Using AI to build CRO-ed SaaS Websites

Okay, we are live, five minutes later than expected. We were trying to fix some challenges which are now fixed. So it is perfect. We just wait for our first few viewers to join in so we know that we are actually live. Next time, one of the things that we should also do is we should just do cold open. So like just continue to have our conversation or just directly go live.

We don't have to wait and have an awkward silence and see if, okay. So now, see, now we have some people. So we know that we are, we are truly like, but next time we'll just do this cold open. think that's better, but let's, let's begin for today. Hello and welcome to Momentum Officers. My name is Yash and I'm joined by my co-founders Jay and Koushik to discuss topic of the week using AI to build high converting SaaS websites.

Our goal is to provide you with actionable insights and practical strategies that you can apply to your own business. Throughout the session, we encourage you to engage with us by asking questions and sharing your thoughts. This is fantastic opportunity to learn from each other and gain new insights that can help drive your digital initiatives forward. Let's get started. Jay, Koushik, how are we doing? We're quite great. Busy week as always, but new things coming in, so yeah.

Zeeveek, Vijay faced a new problem that we didn't have before, was that we didn't have a place to sit, which is a good problem to have. So we have more people than we expected. Our team is growing and it is causing minor inconveniences in terms of finding places.

These sort of inconveniences make us proud. that's good. How are things with you, Koushik? things in Bangalore? Nice. It's been raining here. Like in morning it was raining, as it was raining. It's raining here also. It's raining heat. Heat. I know. It's raining only. Different kind of rain. So it's Perfect.

Tell us, Koushik, what will we see today? What is CR code? Conversion rate optimized. But why do we need AI to build SaaS websites? SaaS websites have been doing amazingly well before AI came around as well. Why do we want AI to get into SaaS websites also? Yeah. So one bigger factor, the internet is changing. the way things have been. So we have multiple ways in which people are trying to search for a particular

product or things. From the stage of people being able to Google, people are using something like Perplexity or even ChatGPT for that matter to look out for what they need because they think that it's much more faster and easier for them to reach out. And then whatever the websites that come in from that answer, that becomes as a reference. And from there, they go to the website and check it out. So the entry points at which

people were previously trying to enter from are changing significantly. And it is only going to more change and more different going forward. So that is one of the primary reasons. So the entry points are changing. The behavior patterns are also changing. The arrival of chatbots have also made it very different now. So almost all websites are trying to have their own vertical chatbots implemented within the website.

such that the users can engage with it and converse with it. Now what this does is that previously, the design of a website is always in this pattern where it is the scroll that tells the entire story of what we are trying to achieve. And it is also the metric which we use to measure the success of the clicks, the places where the users traveled around, the flows, the pages visited.

All these things were the data. Now suddenly the chatbot comes in and they just ask the thing that they want to know and it just fits out that specific answer. So what sort of patterns are there within these chatbots also that needs to be tracked now? So you need to have a tracking pattern and metrics that are associated with your chatbots too. So these sort of patterns are something that are new and that's exactly why we need to have this kind of this.

Interesting. Jay, what are your thoughts? Yeah, mean definitely a lot of things changing and so it's very it's always great to you know have a guide on what should be the actual process of going through it because the way people are taking things with AI it's always about you know trying to figure their things out but if there is a way where you know they can navigate through and they can just optimize their overall processes. Exactly.

What should be the first step, Koushik, when someone is looking to just optimize their website? I mean, there are lot of parameters to check upon for sure. Is there a way you can give a microscope if you want, how to get things started there? Yeah, let me share my screen. I just had compiled together as I said. Do you see my screen? Yeah, I brought it here. Yeah, just a second.

Basically, the way we could think of this is that, yeah, it's full circle. That's how we know that Koushik is a UIUX designer. He did a presentation in Figma. In my defense, this time it's a little better because it's Figma slides. Oh, okay. Figma came out with the product. They must be a lot of people like you. Yeah, that's right. So they thought that all these designers are doing it Figma directly. So they came up with Figma slides. Actually interesting. Like this is the interface that they have. It's cool.

You can add a slide, create a slide on it. So first thing is that the usual way that we currently are doing using multiple different platforms is we try to capture the behavior. We do visitor segmentation based on the behavior capture. When I say behavior capture, I'm talking about clicks, scrolls, dead clicks, all sorts of behavior that is happening within the website and across all the pages that you currently have. And then based on this, we usually do the visitor segmentation

based on the intent and the journey stage that that particular visitor is having. So we try to categorize the visitors visiting each pages. Based on the visits for the relevant page, we try to classify them as one intent group. And based on the clicks, we try to classify them as different intent groups. So that depends on website to website. And also, we would also want to understand what are the drop-off indicators that are there across these pages.

And let's say, and across, like for example, the drop off indications that are in my homepage versus the drop off indications that are on my product pages or feature pages is something that needs to be studied because these are the major pages that we want the user to click on and to get converted. So these three are usually the things that we currently use different platforms to analyze. Then what we need to do.

is that now with emergence in AI, we can feed these data to a machine learning setup and we can start creating a forecast of what could be expected further in next few coming days or months or weeks. now this, so we have data of behavior capture, visitor segmentation and drop-off signals over a period of.

And based on that, you can create a forecast of the upcoming period of time. So that is one interesting way in which we could use AI. And that is a machine learning part that is there. And then we could also start asking AI for recommendations and fixes in ways how we could do it. So one thing that AI is very good at is almost all of the LLMs are very well trained upon structured data and unstructured data.

So the suggestions of recommendations that it could give will be much more better and accurate because it is trained on the internet. it has access to good websites as well as bad websites at a larger scale. now the first three would build the base for us to be fed to the AI and then AI can give you predictive analysis and recommendations.

and also fixes and recommendations with respect to individually at section wise how you could correct things. So this is it. Yeah. So like at what scale would you recommend this as an approach? I'm assuming not zero to one for sure. So like if I'm just starting my SaaS company and if I'm creating a website for that SaaS product that I'm building, generally I'd use a theme or a template or something from.

marketplace and it could be like a web flow marketplace or a wordpress marketplace or framer or whatever ecosystem that I'm comfortable being a part of. So definitely not zero to one but for any LLM to understand and capture behavior, capturing behavior can happen at any scale but segmenting the visitors, indicating drop-offs, predicting paths and all of that and what scale of visitors or at what size of

use it, does it make start to make sense that this is something that you should try to do. So I think at a very initial zero level behavior capture as well as drop off identification is something that is for sure you need to do. Visitor segmentation is something that comes later but just based on these both data what you could do is that asking AI for

UI or UX recommendations to be fixed is something that is possible, even at that level. For example, could create a... So I can give you a very quick trick of doing this. There are multiple AI-based tools also who does this, but apart from that, for example, let's say if you have ChatGPT Pro with you, could create projects in your ChatGPT, and you could feed the behavior capture data and the drop-off identification data.

in the project as the knowledge base. knowledge base is what you're trying to do using a project in OpenAI's Chalgibity is that you're fine tuning it at a level. So you're just customizing it, making it first refer to the knowledge base that you're giving and then using the rest of the tools it has. now then what you do is you give your all the pointers or checks that you need, you think that are the right things.

For example, let's say that you wish to have a sequence in which your user should scroll. For example, what is it? How does it work? What information does the user need to take action upon? what is the combination of CTAs that needs to come in and all these things? And now feed it to the knowledge base. And then give your website link to the AI. And then

After that, the chat, if you ask it to analyze this and start giving recommendations, what it does is it does two things. One is it first refers to the knowledge base that you just gave it as doc. Then it goes back to the internet and collects the best set of or the best practices and then compares both the data and applies it on your website link, analyzes it, identifies loopholes or gaps, and gives recommendations accordingly. So this is one quick way to.

do it when you're at a smaller scale. And is it possible to make sure that AI is only looking at certain part of my data and not other parts of my data? So I'll give you an example. Because if I'm running ads, so if I'm running, let's say search ads or if I'm running meta ads, retargeting and stuff like that, the behavior of people who come to the website through those ads are

fairly transactional in nature, most likely. However, the behavior of people who are organically coming to the website for reading an article or getting a resource or an ebook or something that we put together, that would be very different. And so we don't want the whatever AI engine that we are using, for lack of a better term, I don't know what to call it, but whatever AI engine that we are using, we don't want it to get confused and give us

insights across both different behaviors because the add piece is that we know that that behavior is very different. is there a way to like segment first segment that this is the organic and then just look at that and give me all the other things like look at that and give me path predictions and stuff like that. Yeah so basically what happens is that basically what happens is that the knowledge base that you are adding the data to it

as you keep adding it, what's happening is that the LLM is trying to segregate the knowledge base also or try to analyze the data also within the knowledge base. Now within that, it has all of them as data points. And in the chat, when you specifically engineer your prompt in such a way that pick only this data or specifically when you mentioned it only picks up that data. It doesn't go to the all the other data that is there. So it is the skill of

engineering the prompt over there that comes up and how you are specifically asking for what you need, where to pick from. That's exactly why I asked to create it like projects because then you can very specifically create it. Or what you could do is that you could create another project and feed only that data and ask that question also and you could still ask AI to refer to the different project that has been created as a reference.

So you could do that a combination of projects also. So that is possible. That's interesting. So Koushik, I had a question with respect to optimizing websites, lots of marketers are already used to platforms like GA4 and understanding and doing some analysis. Is there a way where, let's say for instance, let's talk about GA4. Is there a way to integrate that with any LLM and just take?

further insights from it in terms of better predictability or doing some A.I. testing on what I would change in form of my content or my design and that could lead to a certain amount of behavior. Is there a way to do that? Yeah. So most of the A.I. testing platforms now already are having A.I. within them. That is one point. But still, if you have any other A.I. platform that you're currently using and based on that, you want to

connected with an LLM, it is completely possible. In recent updates, we now have MCP, which also does the same job. To give you a very simple idea on MCP, we'll have a much more longer discussion on MCPs coming in the future. But MCP is basically API for all APIs. So it connects with any tool that you need with the LLM. So that is one thing that both

as well as open air currently working. So that would help in larger level. But adding to what you just mentioned, Jay, is that previously these platforms were just giving just the data. Now people start seeing more statistical power and more use case with respect to how the statistics can be used further for forecast as well as

it would start recommending you multiple AB options for you to take up and go further. So those sort of improvements are something that you could that you will start seeing further in the future. But yeah, this is mostly with respect to testing as well as connecting them with the LLM. This is one way of doing it. This is how currently those platforms are also doing it. How when they have AI within their platform.

So adding to one more thing that you mentioned about design optimizations and strategies, this is something that AI can actually do much better. In fact, one thing that recently that was going strong in internet was there is a Chinese based AI company which can create all Figma designs directly. there are there's also cloud, are certain designers in certain companies and YCB back startups.

who design in cloud. They give prompt and design. So now we seeing a lot of involvement of AI within the design optimization and strategy with respect to executing the design itself. So with respect to that in mind, like you mentioned, we will start seeing adjustments within the design tokens, like for example, the buttons, the color of it, which color is more, you could create testing within the color variations or the color variations that is available.

Because if you think of a website, graphics is a critical part of a website's conversion rate. let's say if you have a low contrast background and the contrast ratio or the contrast ratio between the button upon the background is poor, it actually leads to conversions. So which better contrast ratio leads to better conversion is something that AI could recommend you. So you could think of

AI being on a company level here and trying to suggest the designer on which leads for better conversion and design accordingly, which is not the case with whenever we design insights usually, right? Like we usually, there's no AI that is aiding me and telling me database insights while I'm designing. So now that will, that is something that we'll start seeing more.

One of the other challenges that I've seen a lot of other SaaS founders face is also maintaining, how do I call it, design sensibilities. So I'll give you an example. So as an example, for a website, so if I have a SaaS website that is targeting, let's say, procurement teams of manufacturing companies, versus if I have a SaaS website that is

targeting early stage career upstarts. So most likely, people who are in their early stages of their career journeys versus procurement teams of manufacturing companies. The design that my SaaS website needs to have is very, different. And what is happening today is that the person who's looking after the website is the person who has that sensibility.

and that sensibility has developed over a period of a few months or a few quarters. When that person moves out, that sensibility also goes away. I believe that the answer to maintaining that sensibility was like having brand guidelines or a brand thesis or something. But that is not true. So can AI help in that? Can AI ensure that every new thing that we are designing?

for the website has, I don't even know whether sensibility is the right term, but I am not sure that I'm able to convey the question, right? but can I make sure and check that every new webpage that is being created for whatever purpose that it is on the website has the same sensibility as the existing pages, right? So, as an example, if all of my existing pages have rounded corners versus sharp corners,

for images and graphics. another example is going to be, let's say, if all of my other pages that I've designed have photographs instead of graphics, then my new pages should also have photographs of real people and stuff like that, which is not necessarily part of a brand guideline, but it's just something that has to be maintained. Can I help with that? Yeah. So currently, there is no product that is

The closest that I could think of is in Canva you can create brand locks. It's like brand control where it's only the brands that you have fixed.

is what you could use. We can create those. But what we're trying to talk here is a vertical level AI agent or a chatbot, more of an agent, that when the designer is designing in Figma or anywhere, and when he tries to publish it, that published item is being analyzed. And if it is crossing the brand guidelines, it needs to a list of things that the designer has violated.

or crossed and then asked him to correct it. So it's almost like an accompanying agent along with the designer. Now this could be a UIUX designer, this could be a graphic designer because what you mentioned is a very genuine problem and it's a bigger problem as the scale becomes bigger, right? Yeah, it doesn't get solved with scale. This actually escalates with scale. So what if the one way to do it is you

create a fine-tuned AI assistant which is trained upon your brand guidelines. then, which is one factor, then whenever a designer creates any design, it could be a graphic design from the stage of a graphic design till a screen that is being created for the company. All of them go through a publishing system and then the published output is being analyzed and then wherever it is being violated, like for example, the corners, the colors.

the graphic styles, the image styles, the graphic styles, the sizes. Very often you would see layout changes, inappropriate layout change for the use case. For example, someone wants for LinkedIn, but they used it, different layout for it, not suiting the place where it needs to be published, those sort of problems.

So all these things could be considered and given as a recommendation and put a check on the design team overall. That is always possible. But it also comes as a perk. It's a luxury at a scale again. Probably a company that is at a very initial level might not be to do it. So that makes sense, Koushik. So I had a question with respect to another tool.

We talked about how chat utility could be helpful. We also talked about other models which could be helpful for a specific use cases. talking about SaaS websites, let's say for a company which is growing, let's say two-year-old SaaS website, two-year-old SaaS company who want to revamp their website and want to make sure that certain things are well set. Is there a dedicated tool which they can utilize where all of these things, when we talk about like

all the predictive analysis, all the AI power here. Audit is also done, and more suggestion is also given on how to optimize the same. Is there a dedicated tool where these things can be placed all together in a single place? And that's the only one way where you check things out, right? Just like how, for instance, just like so far, how the process has been is one checks about which pages are good through GA4. People check on heat maps.

through Hotjar or Clarity and other platforms. All I'm trying to ask is if there is any one tool where all of these things can come together and also what next steps to be taken in the existing set can also be showcased. That is a great question Jay and before I ask, before Koushik answers it, I'd like to take a quick stab at it and for all the people who watching it live, believe you me, this is not a setup. So Jay, like we didn't.

ask Jay to ask this question. This is just a natural question that he has by himself. But so Jay, one of the things that we will do, you know, along with this office hours and going forward also for all the sessions that we do is one of the recurring requests that we have is we talk about a lot of things over here, but they are not very documented. if someone is consuming this video after like a while, like not live, but after a while, they need to be able to have the list of things that we that we've spoken about. So

Over the next week or so, we'll be publishing a list of like 10 or 15 tools and platforms that you can use that are AI platforms that can help you with everything that Koushik is talking about, as essentially as an annexure to this conversation. So that we will have for sure, which will be like a really long list of things that can be used.

I just wanted to add that but Koushik over to you, you can say the tools if you know. Yeah, so Jay like you mentioned, if you ask me if there is one single stop tool, there isn't one. Hotjar and I'll tell you why also. Hotjar as well as Clarity and all of them are trying to come up with AI within them but we are seeing more of predictive analysis and recommendation AIs within these platforms.

We haven't seen a use case where there is a GenAI application, but we are seeing them separately. is not like we are not seeing both of them combined together. Like for example, I'll give you a new case. for example, let's say Hotjar has identified a particular, it has some AI recommendation engine which identifies UI based or UX based loopholes or gaps and says that this particular thing needs to be corrected.

But it doesn't generate a copy for you or generate a token or an item or a design component for you and just ask you to replace it. So the GenAI as well as the recommendation engine AI or the machine learning is not coming together combinedly as of now. But I think that is something that they're working towards. if you see on other sites, there are a few web flow based plugins. There are also a few Figma based plugins.

which are more JNI focused. Like I could create an entire design and I could select the entire page that I just created and give a prompt of what style it is and what am I aiming to and what is the tone of the copy that needs to be. that automatically the entire copy for that particular page could be written. It's all AI generated copy again, but still it still finishes up the entire copy.

and I can keep a trade up on it and work on it. Same works for images also. Within the figure while I'm doing the design, can generate images and I can just add it accordingly. So all these JNA components are there, but both of them together combined based on the analytics or the forecast that it has done, a very informed JNA output coming out of it. This combination is not as yet come together. So, but it would be great to have.

see any of these products start having both of these components together.

So actually one more pointer that I thought I'll be discussing is the chatbots thing, because it is one of the key things that are growing faster. So one interesting ways in which you could take advantage of the AI chatbot race or the time, whatever you could call, and have a more vertical chatbot built for your website or your product, and is that?

One is with respect to the lead qualifications, how based on the chat patterns that is happening between the user and the chatbot, we could study that behavior and we could convert based on that we could create intent classifications of the people who are trying to reach the chatbot. One way is using proactive prompts like if a particular

user is using a particular word or a keyword or a prompt, then it would trigger an indication saying that this user could fall under this particular category of intent or anything. So this is one way of how you could track a keyword-based track or a prompt-based tracking while the user is interacting with the chatbot is one way for you to do the lead classification or the qualification process.

To be very honest, Chatbots actually made it very easier compared to the websites for us to arrive at the how good is this particular lead for me to reach out to such that they could convert or use the product or the service. Because they are definitely asking for one item and Chatbot is trying to give answer definitively for the second item. And all these things, all this behavior and the

outputs that you're generating out of these behaviors could be integrated and with your CRM and the CRM will again be used by the chatbot itself to do continuous learning for it to keep updated. So it that it knows that, fine in my CRM, I could see that these are the regular kind of people that have that are of use. So then I start looking out for those kinds of chats that are happening within the system.

We are constantly seeing fine-tuned set of chatbots. We are also seeing more RAG-based chatbots. So, RAG-based chatbot is where it is not just the website information that is involved. Like, let's say if you are an insurance company, right? There's a lot of information in the website, but there are also a lot of information that is not on the website. That is probably in some white papers or something that you have published or some documents that you still have within the company. You could train the chatbot.

On this particular document data also, when a user asks very niche and specific questions to these chatbots, they can refer to these both and give an answer. So yeah, that's one pointer about chatbots, is the trending and the rising asset.

Awesome, I think this is a great thing to put together. This should be helpful for a lot of people and as I already mentioned, one of the things that we are going to also do is we are going to put everything that we have discussed in a well documented fashion on our website and we will add all of those things as links in the stream as well.

So that way that also will be helpful. But thank you to everyone who joined in on this conversation. I hope this conversation around building conversion rate optimized SAS websites has been meaningful and helpful for you. We always try and come back every week to sort of put together everything that we are learning and everything new that we are also coming across in the space of AI.

AI as well and we will see you in our next stream and before I let you go, one of the things that I have to always mention and this is a mandate from the marketing team is ask you to subscribe. If you subscribe, if you leave a comment, it will be like giving a high five to the algorithm. So you can give me a high five and thank you for giving me the high five. But if you like the content that we are putting together,

do subscribe so the algorithm also knows that you like the content that we are putting together. And so your future self will thank you later for subscribing to everything that we are bringing to you every week. But thank you again for joining in and we'll see you again next week. Until then, bye bye.