How Phospho gives AI scaleup companies actionable data about their users

Crazy news guys, we’ve just launched a startup program for AI founders (the perks are crazy).

You can get $2000 worth of credits (Anthropic, Mistral, OpenAI, and Phospho) + a call with our amazing team to guide you in your product-market-fit journey.

You can apply here.

For scale-ups, growth is often the north star guiding every strategy and decision. However, amid the rush to expand, a critical component is often overlooked: the insights derivable from your own internal data if leveraged properly.

The Importance of Actionable Data for AI Scale-ups

A company cannot scale effectively without a deep understanding of its users and its data to derive actionable insights for sustainable growth… unless you want to rely on luck or sheer trial and error, but we don’t always have the runway for that.

According to a Forrester analysis, on average, between 60-73% of all data within an enterprise goes unused for analytics. Simply put, many companies struggle to convert data into meaningful intelligence or actionable insights, but the real issue is this puts you directly at a competitive disadvantage in the market against those who can. This is because it prevents you from capitalising on opportunities, and slows down the time to product market fit.

But early stage scale-ups struggle with limited resources and data processing capabilities at scale. This is where AI can be used to leverage internal data more effectively. It’s not about finding a silver bullet that leads to an explosion of revenue or users, it’s more about continuous deep insights to help iterate more rapidly and improve products.

The Evolution of Data Collection: From Basic Analytics to Interactive AI Layers

Traditional data analysis relies on static metrics and dashboards composed of visualisations. It does have reliability from its tried and tested history but its limitations become very apparent when faced with the volume, velocity, and variety of modern data. The manual nature also poses big bottlenecks to scalability, but more specifically, cripples the speed at which we can make decisions and respond to market changes which are quick and frequent with AI.

We might not all be fully aware of the significance of neglecting AI when considering the world around us right now, 76% of SaaS companies are currently using or exploring the potential of adopting AI.

The data collection methods we’re used to with static metrics (page views, session duration, conversion rates) and basic analytics dashboards fail to paint a rich picture, or capture the nuanced user interactions that come with interactive AI layers within most apps today. In other words, it’s not effective for driving effective product improvement.

Unlike traditional data gathering, AI data analytics is more than just numbers. It’s enormous amounts of unstructured data that’s translated into actionable insights in real time. This allows us to operate and answer questions at far greater speed which is essentially the foundational leverage behind faster growth today. Traditional analytics answers “what”, whereas AI analytics answers “why” and “how” at scale.

Most AI integrations often facilitate a conversational user experience, comparatively speaking, ignoring the underlying feedback and understanding of user interactions directly with your product would be like ignoring surveys and interviews. What better feedback than real-time verbatim in-app conversations? This is of real benefit to scale-ups that utilise AI analytics so they can leverage this user understanding to continuously refine and optimise their features and offerings.

What’s interesting is the varying ability of scale-ups for competent data collection and interpreting has itself become an added layer of competitiveness.

Data analytics proficiency is now a big component to the success of a scale-up against competitors, this demands more sophisticated tools with AI capabilities that can handle more complex data in real time and ultimately provide better quality of insights.

With AI analytics tools like Phospho that integrate these AI layers into its platform, you’ll have real-time data and actionable insights to help you correctly identify customer problems, prioritize initiatives, and streamline iteration cycles.

If you’re a scale-up and want to understand your users closely to optimize your LLM app for faster product market fit, sign up here and try out Phospho for free.

Three Steps to Using Phospho for Actionable Data Insights

Now that we have firmly established how proper data collection and interpretation is a legitimate competitive advantage, let’s look at the practical steps in deriving actionable insights with Phospho for user aligned iteration and faster product market fit.

Step 1: Monitor User Interactions in Real Time

User interactions are a rich pool of data we should tap into, especially for apps that integrate conversational AI interfaces.

With Phospho we can automate the monitoring of these interactions in real-time and get an immediate overview on user behaviour.

By logging these user interactions, Phospho allows teams to tag and annotate (without code) individual interactions to link context to specific user needs that creates a clear feedback loop for continuous improvement. When tagging and/or labelling interactions as ‘success’ or ‘fail’ you are also training the model with your own feedback so it constantly improves itself over time for better ongoing analysis.

Step 2: Learn from the Data to Uncover Actionable Insights

Collecting user interactions is one thing, let’s take this feedback loop a step further. Let’s say you derive some insights from your data collection and you want to test some assumptions.

With Phospho’s custom KPIs you can set your own metrics to track and ‘flag’ for automatically. It’s like having a full time data analyst watch your user interactions, alert you to prompt investigation, and share insights based on your specific criteria. The flexibility to set your own metrics that are directly applicable to your assumptions or company goals, ensures your data gathering is highly relevant and actionable.

The value behind flexible, real-time nature of monitoring user interactions as they happen, is that scale-ups can now quickly target and identify patterns, edge cases, and any potential issues so they can act on them more proactively.

By obtaining highly relevant data based on specific needs, scale-ups can prioritise the most effective areas of their app to optimise for their users and ultimately scale faster.

Step 3: Evaluate and Improve Your AI Models Continuously

By continuously monitoring and evaluating your product’s performance with custom KPIs you can also fine tune your AI model and train it with iterative deployments and automatic A/B testing to see which versions perform the best for your users.

With Phospho’s automated A/B testing you can comfortably iterate and test multiple different versions of your app to see which ones perform better with your users. The rate or speed at which scale-ups can do this AND understand what “works” best with their users conclusively with data, IS the competitive advantage in today’s saturated AI app market.

If you’re a scale-up and want to leverage deeper user understanding for faster growth, try Phospho for free by signing up here.

Conclusion: Empowering AI Scale-ups with Actionable Data

The value behind actionable data is the insights we gather can immediately inform our decision making. The efficiency at which we can iterate with real user needs will mean faster market entry and time to product market fit.

That’s why we’ve specifically designed Phospho’s features to provide this comprehensively for AI scale-ups so they can easily leverage insights from their users to fuel highly effective iteration cycles.

To start taking advantage of your own untapped data, try integrating Phospho into your AI app by signing up here. We have a free tier and it’s really easy to integrate with all the popular tech stacks.