SaaStr 812: What's Working Now: AI's Real Impact on Sales with SaaStr's CEO and Co-Founder, and SVP & GM

0:01Welcome to the official Sastr podcast where you can hear some of the best Sastr speakers. This is where the cloud meets. Up today on the Saster Podcast. So an interesting thing from all of these things, whether it's slides, whether it's QA on the SGR, which now can do 5 ,000 interactions instead of 50. But if you add it all up, it's more work for your overall GTM team. It's more work. It's more work. So I think what a lot of folks, especially CMOs that I talk to, or sometimes CROs that don't really want to get in the weeds, they're looking for solutions that are less work. They want AI to be less work for them, right?

0:40Why can't I just turn this? But it's more work. It's more work managing these AIs, right? You get higher output. You get higher quality. But there's no less work tradeoff here. That does not seem to exist in high-quality GTM AI today. More work for better output. Right. You might need less humans, so you could have less humans, but the ones that you do have are going to be working harder.

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2:28Welcome back to Zaster AI Live. We thought we would do something fun today. Since we've been using AI ourselves a lot at Zaster, I know you guys have seen Jason's tweets. You've seen some of the content we've been putting out. Seems like you guys have an insatiable appetite for some of this stuff. So we thought we'd share a little bit about what's working now. I've been using it a lot, specifically for sale, outbound calls, a little bit in marketing. But we thought we'd share with you some of our results, what we're seeing, some of the pitfalls. And just to preface, even though it's Zaster and we do use a lot of AI, I would still consider us early on our journey.

3:08We've used different AIs now probably for a year or so, if not more, in different ways. But I'd say specifically in sales and marketing, we're still early on, but seeing some good initial results that we would share with you guys. So with that, we'll go and get started. Let's start with Outbound because I feel like this is where we have... I just got the bad Outbound-y and all this warning that was, I hope, from an AI. Maybe it was actually a person. I assumed it was from an AI because it was so bad. So let's talk about Outbound. What's actually working? What's actually closing deals using AI for real?

3:46So in the last two weeks, and that was, at the time, The only time we had been on this platform that we're now using. In just two weeks, little old after, it became the highest response rate on the platform that we use for our AISDR. We opened pipelines, we booked meetings, we touched LAPS accounts. It both seems fast and slow, but it's better than historical human averages we had with any human SDR we've had. Maybe even some of our human AEs that we've had in the mix at Zaster. user screenshots here if you can see how many leads it's touched right like there's no way i could send 4 ,495 messages by 1 ,002 weeks and to do it a hyper personalized hyper fail that actually is adding value so you'll hear me talk about it a lot but i think the overall consensus you'll see in my key takeaway is your ai has to be good enough to also add value if you want to see these kind of results.

4:47Like if you're doing, you know, AI that's just, hey, names on our website, you're probably not going to get these types of results we're getting at Saster, even though it's early on and even though it's already doing better than any historical human averages. There's a lot that goes into that, which I'll talk about next. But just to preface there, It does take, one, a lot of tuning, and it does take a lot of data and a lot of conscious effort to get these AIs to work this well and have this good of a overall response rate, positive response rate, and also to make the emails seem like we actually wrote them.

5:27There are screenshots in this deck of people asking me two things. Like, one, is this a real person? I'm like, yeah, here's my LinkedIn. I'm real. I'm a duster. I'm on this webinar. I'm not an AI advocate. But also, half the time, they don't care. I think it's just a big learning, too. Like, if your AI adds value to the sales cycle, why does it matter if it's an AI versus a human? Amelia, maybe just add, let me add one context while you're doing this, just for folks. This is great. And these are good results. A few caveats for folks, depending on where you are in your journey. One is, we have a pretty big brand.

6:01Yes. Okay? So that helps. People have heard of us. Sasser's been around since, my God, since 2012. I've been writing content. We've been doing events since 2015. We've sold over 100 ,000 of sponsorships. People know us. And these 4 ,495 messages that Amelia's AI sent, and that is important. It's so many. So many. We could never send this many good emails. We could send this many emails. Just send them. We could never send this many good emails. But we have a brand. And two, this is all to our base. You have to do a two-by-two. Do you have a brand? So is there some reason people will interact with your email?

6:35And two, is this just raw folks you're emailing out of the blue that have never heard of you, did not opt in? Or are these folks that you already have some sort of relationship with? And I think, Amelia, all 449 of these folks are folks in our database that have participated in SASTR, that have opted in in some scenario. So it's a pretty good set of folks to be hitting up. It's just there's no way we could do this at scale with humans. Yeah, some of them are website visitors, right? And there's a lot of AIs that do this now. So there are folks that are on your website. They know us, but they know you.

7:08So that's the key. So I just think these are some good learnings just to provide the context to you. You know, if no one's ever heard of you and you have no base to email from, no matter how well you do, you're not going to see these results. You may see, you should see results. But I think that's one of the many reasons. There's probably three reasons we've done so well, like off the charts well, brand, our own base, and lots of training and lots of iteration, not just turning on and walking away like most people do. So just for some context, those three points. But yeah, keep going. No, super helpful.

7:38You talked about it. I want to talk about it a bit more in depth on how to train it. I already see some comments in the chat. There's a lot of implications here, right? I literally was just emailing with somebody today that was like, I'm coming to the webinar. I have a quick question on what can I do that's quick. I'm like, it's not quick. That's already one that's maybe like the wrong mindset. Like if you think you're going to turn on the AI SDR and it's going to give you the same results that we got, it ain't going to happen to you. Like a lot of trading. I would say I have almost dropped a lot of other things I should have been working on.

8:12And I think, Jason, you were talking about yesterday on Twitter, like you don't spend as much time in some of our AI as you used to. And it's becoming the same now for this particular sales tool we're using for outbound for me. But it still does take a lot of time. At the very start, you should expect to spend the most, basically all your time with it. These two weeks were also the two weeks I spent the most time on the AI SDR. I spend less time with it on the outbound now because it got right. That's the thing that AI will get better at the outbound. But if you don't have two weeks to onboard your AI, just like you would spend two weeks onboarding a human being, then don't do it.

8:48You literally need to spend the same amount of time, maybe more, on training the AI. that you would a human being. Like you gotta train it. You absolutely gotta train it. That's one maybe trade-off or nuance people don't realize yet. But I would say the nuance of that being you can do it when you want. Because it's not a real person, like I would tune my AI first thing in the morning and then last thing in the evening, right? Probably 90 minutes in the morning, maybe an hour in the evening, maybe more. And then I would respond throughout the day pretty much in real time if I could to any responses that our AI was getting in real time.

9:27But that's the upside of the AI is not a human being. So I don't have to set 10 to noon Pacific to do the training that I would a human being, but it's still the same amount of time. So pros and cons, but let me get into one of the biggest things is you got to train it with all your data. And this has been, I would say even for us, a little bit of a hurdle. We have so much data to answer. Like you guys have probably talked to Jason's, AI on saster.com. It's got something like 20 million words in it, I think, and counting, right? Because we post new content on Saster every day. That's one data set for us, right?

10:07Like if you think about, or if you have multiple product lines, or if you also produce a lot of content, or if you've been around a while, you're going to have a lot of data. That's just one set. That's just saster.com. That's not every data signal that's in our Marketo, which is millions upon millions upon millions. That's on every data signal that's in our sales force, which is probably another couple million. Like there's so much data you have to train to work with your AI. And so, you know, I do think you'll start to see more rev ops. You will become AI rev ops. But I get why now after spending like almost two full weeks with our AI, it takes a lot of time.

10:45And like to do it right, you do need to know the business. Don't hire somebody off the street to do this for you. if you hire someone to do this and you don't do it yourself, it's got to be somebody wicked smart that actually knows how to use AI and actually knows your business to feed the right data inputs to the AI.

11:06On our AI, our general purpose AI you can chat with on the homepage, you know, it's trained on almost 20 million words of SASTR content. And to Amelia's point, for the first two months up until SASTR annual issue, every morning I would wake up, I'd get my coffee and I'd spend 30 minutes training it. And training can mean different things. Like I'm not an expert. For our AI, for the base AI, not for sales, which Amelia didn't do, she can talk about how she trained it. For our sort of generic AI, which is pretty good. We've done a hundred thousand chats on it. It had to ingest 20 million words of SASTR content, all of our SASTR annual sessions, all my tweets, all of our YouTubes, all of our videos, everything.

11:44But their very specific questions were really good because it had ingested all that data. Where it would hallucinate or make mistakes is where it just wasn't in that data. For example, if you asked it about a speaker that we had at Sastra that actually spoke, what does Stuart Butterfield say the two times he came to Sastra? It'd be great, right? Then if you asked what the CEO of Notion said at Sastra, who never came, it would make something up. It'll wake it up, yeah. And the worst, the most, so those are a little funny. And the most extreme one was we had not published the dates for SASTR 2026 or SASTR AI London this year yet.

12:18And so we didn't know the dates, so it would make them up. So what I would do is every minute I would just audit the chats, audit the questions for 15 or 30 minutes. And there weren't that many errors, but the ones that were, were egregious like that. Right. And then I would go in and I would manually write the question and manually write the answer. Okay. In the training, in the training section. When I did that, it wouldn't make the mistake again. I'm like a human, like a human would make the mistake the next day. Right. But I had to do it literally for almost half an hour for two months every morning, every morning.

12:47And so now, now I do it once a week. Okay. okay and um but i think that's a metaphor for even if you have all your data in even if you have it all in you may have to qa it every single day for the first couple months yeah i will say there's on every ai str tool all of them have different rule sets this is trained on most of our data not all there's a lot right so like some of our ai is trained on more of our data some ai is trained on like the core of our data um doesn't mean you have to train on all your data like more context is good. But at the end of the day, what your AI is looking for and what you really need to trade it on is what do you want it to surface to this person?

13:27Okay, if you're using it for outbound, but you're using it for, let's say warm outbound in the way we are or people who are visiting your website who are already on the website and kind of know at least a little bit about who you are and what you're doing, then basically what you want to tell the AI is, okay, if they're on the website, what are they doing on the website? Okay, great. The AI will tell you that right away. It already knows what this person's doing on the website. Next, okay, let me go look in maybe your CRM or your marketing automation platform. Maybe I don't necessarily need both to see, okay, is this person, let's call it a VIP account.

14:02Like, have they been to Saster? Have they sponsored with you? Are they already a customer? Don't need to give it every single email you've had with this customer and every closed-box opportunity. Half the time, let me tell you, the people on the website might not know that. They might be employee number 10 at Oracle who doesn't know all the history and context of SaaS or Oracle from back in the day. And like a few words of that context from your AI is nice and handy and it's personalized and it will get them to open and respond to your AI and probably take the call. And that's the level you need.

14:35So I think you also need to just think about the mindset of, okay, a lot of data is, but it doesn't need to be perfect. Like it does not need to be perfect. And we started to hyper-segment more and actually like rip out some of the data from this to make it more hyper-segmented and actually a little bit more simple. And that is part of why it's also working so well for us. So just to train your data, if you will, to get this to a working state. I outlined six steps you'll need to take in the early days if you're serious about doing this, right? So one, yes, you're going to need to start with your CRM, your marketing automation platform data.

15:12hopefully that is in a working stage in which you can use so just keep that in mind if it's not already living somewhere it'd be hard for your ai to adjust it each tool kind of has its own way where you can say okay make an artifact and say okay um hey by the way ibm was a sponsor this year it wasn't in salesforce and it's like okay i've recognized that data kind of like you would in your brain right like the same way a human brain works you can tell the ai's brain like hey let me do a brain dump of my human brain to the AI brain of things I know that are in our theorem. You can do that. A lot of these platforms have a way you can do that.

15:49Man, good to do that at least once. So it won't know what you don't know. Hook up any of the major, both of the tools. Hook up to all the major platforms. Some of them are listed there. But yeah, you're going to need to probably do some data cleanup to a bearing degree based on where you are and how much data integrity you've had historically. Not to say you can't start with zero. It's just to the point Jason was making earlier. It probably won't work as good if you started from zero. So if you're like, I know I'm behind on AI, I probably need to get started with this, but I don't have a lot of data yet.

16:22You might want to spend the next, even like six weeks of data might be enough for it to get up and running and going with something. So it has some historical context to help your open rates i think otherwise it will start to default to being too generic and that's where you'll see like lower open rates and like lower positive sentiment scores the other thing i did as part of this during a lot of where we send our time now that we did an additional data cleanup is enriching our current data and those warm leads the warm outbound so like people on our website people covering the sound store event with fresh data right so there's different there's like i I think 20 different vendors that do this.

17:03Obviously, a good way is to scrape your website itself and see what you have. So maybe in your journey, you might need to do step two more than you need to do step one to get all the fields you need to work with AIs. So the other thing I'll just mention is a lot of these different AI SDR tools have certain requirements of fields that you will need to have in order for it to send the really good emails. I mean, each of them have kind of weird, quirky certain fields they need. and that's because it wants to 100 % make sure it finds the right person on the internet to say, okay, this is the person that you are trying to prospect.

17:38Like beyond a certain of a doubt, you know, that is what the AI is trying to do. So there's a good reason it needs those kind of quirky certain fields to do your prospecting and outbound for you. It wants to make sure it's doing it to the right person. Not like, you know, I typed in Amelia LaRue to test her. There's only, I think, one of me on LinkedIn. But if I've got a more common name like Joe Smith and there's 10 of them, well, this is why the AI needs all those fields so it can make sure it's affecting the right Joe Smith at the right company. The last thing you want is for it to send an email to the right person, but wrong data points of, oh, hey, I saw you're at Fox.

18:16And actually that person works at Dropbox. That's what the AI is actually trying to avoid. So that's part two. And then, yeah, step three would be actually thinking through before you start a structure for the AI. Two and three go hand in hand, right? You may need to even take it a step further. Choose structure your AI, SDR. I'd also start small. It's not something where, you know, you want to necessarily start blasting everybody that comes to your website in real time because you can. Doesn't mean you should. What are some of the things that perform the best is, yes, what do they do on our website?

18:51What are they doing on Souser? Have they been to an annual? Have they been to Europa? Have they been to all these workshops? What stage are they in? All those things like that. Recent funding news, job title changes, pain points, like some of this that will scrape for you automatically. All this to say, points one through three, you're trying to get your AI to a point where it knows enough about the exact person you're trying to reach that it will write a really good email that you would have otherwise had to spend probably, let's say, 10 to 15 minutes writing yourself. That is the whole point of trading the data and getting the structure right is to write the email good or better than you would have yourself, obviously at a much faster rate at a much higher scale, right?

19:35Like I couldn't email those 4 ,000 people myself in two weeks. So we talked about it a bit. So I'll keep going. But yeah, just for hyper segmentation, you know, it's been true of marketing since before. The more you can hyper segment it, the better it will work. Ask yourself, what have you, ask yourself, what has this person done with our company lately? Have they been to a customer dinner? Did they go to the website? What do they read on the website? So ask yourself, what have they done with you lately? And how does that tie to them maybe showing intent to buy? Or could you get them, you know, down a path where they have an intent to buy?

20:14Jason mentioned it as one of the caveats. I have it here at number five. Like prior tracing, what I call, what we call warm-bound bound always works, right? This is always going to work better than any like bold list you could scrape off the internet and say, hey, AISDR. I just got this list of 100 companies that are in the top 100 companies. Start emailing them. That's probably going to work as good as you giving that list to an SDR. That's a human. Say, hey, start emailing them, which is probably not that good. if they don't know you or you haven't added value to their company. Again, at the end of the day, you want your AISDR to know exactly who you're targeting, have some context with all your data of what they've maybe done with you and what your value add to them is so that it can write an email that will start to add value to the person.

21:05You can call them a prospect, you can call them an account, call them whatever you want. At the end of the day, if your AI is adding value, We will not care. It's an AI. Amelia, maybe a question on this. With your AI, you're basically trying to do two things today, if I have it right. One is re-engage folks to buy a ticket and come to our Sastra events in London in December and May next year. And two, reactivate folks to buy sponsorships. I think we're running both. We're running both. How do you do both at the same time? Is it two different instances, two different orgs, two different iterations?

21:40And so like how many variants of this AISDR are multitasking, are running in parallel? Yeah, it's a good question. I would say in the first iteration, this was an experiment for us. I know you're all in on AI, I'm all in. We all agreed we should try this. Before this webinar, listen, there's not a lot of content out there on how to do this for real. So I just started it. I was like, okay, let me use the marketing principles and sales principles I know since the dawn of time, but also let me just try different things. So I started it on three different experiments, one for each bucket, one which was a totally cold outbound, literally to a list of people.

22:21It was a mix. Some people knew Sassar, some people did not know Sassar because I wanted to see how the AI would do. Then I did lapsed accounts, right? So these are folks that have known Sassar, but maybe haven't engaged with us in a while for sponsorships. And then I did I did one for tickets of people who have been to a previous disaster event, haven't been to one in a while, see if that would convert. And that fourth was I had the website one on in a version that I don't have on anymore, but it was like basically doing some light qualifications of folks on the website to see what would happen with the AI.

22:59yeah i would say across the board results are better or worse in each of those four buckets probably most relevant for folks on this call as different product lines you do have to kind of force rank what product lines and different campaigns you want to run with your ai because already like my inbox is out of sense like i have you know i think there's like 30 personas i have on our tool and I'm already like maxed out. And so I literally have David from our team joining now, who is now I'm segmenting some of the sends through his inboxes because I'm like, okay, I'm going to use mine for certain campaigns.

23:40I'm going to use his for certain other campaigns to work because even the AI, as much as it can send, even for us was not enough. I was like, I already need more. There's that. But before we move on to things like other ways in the sales cycle, then pure outbound is don't expect the AI to do it all for you. I think when folks turn on or even if they make a conscious decision to buy something like an artisan or something similar, they're like, OK, I'm going to buy this and use this. I'm all in on AI. Like, it's going to work magic for us. Even at that stage, I would say don't expect it to do everything.

24:21Right. Everybody has different data signals. You have different campaigns you're trying to run. It doesn't mean it's a genie and it's going to grant your wish right away of like more pipeline, more close to one fields, more reactivated accounts, more sales than the way it has for us. Like it may not work that way. So just know that going into it, it will require a lot of tuning and training. The other learning was once we turned this on and people started responding to our AI and we had a human in the loop, which was me and it worked for a while and it worked the best. You have to respond instantly.