Maximize the value of SaaS application analytics through AI

Software as a Service (SaaS) applications have become a boon to businesses looking to maximize network agility while minimizing costs. This provides app developers with on-demand scalability and faster time to benefit for new features and software updates.

SaaS leverages cloud computing infrastructure and economies of scale to provide customers with a more efficient approach to adopting, using, and paying for software.

However, SaaS architectures can easily overwhelm DevOps teams with data aggregation, sorting, and analysis tasks. Considering the amount of SaaS apps on the market (over 30,000 SaaS developers operating by 2023) and the amount of data a single app can generate (each enterprise uses approximately 470 SaaS apps), SaaS Provides businesses with structured and unstructured data to parse.

That’s why today’s application analytics platforms use artificial intelligence (AI) and machine learning (ML) technologies to mine big data, deliver valuable business insights, and provide superior data observability.

What is application analytics?

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

App analytics includes:

• App usage analysisIt shows app usage patterns, such as daily and monthly active users, most and least used features, and geographic distribution of downloads.

• app performance analysis, It uses metrics like response time and failure rate to show your app’s performance on the network and identify the cause and location of app, server, or network issues.

• App cost and revenue analysis, Track app revenue and customer acquisition costs (the costs associated with acquiring a customer), including annual recurring revenue and customer lifetime value (the total revenue a business can expect from a single customer over the life of a business relationship). new customers).

App analytics services use sophisticated data visualization tools, many powered by AI, to help businesses better understand their IT operations and help teams make faster, smarter decisions.

AI in SaaS Analytics

Most industries have had to consider AI proliferation and AI-based business practices to some degree.

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

SaaS app development and management is no different.

While SaaS provides businesses with cloud-native app capabilities, AI and ML transform 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. ML algorithms allow SaaS apps to learn and improve over time.

Using comprehensive AI-powered 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) are sometimes inadequate to handle the large amounts of data that SaaS apps generate. You may also have difficulty taking full advantage of the predictive capabilities of app analytics.

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

1. Data insights and reporting

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

AI technologies can also reveal and visualize data patterns that can help develop features.

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

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

2. Predictive analytics.

Predictive analytics predicts future events based on past data. AI and ML models, e.g. regression Analysis, neural network and Decision trees—Improve the accuracy of these predictions. For example, an e-commerce app can predict which products will be popular during the holidays by analyzing purchase history data from previous holiday seasons.

Most SaaS analytics tools, including Google Analytics, Microsoft Azure, and IBM® Instana®, offer predictive analytics capabilities that allow developers to predict both market and user behavior trends and shift business strategies accordingly.

Predictive analytics is equally valuable for user insights.

AI and ML capabilities enable SaaS analytics software to run complex analytics on user interactions within your app (click patterns, navigation paths, feature usage, and session duration, etc.), which ultimately helps teams predict user behavior. It helps.

For example, if a company wants to implement a churn prediction protocol to identify at-risk users, it can use AI capabilities to analyze declines in activity and negative feedback patterns, two indicators of user engagement that often precede churn. After the program identifies at-risk users, machine learning algorithms can suggest tailored interventions to re-engage those users. Subscription services can offer discounted or exclusive content to users who are showing signs of churn.

Deep analysis of user behavior data can also help companies proactively identify app usability issues. During unexpected disruptions, such as those caused by natural disasters, AI and SaaS analytics provide real-time data visibility that can help you continue to operate or improve your business during difficult times.

3. Personalization and user experience optimization.

Machine learning technologies are often essential to delivering personalized customer experiences in SaaS applications.

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

For example, a news app can highlight similar articles to ones the user has previously read and liked. Online learning platforms 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 times they are 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 through your app and streamline navigation for your entire user base.

4. Conversion rate optimization and marketing.

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

AI-based analytics programs include funnel analysis (identifying where users are dropping off in the conversion funnel), A/B testing (where developers test multiple design elements, features, or conversion paths to see which performs better), and click-through. You can automate your call-to-action. Button optimization to increase conversion rates.

Data insights from AI and ML can also help you improve product marketing and increase overall app profitability. Both are important components in maintaining SaaS applications.

Using AI, businesses can automate tedious marketing tasks (such as lead generation and ad targeting) to maximize advertising ROI and conversation rates. ML capabilities also allow developers to track user activity to more accurately segment and market products to their user base (e.g. with conversion incentives).

5. Price optimization.

Managing IT infrastructure can be an expensive task, especially for enterprises running large cloud-native application networks. AI and ML capabilities help minimize cloud costs (and cloud waste) by automating SaaS process responsibilities and streamlining workflows.

Using AI-generated predictive analytics and real-time financial observation tools, teams can predict resource usage fluctuations and allocate network resources accordingly. SaaS analytics can also help decision makers identify underutilized or problematic assets to avoid over- and under-spending and free up capital for app innovation and improvement.

Maximize the value of SaaS analytics data with IBM Instana Observability

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

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

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