Speaker 1 (00:02):
All right guys. So we have Mr. Paul Kleen here from, weadvertiseyourbusiness.com and he has a pretty interesting story. As an agency is a pretty unique background and he is doing well. We're going to be talking about some advertising strategies, how to improve the ROI of campaigns in today's landscape. But first let's introduce let's introduce Paul and get his story. So Paul, why don't you kick us off and, you know, give us the, give us the story. How'd you get here? What's maybe the last 10 years or so. What's, what's your background.
Speaker 2 (00:38):
All right. So Paul clean founder of weadvertiseyourbusiness.com. We, we were founded in San Francisco and I moved to San Francisco after working in agency life in the Midwest for, for five or six years, because I wanted to learn how to build a business with that skillset. So we moved there and I became a data scientist. And my job at that time was, was working at a Y Combinator graduate startup who's who's backed by Google ventures. And we were, we were creating the sot, the SAS business that would help consult other business owners on who is going to churn from their subscription-based model. And my primary job was looking at all of our portfolio of clients and looking at their data and telling them here's the people that we think are going to go, we need to get ahold of them to stop them from churning using marketing strategies, depending on how, how many people were showing up in that report, we would have various strategies to help them acquire that customer longer than what they would have done if they just kind of left it and they ended up just churning.
Speaker 2 (01:38):
So that was, that was a lot of consulting. But the thing that I learned was how to start, how to start a business. So, so I combined the two skills advertising and marketing was starting a business to create this company. And we scaled it really fast. We've been in business for a couple of years. We got to our first million and two and a half years, but I wanted to find a way to teach our clients how they could do it faster. And we have a couple of case studies that we're working on now, people that have taken our strategies and grown to their first million in a year. So now we have a program that doesn't the year. So we came to sales process mainly so that we could figure out how we could better position that service offering on our website, how to price it better so that we ended up making a better condition when we hit those goals and how we can in general, create this program so that it's cohesive from seeing an ad, going through the sales funnel, signing up, getting onboarded, staying with us for a year, giving us a testimonial.
Speaker 2 (02:39):
We, we kind of do that randomly. Now we wanted a better system. So that's, that's why we're we're here with with Nick today is because we're, we're in this program now learning how we can combine all of these things and turn it into a system.
Speaker 1 (02:52):
Got it. And so who's your target customer who can benefit the most from your from your services? I would
Speaker 2 (03:00):
Say that whether or not they're they have advertised in the past, isn't necessarily something that's required. What, what would be required though, is they need some form of cash funding to be able to invest into this. It doesn't need to be a long time horizon, just, they do need to be able to afford five to $15,000 in cash before they start seeing some of that return. So a lot of times we work best with people who have a small seed funding round maybe a large friends and family round or an average series, a round would be plenty. So those are the, those are the three people. We usually work with people who have, who have at least a hundred camp funding is probably the safest cutoff point. If I had to pick one or their business makes 300 K to 600 K a year on its own, you can usually find the budget with that revenue by cutting somewhere else to make an investment. Or you probably could just afford it in cash terms on a credit card or something to afford it for 90 days.
Speaker 1 (03:59):
Right. And that you're talking about the monthly advertising budget.
Speaker 2 (04:02):
Yeah, exactly. Because they have to spend money on advertising in our system. That is one of the requirements is that we do a lot of it through advertising. Right.
Speaker 1 (04:11):
And so maybe expand on how you were able to keep that an interesting point is how are you, how are you able to keep customers from turning with marketing? It sounds like I want to make it as useful as possible. So yeah, maybe some strategies on how to do that.
Speaker 2 (04:28):
Okay. So we, we learned early on that if advertising didn't return within like two weeks, the people were just going to straight up cancel. Okay. And we used to do almost no barriers to entry where it was month to month contracts, no minimum spend no any setup fees or anything like that. And that was really good for taking our clients, but we also found that a lot of clients won't get returns at two weeks. They'll cancel. So our strategy to improve that has been create a actual 12 month plan ahead of time, get, sign off, get an interdepartmental sign-off. So everybody on the team sales team, marketing team, advertising team, if they have one CEO, especially even service team needs to all be on board with this influx of clients, this long-term investment and a new game plan. So building relationships with the whole team has helped us a lot.
Speaker 2 (05:18):
And then the other thing was we had to stop being just this broker of Google advertising and YouTube advertising and Facebook advertising services. Because if, if that's all we're doing is running Google ads for them, they can get free support from Google. They can get another agency who's always going to do it cheaper. They can hire freelancer, they can do it themselves. There's so many less expensive things. And if the Google ads don't work, then they're just going to want to cancel immediately and find that cheaper thing. So having the system that addresses analytics, advertising and their funnels helped us keep them longer because we were more involved than just a consultant or an advertising agency usually is where it's just, we're running a thousand dollars ad budget on Google. That's our job. We just want to keep this retainer. So we're going to do as minimum work required possible just to show results and send you some random reports every month. So, so we're trying to leave that kind of agency mold and move more into an actual team member, but a full-time employee probably would be doing if you hired them. So that I think that's one of the, one of the main things. And then the other thing is that our campaigns do actually get better results than most people are able to produce. Mostly because we just have a lot of experience getting in the weeds with our client accounts. So we build campaigns differently than most people do. So, so that's another factor too.
Speaker 1 (06:41):
Got it. Yeah. So maybe, maybe let's talk about some of the campaign strategies. So what campaign strategies have you seen work? Cause I, I, I run ads every day. Like I'm pretty versed in this stuff. Maybe we can compare structures and what works and yeah. Yeah. So what have you seen work really well?
Speaker 2 (07:02):
Yes. Okay. Let's start with let's start with our case studies. That's probably the easiest thing, cause those are, those are people who followed our system properly and they got really good results fast. The first one is a company called paper tools and they're an e-commerce business. And they sell tools to people who are trying to make Juul or jewelry. And they came to us, they had a $300,000 ARR in March of 2020, and they had another agency that was running the ads for them. And they were doing Google shopping ads, which was just a smart shopping integration with Shopify, really simple, like 10 minute build kind of a set it and forget it, just let it run on its own using machine learning strategies. And then that's it. We took them on and by December of 2020, so that's nine months. We got them to over $3 million ARR in the month of December alone, they made over, I think it was $400,000 just that month and their advertising budget scaled, but not that much.
Speaker 2 (08:03):
So their ad budget when they hired us was seven K in December, it was 20 K, but their sales was 10, 12 times higher. And it's because instead of just doing automated, Google shopping integration campaigns with Shopify, where it's just a product and a price, and we started doing Facebook ads that had videos of them actually using these tools. So showing what you could do with the tool instead of selling tool has been stellar for them. They had a lot of people interacting with these video ads on Facebook driving traffic, and then we had retargeting ads hitting them. And then we set up a series of different mid funnel tactics. So capturing text message or sorry, capturing cell phone number in exchange for promo codes that they can use Klaviyo to send out text messages for promo codes. That's been great at converting for them.
Speaker 2 (08:51):
We added a live chat to give free shipping and exchange for email. And then we created automated email system to try to follow people who don't finish checking out right now, we're working on integrating HubSpot so that he can have all that in one place. But last year we just had all these different softwares that we kind of stacked on top of each other. So, so the Facebook ad drove them to the site. Re-Targeting ads helped convert them. We still had a Google shopping campaign and it did really well, but we didn't just throw his entire budget into it because it needed support. That's just good at driving traffic, but it didn't have a strategy behind it. And it's just a product based advertisement. So we kept that in and then we started pursuing ads on YouTube as well to go really top of funnel.
Speaker 2 (09:32):
So people who are looking up how to build jewelry, maybe they haven't yet purchased one of these tools from him yet. So that was not necessarily for driving traffic, but just to get them to see his name. And then we combine those ads into a full funnel campaign and found that we were getting profit as long as we scaled Facebook. So we could keep YouTube and Google shopping ads pretty flat and not increase the budget. They did their job in the stage of the funnel that they're in. But Facebook was kind of in the middle of his funnel, where they had seen as YouTube ad. They may not have visited his website yet, but they were jewelry tool owners. And we're looking to acquire more of these assets to build more jewelry. So we targeted them there. And once we started making sales and making profit, we started importing those audiences of who bought into Facebook so that we weren't just targeting jewelry owners and business owners.
Speaker 2 (10:27):
But we were doing, we were targeting people who are lookalike audiences of a sale. So, so there is this 90 day approach of getting the right network in place, scaling it, and then finding data from that 90 day run that we could then feed back into the machine learning to make the second 90 day loop better. And it really hit its peak in December because that is also his biggest season two too. So it was it was really a nine month look at how do we get your business to its absolute max when the season is best for you to sell these products? So that's, that's probably the best e-commerce case study that we can give. If somebody really took all of our advice and next year, you know, 20, 21, here we are, he's hitting a slow season. So what we're doing is we're redoing his website and we're literally just thinking, how can we get this ready for this coming December again? So we're just repeating and now we have more money, so we can invest in things that he couldn't do last year, like a web design, which is 30 K. So now we can actually really invest heavily in shoot for a million in December instead of 450 K.
Speaker 1 (11:29):
Right. Got it. So, yeah, so it sounds like you're involved in the whole process. And so from what I got the demonstration ads work better than the, just here's the product, here's the price ads. Yep. Where are you re where are you doing cold on? Oh yeah. I guess you were using a lookalike on Facebook to scale the cold. But you're also using cold on the Google or YouTube and search, right. Just to find people and put them into the, the pool. Yeah, yeah, yeah.
Speaker 2 (11:55):
Search searched in YouTube. The way that we found the audiences cold there is, I mean, you could target channels. It's just his, his, his industry is so niche that the channels just don't have enough inventory to scale. So they might be really profitable, but we can only get like a hundred impressions a day. So what we did there is we put observation audiences on is search campaigns and this display campaigns, and we found out which of those audiences did the best and that's what we targeted on YouTube.
Speaker 1 (12:22):
Right. Yep. That makes sense. And so at what rate did you scale your, the ads, like, have you found like just doubling in one day? I I've seen, I've seen FA like Facebook kind of throttle it a little bit. Like yeah. Yeah. What, what have you found?
Speaker 2 (12:40):
Yeah, we did not do it every we didn't do it all at once. Yeah. It was mostly I mean I would advise to him when to do it, but it usually came down and when he said yes, and that was usually once every 45 days or so when he had kind of consumed the sales, turn it into a profit and had the cash. So it was once every 45 days or so that we would kind of walk up the budget and I had it split between four or five different campaigns so that we weren't just like hail, marrying on one campaign. So we would increase the budget equally across all five. And you would find that they would scale better than if we had just combined them into one and then increase that one, a significant amount.
Speaker 1 (13:17):
Why do you think that is?
Speaker 2 (13:21):
It is the machine learning. That's trying to look at it. Usually it's not the audience. The audience is usually pretty big on Facebook. I think it's because most machine learning models are going to take seven days to train and big changes like cash significantly alters their formula. So you have this model that's designed to get you the best results, but it also is limited by your cashflow. So when you change that factor, all of the other variables have to also adjust because you have more money to spend. So now you have a more maximum amount of conversions that you can get. So it takes time for them to kind of learn. And a lot of times we found that if you don't wait long enough, it's either going to break or you're going to change it before it can actually figure it out on its own.
Speaker 2 (14:03):
So, so the, the key here is you can't just set it and forget it because sometimes the machine learning will figure itself out after like seven days. But sometimes it doesn't and it just gets stuck. And there's, there's actual reasons for this. And we have this book on machine learning that we always pull up with our clients. And we were like, here's the chapter it's talking about, which model this particular algorithm is using. And, and there's ways that machine learning can get stuck at a place that youth that it might think is the best cost per conversion, but it's actually not. And you need to almost kick the model into trying again. So it doesn't come to this conclusion that 50 bucks per sales, the best that it can get. So sometimes slight changes just forces it to go back into learning mode and find that $30 trough and maybe 30 bucks. This is where, you know, as the lowest, right.
Speaker 1 (14:50):
Just question, where would it get stuck? Like why, or why would it get stuck? Just like the initial initial conditions were set wrong or like w does it get caught too early? A conversion was found too late. Like wh what's the mechanism behind why it wouldn't be optimized. And by making a small change, it would be,
Speaker 2 (15:11):
Yeah. So on Google and Facebook, both of those channels have the, the maximize conversions equivalent where you just tell them, get as many as you can for as low as you can. The definition of that is entirely vague. And the machine learning model sets its own definition of what the maximum conversions for this budget could be based on how it's been able to convert and that learning phase, which is only like seven days. So that's the thing is if it doesn't figure it on seven days, it's not going to figure it out. That's as long as that model is designed to harvest new data at that point in time, it just, it just uses the data and it just optimizes on it, the best you're going to get out of that as like a 10% improvement over time. So that's seven days is critical. The, the better model to use when you're trying to force it down on Google is target CPA on Facebook.
Speaker 2 (15:58):
They have one that lets you set your own max, those are necessary. So if it's not like 50 bucks a sale, you want to change it to 30 as its maximum. And see if that forces it to say, okay, well we need to find less expensive audiences or really just stop bidding so much on these audiences because that's the only way we're going to get to 30 bucks. So that that's how you have to force it down. If you are already using that. And it's not getting to that number, sometimes literally just duplicating the campaign and starting fresh is the best bet
Speaker 1 (16:31):
I found massively true.
Speaker 2 (16:33):
You just erase its data and start a new data set. And then you've got this new machine learning model that sometimes can do better. So you're thinking of it as like an SDR, you've got two SDRs and one does really well. And one doesn't do really well and you let that one go and replace them. And sometimes the system works again. It's the same thing. Machine learning is it comes to these conclusions and you can't necessarily fix them. So you have these ways of trying. And if you can't, you just abandon it, duplicate it and create a new one. And if, and if the second one doesn't work, maybe you need to reduce the audience or change something about your ad set targeting so that there's less audience in it. And maybe it can come to conclusions faster, but there has to be something that changes because again, you know, after seven days it's not going to fix itself and a lot of people will just give up or they'll just kind of set it and forget it and think that it's going to figure it out. And it doesn't, neither of those outcomes is going to be good for a business owner.
Speaker 1 (17:23):
Right. So I guess, and also the size of the audience and the amount of money spent in that seven day window is significant then, because if you don't spend enough money, you don't have enough people. And then you're less likely to get to that get to the first conversion or first few conversions is that yeah,
Speaker 2 (17:40):
Yeah, yeah. I mean it needs, they, they always say on paper that it needs 30 conversions to exit learning phase, but it will exit learning phase, even if it doesn't 30 conversions. So that that's the key here is they didn't use to let you use these automated strategies until you had 30 conversions, but people complain so much that they were stuck and couldn't get 30 that they just changed these models to allow it. So, so 30 conversions at least is required. And if you have a large enough budget, you can easily get that in seven days, if you aren't using a budget high enough to get that number, then what you're doing is you're kind of using a sample size. That's probably a third or a fourth of what it should be in order to come to these conclusions. So it might be able to do pretty well for you. But again, if you're going to scale on that dataset, that's only 25%, right. There's going to be an accuracy in it that, that won't work when you start scaling. So yes, you need a good enough budget in that way.
Speaker 1 (18:38):
That's what I found too. Yeah. let's talk about a campaign structure. How do you set up your retargeting campaigns in in Facebook? What is what have you found to be the best structure?
Speaker 2 (18:52):
Yes. so on Google and on Facebook, we try to use a Scag equivalent set up where each ad set, it's really just testing one variable. We try not to throw 20 different audience targets inside of one ad set, even though it might be easier to build the campaign that way it's harder to optimize it that way. Because when you're looking at performance, they don't always give you performance by the audience target. They just give it to you at the ad set level. So if you have a campaign that's doing great or poorly, you don't know which of those audiences inside of it has led to it. And you also don't know which of those audiences is kind of maxed out. So if you increase budget and your audience of targeting business owners is what led to all of your high profit margin sales, but you've reached all 100,000 to them.
Speaker 2 (19:41):
And they've seen your ad three times and you have 10 other audience targets in that ad set, maybe scaling is going to now reach the other nine that you weren't spending as much money on, which might be like people interested in biking. People interested in skiing, right? Depending on who you're targeting, you have all these different people, maybe those audiences aren't profitable. So now all of a sudden your campaigns tank, it's not the business owners. Isn't a good audience set it's that with a higher budget, it can now afford to bid on some of these more expensive audiences that it couldn't afford at your lower budget. So now it's trying them out. It's not doing well. And you don't know how to cut that out because you combine them all into an ad set. So I try to limit what's in an ad set, because I know from my point of view, that if I'm going to optimize this campaign, I need to know what the machine learning knows. And the only way to know that is to label your ad set with the exact target. So when you turn off the ad set, you know what, you're turning off and what's included in that ad set. So that that's one big thing that we do. And then the other thing that we do inside of our targeting, as we try to use exclusions as much as possible. And sometimes we'll even turn off the expanded audience because you just, that's a big mystery box.
Speaker 1 (20:57):
That's a, that's a, it's like giving your girlfriend your credit card. Like I'm on trust
Speaker 2 (21:06):
That button, that button can it can do great. If it's really easy for your clients to sell stuff, it can do terrible. If you're going after a niche audience, selling a niche product that cannot do well with a lot of different people. So like for pay tools, for example, it's terrible because he only sells to people who make jewelry, which is very niche. And if you like jewelry, which is almost every girl it's gonna, it's gonna think, well, they're similar, let's target them. And then it starts serving ads to people who like to buy jewelry, not make jewelry. And we get all these comments, like, what is this tool? Why is it? Why, why am I seeing this ad? And then you look at them and it's like this random person who, who works as like a roofing contractor, or it's like the wife of some, some random person who doesn't even work in the jewelry industry. And like, that box is dangerous if you're, if you're targeting niche people.
Speaker 1 (22:00):
Yeah, I agree. And so I liked the strategy of creating lookalike audience or getting the customer list, creating the lookalike audience and scaling it. Do you guys still use interests? And like once you find that lookalike audience, do you still scale interest audiences or do you still do some experimentation as you're scaling look like?
Speaker 2 (22:22):
Where's that I do usually. So we try to have those different campaigns. We have several campaigns running at a time, and then I was talking about this earlier, but to be specific, we usually have one that's focused on retargeting. And then each ad set will be a different, a different type of retargeting, maybe a different number of days out, maybe a different action. They took on the site. What they're always going to exclude people who purchase one might be lookalike audiences. So we'll have a lookalike audience based on add to cart, but didn't buy a lookalike audience based on high checkout value. A lookalike audience based on repeat customers. Those are all going to be housed instead of a lookalike audience campaign. But then we have these cold traffic campaigns that are using interests based targeting. And we try to keep them separated with their own dedicated budgets.
Speaker 2 (23:08):
And we keep them on at all times, because the reality is that the one limitation of lookalike audiences is that you have to constantly be making sales at a high volume to keep them active and fresh. And sometimes you might have a low month in sales. So those audiences aren't going to be as good this month, but those interest based audiences are constantly reviving themselves with people's behavior on Facebook and Instagram. And then they're totally independent of your company's success. So having them active, just hedges your bat, it's probably going to be more profitable to just pile your money into localized, but at the same, you have this snowball reliance where you have to make the sale to get the data, to do the targeting. And if your sales are low, your targeting is not going to be as accurate that month. But interspaced audiences are so vast and people use Facebook and Instagram so much.
Speaker 2 (24:01):
They're constantly a good bet for just harvesting new people and bringing them into your funnel. So I try to keep them active unless they're just total dogs and can't perform. But we're always trying to find new ones that we can introduce. And, and even if one doesn't work this month, we'll try to introduce some new ones the next month and see what we can get from it. So Facebook definitely has a lot that you can choose from in that, in that respect Google Google, we try to do a similar strategy. So you might find from your search observation audiences that your, your display advertising and your retargeting convert best when people who are interested in business loans, the ad. So that's an interest you can use on YouTube. And we always try to keep those active for the same reasons, you know, that that particular channel or keyword target that you're going after might be a really good one, but having an interest space, one that can bring an additional type of targeting into your funnel is, is scalable almost continuously. So it's important to keep those on, I think.
Speaker 1 (25:05):
Yep. I agree. And what have you been seeing with respect to the new like, environment, right? Like there's a lot of online business. Like every, everyone has to be an online business. What trends have you been seeing in the last, like since March, 2020?
Speaker 2 (25:22):
I think that in the e-commerce world, the biggest trend is there is a massive, there's a massive migration from WordPress into sites that don't require stacking plugins and themes on top of each other. Because as easy as those might be for people who are tech savvy, they're not simple for people who are trying to do side gigs and have a lot of free time. So it's Shopify and web flow and Squarespace. And some of these easy site builders have, in my opinion, really improved their, their business model and their service offering. And they're capturing a lot of people who probably wouldn't have started a business if making website were so difficult and expensive to do so. There's a lot of people who are like solo preneur side gig businesses that are going on these channels. And the other big trend in e-commerce is that they don't have to sell on their marketing website anymore.
Speaker 2 (26:19):
So a lot of people are going on to Etsy and some of these more niche marketplaces to sell their products and having amazing success on there. And some of those channels like Etsy also allow ads that are really profitable and easy for business owners too. So I think that, that, that's a really big one. There's a lot of people that are, that are popping up businesses out of nowhere. And in the lead gen business, we have a lot of psychologists and lawyers in that industry has changed a lot because they can't do in-person visits anymore, but it has allowed us to target people that are further away because they can do virtual consultations and things. So their model has changed in how they close deals and also how it's easier to track those deals. Because a lot of the interactions that they're doing now are online and an in-person visit used to be impossible to track, but now showing up to a web visit or sorry to a, to a conference call, we can track that.
Speaker 2 (27:14):
So we can see a lot of the mid funnel work that a sales team might do, and actually use that as data that we can plug into our, our targeting saying, Hey, if somebody showed up to a sales call, that's a lot better than just asking for a sales call. They showed up and they made it to that discovery call. And if they make it to the, to the second call, like the close call, that's even better than a discovery call. And we just never used to be able track that stuff because they go into the law office and they'd fall into this law office is total void of, or this like black box of, well, we just closed 50%. That's the business owners. You should tell us if we just had no analytics on that stuff. And now we're uncovering that they close way less than 50%.
Speaker 2 (27:55):
They close like 10%. They don't answer the phone for half of these people. And their sales team doesn't even schedule appointments or asked for sales closing on the other third of them. So it's like this whole funnel is broken and now we have better clarity. And I think that I think that the other big trend, which just has to be discussed more by agencies is that our tracking is just terrible. Now pixels do not track accurately at all anymore. And we've moved all of our clients over to a CRM where it's first party data, because the CRM is the only thing a browser is going to consider first party, anything being tracked from Google, Facebook, LinkedIn, Snapchat, Instagram, all those pixels are third party pixels in any browser. They die after a day. So if you don't close them in a day, it's using machine learning and artificial intelligence to guess what percentage of them converted and the way Google does it is if you click a Google ad, you're on an Android phone using Google Chrome, and you're not blocking it in an incognito browser.
Speaker 2 (28:58):
It looks at the percentage of people that convert in that cohort. And it says, everybody converse to that percentage. So if you're on Safari and you click a Google ad and you convert a week later, it's just going to say 10% of you. They don't know. They don't know. They just say 10% of those people converted because 10% of the people who were similar on Google phones that we have full data for convert it. And that's terrible. You know, it's super inaccurate what we have found using HubSpot. And some of these other CRMs with our clients too, is that they might Google might say they got 10 conversions, but we're really uncovering about 15 or 16 that came from Google ads later on after that first, second, third day. And we all, we also find that a lot of people will convert like 90 days later.
Speaker 2 (29:41):
And you just can't track that with a pixel anymore. So CRM CRM is just necessary to store data fingerprint that person who came in know who they are and the CRM can't just be this Excel sheet that stores customer information. It has to be an analytical tool that has some way of putting a pixel on your site to store their IP address so that they can see the behavior they're conducting on your website as well. Or it does it, it doesn't have any way of saying Google's data is wrong. These five people actually came from a Google ad. So what we have done with a lot of our HubSpot clients is we've created pages on their site. That's Ford slash Google forward slash Facebook. It's just duplicate. And if they hit that page at all, instead of saying, what's the G-Cloud from Google saying they came from what's the Facebook pixel saying it came from, we're just saying, if they hit this Google page that came from an ad and that's how we're uncovering tons more data than what we were getting with the pixel. So that's, I think that's probably the, I should have started with, with that, because that's the biggest change that we've seen with all our clients e-comm and lead gen is that we just have to stop relying on pixels to track things.
Speaker 1 (30:51):
Yeah. Well, the Google pixel, the Facebook pixel, and I guess the LinkedIn pixel, those are the ones that we can't rely on anymore. Yeah, like that's a, that's been a little bit of annoying. We always did this with custom code. Like we had to create custom cookies and then the parameters would shoot up into the custom cookie. And then we called and then pulled down whenever we called it into a form, like I never trusted the pixels in the first place. Like I was like, I always wanted to make sure that we, like, we basically just built our own pixel and and pulled it down on when necessary. Yeah. Yeah. Like I was I found I D yeah, I didn't, I didn't trust the, I didn't trust the data. So yeah. How maybe explain how HubSpot's improve. Like you're advising a lot of your customers who use HubSpot. Yeah, I I've tried it a few years ago. It was a little bit clunky. How, how have they improved their their offering?
Speaker 2 (31:51):
Yeah, it's, it's, I'm bullish on HubSpot because they're because they're, they're an environment that can do all of the automated marketing that you need mid funnel and has the analytics chops. I think, I think where HubSpot has, has failed past customers is that their pricing model is extremely confusing. It can get expensive really fast and they don't offer onboarding. And that has led to a lot of people spinning up free accounts and never doing anything with it. And just getting stuck. They'll say something like this is just a big bottleneck and actually someone on the Spyro Facebook group said that the other day. And I remember thinking like, I don't blame you because you signed up on your own and they don't help you onboard at all. Their account management is really bad, but that's why they have agency partnerships because agencies aren't trained to make HubSpot instances for them.
Speaker 2 (32:36):
And, and, and what, what you can do with HubSpot that I think is valuable and should start with is just create contact lists. That's the first thing you should do contact lists to interact with your ad campaigns. So make one for Facebook, make one for Google, make one for LinkedIn. And simply just say, if, if HubSpot knows the campaign name from Google, put them into the Google list, put them in the Facebook list, put the no LinkedIn list. Then you can make reports that pull how many contacts are on this list? What month did they join this list? That's how you see your analytics on how many people came from your ad campaigns on a given month. That's not intuitive to do. They don't tell you to do that. And HubSpot, in fact, they basically just tell you to connect your ad account and then create a report.
Speaker 2 (33:22):
And the report just basically shows you what the pixel shows you. That's it, there's no additional insight being provided. And that's the big issue is you have to know how to work the HubSpot pixel to pull that data out because it's there. They know the campaign that they clicked on only as much as Google knows the campaign that they clicked on, but they know what page they hit because their pixel can store that forever. So if you, if you build your report on the page, they viewed as opposed to the campaign they came from. And what we do is just an aura clause. So either, you know, the campaign they came from, or they hit the Google page, put them on the Google list. Now you have all the data that you need. I don't know when that feature was unlocked, but it's been around since I've been around.
Speaker 2 (34:04):
But it's just one of those little line items that they put in the, in the marketing professional plan that is so important. I just think that they themselves don't really know how to market it that well, because because it is a new trend that started just a year ago and now it's so important. I would imagine in the future, HubSpot's going to really push that a lot more to people. We found a really big opportunity ourselves, because nobody seems to know about this, but HubSpot's really been awesome. And I know that there's only a couple of other CRMs that can do some of these capabilities. They all say they have marketing analytics, but this is a very specific type of
Speaker 1 (34:39):
The multi channel attribution, right? Like, yes. Yeah. That's been a tough, a tough one to solve.
Speaker 2 (34:46):
Yeah. So what we do in our reports as we'll make one channel or sorry, one report that gives full credit to each channel individually. So we'll have one for Facebook that says if they ever came from a Facebook ad, no matter what stage and funnel I want full credit given to Facebook on the month that they signed up saying they came from us and it will give you the aggregate number. You click on the aggregate number. It pops up the people, the actual names of who they are so that you, as a business owner can go in and say, okay, was Mark Campbell really from Google ads? And then you look at their timeline and it's like, Oh yeah, they did come from Google ads. So instead of saying like, Oh, that was a word of mouth referral, which a lot of business owners will say, like, all my sales team said, they brought that in, right.
Speaker 2 (35:27):
Or they said they searched Google and type my name in, right. But the analytics shows you when they clicked on the Google ad, that's the big benefit for us to, to basically prove business wrong that it's not word of mouth. It was an ad. But then we have a second report that is I think the first report was more of like a linear approach. You just give full credit, no matter what stage of the funnel they're at. But the second report we make is, is giving first click attribution. So we, we say, what was the campaign that brought them in the funnel? And then it duplicates it because you might have 20 leads in a month. And it might say, Google brought in 15 and Facebook brought in 10 numbers, don't add up, right. It says 25, you only have 20. So you have to make that second report that only shows the 20.
Speaker 2 (36:11):
It has to give credit to just one channel that brought them in the funnel. That is important because now you've got your full channel. Omni-Channel analytics that say here's the contributing channels and here's the closing channels. And you can actually make a decision as a business owner. And instead of just killing off the campaign that never makes the sale, which is what a lot of our clients do. Now you can start thinking strategically, okay, well, YouTube brings them in the funnel. It doesn't close them. Facebook closes them, but YouTube brings them in. If we kill this, we're never going to get this. So that that's, that to me is what HubSpot can do aside from support ticketing and live chat. And some of their automated email marketing that you can do. Those things are incredible, but there's other softwares you could stack. I just haven't found a great CRM or analytics tool that can do what we just talked about.
Speaker 2 (37:02):
And there's some other stuff like heroes that's doing something similar about you, you guys do where they kind of piggyback on a UTM and then they store it in their own environment. Yeah. Alex is a customer. Yeah, yeah, yeah. So we've used hieros, we've used hieros and and it can do a lot of a lot of what we're talking about here. It's more positioned for e-commerce clients, but lead gen clients need something like HubSpot that can serve as a CRM and help them close the deal in addition to tracking the deal. But hieros is great for e-commerce customers. It works like what you said. And as long as UTM parameters are going to be allowed by browsers, they're set. I think the other strategy we've seen some customers do is they'll create a custom database where they store the data on their own exact same way that HubSpot would, where they just look at UTM parameters. But instead of, Oh, is that what you do? Yeah,
Speaker 1 (37:52):
Well, we use like Alex Alex's theory, right? Like we just built a custom cookie that captures everything and then we spit it into head and field into, into forms. And then we can see exactly when they came in, like what campaign and when, when they did. And if there's multiple, if there's, if there's like, there's diff we defined our own variables, right? Yeah.
Speaker 2 (38:13):
Yep. Yeah. Yeah. I will say for, for the standalone tracking purposes, hydros does do what it says it would do. And, and I think they have the same, the same struggle where it's hard to set up and hard to read, but that's just because this is, this is not necessarily an easy thing to do. It's a very difficult thing to do tracking. And that's the only thing they have going against them. Same thing as HubSpot setting up is hard. But when you, when it is set up, it can provide amazing analytics, especially if it's been running long enough to actually give you good data. And I think that's another struggle HubSpot and hieros have is that it doesn't give you great data after 30 days because customers take longer than that to sign up. So you have to literally give it six months and look at the six hour time horizon.
Speaker 1 (39:00):
So I did the D my own custom cookie. Cause you can find the time that the cookie exists. If you write a custom one. Yeah. Yeah. So
Speaker 2 (39:10):
Smart. You guys are, you guys are getting it on your own. I mean, most people don't have the resources to make that, but you guys do and you have a team that can do it, but housewife is good for people who do, who, who just want to pay someone else to set up that like that software is there and they just pay for it. Now they have,
Speaker 1 (39:26):
How do you tie it back to cash? Like, because like, that's another big thing for advertising. It's not necessarily revenue, but the cash collected. Yeah. How do you guys make those decisions or how do you help your customers make those decisions based on cash?
Speaker 2 (39:42):
Yeah. So you guys, you guys, the way your system would work is at least when we signed up, we were sent a signup link that was linked to a checkout. And you would basically just integrate that checkout with your HubSpot instance so that it you're sending the deal proposal. They call them deal, tickets you to sign up a quote to a deal ticket. And then you would email the quote link, which is a HubSpot link hosting your payment checkout with HubSpot. You'd send that to the client. They'd sign up, HubSpot sees the purchase and then moves the deal ticket to closed one. And your report ties that to the contact and company that came in early on as a lead. And it, and now you make a report that says, show me the leads, show me people who showed up to meetings, show me the people who bought. And it has that information because you've used all of HubSpot's services. Now they, you know, they integrate with other stuff, but sorry,
Speaker 1 (40:36):
But the actual cash collected, cause sometimes that's really important. Like at the, what hit the bank account is important to determine the amount of money you can spend in the ads in the future. Right? Like it let's say there's payment plans or there's subscription, like or there's delinquents or refunds or something like that. Right? Yeah, yeah. Yeah. How do you, how does how can you use HubSpot to handle that or, or provide visibility to that? Yeah.
Speaker 2 (41:03):
I will tell you what we do and it's not necessarily something that I think everybody's going to want to do, but we, we actually stack QuickBooks and a company called giraffe to track our financials that shows us cash collected after Stripe fees and refunds. And then it puts it into analytics for us. So we know our totals and cash. The only problem with with HubSpot is that there's no way to layer that into your reporting. So we have a, we have an accounting client who is a software that provides really forecasting services to different accounting firms and different business owners who care about their accounting. And they have all these amazing reports that they built in HubSpot that addressed some of those conversations. So, so they would probably be able to make something and HubSpot that's specific, but they're accountants, right? So we don't generally get into that level of granularity, like sales process does with how much cash are you making and taking into account that to, to help feed into your sales system.
Speaker 2 (42:01):
But there are ways to take that data and find it in other softwares. I don't know if HubSpot's going to be great to use for, for expense tracking now, which is what I think you're getting at, where you've got this fee of refunds, those who need to be taken into account. I know that you can integrate WooCommerce and Stripe with HubSpot and Stripe knows the fees and the refunds that you have. So there must be a way to create a refund report and HubSpot I've just never used it for that, you know, for that use case.
Speaker 1 (42:29):
Yeah. Well, for listeners, if you can tie in, we use a service called ChartMogul, it's like a calorie tracking and then we take the tags, like the tech, the UTM tags, or the campaign tags. We tied to the customer instance in ChartMogul. And then we can look at the, the lead source, the cash against lead source, the rap against lead source. Like that's the source of truth. So as long as you've done, like if we're using your HubSpot solution to get the tracking, right, then you need to, then you need to take the tracking and put it into like the cash collected part with another song, with another software. And then with that, like, I've, I wouldn't be able to run the ads without that. Like, cause the price points are different. The cash, the terms are different, right. There might be a different, a customer is more likely to refund or a customer who's more likely to expand like the or pay up front. Yeah. So
Speaker 2 (43:28):
Yeah, I think you guys would probably end up doing what what our client did, which is hire at HubSpot engineer to work with the API, to just pull that data in. And that's there, it's just not going to be an out of the box solution because it is so specific. It is extremely important for people who work and need cash in order to make these decisions. And that's almost every business owner, but I will say you probably would just hire an engineer to pull it in through the API. Their API is pretty easy to work with and, and you can do a lot of cool stuff with it. That's probably what you'd have to do. I don't, I just, I don't know if they have an out of the box solution for that particular use case.
Speaker 1 (44:03):
Yeah. Well, yeah. This has been, no, this has been really helpful. Like this is probably more of an advanced advertising discussion. So Paul, where can they work? Can they find you if they want to work with you or have a consultation to check out there? Like, is it equal? Like who's your wheel as, is it mostly e-commerce it sounds like you have like a lot of experience with the e-commerce folks and you, you, it seems like you can provide a lot of value or who should be who who's the best type of customer to work with right now?
Speaker 2 (44:37):
I think that e-commerce is big. As long as they have enough sales to come, our way is going to work. And the case study we gave, you had 300 K in sales. That's usually a really good starting point. We can turn that into a million dollars pretty easily because you have a ton of data and, and you clearly have product market fit and a good price point where you wouldn't have gotten that high. The the other person that we really work well with is those those kind of hybrid companies. So we have an online travel agency, technically they're e-commerce, but they're also professional services. All their services are booked online and they're purchased like an e-commerce product is we grew them from zero to 250 K per month in six months. So from October, 2020 to March, they were totally zeroed out flat line by con COVID.
Speaker 2 (45:24):
We took them from making nothing to 250 K in March alone. And it's a similar model. It's a lead gen that sells online a product or service. That's another good client for us. Or just, you know, the, the software SAS startup or bootstrap startup is another good one. And we have tons of case studies of people we've taken from from 300 K to 2 million in that space too. So those are three really big spaces that I think we just have the most relevant case studies and have, have immediate strategies that we know are going to work. We can work with other people, but it's, it's easier if we know how to get to that finish point and have someone that they can call as a reference to, to ask questions to and make sure they like us and that sort of thing. So,
Speaker 1 (46:08):
Absolutely. Well, where can they find you if they want to contact you Paul?
Speaker 2 (46:11):
Oh yeah. Yeah. Weadvertiseyourbusiness.Com.
Speaker 1 (46:15):
Okay, cool. And any questions before for me, if you want, before we end the call, I think has been really productive. Yeah,
Speaker 2 (46:22):
No, I don't think so. I'll get, I'll be in touch with you though over time.
Speaker 1 (46:26):
Okay. Awesome. Well, I appreciate the time and we'll end the call here and have a great rest of the day.
Speaker 2 (46:31):
Thanks man. Bye.