Marketers of Technology
00:05:08 · Andres Figuera, Elizabeth Arnold
[00:05:08] S1: Okay. Understood. Understood. And how does the process for a potential customer look when they wanna work with you guys and start using your services? Like how would they go about finding out if it's a proper fit and and getting like a demo or something like that? [00:05:22] S0: Yeah, definitely. So we've actually been acquired by Sprinklr. So recently we've gone through that acquisition journey, which is quite fun. So we'd reach out to somebody at Sprinklr, myself, whoever that is, and we get them in touch to go through demo and understand what their use cases are. We have a whole bunch of use cases from, I don't know, understanding my brand to try and find trends to meeting new product development. So we wanna understand what it is that you wanna do, what team are you sitting in, and then we can go and give you a demo. We can pull data for you and show you what it looks like. And then you can watch how that layers into the other social listening and social intelligence data that you might already be accessing through other platforms. [00:05:55] S1: Okay. Okay. Understood. And now Elizabeth, shifting the perspective a bit more to what the show is actually about. How has marketing this product been for you? Like how have you been able to generate demand and have potential customers see the value of what you offer?
00:06:10 · Elizabeth Arnold
[00:06:10] S0: Yes. So this was actually a really interesting product to bring to market because it was the first of its categories. So we were a category creator and was actually a bigger uphill battle than I thought it was going to be. When this tool came on, it was also one of those first AI tools that were coming out and not like the broad sweeping ones where everyone's making videos with cute little cats running around. It was like, oh, like an AI data layer that can actually go do something. And this was just as people were getting access to chat GPT generally and using it before Claude took off. So it was actually a little bit harder of a conversation to then go to the Gartner's and the Forrester's of the world and say, hey, this is a category. Don't bucket us in with just social intelligence or social listening or just general social insights. This is a whole new category of understanding video data. Because when you go to other sorts of social listening and traditional listening tools, you're just looking at text. And text is fine, but it's also just one dimensional. Right? What I write in a tweet is what I write in a tweet. Maybe I have a picture on it. Okay, cool. It gives you a little bit more room to the message, but a video, you have gestures, you have facial recognition, you have the tone, you have the music in the background. You have all of the different things that people can say and do in a ten minute video that puts off a lot more meaning. I could be walking around and be like wearing a Nike t shirt and talking about something political and standing in front of the Eiffel Tower and you can grab all that information and then try and understand who's this customer would be talking about, why does this matter? And so when it came to marketing this product, it actually was a uphill battle trying to actually create the category. So we did a lot of different work with analyst teams, a lot of different partnerships with social intelligence spaces and communities, and then worked on building whole partnership pipelines as well. So working with the researchers to then empower them to learn and understand how this works because it functions differently from a regular social listening tool. And then take that to market. How you go give this to your clients? What additional information can you add to your world cup report and things of that sort?
00:08:07 · Andres Figuera, Elizabeth Arnold
[00:08:07] S1: Okay. And that was about what, three to four years ago since the ChatGPT and all that AI boom started? Or how long has ViralMoment been in the market? [00:08:16] S0: Yeah. ViralMoment has been in the market, I believe we're at about five years now, just around five years. So it all started around the AI boom. It took about a year to try and get in front of the right people. Would say you have to get the right traction, the right reports, and then all of sudden the Gartner analyst like, oh. [00:08:33] S1: No. And I imagine it would be a bit of a longer process to get the market to really understand that you're a category creator. As you mentioned, that was a bit of a difficulty you had. But after that obstacle has been surpassed, now let's come to the present. And how is marketing looking like for you guys nowadays? Since I believe you've already then situated yourself as a leader in this category and are pretty much the authority in AI understanding and analysis of this type of content, then do you feel a lot of competition has been created within the category after you guys? Or how is that looking?
00:09:06 · Elizabeth Arnold
[00:09:06] S0: Yes. So there is definitely more competition in the category. Now that AI is more widespread, it's easier to access. Models are opening up. Training models is becoming a little less cost heavy, little more efficient. People understand how to do this now. So more AI engineers are out there able to build something like this. So there's definitely been a lot of competition which has come through, which is great. It's validating, right? You're like, Hey, it's a thing, which is fun. Also a little scary. So there's like two worlds you kind of live in as like an executive leading that space. But there is a lot of competition coming in. I think what we're doing now is actually a little bit ironic is I'm breaking down that category. And I'm saying it was a category. And now I'm like, great, we did this. We made it a category. We told you this is a thing. But at the same time, we're not just this sidebar thing that you've only spent like you got a $100,000 on. Like this is data and it is important data that layers into your other data. If 82% of all the content on the internet is video and you're not looking at any of that, then you're missing all of the content that's important to you. Like your people are gonna be on TikTok, on YouTube, on Instagram reels, whether or not you're there. So what are they saying? And how are you gonna figure out what that is? And so that is just another data layer that doesn't necessarily have to live separately from your current intelligence layers. So although it is a new category, the data layer itself really should be an integrated data layer on top of the rest of the social listening that you have.
00:10:31 · Andres Figuera
[00:10:31] S1: And differentiating yourselves, Elizabeth, that's another question I always like to ask with these AI companies or anybody who is heavily using AI to provide a service, and that is, how are you being able to get customers to know all these values that you provide and that it's not just another AI company? Because I know you guys are situated as a leader and the authority right now, but do you feel that you have to educate still in order to not be confused with other companies that might be arising or that might be within the market right now?
00:11:00 · Elizabeth Arnold, Andres Figuera
[00:11:00] S0: Yeah, absolutely. Some of it comes down to basic things where over the next eighteen months, I just see not really being quite big differentiators. But right now it's things like data access. We've been around for a long time. We have a lot of different ways that we can access tons of data. So we have a lot of it, which is the most validating that you need. You're like, well, I need to see everything on dog, everything on YouTube. I need to see as much as I can. So having that a massive amount of data is very helpful. I think we're getting to a point in technology in the world where just having all the data isn't gonna be such a differentiator anymore. I think with the way that AI is going and access to these tools, that's all gonna collapse. So a lot of what we do is actually more multimodal now. So it's not only creating our own AI models on top of what we're running to make sure that we're grabbing the best taxonomies, the best sentiment emotion layers, best different layers to tell you what's happening with logo recognitions and visual recognitions. So what can we put inside of these videos to make the data richer and deeper? But then on top of it, it's layering that data and telling one cohesive story. So if you have your Reddit and your Twitter, sorry, axe data of the world, I really can't get over that whole Twitter fax thing. Like, still, it's been years now. [00:12:04] S1: I missed the little bird, honestly. I missed the blue logo, everything.