How to launch an AI SaaS in 2024

It’s no longer just a trend to integrate AI into SaaS products anymore, especially now with most apps offering AI features and more everyday users setting them as standard expectations.

The impact this has had on the overall quality of SaaS products can’t be overstated. It’s ability to learn and adapt to provide more personalised experiences has made it a necessity for apps launched in 2024. That’s on top of the automation of workflows and real-time advanced analytics that further streamline the development and iteration process.

However with big promises come distinct obstacles, AI native SaaS is actually harder to differentiate and successfully launch compared to normal SaaS because of saturation from the lower barrier to entry and standardisation of backend LLM integrations.

Only a fraction of people with the right data-led approach will succeed. In this article we’ll be going over most strategic approach to leverage data easily and successfully launch your AI product. We’ve broken it down into 5 actionable steps

1) Conduct Market Research

This should come as no surprise but every SaaS needs to solve a real problem, so we recommend starting somewhere you are well positioned to apply a solution. For example an industry you have experience in, workflows you have experience of and understand really well, or challenges in your own day to day.

Once you have a problem we need to prioritise identifying our target market and understanding what their needs and pain points really are through market research:

Actionable steps to find target market:

• Create organic content around your identified problem and analyse the demographics it resonates with (Highly recommended as its free to reach thousands of people)
• Engage with different personas of potential users on social media (recommend LinkedIn)
• Network with relevant people in the space (in person events and online)

Actionable steps to find pain points:

• You can try to isolate pain points by analysing market trends and popular searches around a problem space or industry with google trends and free SEO tools
• Engage on social media platforms and forums about pains and bottlenecks (tip: use AI analytics tools to gather insights from online conversations)
• Analyse direct and indirect competitors in your space to find gaps in their offerings you can capitalise on and to find opportunities to differentiate your SaaS

Ideally we want to be working with as much data to inform our assumptions around our problem space which helps in outlining a compelling value proposition that would resonate with your target audience.

But equally as important, this stage is all about validating hypotheses we may have about the market. It’s crucial that we develop a SaaS with this understanding to build with the most confidence in addressing a problem and to also minimise any wasted effort when developing our MVP.

2) Build a Minimum Viable Product (MVP)

When building an MVP, the importance of assembling not just a team but a diverse one with developers, designers, and AI-proficient individuals is often understated. Covering each other with different strengths and areas of expertise is really high leverage to develop your MVP fast and generate ideas that challenge assumptions from different perspectives.

An MVP is basically a very lean representation of our ideated solution that we can use to confirm our confidence in building a product that people actually need.

Using our market research and validated assumptions we can lay down a list of only the core, prioritised features that address the primary problem and its pain points to include in our MVP.

We gain more confidence and insights into the direction our SaaS needs to take with MVPs to allow for early testing, data collection and establish feedback loops to inform our iteration cycles and further guide development as best we can.

With the ready availability of AI tools on the market right now, one of the best ways to leverage rich data is through AI analytics platforms like Phospho which can be easily integrated into your tech stack. Basically you can use Phospho to extract insights from user interactions with real-time monitoring and setting up custom KPIs to automatically ‘flag’ for if triggered. You can also A/B test different versions of your SaaS to see which ones perform best.

This provides a huge pool of insights to offer data-driven iteration cycles that are directly in line with what your beta testers or early users are really trying to accomplish with your SaaS. This data can also help identify any bottlenecks in their user journey from a very early stage to address before a bigger launch.

To leverage data like this to fuel more effective iteration cycles, sign up here to try out Phospho and see for yourself.

3) Develop a Comprehensive Business Plan

As builders and creators we dread the thought of writing up a business plan but it’s important so let’s quickly go over the key things we’ll need to consider:

Monetisation strategy

Here we’re trying to outline our potential revenue models and financial projections. The most common tried and tested pricing models include:

• subscription based
• freemium
• pay as you go
• per user or per feature

As for financial projections it would be wise to leverage your market research again to consider the size of your addressable market when calculating these figures. There are many templates online you can use to help but ultimately it boils down to estimating the operational expenses and weighing it against the estimated number of potential users and expected growth rates. This helps in setting realistic sales forecasts.

Marketing and Customer Acquisition

One of the expenses we’ll have to consider as mentioned above is the variable costs that come with marketing channels, customer support and hosting solutions that will have to scale with revenue and number of users.

Optimising the cost of acquisition (CAC) and improving customer lifetime value (CLV) are key metrics you will have to implement a strategy for in this stage as well. For example, things like organic content marketing can drive down CAC with less reliance on paid ads, and extracting more insights from product usage with tools like Phospho can provide more personalised user experiences to reduce churn and improve CLV.

It’s important to have a clear plan to accommodate any influx of user adoption with your SaaS by implementing scalable hosting solutions that can handle any growth and performance needs. There are providers with flexible play as you go models or ones with the ability to easily upgrade plans without significant downtime. It’s a question of choosing the best option for you based on predicted user growth or resource requirements.

Don’t be too harsh on the accuracy and veracity of your figures in the business plan as we can only estimate, as long as they are justified by market research and any data you can obtain already it’s reasonable enough to understand your basis.

4) Launch and Acquire Initial Customers

Before launching anything it’s useful to consider whether you want to release in beta or wait until you can be sure of its traction (based on data and metrics). Launching in beta can be riskier because it’s less ‘fleshed out’ but it also means you can test different marketing strategies before a proper finalised release later on.

Best practice for successful launches now demand a serious multi faceted approach - a combination of highly targeted promotional tactics and free sign ups to incentivise adoption. Read our previous article here where we go into launch strategies more in depth.

Let’s start with marketing, effective marketing strategies will depend and rely on where your target market shares most of their attention and investing there. Understanding your target demographic of users will help to craft targeted material on these channels that catches their attention and ultimately create buzz and attract early adopters.

Example channels: social media, email, blogs, forums, webinars.

Another thing to consider is free trials and freemium plans to incentivise sign ups and lower the barrier to entry. Users are obviously more likely to try something if it’s free but it keeps the door open to upsells with premium features. Users who have experienced the value in your SaaS already are much more likely to convert to paid plans and become paying customers.

The freemium model also provides a way to get a larger number of people to try the product and test product market fit by getting more user feedback. Gathering as much data and feedback as possible is paramount to the success of a SaaS let alone its launch as it provides the insights to refine the product and ensure it is of value to the target market.

So how can we do this effectively? Let’s look at using Phospho practically to gather insights from user data to improve your AI SaaS with more effective, data driven iteration cycles.

5) Analyse user Data to Improve your AI SaaS

We mentioned at the start of this article the importance of having the right approach to launching an AI SaaS in such a saturated and competitive market. One of the key factors is leaning into as much data about your users as possible to provide an experience tailor made for their specific needs, one that can’t be matched by competitors. The speed at which you can get closer to this ideal user experience is where the competitive edge is for SaaS products.

Therefore, it goes to conclude that the market belongs to the teams who can best understand their users and apply these insights into targeted iteration cycles quicker than their competitors.

But to get these insights in the first place you need to understand on a granular level (in-app) what’s working and what’s not. At Phospho we provide a text analytics platform that helps monitor, optimise, and iterate AI products by extracting actionable insights from user interactions to guide more intentional iteration cycles that help you retain and attract more users.

Here’s a non exhaustive list of Phospho’s key features:

• Real-time monitoring of user interactions lets you track and log user inputs to identify issues or trends as well as continuously fine tune the performance of your LLM app.
• Automated insights extraction and KPI detection so you can create your own KPIs and custom criterias to ‘flag’ for, and you can label if it was a successful or unsuccessful interaction.
• Continuous evaluation and iteration support. You can use our automatic evaluation pipeline that runs continuously to keep improving your AI model’s performance.
• User Feedback Linking: collect, attach, and analyse user feedback in context to make targeted improvements toward overall app performance.
• Easy Integration: simply add Phospho to your tech stack with any popular tools and languages like JavaScript, Python, CSV, OpenAI, LangChain, and Mistral.

Having a clearer view of customer behaviour, where people are getting stuck, or what actions users aren’t taking with product usage data is key to evaluating if a product is truly “working” and which areas really need the most focus for further optimisation.

If you’re building an AI SaaS, sign up here to try using Phospho for more intentional iteration towards what your users really need.

Conclusion

AI has been a game changer for many industries and as we can see it’s no different for SaaS. It’s presented more opportunities for faster, streamlined development but also opened the door to saturation by lowering the barrier to entry.

This democratisation is great for building software products, but to launch a successful AI SaaS now requires a strategic approach that leverages the best data to derive actionable insights into faster more effective iteration.

The speed at which teams can do this will determine how effectively they compete in the AI SaaS market and for the most leverage you need AI product usage data from tools like Phospho.

If you want more data driven iteration cycles for faster market entry and stickiness, try using Phospho by signing up here and testing it out on your app. It integrates really easily with all the popular tech stacks!