How to improve your AI SaaS performance in 2024 in 3 actionable steps

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We are undeniably in the midst of an AI SaaS era, but it’s not just a trend to integrate AI into SaaS products anymore. Most everyday users now consider AI powered features and capabilities as a standard expectation, and it’s reflected in the market with over 70% of new SaaS products having an AI LLM integration.

This presents an issue because in today’s saturated market you can’t just sell software anymore, in order to compete your AI product has to constantly learn and evolve on the fly to stay relevant with users. In other words, your ability to obtain and leverage data is fast becoming your biggest competitive edge.

Feedback loops have always been the cornerstone of constant improvement, but it has never been this important. Traditional SaaS relied heavily on established market strategies and incremental updates but AI SaaS is about agility and real-time learning. The most alarming thing to note is that 80% of data in AI SaaS applications comes from user interactions, which traditionally we were either unable to or didn’t need to consider.

This pool of rich data is often unstructured and requires AI analytics to extract any actionable insights. The value this data holds cannot be overstated - Gartner reports that companies leveraging real-time analytics will see a 30% increase in retention rates by 2025.

In fact, 86% of enterprises now consider the use of AI to be key for success, but only 14% have actually deployed any AI systems at scale.

This gap highlights a few things:

• Better overall utilisation of data to improve AI performance is a competitive edge
• Teams need more accessible and comprehensive AI analytics tools to leverage data
• These tools need to be AI native - they need to be made specifically for AI products.

Phospho’s open source platform for AI product analytics is built to bridge this gap with highly accessible real-time data and analytics. Unlike traditional analytics tools, Phospho is designed especially for AI products and conversational interfaces in mind, positioning this as a tool specifically suited for AI integrated SaaS products. You can try Phospho for free by signing up here, we integrate really easily into popular tech stacks.

The 3 actionable steps we’ll be going over in this article are not best practices. They are essential strategies AI SaaS companies can implement with Phospho to stay competitive in rapidly evolving saturated markets.

1) Embrace Real-Time Monitoring for Proactive Improvement

The AI SaaS landscape is evolving faster than we give it credit for. With 89% of AI SaaS applications expected to include real-time capabilities, it’s important to match that with our analytics.

Traditional analytics are often retrospective and simply put, don’t let us operate with data and insights to the level we need for effective development of AI products. Companies that iterate based on real-time feedback see a 25% faster product improvement cycle.

These AI products are only becoming more sophisticated as well, for this we’ll need the right tools built specifically for AI product analytics. With Phospho’s real time logging of user interactions, teams can continuously monitor the health of their AI model and its performance. But this isn’t a marginal gain - Phospho provides a 20% faster detection rate of anomalies compared to traditional analytics.

The ability to pivot quickly based on new data, feedback, and pattern changes can largely determine the success of your product in the market.

2) Leverage Data-Driven Insights to Uncover Edge Cases

Edge cases are an important aspect of product development we often overlook due to their inherent difficulty to predict or detect. Traditional analytics might highlight general trends but again only with retrospective data, and often miss the nuances of user behaviour in conversational AI SaaS that are prone to misinterpretation. This is why constant refinement with data driven insights are key to maintain AI model performance.

Edge cases, though rare, can lead to considerable user dissatisfaction if left unresolved for too long that it becomes problematic. Access to real-time data and Ad hoc analysis in Phospho can help teams identify any emerging trends in user behaviour before they become obvious or a cause for concern.

Let’s take an example: an AI SaaS chatbot in healthcare might misinterpret medical jargon or misclassify figurative speech from literal, leading to incorrect advice and recommendations.

These performance issues derived from edge cases are more significant in high stakes environments but for any product are crucial to your users’ experience. Phospho has a feature that lets you ‘flag’ for any detection of patterns in user interactions that deviate from normal.

By identifying these early in real-time teams can then investigate and refine their models to handle different edge cases as they arise. Companies that focus on edge case detection report a 15% increase in user satisfaction by addressing these rare but impactful scenarios.

When refining AI models we can take it a step further with Phospho’s customisable KPIs. Traditional KPIs are too static, don’t paint the full present picture, and rely on time to deliver any observable insights. Phospho allows teams to define their own KPIs specific to their app and user base. For example, a KPI might be the frequency of clarifying questions asked by the AI, which indicates how well the model understands user intent.

What we’re trying to showcase here is the flexibility and control you have with our features when obtaining relevant data for any scenario, edge cases, or unanticipated issues. You can also do all of this without any downtime or halting the development process so you can achieve much faster, better informed iteration.

If you want to derive insights to identify and address edge cases efficiently, try testing Phospho with your own app/data with our free credits by signing up here.

3) Implement Automated Evaluation Pipelines for Ongoing Optimization

It’s an understatement to say that AI SaaS needs constant improvement. The market is so dynamic and users needs evolve so rapidly, that static models are completely outdated and lead to user frustration faster than ever. Real time monitoring is key for this as we’ve mentioned but so are automated evaluation pipelines.

But the issue is not in our understanding of its importance. While 94% of AI driven companies agree that evaluation pipelines are crucial, we lack the right tools to implement it effectively or at scale. For companies operating at scale, manual evaluation is impractical. Phospho allows for automated evaluation of AI models by constantly measuring performance against key metrics. Automated pipelines have been shown to reduce the time spent on evaluations by 40%, which frees up more resources.

You can also automatically A/B test different versions of your AI model to see which are performing best, ensuring your AI product is always optimising and improving based on real user interactions and not intuition.

Transform Your AI SaaS with Phospho in 2024

It’s true that analytics tools have not kept pace with the speed at which AI itself is developing, which is exactly why we built Phospho to provide robust data analytics and actionable insights from user data specifically for constantly refining AI products.

In AI SaaS, fast, data driven iteration is the highest growth lever for your product. It’s more than fixing bugs, it’s a competitive layer in itself where the companies that can obtain, interpret, and act on real user insights the fastest will position themselves as market leaders.

Those who fail to adopt real-time data analytics and AI capabilities to improve AI SaaS products don’t just risk falling behind, they ensure it.

If you want real user aligned iteration and scale faster, try Phospho for free by signing up here and start leveraging untapped insights.