Jeremy Weisz 13:32
I’m sure when that happens, they may want you even more at that point.
Gil Allouche 13:37
Sometimes it did happen to me one time, they said no to a customer and the customer really wanted to work. And I was like, Look, I’m not doing this for this purpose. I actually said I’m actually went and met them in person to really make the case and see, look, if it makes sense. We’ll of course continue to work together. But this is what we see. And we want to be fair, you know what, we don’t want to do something that is short term. And so I think that confidence comes together with a little bit of doing the work. And it’s just like a relationship, you know, you have a thick, toxic, toxic relationship. If you’re really afraid of being alone, and you don’t have experience of being alone, you’re just like, oh, my God, I’ll stay with at any point at any price. I’ll stay with that person. But if you’ve matured from that, you’re like, this is not good for me. I’d rather be alone and grow. You can make healthy decisions for both sides.
Jeremy Weisz 14:19
Now, I want to talk about the evolution of the product for a second, like where was it in 2015 2016? What did that look like? And then now if you’re watching the video, you can see you can go to metadata.io. And you go to the products page, and they have all these different products and features. But let’s start off take me through the evolution of what was built and then we can talk about today.
Gil Allouche 14:49
Yeah, so actually, when we started, the product I built with talking about that pair programming was essentially a product that enriched leads. That’s all it did. So you put an email in there and he tells you the full business profile all the information, actually some crazy information that we had there. We had a lot of personal, psychological marital, there was a lot of great data that came out of that product. And it was a popular product. But I realized very quickly that it’s not a unique product at all, that this product is essentially going to be a race to the bottom, because I’m going to be competing with 50 orders. Also I realized that the database itself is becoming a commodity, what you do with the data is the actions that you take, that’s where the unique value is. And so that’s when we made the tough decision. So essentially, rebuilding and saying, we’re gonna build something completely different. I remember. So I built this first product for the lead enrichment, and barely was working, but it was crazy demand for it. And I had and I joined 500 startups, I started doing distribution using their best practices, we got something like 1000 users, within a month and a half, not customers. Many of them were free trials. And so other than a lot of the emails that I got of, this is great, really not working, perhaps so and so forth, I realized that there is nothing unique here. There are many other companies that can offer the same thing. So we converted from that into okay, now that the data is there, what can you use the data for in order to guarantee pipeline, that’s how we essentially put the experimentation when the next stage and said, we’re not going to rely on the marketer to take the data and figure out how to do the execution, we’re going to do the execution for them, because they’re already lacking time, we’re not going to give them yet another dashboard to look at. And so we focused completely on that. And that took us a really long time to build. There’s a lot of complexity that went in there, it’s essentially we built something like a Command and Conquer system, like a game, there’s a real-time clock and metadata, it’s not just you click here, and something happens, the system runs millions of actions every day. And so that’s the second thing that we build, right. And that was really the majority of the lifetime of Metadata. And it’s still the core product. But over time, we found that there are other technical, repetitive, mundane tasks that are equally important to generate pipeline. And if you don’t get them, right, even if you have the best pipeline in the world, it doesn’t convert into sales. And also there is a lot of bottlenecks and a lot of waste if you don’t fix them. So for example, personalization was one of them, you spend $1,000, to get a lead from Google, to your website, and then your website is a generic website. That’s crazy. You spend $1,000, once you spend like a little bit more to make the website, I don’t know, I’m not asking you to be Netflix or Amazon, although that’d be great. We’ll get there. But somewhere in between and do better than just like, hey, company name in our whichever new website in the world has. And so that’s personalization for you. And there’s a lot more right lead enrichments, for example, something super simple. But if you bring in the right people, the from the buying decision-makers from your named accounts, and you don’t know exactly who they are, because they sign up with their Gmail or MSN to Facebook, and it’s how are you going to lead? What are you doing? Why don’t you pay like another dollar another half 150 cents, and $1 and a half to get that full business profile. So we added a few more things that we know are critical based on trial and error. And we’re making sure that to plug those things in. So that end to end, you have a profitable marketing machine in your company.
Jeremy Weisz 18:25
Did that come internal? Or were you getting suggestions from your customers at that point?
Gil Allouche 18:30
A lot of suggestions a lot. And prioritizing the roadmap is, is a balancing act, you have to innovate, because what you think is Henry Ford, right? If you ask him, what he wants is like another horse or a faster horse. And so like you have to innovate and lead, because some people don’t know what’s possible. And that’s one of the advantages of being an engineer, right, you can begin you can start seeing what is possible, given your experience building other stuff. Second one is customer feedback. And that’s you have to be religious about that. And be comfortable with negative feedback and critical feedback and even be comfortable with you’re going in the wrong direction and pivot. And then your field, the frontline, like people, the SDR, the sale, the customer success, they know what’s up. And so you should definitely take into consideration and prioritize based on what they see.
Jeremy Weisz 19:20
I want to talk about some use cases. Before we hit record, you’d mentioned a company used it to do 13,000 experiments.
Gil Allouche 19:31
Yeah, we have a customer he’s been a customer for like two three years now. An enterprise customer. Their budget is I think it’s something like 3 million a year in marketing programs like, it’s not Coca-Cola billion, but it’s like it’s pretty substantial for b2b to spend three, three and a half per year. I think it’s even more than that. And that means that they also have a lot of creatives, a lot of text very In a lot of channels, a lot of campaign types have a lot, their marketing mix essentially is huge. It’s like a big bucket of like, all of these variables. And that’s perfect for metadata because a human, even a team of 50, humans, a team, let’s say you’re hiring an agency, and there you have 50 people just for you running campaigns, they’re going to pick and choose the campaigns that are, first of all they know how to do they know how to run the campaign to have the most experience with based on the time of day, because it’s not just executing the campaign. That’s even if you’re amazing, and you can run a campaign within half an hour, it’s auditing that campaign day in day out for pipeline results pipeline doesn’t happen within a day, it happens in a complex sales can happen within a few months. And so that operation takes a lot of time, which means people will always skew towards what they feel more comfortable with giving their limitation, expertise timezone, so forth. So out of let’s say, if you multiply all your creative, all your text variation, all the channels, all the campaign types, one time it’s a sponsored update, one time to Legion for a one time, it’s a chatbot, you can get to a universe of let’s say, 15,000 campaigns. And so as a team, you’re going to choose, if you’re reasonable 500 400 campaigns run this year, it’s still a lot. But you can run that kind of operation, what our system offers, is to actually execute the entire span the entire bell curve, because that’s what it is, eventually, it’s the entire bell curve of campaigns, some of them are going to be extremely bad, and you should shut them down within a few hours or a day, many of them are going to be Everett’s so you should also shut them down really after a little bit more time, depending on how much volume you need, then you’ll be more forgiving and less forgiving for b experiments. But then can you focus on the 10%? It’s not even a 20 80% rule. It’s like, can you focus on the five to 10% of the campaigns that are generating yielding all of your pipeline for the smallest CAC, and that you can find out only by experimentation. And that’s essentially what the system will do, it will fine tune into that 5%. And then to create clones of it, and derivative, let’s see, if it works on LinkedIn. That’s great on Facebook and on Twitter, if it works with a sponsored update, let’s try a carousel ad in a video. And so that’s what the system does over and over. Essentially, it games the system for you, the channels, they optimize for you spending more money on them. But our system, it optimizes for pipeline results, which is unique. And it’s agnostic towards which channel which campaign to which audience, it does, it whatever makes sense that brings results.
Jeremy Weisz 22:32
What about cutting customer acquisition costs you had you had someone that cut their customer acquisition half?
Gil Allouche 22:40
Yeah, that happens a lot with metadata. So there are a few reasons why that happens. So once we find an experiment that works well, we’re going to now clone it across different channels. For example, a regular marketer, in b2b world, what ads do most popular channels, it’s AdWords, the most classic, right? And LinkedIn, because LinkedIn, put a lot of effort, and actually built a very nice campaign execution platform for b2b with the criteria for b2b. If you’re going to Facebook, which most of their customers most of their advertising dollars comes from political and consumer, you cannot choose job title or seniority. No one puts their company name on Facebook. And so with metadata, we’re applying the same b2b graph across all different channels, Instagram, boom, it’s a YouTube channel, Facebook, of course, YouTube, AdWords, and Twitter is becoming another one. And the list goes on and on. And so just that because you’re one of the 5% of the b2b marketers, were actually spending money on those channels, guess what you’re getting it getting the same lead, that you would get on LinkedIn for a third of the price. Because you’re not competing with other marketers on these particular people, those particular people usually don’t get ads. And so that’s one of the reasons a customer acquisition cost goes down. And of course, there is optimization part, the system will does not optimize for vanity metrics. It doesn’t care about impressions, it clicks on much it cares about are you generating a conversion from the right company, the right person within the company? Is it converting to pipeline, and because of that, two campaigns, one, we may generate 100 leads, one may generate two for the same price. But out of those 100, leads, zero became an opportunity out of those two, one became an opportunity, the system will know to optimize towards what actually works, and that starts cutting the cost. The customer acquisition cost is significant. It’s essentially economies of scale.
Jeremy Weisz 24:36
Gil, talk about who is the ideal customer for you because I know, like there may be someone from a larger organization, maybe they’re a head of marketing, maybe they’re head of paid media. I’m wondering if agencies use you for their clients because there was one case where someone kind of built the career, build their career using yours. And I mean, we won’t have time to go into it. But I’m curious on how you come up with the pricing, right? Because your pricing is based on, obviously, what you’re generating for some of these companies. So talk about the ideal client using metadata.
Gil Allouche 25:22
Yeah, it’s a very good point. Some marketers that are early adopters totally build their career on metadata. And that’s wonderful to see, I think I know over a dozen who moved from like a campaign Junior campaign manager to a VP in the span of three years. Some of them work for metadata, we have 30 of our investors are former customers or existing customers, and many of our employees, actually our former customers, our ideal customer profile, our mid-market, companies like zoom that we’re using right now with the customer, Yelp, Drift Gainsight. So, I would say a few 100 employees do a few 1000s are perfect fit for our full platform, they’re spending 5060 grand a month, even 40 grand a month, when you start spending at that level, if you’re spending 10 grand amount a month, you should use our meta match in our low price Free Trial, essentially, this only audience the creation, so you make sure you don’t spend money on the wrong company on the wrong person. But you should manage your campaigns at that level, on the native channel, don’t spend too much money on the management software because they don’t need it. That’s exactly that’s one, but if your spend is high, it’s like you want to have an investment manager, managing your half a million dollar, and so that’s essentially what the system does. And so mid-market, customer companies, B2B complex sales. So, it takes them a few weeks or a few months to sell, and then high ACV. So if you’re selling like a 50 bucks per month product is not a fit. But if your ACV is like 20k 30k, it could be a 200k. It’s a great fit, using modern software. So you have to use Salesforce CRM or HubSpot CRM, and one of the top four marketing automation systems. So we have a good amount of those requirements for you to be a good fit for metadata. Because if you’re coming in as a customer, we want to make the money in money out ratio. So amazing that you don’t have to ever think twice whether it was a good investment or not.
Jeremy Weisz 27:17
Do you have some agencies that maybe serve a lot of b2b SaaS companies using the platform too?
Gil Allouche 27:24
We do one of the agents we work with is actually the former customer who builds discovery with metadata and did so well, that he made his existing company a customer now he has 10 other customer, we even send customers to those agency, we work with agency, they have special pricing and special terms, we have a select number of agencies that are, future looking early adopters of this methodology, which is different than the usual. And they focus on what they do best, you know, content strategy, creative, and they don’t have to worry about the performance marketing side of things. And those are perfect agencies for us to work with.
Jeremy Weisz 27:57
I have one last question. First of all, thank you. And everyone, check out metadata.io to learn more. Last question, Gil is, what’s the one thing you learned you were in the Israeli Defense Forces? What was one thing you learned there that you take to running a company like you’re now?
Gil Allouche 28:21
When overwhelmed, which happens a lot, managing the psychology. So taking a breather, kind of understanding the surrounding writing a mental or physical note of what’s going on and where am I here? What do I need to do? And kind of going back to fundamentals versus getting into flight or fights or into reactive mode? And so this is one of the things I’ve learned from one of my superiors and took to my civilian life and into running a company.
Jeremy Weisz 28:55
Gil, thanks, everyone, check out metadata.io more episodes of the podcast. Thanks, Gil. Thanks, everyone.
Gil Allouche 29:01
Thank you very much. It was great to be on your podcast.
