The Sales Engagement Podcast
The Sales Engagement Podcast

Episode · 3 years ago

Outbound Ops Part 2: Relevance at Scale w/ Ben Salzman and Kyle Williams

ABOUT THIS EPISODE

Max Altschuler hosts Dogpatch Advisors to discuss the future of sales engagement. They discuss the Outbound Ops model, custom code to find relevance at scale, visual prospecting and other concepts from their research and development efforts.

 

Welcome to the sales engagement podcast. This podcast is brought to you by outreach dot ioh, the leading sales engagement platform helping companies, sellers and customer success engaged with buyers and customers in the modern sales era. Check out sales engagementcom for new episodes. Resources in the book on sales engagement coming soon. Now let's get into today's episode. Excited to welcome back dog patch for part two of their first episode. Just had them on recently. If you haven't, please go back and listen to part one. It's pretty awesome. We're here to talk with Kyle and Ben from dog patch. Welcome back, guys. Where are you getting your data from? Do using similar web for this example? But let's talk about data sources. We think about data sources and three main categories, right, like there's the data that you buy, right. So you're going to talk to vendors, whether that's some Menfo, clear bit and others. There's data you would outsource rights. We're seeing this search to crop up more and more where teams are spending manual effort going and looking up, you know, does this company have x employees on Linkedin specifically, and they'll just outsource it and there's collect, and collect is either things that you're asking your teams to do directly or the things that you're you're automating, that first party data that's really most relevant to your company. And often that in this example ample for the similar web to so similar web, that spot data, the APP store data, that was outsource. In this case, we started with having outsources pull it and then we eventually we automated it and then the collected data was actually the computed attribute between those two data points, right like the innerlock, the overlap between languages supported versus languages that could be supported based on demand. That was a computed attribute. So we think about by go to the vendors, outsource, have someone else do it, where the...

...throughput or the calorie spent isn't put on your sellers or your teams that are driving conversations, and then collect, either things that you're automating because there's a way you can do it and it's first party data that really matters to your specific brand, or it's so valuable that it's worth putting the teams that you're you're paying salaries to go out and collect directly. And you know, I think one of the next you're asking a really important question like the data pipeline is we think of it is it's probably more important than any individual contributors that giving company has on their team. So a lot of times we're not thinking about which provider do we pick? We're thinking about what are all of the different types of data across companies, people, technographics, all the attributes that we could know about any of those categories of company, coming up with sort of backing into how do we use those attributes? You the drive target or drive personalization and campaign, and then how do we prioritize and sort of build out a longer term plan for acquiring and normalizing all that data? And one of the things that we found was when we go in the companies and ask, you know, what are you doing to source company data, they basically say we're going with x vendor. When we say why, and stay of the art response to that in many cases is, you know, this is what my vp of marketing chose at my last company. Right like that's literally fifty percent of the data purchase these days are what was my vp marketing doing at the last company? And while that can be a helpful data point. The data industry move so fast it's actually probably wrong for most companies, if you haven't looked at it recently. So a very large startup of ours, it's a client, actually had us go and run a large scale test across twenty four vendors globally to really understand out of statistical significant level. Right, we're talking thousands and thousands of rows of data to say who has which data where, what are the fill rates for that data and how would we use that data? And that is probably the right conversation to have instead of which vendor should we choose? So most of the companies that were going into are sometimes sourcing, you know, five or six different third party vendors while spinning up large scale collection efforts and large scale outsource efforts, all to be brought...

...together and sort of inform the sales playbook. It's a much more complicated process than I think most companies recognize when they first go after this. Have you done any reports or insights into US and global data providers? Yeah, so we did both. So it was first for this start of an effort to profile the US vendor landscape, which we first thought would be pretty clear cut. It it turns out to be quite complicated. There's a lot of confusing messaging and some claims that are dubious at at best. And then we did it globally, and in the global look was really interesting because we thought we would see a bunch of really specific sort of regional vendors who do really well in particular countries or companies who focus on a particular type of data who would do well there but not other places. And what we found was the folks who are winning today in the market are winning everywhere right. So the best providers tend to have data country to country that is better than the vendors who are based in any given country. The vendors who are doing the best on company data, they're also doing the best on people data. So it has a lot more to do with building the flywheel and having sort of the DNA to build an engineering approach to build a company on the back of data, not sort of thinking out of tactically of you we want to go and find a certain number of companies. It's how do you build the infrastructure to long term continue to collect that data? That makes sense. So I don't know disclose it. Where were the findings? Will be careful not to share too much. I don't think the startup that had us do this would want us to give all the results. I think what we can say is that the some of the interesting takeaways. One was there was a cluster of about three or four companies who are in these sort of eighty seven to ninety three percent match rate. And when we say match rate is is this company in your database? What's probably more confusing in most companies really haven't thought deeply about is what is the fill rate for all the attributes that we care about? So...

...a lot of companies that we're working with, we're helping that think it would don't just worry about is the company in the database? Do they have the attributes that you need to do your filtering, your targeting? It's Sarah. What we found was that was anywhere between about ten percent to ninety percent in terms of actually having the attributes. So, for example, funding very, very difficult to pull that. Obviously crunch base does a pretty good job. When you talk about some of the other global providers, often we're seeing fill rates in the sort of twenty five to fifty percent. So you know when you go outther and say show me every company is raised ten to a hundred million dollars, it's likely you actually have about half the answer, and so it's not actually as cut and dried as people, I think, imagine when they start this analysis. You know, I want to be careful of giving away everything, but there were some really interesting insights. Yeah, I'd say the big things are and and turning this more into like if someone wanted to do this, what should they do differently than just the what if we buy at my last company? Or One of the common mistakes we see is the selection of a data vendor is more part of a Tam like identifying the size of your market exercise, and so then you're able to tell the board, all right, there's a billion dollar opportunity that we can go capture and business services, because there's this many company with business services. Great, there's not a differentiation in that vendor data. If you to go say something differently or say something relevant beyond just they have a tag from a vendor. So that importance of saying what are the attributes we actually care about, not just for who could we talk to, but why should they want to talk to us? And that's sort of the pivot. We're often talking to companies and saying you've already done this exercise that says who do we want to talk to? But often there's that missing piece of why should they want to talk to you? And that follows that entire spectrum of data you could buy. So whether it's technographic data that are correlated with the types of companies that are ready to talk to you, could be employee size, could be presence of certain title, could be something that you go and collect that tells you they're doing something interesting on their site. But that extra dimension of thinking through what are the...

...attributes to tell us why do certain types of companies want to talk to us today is very different in a lot of these evaluations. So is this what you consider getting into data driven copy? What is that? Yeah, it's a great question. So I think the it's a starting point right. You have to have a comprehensive plan and stance on which data is going to be important at which field rates that will enable which campaigns, where the density of context will have across domains? What technographics can we expect at what actors to write. So you have sort of answer all those baseline questions. Where data driven copy. Gets really interesting is when a message is actually higher quality because of all this data normalization and really taking advantage of an outbound obs function. So in the example that Kyle just laid out from this, you know, language translation company the opening Hook for a message in that partular cases. I noticed you have seventeen two percent of your traffic coming from France, but you only support English and Spanish. We have some ideas for how we can help you expand into giving country. We found thos people were responding and saying, Oh, this is really helpful. Right. It's not that we're doing data driven copy to save time or because the throughput is higher, which it is, but it's because the quality actually ends up being higher because of the relevant or because of the scale and because of the automation. And it's really interesting to see the replies where you actually have people sometimes replying. I can't tell if this was automated or not, but I don't care, and that's actually a whole new category of outbound that we're really excited we think of a sort of relevant automation and it follows, you know, very similar trends to what happens to consumer five years ago. You search for something on Amazon and see a display ad. The next day you're pissed. You're like, oh my gosh, they're invading my privacy. There's all these issues. Now I see a display out or something like thank you for showing me something relevant. Right. So that same thing is happening in outbound, where data is really driving a lot of the quality and the relevance and people don't actually care as much as even two or three years ago as to whether there was automation involved.

They care about value and care about whether your vendor can solve a problem for them. Right. Do you guys use intent data at all? Surge data? So you mean like search trends or keyword data? That type of thing? Will also look at like are they on GT crowd, you know, looking at your company, or are they using clear it reveal? Right, yeah, yeah, so that is an actually it's an interesting segue into this topic of relevant automation where we're not so much focused on being human. Is Like I'm sure you've seen the examples of drifting segment or these cup you go to the site and it says you want to see how we can help outreach and it says your company name on there. If you did that five years ago, you get a ton of letters saying like, what are you doing? You're tracking my IP address. would be this creepy thing and I don't think anyone who goes on the site thinks that a human is specifically tracking that outreaches on their site and then crafting this message. I don't think that goes into the calculus. It's just more relevant. It's intriguing to say you want to see how we can help outreach for the banner changes. So we absolutely have seen a ton around using intent data. It creates a better lane for relevance, right, because we know there's some type of hand raise. It says there's some relevance to happen here and it actually reduces some of the requirements of how far sometimes you need to go with personalization, because now we're getting into the tap on the shoulder. Yeah, and there's other ones that are just really straightforward that I think a lot of people still think are too good to be true. Right. So anonymous IPS being surfaced is like this person was on your website. It's actually been around for quite a long time. Clear Bit anothers have recently made it accessible. Right. So the price is going down to the point where people can identify anonymous busitors to their site at a relatively low cost, and it does a number of things right. One is it helps you understand who should we be targeting? WHO's important to us? It also does a number of other things across the sale cycle, right. So a lot of companies probably end up having visitors to the website that are midsale cycle. That's super important to know right your mid sales. Like you're scheduling a call, you see a bunch of traffic the day before the...

...call. Oh, someone cares, right. Or you're closing a deal and all of a sudden people start showing up on your site. Guess what it's finance wondering like who are we contracting with? There's so many signals that you can pull out to better understand what's actually happening on the other side. And again, it's much more accessible that it was even a few years ago. I would say. The one trend we've noticed to be interesting to see what you see on this Max is, you know around saying you don't have to prove that you're a human, you can just be relevant. Is there's still a creepy line that exists. So calling out and saying hey, we analyze the IP traffic on our site and someone from your company was on our side. Are you interested? Probably doesn't pass that bar. And it's what Ben and I call the Shakespeare effect, which is you're going to dinner with someone, you see they've liked Shakespeare on facebook. You don't sit down and say, I saw you like Shakespeare on facebook. Let's talk about that, because it could be a number of reasons. Why? Right, I did a paper in ten grade and I had to click the box, or why are you on my facebook looking at what I like and using that for topics? But well, we would say to do instead is use that intent data to say something relevant. So you should just quote act three, line two of Othello, and if they really like Shakespeare then they'll say, oh my gosh, I love Shakespeare, and if not, then you said something kind of smart and it passes by harmlessly. It's that concept of using that intent data to be relevant, but you don't necessarily have to call everything out. I like that. I like that a lot, so let's talk about that some more. So you're talking about, you were talking about earlier the two by two Matrix, the goodness of fit for vendors to goodness of fit for prospects. How does that work into these plans? Yeah, so, and often that's even a selection. So we often talk about the difference between your targeting data and your messaging data, and what often gets mixed up is treating those the same. So usually you end up locked in this dichotomy of we're going to send scaled messaging, which is we're going to make a list and they're going to write the message that matches everyone on that list, or we're going to write super relevant messaging, and so we're going to go one by one...

...and write the message for each person, going to match the message to each person. And we would say there's a combination of the two. So you get the list to a certain size and then you adapt the message within that list. And often those points are going to be who were actually the people who care about US and why? So that you know that Matrix is basically saying, on the one side, who do we really want to talk to and why? Right, like they have a certain amount of funding, they're in a certain business model, and then what are all the reasons that people want or don't want to talk to us right. So if you're a startup, there's a certain class of company that doesn't like talking to startups and there's certain class of company that really does like to start to talk to startups. So there's probably thirty different attributes that are a plus or a for the message you're trying to send, and for some of those you would simply not send it. Right now. We're not going to focus on that particular part of the market. Right now. We're going to win where we compete, can compete, and then within a certain amount, it's how do you adapt your message to talk through? You know been then I talked about this example of when we're at Google, and this is before the data providers. So I'd written this little Bot that would paying email servers, because email servers will say hey, I'm I'm alive, I'm here. But every spam provider, email server has a different flavor of how they give that what's called a hello command. So profiled though, so we can know all right, are using Cisco Ironport, which is really expensive and the heavy it decision, or using something open source and you could totally change the message based on Cisco Ironport. I'm going to talk about how Google has five hundred security engineers and with the most secure platform for this, in this case selling Gmail. But if you have something open source, when to talk about cost of ownership and how you don't have to deal with maintenance? And it's that difference of like tuning the message versus deciding maybe who you don't even go after. Yeah, I think I'll give you another story. You know from our last start of Kyle's first day at a mobile marketing compy that we both worked at, he asked you, how do we identify the week competitors and I said I wish that that was out there. Right, is literally predataized rights. We did nothing to know...

...what people had tech install and there was a bunch of we think of his legacy providers out there who are doing relatively uninteresting things. And Kyle said, okay, I'm going to, in all of my glory, write some code to sniff my own Wi fi traffic, download the top thousand APPs in the APP store and see what the payload contains. So we were able to all of a sudden identify here's what sdks are installed in all these different APPS and it was the lowest hanging through. Right, you find the weakest competed editor and you start they're targeting those people straight away. So what's interesting is the explosion of raw data makes this so. Now every company has access to that and it creates this downward pressure on relevance because everyone sending that same thing. Right. So someone's I noticed you have ex tech installed and I want to sell you my product. That's very focused on what you want for your target customer. Right, what is your ICP? That doesn't really speak to whether they want to talk to you. So what's much more powerful again, is going from observation, which is you know, I saw you have x tech installed, to inside. So I saw you have x tech installed and you've hired a number of new employees. Are expanding into this new market. Here's how we can help to solve a problem that's created when all three of those things are present. Again, very easy for an str to go figure that out in an hour for a single company. Very hard to do that across your entire targeting data set or your ICP without the right function in place to go and sort of normalize that data to be used. I love how you guys have merged and you know engineering and sales to really like master really put together this outbound ops. I don't know if it's a new role or position or industry or whatever it is, but it's certainly necessary. You know, it sails ops, sales ops hooped up for for outbound, for pipeline building. Right, totally. Yeah. I mean, look, the salesops function has been important for a long time. They never really touched outbound because it was such a human driven, in manual thing they didn't really need to. So we we do think it as a new function. We also think of it as a new model. Right. So the the idea of the the sort of predictable revenue model and Aaron Ross invented this in two thousand and two.

He's done a lot for the industry the world. Simply change around that model, right. It's so from a time when specialization was breaking out quart of carrying reps into prospecting reps. that same specializations happening in outbound right. So it's been having a specializations to having an economy for a hundred years. In this case there's all this raw data and this is sort of explosion of structure data that exists to power outbound. So it is a new function and a new model and you know, as we've said, it's not that. You know, it's data for data sake. A lot of the relevance that's coming out of these messages is driven from the raw data, not from the humans. So for a lot of our customers who talked about sort of modeling out their headcount of their growth projections, we would in almost all cases recommend a company goes and spends seventy to a hundred thousand dollars on more data than another single individual who, you know, maybe fully loaded in the area, is going to be close to that hundred K mark. There's so much more leverage that comes out of the the data than there is from sort of like one by one or linear effort. Yeah, I agree and really love what you guys are doing. So you work with companies like clear bit or we're allowed to get in a set of segment. I think there's another one. Your guys are in some of the have to believe at least one of those. But yeah, keep going, okay, will you guys are working with a lot of the fastest growing companies in sucking valley and most forward thinking companies working on APP bound ops. We're excited to get you on future episodes of the show to talk more about kind of the future of outbound and, you know, some of your tool kits and stuff like that. But really appreciate having you on and taking time out of your day to talk to our audience and looking forward to working with you more. Where can people find you right now if they want to learn more? Yeah, I mean good, to our website. We put up a decent amount of content and we sometimes speak at events, but you know, just come talk to us. Right we love talking to companies about what's going on in the world of sales playbooks and an outbound and we really focus, that you sort of mentioned, on the very highest end companies right like we don't tend to be the company who comes out with a report about what are a hundred sales leader saying across the industry?...

If it's the middle of the market, we don't care. We're really focused on one are the very highest end companies doing to push the conversation forward and we love talking to about that. So you know, come find us on twitter, linkedin wherever. And Yeah, Max, really appreciate you having a song. We're excited to be on again and congrats that everything you guys are building at sales hacker and outreach. It's awesome. Thanks so much, Max. Thank you. Yeah, we'll Sienna future episode and that's offer. Today. This was another episode of the sales engagement podcast. Join US at sales engagementcom for new episodes, resources and the book on sales engagement coming soon. To get the most out of your sales engagement strategy, make sure to check out outreach die I oh, the leading sales engagement platform. See you on the next episode.

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