No-Code Programmatic Pages for SEO and SEM Purposes

Hello and welcome on Productivity Conference. Today I have the pleasure to speak to you about no-code programmatic pages for SEO and SEM. But first of all, let me introduce myself to you. And so, my name is Bożej Abel and I'm a CEO and founder at Lendingy.

Feel free to connect me on LinkedIn. Here you have the QR code so after the presentation, you can shoot me the message or ask some questions. So I can help you with your ideas. Besides being a CEO of the company, I'm also the leader of the growth team so the case studies I'm presenting today will be something that I'm involved in on a daily basis.

The short agenda for today is the two case studies. One is about Lendingy and how we use the programmatic pages and the second one is the SES gaming group which is more about ads and SEM use case of programmatic pages and a short summary.

So first case study about SEO. Let me tell you about this and give you a short background. It's not about selling Lendingy. I just need to give you this background to let you know what the problem we had and how we tried to solve it. So Lendingy is the B2B SaaS working international and we are delivering landing page platform for marketers and business owners. So you can imagine that there's a lot of similar solutions on the market and this is a very crowded space. So there's a lot of challenges in the marketing team and how we acquiring customers because for us something that is very important is to generate more and more signups because more signups means more sales for our company and of course we are in a red ocean.

The Martech space in the marketing industry is a red ocean. There's a lot of solutions as you can see here. I checked G2 and you can find two hundred thirty three landing page builders or landing page solutions on the market. So we need to compete with a lot of other players on the market to find our customers and because of that we try to find another idea how we can generate more leads for our business and without engaging too much people from our team.

So we try to somehow hack how we can get more signups and the signups are coming to us from the high intent keywords. By high intent keyword I mean this is a keyword related probably close to the transaction or to the signup. So we have four groups of this type of keywords. We have the brand keyword which is obvious. If someone is looking for landing you probably will sign up. Of course everything that is very related to a landing page and two other phrases that we know that are converting very well are phrases that are related to alternative to as well as versus because it means that someone is already using a landing page solution and trying to find something that fits better to their business.

So we have a different type of those matches. Of course we are covering this with content but preparing those alternative blog posts is a very resource consuming activity because we have a team that is in the research we are trying to find with whom we should be compared. We need to check what's going on on the website and trying to bring a huge value to the customer. Because we have a such a lot of number of competitors on the market it's super tough to try to cover everything. So we also try to find the new way and this easy way to do this. So we dig deeper into Ahrefs and the basic idea that we had it was to be a third and meaning to be a third is that there's a lot of pairs where customers trying to compare two players on the market and we figure out that we can build the programmatic pages for many of businesses that are in our category where we compare them together but also suggest Lendingy as a search solution for consideration. And that was the basic idea.

So how we execute the project. The execution was divided to the few steps. First of all we had to prepare the data. So we used four different type of sources of data to help us understand how we should this data prepare because to build the programmatic pages first you need to have this data that you will put into the placements in your page that will be in our case comparing two solutions to each other. So we have our own database about our customers. Lendingy is over 10 years on the market. So we have plenty of information about our competitors. As I mentioned we are also describing Lendingy and alternative to other players on the market. So we have a lot of our own content where we already did the research. We also have the public listings like G2 and Captera to understand who is our competitor and what are the secret sauce to be unique. So there is a lot of information. Of course there is the AI to GPT or other AI agents where you can ask for the same type of information and try to find the comparison of those solutions. So we tried to gather all this information and also we knew that it's not possible to cover all 233 solutions.

So we tried to figure out with whom we should be compared and what kind of purse we should compare. We prepared and of course the good old spreadsheet comes here because we had to prepare a CSV file where we decided what kind of information we will be comparing and what players will be comparing and build the one huge spreadsheet where we organized all this data. So as you can see here we have the each row means one lending page and in the columns we have variables that we put to the placements on the design that I will tell you shortly.

So because we, as a final decision, we picked 97 vendors and being honest we didn't think about how many combinations this will bring to us but we just focused to gather all this information. But trying to compare all 97 vendors to each other, it means almost 5000 combinations because there is a math behind this and you can see how it's working. So we were surprised by the number but we don't worry about that because we knew that this project will be programmatic so there will be not a lot of human resources that we will need to engage to build this huge number of pages about comparison.

So once we prepared the spreadsheet we moved to our builder and in LaDingie into no-code way we prepared a design. What is important here, we built a template where we put the placements for these variables that we have in a CSV file so we can plan what type of content and how we promote this content in our design. Something that was important preparing this design is we would like to be so close to our blog post and we would like to give the experience of building a webpage, not necessarily a landing page or something that looks more like ad. It should be a true content. So we used our navigation elements as you can see here as well as the pages built with the hero section, the content section, and the CTS that I will present you here.

So you can see that there is some kind of information where we compare two vendors as well as we put the placements and this is this being a third where we compare, where we suggest LaDingie as an alternative to those two players we are comparing.

So once we prepared everything we used our programmatic solution, we picked the template, we upload the CSV file and generate pages. Of course, it won't happen with the one move because the number we prepared was so huge we had to divide it into a couple of some kind of loop of doing this until we built all those landing pages. You can see here that once we generate those pages they already had the URL, they were published and ready to use and here you can see how it looks live. So you see two players that we are comparing here and you can see also the call to action that we used to convert customers who use this content. So that was the basic idea. It was very quickly to do. We are talking today about the productivity and how we can use the automations. So it was really easy to prepare such number of pages.

The last thing we had to prepare, we had to let Google know that we're publishing something like that so we prepared a sitemap and upload to Google Search Console so Google can index our pages. So what was the results? That was a huge question to us. Being honest, we didn't know what to expect but because it was something new and we couldn't find any learnings about this kind of project, we built our experience on our own. Here you see the results on Ahrefs. Very quickly we have a wave of impressions in visibility in Google. We were surprised. The first two days we were surprised on how many pairs. Comparing two names using versus word, how many of them we can cover. It was a very good position. It was quite good for us. We hope that this will bring something to our business. As you can see here, this is exact data from Google Search Console. We hit the impressions very quickly but that's one of the takeaways. We didn't bring such huge value to the customers. There's such a lot of great players on the market that are delivering this information about the vendors that we just didn't bring enough value and Google realized that they are displaying our software but there is not a lot of clicks and there is not a lot of engagement with the content we prepared. Overall, I think that was not a good assumption to build such a number of pages. But starting this project we were not sure how it's going to work but what is interesting and you can see here that we were able to understand also what's going on in the market. We have a very precise data about the pairs and we can understand what competitors are comparing to each other, what players are the most active on the market and what switching thinking is behind our customers. That was very interesting for me when I'm planning this strategy and also I'm keeping an eye on the competitors and what's going on in the market. That's the market perspective and it was very interesting. Once again, the take away from our idea is that too many pages at once.

It was a mistake to bring so many pages because we indexed too much with Google without bringing enough value. It will be probably if we will divide this to the steps like publishing 200 pages, learn from that and improving the data and publish another 200 pages. That will be something that can grow this project and this will be the approach we'll be using making the second iteration of that. We lost the value per webpage because we indexed a lot of pages. We did everything under our main root domain so it also cost us reputation. Not enough value for any user so Google knew about this and with that we decreased our positions and of course there's a lot of competitions in the market, it's very strong. We have a great market leaders in delivering this kind of information like G2 and Capybara and other players so it's tough to compete with them on the very similar information that we were able to prepare. But interesting data about the competitors and what's going on in the market, that was something very interesting and of course a lot of learnings how we can do this next time and I think this is also something valuable. We weren't successful with this idea but overall you can understand how you can use this in your SEO strategy and on what type of things you need to be very careful.

So very important because we are discussing productivity and how you can build something fast and how you can improve your marketing. I think it was a one-week project for two people so very easy to implement and check so even if we weren't successful we didn't put too much resources and effort to do this. And now the second case study is about SEM and display ads and how our client is using programmatic pages.

So again let me bring you the background of this customer. So STS Gaming Group is the leading company in CE region about bets in the gaming industry. So they have a lot of sports events that they are covering with their portal. And the idea was to use real-time marketing.

So because they already gather a lot of data and they are somehow the owner of this data because they need to do this to run their business, they exactly know what's going on in sports, what kind of events are going on and when it's happening. So because they have a lot of categories of different sports and there's such a lot of things happening in sports each week, they figure out that they can build pages with events that are happening each week but it is a dozen of landing pages each week to build. And there's a very easy way to make a mistake if you are doing this manually one by one.