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Artificial Intelligence (AI)
December 12, 2023
The Critical Role of AI-Powered Analytics in Your Product Strategy
AI-powered analytics are becoming essential for SaaS product teams, whether it’s for creating new features or entirely new data products. It’s now easier than ever to integrate AI into applications. However, it’s important to understand the value these analytics can bring to your product and to choose the right strategy, format, and tools for their implementation.
Why Integrate AI-Powered Analytics into Your Product?
Shifting Customer Expectations
Most SaaS products offer analytics through dashboards, allowing users to filter and drill down on data. However, these dashboards are often separate from users’ workflows. Today, users expect analytics to be embedded within their work processes. By 2025, Gartner projects that 60% of self-service analytics activities will start, and 30% will be completed entirely, within digital workplace applications. Traditional dashboards are great for reporting but users need more – they want analytics that inform daily decision-making and workflows.
Vendors like Sisense are making it easier to go beyond dashboards with composable, AI-powered analytics that can be seamlessly integrated into products using APIs and SDKs.
Enhancing User Experience
Embedding analytics within workflows helps users understand what’s happening and why, without interrupting their tasks. Combining AI with analytics allows products to suggest actions, simplifying the user’s decision-making process.
For example, a hotel event coordinator can not only see which rooms are available but also book a room, create a waitlist, or offer a discount all in one place. This is possible by applying AI to room utilization data. AI-powered analytics can transform static lists into dynamic tables with actionable insights.
Differentiating Your Product
AI-powered analytics can significantly differentiate your product, especially if your competitors haven’t adopted similar technology yet. While dashboards alone aren’t a lasting advantage, the advanced functionalities offered by AI analytics can provide a unique edge. Generative AI allows for the creation of personalized experiences based on user data and preferences. These tools also offer flexibility through APIs and SDKs, enabling continuous experimentation and fine-tuning.
For instance, Sisense enables building custom chatbots or adding individual AI components such as quickstart questions and data storytelling features. This allows you to continually enhance your product and surprise your customers.
Three Ways AI-Powered Analytics Boost Revenue and Meet Business Goals
Improving Account Expansion and Retention
Expanding and retaining customer accounts is the most common goal for data products. By adding analytics features that provide more value to existing customers, you can increase user adoption and usage of the product. This leads to easier renewals and potential upsells.
Capturing Additional Market Share
Embedding analytics can allow for tiered pricing or segment-specific bundles. This can maximize revenue by catering to different customer needs. According to the Eckerson Group, integrated analytics can increase the value of applications by 43% and enable charging 25% more on average.
Service tiers often offer basic analytics in lower tiers and more advanced, costly analytics in higher tiers. Segment-specific bundles can include industry-specific metrics or regulatory features, supporting higher price points.
Expanding into Adjacent or New Markets
Consider if the data collected through your product might be valuable to other market segments. For example, operations staff might find analytics useful for assessing efficiencies and improving workflows. AI analytics can provide recommendations and insights, driving additional usage.
A practical example is Salesforce, which started with Sales Cloud but expanded into Service Cloud to capitalize on customer data’s value to both customer support and service teams.
Elevating User Experience with AI-Powered Analytics
AI enhances analytics from simply describing “What happened?” to predicting and prescribing “Why?”, “Will it happen again?” and “What should be done?”. Diagnostic tools can analyze root causes, predict outcomes, and suggest actions. Generative AI narratives can explain insights in charts, while conversational AI chatbots offer a novel way for users to interact with data.
With tools like Sisense’s Compose SDK, advanced analytics can be smoothly integrated into your application, keeping user experience at the forefront.
To discover more about how Sisense’s AI-powered analytics can enhance product retention, value, or offer new monetization opportunities, consider requesting a demo with their experts.