Name: Decoding data to fuel SaaS Marketing analytics
Uploaded: 2024-09-11T17:57:35.026Z
Duration: 31 min
Description: Decoding data to fuel SaaS Marketing analytics

Transcript for "Decoding data to fuel SaaS Marketing analytics": Alright. 3, 2, 1. Here we go. Hello, everyone. Good morning. Good afternoon. Good evening, wherever you're joining from. Welcome to our 3rd data warehouse and analytics webinar series. And today, we'll be talking about decoding data to fuel SaaS marketing analytics. We have 2 amazing speakers from Silego, Brian and Jerry, and we'll shortly get an introduction, who they are and what they do at Zilligo. But before we start, would love to hear where you are all joining in from. I see folks are joining in, so welcome. Type in the chat, and, I'm joining from San Francisco. And when Jerry and Brian introduce themselves, I'll let them share where they're joining from as well. As you can see, this topic is very hard, near and dear to us because when we think about SaaS marketing operations, having the data for insights, looking at data extraction, all of these things are fundamental to helping our teams be successful. Whether we are thinking about what do we do with all these complex data structures, are they ready for, taking into our analytic platforms like CaliberMind or ThoughtSpot, leveraging the cloud data warehouses like Snowflake, and thinking about connecting the dots between HubSpot, any of the analytics and data that exist. Today, we'll be tackling data inconsistencies. What does it look like to have full leverage for your data? Bridging the gap between complex data and actionable business insights. So join us as we untangle the data that exists today in Soligo into streamlined analytics driven formats. So I'll take it pass it over to Brian and Jerry to do a quick introduction. For folks who have joined us before, You know the drill. This is our 2 truths and a lie. We would love to hear a little bit more about Brian and Jerry other than their job titles. So, Brian, you can go first and maybe guess what, Jerry's lie is, and then, Jerry, you can do the same for Brian. Okay. I will call you too. Thank you, Dev. It's really nice to be here. I've watched this podcast or this, webinar series in the past, and I'm really pleased to be here. So thank you. I am, I'm joining from my basement in Boulder, Colorado. I have a, sick kindergartner who's just learned how great an Xbox is. So he's been I think he's playing racing right now. So his his day is going good. So that's where I'm at. Jerry, what about you? I am joining from Lewisville, North Carolina. Just moved there about a year ago. So I used to live in Michigan. It's much nicer. Nice. Yeah. Yeah. Beautiful part of the country. So, Jerry, we've worked together for almost a year. I like to think I know a lot about you. I've met you in person. We've spent a lot of time talking, but I'm stumped. I look at these 2 truths and a lie. I think 2 are like the same, and one is not like the other. The one that's not like the other is that you played college basketball. And I think that you went to Syracuse, if my memory serves me right. So that would mean you'd play basketball at Syracuse, which I'm not doubting you. But, you know, I think that's the one I'm gonna pick as the lie. Well, it's the wrong color orange, so it's Clemson. Clemson. Their basketball team has not been good for a very long time since Forrest Grant. So but, yes, that is a lie. I didn't play college basketball. I wanted to, though. Okay. So for yours, so we talked a little bit. I'm pretty sure I know your uncle did invent battleships. I know that's that's true. And then the dead ends of the banana seems too fantastical to be false. So I'm gonna go with you being a snowboarder. You probably ski instead. Well done. Good deductive reasoning. You passed the test. Look at look at you get getting through data and getting through the insights from yours. Love it. Awesome. Now that we know a little bit about our speakers, we also have a q and a section at the very end. So we encourage folks to as we're going through, if you have any questions or something that Brian or Jerry shares, and you're like, how do we do that? Type it into the chat, type it into the q and a section, and we'll definitely cover it at the very end. So let's get started with our webinar. Brian, we've probably talked about this a lot. What does your day in a life look like as a VP of GTM operations at Tsolegal and even the decade of experience that you've had figuring out what to do with all the sales and marketing data that exist today? Mhmm. Yeah. A decade sounds kinda wild to say, but it's true. You know, I think my day is a lot like many of my peers' days where we are trying to uncover the signals of our business, trying to understand what's working, what's not working, thinking through how to reallocate resources that might be people or investments in order to capitalize on our biggest opportunities. And that type of process all starts with some of these tools that you see here today. Many of you may have them or related tools. But I like to say, you know, Soligo helps all of these platforms tell a story about our data, but more importantly, allows the data to tell its own story. And a few ways that I do that, you know, every day I start my day with Clari. Clari is the the source of truth for our forecast where our sales put puts our their calls for the month and the quarter end, try to understand where we're tracking and trending in that regard. I then shift to ThoughtSpot and Salesforce to understand our pipeline, our sales productivity measures, our win rates, understanding our lead volumes, really getting a clear picture on sort of the leading indicators to that forecast, that we need to hit. All of this is centralized, in a a couple places. There's 1 in our data warehouse, which allows each of these tools communicate with one another, so subsequently feeding information to make each of their calls and their signals more relevant than the last. And CaliberMind is one of the most important tools to help us really look at the whole picture. CaliberMind is a platform that helps us understand our go to market efficiency and effectiveness where we can measure ROI, pipeline influence, and ultimately understand which tactics are working to help our customers be more successful. And all of this is also centralized, you know, in an open AI environment where we can query all of this data in a really concise way. You know, I think, again, this is sort of a case of every one of these tools has native integration, and, those can can help you get to a certain point. But as you start to add more tools into your stack, creating an environment where you have an IpaaS orchestrating data across systems really does give you the 1 +1 equals 3 approach. So with all of these tools, they may be great, they may be perfectly configured, and your users may have fully adopted them, but you're also gonna have be left with 5 problems, which every one of you deal with, no matter if you're in operations or in you're in the data field. And these are quality consistency, the elimination of manual data entry, data silos, and compliance. You know, poor data cost companies 1,000,000 every year, and it could be a missed opportunity that you you didn't get a chance to capitalize on or you made a bad decision based on bad data. In addition, how many times have has the audience been on a call where, you're comparing notes and comparing data points, but 2 people are using 2 different systems, which may have synced at a different schedule and different times. So, subsequently, everyone's data is wrong, and it subsequently abandons the meeting because no one can agree on the data. It's a big problem, and it sucks a lot of time and efficiency out of an organization. The third is, you know, manual data entry, is was a means to an end in the past, and now it's time to really innovate past that. Every system must must talk to one another. We need to eliminate manual redundant actions and the room for error. Data silos are really kind of a central part of creating this orchestration is when we think about a data silo, prohibits a hive mindset. And that's one of the most important things as we think about, you know, not every marketer is the smartest person in the room, not every seller, not every IT member is the smartest person in the room, but really the value is when we all align on common set of data to advise decision making and collective thought, and that's really how we move forward faster. And finally, security is always a concern, especially in this day and age, you know, things like encryption at rest and in transit, event, role based access, event based syncing, audit logs, SSO, all of this enable, really an extensible experience for the the IT teams and our business users to operate. Next slide, Div. I like to think, that I know technology. I've been in operations for as Div dated me over a decade. And, you know, I I ever since I was a kid, I loved technology, in every way, shape, and form. And although I had all these tools and I've used all these tools, and this is just a sample of what you may use in your organization, I didn't realize the power of an IpaaS and how an IpaaS can really accelerate not only the development and the progress of these tools, but also provide more insights. And today, it's Allego. I am I have the gift of working at an integration platform as a service company in a, an operations role. And the autonomy to go really push the limits of our integration and our tools, which is really great opportunity for me personally. Jerry is a member of the sales operations team, and he is a case in point of what we call a builder. Jerry is someone, who really understands the technology. He understands how to integrate the tools using the IO platform, and we spend many times thinking about new ways to use IO. And one of the ways we're gonna talk through today. Right, Jerry? Yeah. That's right. Go ahead and next slide for me. Thanks. So today, one of the ways we execute marketing campaigns is through paid media. That might include ad platforms like Google Ads or LinkedIn Ads or, you know, RealWorks, that kind of thing. Each ad platform might tell only one part of the story as to how well our marketing investments are performing. So we had a way to consolidate that information along with buyer intent signals from other platforms, to give a holistic picture of our customers buying process. So we solved this by creating integrator IO flows that exported data from our platforms and consolidated them into Snowflake, our data warehouse. And then that allowed us to take that consolidated information and send it to reporting applications like Calvermind and ThoughtSpot. And this is just one example of exporting our Google Ads data to Snowflake. So And, Jerry, I'm sorry to interrupt, but you're a builder. I noticed there's some code on this on this slide. Do you have to know how to write code in order to build these integrations? So, I actually don't know a lot of code myself. So, one of the great things about, Integrator IO and our platform is we have things called connectors. So I'm gonna get into that just a little bit, but, that allows for a little bit of flexibility in that We don't need to actually call out the API call as much. We can use drop downs. So our integration platform basically works with a set of building blocks. So flows need some way to source data from 1 or multiple applications, so that it can then import that data into other applications, in the language they'll accept. So we call those exports and imports. And in order to source the data for an export and then send that data for an import, we need to establish connections for each. So, after establishing that connection, we then need to figure out what data we're trying to move between the applications and then how frequent we're gonna send that information. So this might normally be a little tough for someone like me who's a business user and I might know what I need, but I'm not very technical. So, our connectors is is what kind of fuels the ability for business users to kinda catch up to some of the technical users. So based on the application being used like Google Ads for this example, we have connectors that assist with creating that connection I was talking about, and then setting up the export and import with the strap down list that you see here. As well as previewing the data to the right. So my favorite part of the export flow is the preview feature. So once we've configured our export we can preview that data to get a feel for how the data structure will look before moving it on to like the next step, the import. So it makes things a little bit easier to verify. You're on the right track. So That's great. And then what does it do for us? So all of this back end work really is pretty straightforward, can be set up in a matter of hours. And all of a sudden, you've got your data flowing into your data warehouse or your system of record, which we where you run your models. And in our case, we use a vendor named CaliberMine. You can check out their website, calibermind.com, and, really, they're a marketing decision engine. And for us, they're centralizing all of these data points across our sales touch points, our customer engagement, our marketing engagement, our first party and third party signals, both intent and interaction, bringing that all together and helping us distill down into some really simple answers. And and this is an example of a dashboard. It's got some dummy data. As you could see, we've grayed out a lot. But the point is, you know, in this example, we're now able to isolate a very particular campaign and understand the impact that that campaign has had, whether it was pre opportunity creation, post opportunity creation, the overall ROI based on the cost of this. And and this dummy example, this appears to be the Glengarry leads at 832x ROI. It seems like we gotta do a little bit more of that. So this is one small example. We have many, many more, and I'd love to, you know, follow-up with anybody who'd like to connect and kinda share some ideas and brainstorm on new ways to use, use data to really arm our decision making. Jerry, we don't only only use this for sales marketing. Right? Other business units can use an integration platform. That's correct. We, we not only have use cases for marketing teams, but our customer success and sales teams use IO as well. So they use it to source the data in the system that they spend the bulk of their time in. So as an example, we use Gainsight to generate an adoption score based off of usage and other factors. It helps us get a feel for who's doing really well as a customer with adoption versus who might need a little bit of help. So our customer success teams, they spend the majority of their time in Gainsight. But sales teams that this data might be vital for, they spend the majority of their time in Salesforce. So in order to solve for this use case, our first step was to get that Gainsight data into Snowflake, our data warehouse. So this export from Gainsight should look, very similar to the export from Google Ads that we looked at previously. We've got connection, along with the options for what data we're trying to export and our nifty preview feature to the right. For this export though, we were able to use the simple view. For configuration, our our game site CS connector allows us to use those drop down to select what type of API call we wanna make, and it just simplifies the setup for us. This allows us to take data from Snowflake and source other applications like ThoughtSpot or in this case, Salesforce. So our account executive don't have access to Gainsight or Snowflake for that matter, but they do have access to Salesforce. It is what they primarily use in the day. So because of this flow from Snowflake to Salesforce, they don't have to leave Salesforce to get, you know, pertinent information on how their customers using our platform. That's great. Thanks, Jerry. And it's really not limited to just passing ones and zeros into a data lake or a data warehouse and running an analysis on it. There's also large language models, and it's such a hot button. And as stated earlier, many companies have these large language models built into their platforms, helping you decode large amounts of data. In our case, the power of using Soligo IO is to centralize data points from every system, really giving you the complete picture. Here are two examples. We have prebuilt connectors with OpenAI for this example. And, subsequently, we we can query the AI asking questions using whether it be Slack or directly into to other tools such as, tell me about this customer call. Who was on it? What happened in the customer call? Give me the 5 key takeaways that the customer had, had raised with us. Or inversely, if you wanna inspect an account or a company, you could ask a question such as how is this company using us? Are they successful? How many builders do they have? What do they need to be more successful? So again, really trying to take all of this data and decode it in a way that every business user can take action from it. You don't need a fleet of analysts or operations folks to help you understand that. So a hot button topic, I would be willing everybody on this call has talked about it or working on it is customer 360. What does that mean? If IO can help with customer 360. Right? Absolutely. And thank you, Brian and Jerry, for walking us through some of the scenarios. Right? We talked about bringing your paid search, into your marketing engine, looking at some of those analytics. We talked about what does that mean at the end when we have converted those prospects to become our customer and then connecting the dots between Gainsight and your Salesforce ecosystems. When we think about customer 360, it's that end to end experience. So if you look on the very top, you'll see a button which says visit Saligos marketplace. That's the power of having an iPaaS. You can connect over 400 plus applications and data sources, whether you're thinking about your ERP, your PSA, your customer success, even your learning, tools that you have. Right? Litmus, other tools that you have. You can also think about what does that look like on the support side, so bringing your Zendesk information. And we'll be sharing some of those examples in our November, webinar where we'll talk about HR operations and bringing some of that Zendesk information into it. This is the power of having that customer 360 with an iPaaS like Soligo. We're connecting your sales. We're connecting marketing. We're connecting customer success product, all of these ecosystems coming together, and that's what we look for. So definitely click on that button on the top that says visit Saligos marketplace. I also see there are some good questions coming in. So if you have questions as you're working through, feel free to drop it into the q and a section or in the chat, and we'll respond to them. Let's talk a little bit about some of these use cases that exist today. Right? The beauty of, what we could do with the silicon ipass is connecting to your cloud data warehouses, and there are lots of different analytic use cases. We just touched the surface, talking about customer 360, talking about marketing, sales pipeline, and how we connect those things, whether it's your ad spend, whether you're thinking about ticket data, whether you're thinking about customer satisfaction as a whole. Similarly, you can think about operations as another big one. Right? If you have order demands, you're looking at optimizing your workforce today. Right? The utilization of it, maybe even navigating through some of that payroll aspects of it, depending on your demand, whether it's a high season or a low season. Similarly, look at other parallels. You have shipping costs. Right? You have shipping partners that you're working with. How do you find alternatives there? And then your procurement is another key part, whether you're thinking about your supply chain, whether you're thinking about future purchase decisions you want to make, and putting together, especially in manufacturing, other supply chain ecosystems, that single view of the business, blending in your ERP data, looking at your businesses end to end performance metrics. As you can see, Brian is always looking at forecast and looking at some of these metrics on a day to day basis. So it is top of mind. It is to drive the company's business goals and also some of the OKRs we have set for us as a business. Right? So consider some of these examples and use cases as ways to open up and bring data warehouse and analytics into your ecosystem. So with that, I'll pass it back to Jerry, to Brian to maybe talk through for folks who joined us for this 30 minutes, what are the key takeaways, and what do you think they should take as an action item from here? Great question, Dev. I was just reading the q and a. Really good questions. I'm excited to get into that. So key takeaways. I think, you know, number 1, as I reflect on my career and the scenario that I'm in right now, which I have sort of, like, user a access to our platform and product. And, again, reflecting on my past, number 1 is allowing our go to market and IT teams to collaborate and align in a set of high quality data. It's so important, and that's so feels so different at Celica than it has in the past. And, again, I attribute a lot of that to using an IO platform. The second is, you know, the ability to triangulate signals across all of these platforms, allowing people to take action, to support our customers, to win new customers, and subsequently creating a team game. And we all like to say it, sales is a team sport, customer engagement's a team sport, and triangulating these signals helps us. We showed how we can triangulate return on investment of our marketing programs by centralizing all of these touch points, all of these interactions from every disparate system running the marketing programs and sales programs into understanding the the complete picture. And then quality and consistency is always a question. And the more structure and process you have around how data is collected, how it moves between systems, how it's shared, how it's exposed, how it's synced, which is a question in the chat, is is so important. And it really creates kind of, like, benchmarks and gives a baseline for for quality and and understanding. So I think those are the 5 takeaways. The my what I've learned also is the power of an Ipaas, integration platform as a service is frankly limitless. And I always like to think that we are only bound by our own creativity. And the more that we expose ourselves to using an integration platform, the more we can understand really the true power of this. And I'm excited to see over the next 12 months how, our customers are using integration platform and specifically IO and and the lengths that we'll take it. Back to you, Debbie. Love that. Yeah. Love that, Brian. If we think about the art of the possible, like Brian said. Right? Creativity and what we want to do and what we can do is all up to us and our business goals and what we are aiming to achieve. Here are some resources that you can tap into. It's on the doc section in the Goldcast platform itself. We have shared some resources that you can look at when you think about customer 360. Right? A similar example of HubSpot and connecting to a cloud data warehouse. For folks who joined us last month when we talked about fusing AI and SQL, looking at that natural language business intelligence with Yousef, he's also shared a technical blog that will help you, build some of those in your own ecosystems. And folks who have been part of Zelligo and have been customers, thank you so much. We also wanted to share the Salesforce Bulk API 2.0 integration as well. So these are all resources available to you. Let's dive right into our q and a. So we have some good questions here. If some folks have additional questions to add, please do. We'll start with the last one first in. And, Brian, this one's about thinking about data sync frequency. How do we decide what that data sync frequency should be when we connect to an Ipass? It's a great question. And that's a real world example also that came up internally around, improving the velocity. You know, as an example, frequency matters, especially if you're using it, in a real time environment. If you're looking, you know, historically or looking, you know, what happened yesterday, it's okay to sync, you know, overnight or whatever the cadence is. But in our case, there were certain signals that we wanted to pick up and take action on immediately. And in order to do that, we needed to increase the sync frequency. And and, frankly, it's it's, again, back to the you are only limited by your own creativity, your own willingness to execute on something. So in our case, we said we needed to sync every hour. So we created a sync between some of our source systems and our data warehouse to provide up to date information so that hour by hour through the day, we feel that we are really operating in the most, most fresh data. So it's, again, only, only limitation is your willingness to to set it up as a more frequent sync than not. And in our case, we should refresh Brian, if you didn't have that hourly sync? What would happen well, what tends to happen is, again, you go back to the original problem in the past is that you may get on a call with a, you know, a few salespeople, and they're looking at Salesforce and you're looking at, say, ThoughtSpot, and you say, well, hang on. Your pipeline target is different or your win rate is different. Well, I just closed the deal, and I just won it last hour. And that's the problem. And that's just, you know, so important. And, you know, one of my personal passions is efficiency. And I think it it it's really sucks efficiency if you don't have frequent syncs. That's perfect. And that's why the data sync is important. Right? To look at what your business is trying to do. If you are an ecommerce, if you're looking at trends on maybe every 5 minutes, then those are things that you should map it back to that data sync frequency. So the next question I'll probably ask, Jerry, when we think about everything that you've shown, right, you love, working in silico flows, creating them, what's the most time consuming part of setting up these sales and marketing integrations? I would say, it doesn't take a whole lot of time to actually get into the into IO and add and export and add what fields you're trying to map, that kind of thing and test things out. But what takes a good bit of time is the prep work beforehand. You kinda need to know what you're trying to sync from place to place and why you're doing it. So if you don't know the answer to those questions, you you might find yourself creating a whole bunch of flows, and maybe even creating endless loops. You need to understand what day you're pulling from place to place and where you're putting it. And that really takes a good bit of time if you're not really sure about your process. So to me, that's the most time consuming thing. Got it. I agree, Jerry. I thought you were gonna say listening to me before you go build. I'm glad you didn't. Thank you. Well, that that too sometimes, Brian, but we're just being honest here. Because you have to understand the business. Right? What are we trying to achieve? And data can only take you so far if you don't understand the business goals and, aiming to have shared outcomes together. So, Jerry, you also talked about have us as Allego, we have over 400 plus, connector connectors that we can actually connect to different applications and data sources. What do we do if we don't have an existing connector or doesn't have the API calls we want? What do you do in that scenario? Well, I've run into that before because our our connectors are ever evolving. But it's trying to use, like, the most common use cases. So there may be API calls that the connector itself doesn't have a drop down for. And for those situations, you just instead of using the simple version, use the HTTP version. So that kind of flips over to our we call them universal connectors. So that'll give you more flexibility to add that particular API call you need, that may not be in the connector. It's just for those you may need to look at the API documentation just to figure out what call is that you're trying to do. So it's a little bit more effort. But again, our connectors, we revise these things based on usage all the time. So if there is no particular call that you're looking for for a connector today, there might be next month. So Perfect. Awesome. And that's what it is. Right? It's evolving. We have, options for you available today. If it doesn't exist, reach out to our team and we will, look at bringing that into, your ecosystem in the future. Thank you so much, Brian and Jerry. I hope folks who joined enjoyed the conversation, the discussion, trying to understand what it really means when we think about, harnessing the data and what that looks like for SaaS sales, marketing, and customer success information. So as we close, we are excited to kick off our next webinar, so which is on October 9th. It feels like it'll be a month, but, sometimes they seem to be coming sooner or later. What we will be sharing is thinking about that modern data stack. So all of you are here because you're interested in looking at bringing data warehouse and analytics together. We are looking at the next level. Right? This is your data lakehouse patterns. We'll be talking about how that exists today and bringing those connections together. So definitely, connect and reach out as you think about some of these abilities that you have today. And as an organization, the number of data consumers are growing. Right? Today, we have, Jerry here, but there are lots of other builders that are here and trying to actually make sure that you're transforming and consuming the data in the right way. So your modern data stack gives you that flexibility to choose the tools that you need. So definitely check us out, and we hope to see you all next month. Have a great, rest of your day, and have an awesome September. Take care, everyone. Yeah. Thank you.