Moody's
Case study

How Moody’s saves time and boosts revenue with Avo and Adobe Analytics

Analytics is lock-step with any new features and functions that we’re launching. As soon as a new feature comes out we open a new Avo branch, we create the calls to action associated to the new release, and then we start measurement on it as soon as it comes out. – Daniel Kaminski, Senior Director, Head of Data Intelligence at Moody’s 

From credit ratings and research, to investment strategy, and risk assessment—Moody’s does it all. Over the years, Moody’s has evolved from a renowned credit ratings agency to a powerhouse in credit analysis. Through a series of expansions, acquisitions, and internal innovations, Moody’s has developed an impressive suite of solutions to empower savvy investors with cutting-edge technology. 

Moody’s makes this possible through staggering amounts of data. Whether that’s company information, economic data, or risk assessment, customers can quickly find what they need with Moody’s new GenAI-enabled research assistant. The needle is now easily findable within the haystack. 

But these technologies are only made possible with teams of analysts, product leads, and marketers working to constantly improve the user experience. That’s where Daniel Kaminski and his team of data practitioners come in—providing data to cross functional teams, all while making Moody’s proprietary data as accessible to customers as possible. 

With the help of Avo and Adobe Analytics, Moody’s has streamlined its analytics workflow to keep pace with rapidly evolving products. Moving from a painful implementation process to a well-oiled analytics machine, Daniel’s team has made incredible efficiency gains and can finally spend time on what matters most: equipping their colleagues with key insights, and powering next-level GenAI products with high quality data.

The Challenge: slow, manual implementation workflows that couldn’t scale 

When Daniel joined Moody’s, he inherited legacy systems in sore need of an update. While data was available to internal stakeholders, setting up new tracking infrastructure was slow, painful, and decentralized. Daniel’s team often found themselves navigating long Slack threads and emailing local excel spreadsheets back and forth just to keep up with demand.

All of the events that Adobe was capturing were hard coded in the product itself and not documented anywhere. There were spreadsheets flying around, all over the place, it was hard to even wrap my head around what we were tracking. – Daniel Kaminski

Implementing analytics was not easy or fun for the data team. It wasn’t ideal for other teams either. Product managers felt removed from the process, and engineers struggled to delve through disparate documents to find what and how to deploy tracking. 

Before we had Avo, the product team was pretty disconnected from the whole process. It was really my team interacting directly with the engineers. We were just trading spreadsheets and Slack messages and stuff like that. None of it was standardized, none of it was scalable. – Daniel Kaminski

To make matters worse, the slow pace of implementation made it difficult to keep up with new product releases. Sometimes this led to a new feature going without tracking for days right after a new launch, when new insights would have been most useful. 

By the time they released something, we wouldn't be ready with the analytics. So we'd have weeks to a month of gap from when the product went live to when we’d get tracking.– Daniel Kaminski

Facing an unsustainable situation, Daniel knew the team needed to change. As luck would have it, Daniel’s colleagues recommended Avo at a timely juncture. Moody’s was migrating over to a new, react-based tech stack and had the opportunity to make a fresh start with analytics. 

The Solution: Analytics working lock-step with the product release cycle 

After onboarding with Avo, Daniel’s team was able to integrate his Adobe and Snowflake-based stack to a central tracking plan. Within the Avo platform, data engineers found they could work side by side with product managers and engineers to deploy tracking in a more streamlined manner. 

My team manages the whole thing and gives guidelines and best practices. And then our product leads feed us the requirements for what products they're looking to tag, and then the engineers are the ones that actually embed it and go into CodeGen and pull down the code. – Daniel Kaminski

With Avo and Adobe together, Daniel and his colleagues were able to be far more strategic with their analytics. They started experimenting with precise tracking instruments: getting visibility on intricate user behaviors from scroll depth and hover states, to search engine inputs. They could also implement A/B testing on niche aspects of the product, providing highly specific intel they could feed back to internal data consumers. 

We can now get very, very detailed in the analytics that we capture. If there's a new feature or a new type of page that comes out in the product, for instance, we can see CTA clicks, hover states and even just straight up visibility based on how far they're scrolling through the page. And we're starting to do A/B testing which I'm really excited about. – Daniel Kaminski

Moody’s new setup enabled them to deploy much more ambitious analytics. Crucially, it also enabled them to move lock-step with the product team, never missing a beat when it came to tracking new releases. It means crucial insights–those moments when users first get their hands on new features–are no longer missed by the data team.

Now under the current setup, the product goes live, and we are tracking immediately. That's been a game changer because in those first few days of engagement, there's a lot of interesting things to learn. – Daniel Kaminski

The impact on the data team: Implementation is easier, error-free, and 90% faster

With guidelines in place from Daniel and his team, and Avo’s platform to facilitate collaboration, Moody’s has drastically cut down the time it takes to deploy new events. The grueling, manual process that used to take months is now possible in days. 

Now we have very clear procedures. It's very easy. We don't have to retrain people. What used to take a few months or more, we can now get done in a week. – Daniel Kaminski

While Daniel’s efficiency wins are impressive, the impact on him and his colleagues is also significant. Product managers feel a stronger connection with their data requests, now they can contribute directly within Avo. Engineers “love” the ease of grabbing code snippets from Avo Codegen. This has led to a greater sense of cohesion among data producers and consumers across the company. 

Avo has definitely connected our product leads more directly with the analytics. The engineers love it because it’s so much easier than the way we used to do it before. And it's made my life a lot easier, because I don't have to chase people constantly. And it's very easy to keep track of things now that we have it all well documented. – Daniel Kaminski

With total trust in his front-end systems, Daniel can send everything through Avo and Adobe Analytics and Snowflake, knowing his event data quality is guaranteed. Now that the team is familiar with the new process, everything runs like clockwork. 

Every single event goes through Avo and is documented in Avo. My team and product gets it all set up. We get it all ready to go. We merge it to main. Engineers click a button for Codegen, and then they can just do their thing. – Daniel Kaminski

The future of data at Moody’s: impressive growth fueled by great data

Freed up from operational work, Daniel and his team can focus on what matters: “the actual analytics” that data practitioners enjoy. This has opened new pathways for Moody’s, allowing them to engage in impactful work like A/B testing and providing precise insights to other teams. 

When we produce something really insightful that goes to senior leadership, people get recognized, it makes an impact on the business. The stuff that really gets you the recognition, the rewards and the actual insights that were produced at the end of it—that’s what it’s all about. – Daniel Kaminski

But beyond the time-saving and increased job satisfaction, Moody’s has realized significant commercial value from its event data. Thanks to robust data from Avo and Adobe, their GenAI product has been able to “take off”, contributing huge revenue gains to the company. 

The quality of our data structures, thanks to Avo, were the prerequisite for us being able to launch our GenAI solution sooner and make millions of dollars more in revenue – Cristina Piretti, Head of Moody's Analytics REIS

But Moody’s isn’t stopping there. Daniel is keen to explore how to get even more out of Avo to take his analytics to the next level. 

I'm very excited to hear about what's new and then reassess the way that we're using Avo, to get even more out of the platform. It's made a huge difference. – Daniel Kaminski

Industry

Financial Services

Company size

10,001+

Location

New York, NY

Tech stack

Avo, Adobe Analytics, dbt, Snowflake, Power BI

Key takeaways

  • Avo and Adobe Analytics: With Avo and Adobe wokring in tandem, Moody's can deliver precise insights to key stakeholders in Product, Marketing, and analyst teams.
  • Slashing their implementation time: Adopting Avo's workflow means Moody's cut their time to implementation from 2 months to just a week.
  • Greater ownership for Product leads: Within Avo, Product leaders feel closer to analytics and "own" their data requests.
  • Tracking that's fast and error-free: Codegen has made implementation a breeze for Moody's engineers, who can easily deploy tracking calls.
  • GenAI made possible: Moody's high quality data set has unlocked the potential for GenAI products, leading to millions in increased revenue.