How do you know if your marketing efforts are actually driving real business outcomes? Imagine being able to tie every experiment, campaign, and variant to metrics that truly matter.
I am here at Opticon 2024 in San Antonio Texas and getting the opportunity to see a lot of inspiring ideas from some of the world’s leading brands and hearing all about Optimizely’s platform and how it enables 1:1 personalization, streamlined content operations, and incorporates the latest generative AI features.
Today, we’re exploring the power of connecting marketing and experimentation to business outcomes with Vijay Ganesan, Co-Founder and CEO of NetSpring, now part of Optimizely. We’ll dive into how this integration enables marketers and digital leaders to measure what matters most and unlock new levels of insight and accountability.
Vijay – welcome to the show and congrats on the acquisition of NetSpring – Why don’t you start by telling us a little bit about your background and your current role?
Resources
NetSpring website: https://www.netspring.io
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Transcript
Note: This was AI-generated and only lightly edited
Greg Kihlstrom:
How do you know if your marketing efforts are actually driving real business outcomes? Imagine being able to tie every experiment, campaign, and variant to metrics that truly matter. I’m here at Opticon 2024 in San Antonio, Texas, and getting the opportunity to see a lot of inspiring ideas from some of the world’s leading brands, and hearing all about Optimizely’s platform and how it enables one-to-one personalization, streamlined content operations, and incorporates the latest generative AI features. Today we’re exploring the power of connecting marketing and experimentation to business outcomes with Vijay Ganesan, co-founder and CEO of NetSpring, now part of Optimizely. Vijay, welcome to the show and congrats on the acquisition. Why don’t you start by telling us a little bit about yourself and your current role?
Vijay Ganesan: Great, thank you Greg, great to be on your show. I’m excited to be part of Optimizely. We, Optimizely acquired us about just about a month ago. NetSpring, we were an early stage Silicon Valley startup focused on data analytics and Our history, the genesis of NetSpring, we started the company with this realization that there is a market need for next generation analytics platform that’s focused on what we call event data. This is data coming from your digital platforms, instrumentation of your websites and apps and other digital properties. And so we recognized that there was an opportunity to build a more powerful platform there. And that’s a journey we embarked on and we’re glad to be part of Optimizely now. Scale it to thousands of customers.
Greg Kihlstrom: Love it. Yeah. Well, so yeah, let’s, let’s dive in here. So we’re going to talk about a few different aspects of, of just how this, um, integration can be, can be valuable. And so the first thing that I wanted to talk about is, um, experimentation capabilities. So, you know, NetSpring brings these warehouse native analytics to the table. So now pairing that with Optimizely’s experimentation capabilities, you know, what makes this combination so powerful for marketers?
Vijay Ganesan: Yeah, so if you look at how experimentation is done today, whether it’s Optimizely or whether other systems, they’re done in these fairly siloed systems where you measure the effectiveness of experiments based on sort of first level of impact. So for example, you ran an experiment and you’re measuring how many people clicked on a button. But to make the analysis more business impactful and tied to business outcomes, you need to go to the next levels of impact, right? So for example, you may want to understand the impact of an experiment on subscription revenue, for example. And a lot of the data is not in these experimentation systems. They’re sitting outside in the data warehouse. And so the problem we’re solving here is we’re making experimentation more business impactful, more tight to business outcomes. And the way we’re doing it is being able to easily access this rich business context that is sitting in the data warehouse, bringing it to the world of stats and experimentation.
Greg Kihlstrom: So what does that mean from the, the marketer’s perspective? You know, so Optimize League One is, you know, it’s a marketing operating system and, you know, so marketers doing a lot of different things in that, you know, what, what does this kind of integration mean for companies looking to innovate in this, in marketing?
Vijay Ganesan: Yeah, so we’re starting out with experimentation. That’s the first integration we’re doing with Optimizely. But our vision is to make NetSpring analytics pervasive across all aspects of Optimizely 1. If you think about very successful marketing organizations out there. The one characteristic you’ll see of all of them is they’re very, very data-driven in everything that they do, whether it’s in the CMS side, the CMP side, experimentation side, personalization, they’re very, very data-driven. And so, that’s what we bring to the table with Optimizely One. NetSpring Analytics is going to become a common substrate across everything that a marketer does within Optimizely One.
Greg Kihlstrom: So, you know, uh, benefits of this integration, um, include the ability to test against metrics that matter as, as you were mentioning, you know, in the e-commerce space, this might be like return adjusted revenue. You know, those things that to your, to your earlier point are, they’re not necessarily available in a lot of platforms direct, right. They’re, they’re one or sometimes a few steps removed. What do marketers get to do differently when they have access to the, like what, what can they do better?
Vijay Ganesan: A lot of what people do in marketing is very hypothesis driven. You have a hypothesis about influencing user behavior typically and you’re testing that hypothesis. One of the things that make for effective marketing is to be able to close what I call these growth loops. You start an initiative, you launch it, you test it, you iterate on it and there are these growth loops. And so what we help marketers do is to first of all, close the loop, right? So to close the loop, you need access to data that you typically don’t have access to, and that’s the warehouse native aspect of it. And then the second thing is you need to accelerate this closing. Because if it takes a month for you to close a loop for a certain hypothesis you’re testing, it’s too late. The market’s moved on. So that acceleration we provide with our product, which has a very self-service interface for marketers, that they don’t have to wait around for their data teams to come up with a report three weeks later. They can self-serve through our self-service interface. So that’s the essence of what we help marketers do. With the Optimizely One and NetSpring integration, we help you close and accelerate these growth loops.
Greg Kihlstrom: Yeah, and to that point about speed to insights or those closed loops, NetSpring integrates with platforms like Snowflake, BigQuery, places that are already being, warehouses that are already being used by these companies. Um, I think to your point, you know, this is instead of sending a request out and waiting weeks and then, you know, I. I’ve seen this before, you know, a marketing team, they wait for the data and then it takes them time to analyze the data and then it takes them time to act on the data. So, you know, they’re like two months out from being able to act. So, you know, how does, how does integration with some of these known or, you know, currently use platforms improve flexibility as well as data integrity?
Vijay Ganesan: Yeah, so great point. So if you look at what’s happening today, what you said about the snowflakes and the big queries of the world, clearly the center of gravity of data is the data warehouse. And it’s not just business transactions data coming from your sales force and event data is coming to the data warehouse. product instrumentation data as your website instrumentation is flowing into the snowflakes and big queries of the world. So that is the undisputed center of gravity of data in the enterprise. The way marketers have been operating is they’ve got these purpose-built tools that they get some insights from, and then like you said, for the rest of it, they’re waiting around for their data teams to produce a report. which is, you know, the time it takes to get that is a problem. But the other problem is you get that results in a disconnected form. You get it in another tool. You get a BI report. Now, how do you integrate these two? The analysis you’re doing in your purpose-built tool and the BI report that you’re getting from your data teams. You cannot connect them, right? And so analysis becomes very fragmented in two separate tools. You cannot share context across them. And so, you know, it just becomes the ROI diminishes and, you know, you stop doing a lot of this additional analysis that you try to do, right? So what we’re trying to do is to bring all of that into a single tool, working off the single source of truth, which is the data warehouse. And so that simplifies the experience quite radically for the marketer. And then on top of that, when you make it self-service for the marketer, for a majority of what they need, they can just get it themselves without having to call their data teams.
Greg Kihlstrom: And so, you know, to kind of dive into that a little bit more, you know, this idea of democratizing data, you know, I think there’s quite a bit of talk about that. But in practice, it involves things like what you’re talking about, you know, it involves that connection. And I think that you touched on a few kind of challenges when that doesn’t happen is, you know, first the time aspect that we talked about, then the disconnect of, you know, they get one report and they have to tie it to another report. And, you know, there’s not only room for potential misinterpretation or, you know, or things like that, but can you talk a little bit more about What do marketers get to spend more time on, I guess, when they’re not trying to correlate things that are difficult to do? And what does that mean for the marketer?
Vijay Ganesan: I think the biggest thing it means is trust. So if you think about it, yes, you want to close the loop. You want to accelerate and all that. But at the end of the day, if you cannot trust the data, you cannot take actions on it. And you won’t take actions on the data if you don’t trust it. So trust is critical, and so what this lets marketers do is to be able to trust the data enough to be able to take bold decisions based on the data.
Greg Kihlstrom: Yeah, yeah. And there’s also data duplication, and there’s a lot of other things that this also helps to eliminate. Can you talk about what are the costs, so to speak, of doing that?
Vijay Ganesan: Yeah, so data duplication is the biggest challenge in enterprises. Anytime a copy of the data is made, it results in lots of challenges. Managing the pipelines to move data across different systems. Security, privacy, governance challenges, especially when data goes outside of your central systems to some black hole somewhere. Things like GDPR and all that are important. So there’s the cost aspect because this gets expensive. Every pipeline you add, every new system that you add introduces cost, both in terms of managing these pipelines, in terms of paying for duplicate copies of the data, and then all the governance security challenges that it imposes. So there’s a lot of challenges with traditional systems and we’re solving a whole bunch of problems with this approach where we say, There’s one place where your data lives and no copies are made, and we work directly off that data with an interface that is very easy for a marketer.
Greg Kihlstrom: Yeah, yeah. So what advice would you give to organizations that maybe not quite here yet, but wanting to move in this direction, giving greater democratization, tying things more closely to business outcomes? Where should they start?
Vijay Ganesan: Yeah, I’d say, you know, embrace your data teams or the centralized data warehouse teams. If you look at how, say, CDPs evolve, one of the primary drivers for CDPs was marketers wanting to be you know, self-sufficient, you know, that I don’t want to deal with this centralized data teams and I’ve got my own world of customer data in a platform that’s sitting outside of the enterprise. And that’s resulted in a lot of mess with a lot of duplication of data and so on. So, you know, my advice would be, you know, embrace this new paradigm of your central data warehouse being the source of truth and get connected with the data teams And then look for systems like NetSpring that give you the ability to access the data very easily, that not only do you have access to that single source of truth, but you can access it in a self-service fashion and be self-sufficient for a lot of things that you can do. Then the final thing I’ll say is, and this is the crux of this acquisition and the value we bring to Optimizely One, is this idea that you can really make your digital experience initiatives more business impactful. That’s really the crux of growth in your business. You really can take analytics to the next level. You can get an order of magnitude increase in the ROI of your digital experience initiatives.
Greg Kihlstrom: And I want to dive into one quickly, one thing that you touched on too, which is At least from, from what I’m hearing, this also helps, you know, I, I work with marketers a lot. I also work with data teams a lot and it, there’s a, there’s a strong role for each of those teams in, you know, it’s not like all of the stuff is given to marketers and then you don’t need data, you know, you need both, but it actually, it seems like it focuses each on what they really need to do is that.
Vijay Ganesan: Yeah, absolutely. So actually the data teams have an important role to play here too. And data teams love us because what we help them do is what we call a governed self-service. So self-service can be challenging in environments where everyone’s going and creating their own interpretation of the data. So governed self-service is the key. So what our architecture allows data teams to do is to control who gets access to what in the data warehouse. So they control the central data warehouse. They expose certain aspects of the data to certain teams as appropriate. They can audit all access. They can model the data in a fashion that is amenable for self-service. So it’s really a very symbiotic relationship, and it’s a win-win for both teams because the data teams have comfort in knowing that everybody’s working off the single source of truth. Everybody’s only accessing the data that they’re supposed to access. And then, at the same time, they can let the marketing team self-serve and not have to bother them with repeated requests for reports. And then finally, even if there are some elements where you go to your data team to build some more complex stuff, both these groups are working in the same tool. These are not separate tools. So they’re all working collaboratively in a single tool.
Greg Kihlstrom: Yeah, that’s what I call a win-win, right? So that’s great. Well, one last question for you here. I really appreciate talking with you today. And this is something I ask all my guests here. What do you do to stay agile in your role and how do you find a way to do it consistently?
Vijay Ganesan: Yeah, great question. If you’re in a product-oriented company, you’re building software, you’re building products, the number one thing that I think I do to stay agile is to talk to customers all the time. You have to be talking to the users of your product every single day and learning from it and constantly iterating. And that’s the way to be agile.
