Data democratization for Marketing using StreamSets, Snowflake and Software AG

Speaker: Daniel Adayev, Marketing Operations Manager

Daniel Adayev: Hi, my name is Daniel Adayev. I am the Marketing Operations Manager at StreamSets. And my day-to-day mainly involves system integration, API management and hygiene of the martech stack which primarily involves Marketo and Salesforce. I also work with Sales in order to enable any sort of technology or data into the tech stack that is needed to make them the best sellers they can be.

Daniel Adayev: We had a disconnect between our marketing database and our product database. So on one hand, you have your marketing database which has your webinar registrations, your event attendees, your asset downloads, your leads that are talking to Marketing and Sales. And on the other hand, you have your product database which has your platform registrations, your users that are creating pipelines, your users that are in jobs, your users that are running into errors. And this is all information that Marketing Operations was really interested in and we wanted to get integrated into our marketing tech stack. But there were a couple of roadblocks that prevented us from doing so. One of which was the fact that this was owned by a different department and we had limited visibility into the architecture of how this data was stored. We were aware that this data was relatively siloed. And so we didn't really have an easy way of aggregating all that data together, standardizing it and pushing it into Salesforce in an automated and efficient way. On top of that Marketing Operations also had the project of making this data, what we would call platform activities as visible and accessible to Sales users as possible. Our goal was to make it as easy to look at an account's or lead's platform activity as it would be to look at the opportunities related to an account or the email activity related to a lead.

I would say this question is twofold. Snowflake was massive in forming a foundation for building these projects because this data data was solid before we really had no way of knowing where to start. It was pieced out in different warehouses. As mentioned before, we had limited visibility and Snowflake really helped us migrate all the data into one area. So when we were pulling queries or we had to build out an origin for where to start grabbing the data, it was a question of what database, what table do we pull from Snowflake, rather than what data warehouse do we have to start looking for? What authentication do we have to start using? The process of grabbing that data became much, much easier StreamSets was really big in especially our first project where we were syncing up our data between Marketo and our product platform. Due to the fact that we had multiple ways of entry into the platform, one was through filling out a form on our website, one was through backend invitation by a Solutions Engineer or someone in DevOps. In another way it was being directly by another user, and then there was entering through Snowflake Partner Connect. We just weren't capturing all of those nodes and there was some data loss along the way. Even if somebody signed up through our website, all this stuff was getting all these, all this data was getting picked up on the back end of the product data warehouse. However, we were not able to pick it up. And so we were, we had built a StreamSets Data Collector that took all of that product user data, the emails, titles, the company, first name, last name, and pushed it over into Marketo to make sure that both of those were synced up and aligned. Afterwards, we had the secondary project of pushing in our product activities or platform activities. So we were able to track individual events done by users - when they created a job, or they created a pipeline, they created an environment, they created an engine, if they ran a job - all of those, we were able to pick up and push into Salesforce as individual user activities. And we were able to do that through webMethods using the Snowflake table that it was getting pushed into. And one of the ways that we were populating this master table of product events is we were using Transformer for Snowflake to grab different event activities from different sources. We were then transforming those events. We were standardizing them and then pushing them into this one table that webMethods was then picking up and using.

Daniel Adayev: I'm excited to share two major milestones that we've achieved with this project. Firstly, we've greatly improved the way that users sign-up data is passed to Sales and Marketing systems. As previously, only a fraction of user sign-up data was readily available. The second breakthrough is in capturing important platform activities. These include job runs, pipeline creations, environment setups, draft builds and more. And we've successfully integrated this data into Salesforce, linking it to lead, contact, and account records. And this gives Sales and Marketing an easy intuitive way to see who is active on the platform. This means that for lead scoring beyond traditional metrics like webinar attendance, event participation, asset downloads, and email engagement, product engagement is now an additional data point that is factored into the calculation. We've not only set this up for leads, but we've also established scoring rules at the account level which allow us not only to see which users within an account are the most active, but also which accounts among our entire database rank highest in product usage in a way combining our product led motion and ABM motions to create something new entirely. With a dynamic system in place that allows us to quickly and flexibly set thresholds for what is a product qualified account, we can readily respond to Sales feedback and fine tune the system over time to evaluate accounts more accurately. So in conclusion, Sales and Marketing now have an additional dimension of data when evaluating opportunities for new business, renewals and upsell. Marketing can respond in real-time to meaningful events and create personalized experiences and campaigns. And this is a pivotal step in StreamSets' journey to data-driven growth and agility across the business.