identifying where in the conversion funnel users drop off), A/B testing (where developers test multiple design elements, features, or conversion paths to see which one is more effective), and CTA button optimization to increase conversions.

Data insights gained from AI and ML can also help improve product marketing and increase overall app profitability, both of which are key factors in sustaining a SaaS application.

Companies can leverage AI to automate tedious marketing tasks (such as lead generation and ad targeting) to maximize both advertising ROI and conversion rates, while ML capabilities enable developers to track user activity to more precisely segment their user base and market products to them (for example, with conversion incentives).

5. Price optimization.

Managing IT infrastructure can be a costly endeavor, especially for enterprises operating large networks of cloud-native applications. AI and ML capabilities can help minimize cloud spend (and cloud waste) by automating SaaS process responsibilities and streamlining workflows.

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

Maximize the value of your SaaS analytics data with IBM Instana Observability

AI-powered application analytics gives developers an edge in today's fast-paced, highly dynamic SaaS environments, and with IBM Instana, enterprises have the industry-leading real-time, full-stack observability solution.

Instana is more than a traditional application performance management (APM) solution. It delivers automated, democratized observability powered by AI, making it accessible to everyone across DevOps, SRE, platform engineering, ITOps, and development. Instana delivers the data businesses want, with the context they need, so they can take intelligent actions to unlock the full potential of SaaS app analytics.

Learn more about IBM Instana Observability Schedule a live demo

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