Hey there, product folks and data enthusiasts! We recently had the pleasure of chatting with Meytal Markman, VP of Product at Rakuten Advertising. Meytal has been in the affiliate marketing game for over 15 years, and she's got some fascinating insights on how to use data to drive product decisions in this unique corner of the digital marketing world. Here’s just some of our awesome conversation:
Q: Meytal, can you give us an overview of your role at Rakuten Advertising and how product management functions within the organization?
Meytal: Absolutely. At Rakuten Advertising, product management sits very close to the tech organization, which means our product managers tend to be quite technical. But we also serve as a major cross-functional hub across the business. When we kick off a new initiative, we bring in stakeholders from every team - sales, legal, commercial, you name it.
This setup gives us a really nice zoomed-out view of the business and what's important to all the different teams. As a result, the metrics we use in product are often a superset of what other teams are tracking. We're constantly trying to understand how each team measures success and bake that into our decision-making process.
Q: That's interesting that you have such a holistic view. How do you approach prioritization and decision-making with all of these different inputs?
Meytal: Everything starts with the business case. We have a pretty sophisticated governance process at this point, which is necessary when you're a global company with no shortage of ideas!
We look at a few key factors:
- Strategic alignment: Sometimes a project that doesn't have big numbers attached to it this year can have outsized strategic value if we're trying to enter a new market or work with customers in a new vertical.
- Revenue opportunity: This can vary widely depending on the client. For example, some clients may need a very custom technical integration, which we'd approach differently than our more standard offerings.
- Cohort alignment: We consider the value that a new feature will bring to the customer, as well as product alignment with the objectives of our target customers.
For our larger, more custom deals, we're often involved in the sales process. We'll consult on how our existing tools can solve their needs or do an analysis on what it would take to build something new - considering factors like development time, other projects in flight, as well as strategic alignment with our roadmap.
Q: You've emphasized the importance of qualitative feedback in your product process. Can you elaborate on how you gather and incorporate this kind of input?
Meytal: Absolutely! While quantitative data is crucial, qualitative feedback often provides the context and depth we need to make informed decisions. We gather this feedback through various channels.
One key initiative is our "Product Council," where we meet with a select group of clients to get direct feedback. These conversations are invaluable for understanding the nuances of our clients' needs and challenges.
We also pay close attention to feedback coming through other channels, including support tickets, and conversations with account managers and analysts. The key is to not just collect this feedback, but to contextualize it.
For example, if we get a feature request from an account manager, we don't just add it to a list. We dive deeper - The first question is always “Why,” of course, but then we dig deeper - Who's asking for this? Would other clients in the network also benefit? What's the potential value? We look at this data holistically and try to understand the broader implications.
This approach helps us avoid the trap of building for the loudest voice in the room. Instead, we can identify trends and opportunities that might not be immediately obvious from quantitative data alone.
It's also worth noting that in affiliate marketing, there's a wealth of industry expertise that can be hard to quantify but is crucial for decision-making. Our team members often have deep knowledge of the industry, and we make sure to tap into that when evaluating new ideas or solving problems.
The real art is in synthesizing all of these inputs - the quantitative data, the qualitative feedback, and our industry knowledge - to make informed decisions about our product roadmap. It's not always an exact science, but this holistic approach helps us stay aligned with our clients' needs and ahead of market trends.
Q: You mentioned using tools like Google Analytics and FullStory. Are there any emerging technologies or approaches you're excited about when it comes to leveraging data in product management?
Meytal: Yes. We're lucky to be well on our way in terms of incorporating AI into our platform and tools, and I'm really excited about the continued potential there, especially with the advancements in generative AI. We recently hired a VP of AI Strategy and Operations who's going to work closely with several teams across the business including Product and Data Science to continue advancing our use of AI.
One area I'm particularly passionate about is getting to what I think of as a "post-CDP world." It's not enough to just centralize your data anymore - we need to be so good at our data engineering that we can effortlessly point AI inference engines at our data and ask real questions.
Imagine being able to use natural language to describe an industry objective or opportunity, and have an AI look through all our data - from sales calls to support tickets to usage patterns - and come back with insights and recommendations. That could dramatically streamline the business case creation process and allow us to be even more targeted and effective.
I think it’s important for companies to invest in this type of behind-the-scenes work on data infrastructure to make this kind of AI-powered future possible. It's not the most glamorous work, but it's absolutely critical if you want to unlock the full potential of AI and machine learning.
Q: That's a fascinating vision. Any final thoughts on the role of data in product management, especially in a complex field like affiliate marketing?
Meytal: I think the key is to always remember the unique dynamics of your industry. Traditionally in affiliate marketing, there is a model of mutual growth and success. It truly is a win-win-win ecosystem between us, our advertisers, and our publishers.
This means that when we're looking at data and making product decisions, we're not just thinking about one party. We're considering how we can drive growth for our entire network. Sometimes that means taking on projects that might not have an immediate revenue impact for us, but that we believe will drive long-term growth for our partners and, by extension, for our business.
It's a complex balancing act, but having a robust data infrastructure and a cross-functional approach to product management helps us navigate these decisions and continue innovating in a rapidly evolving space.
Conclusion
Well folks, there you have it - a masterclass in data-driven product management from one of the leaders in the affiliate marketing world. Meytal's insights remind us that while data is crucial, it's the synthesis of that data with industry knowledge and strategic vision that truly drives product success.
Whether you're in affiliate marketing or any other tech field, there are valuable lessons here about balancing quantitative and qualitative inputs, thinking long-term about product investments, and preparing your data infrastructure for the AI-driven future of product management.
Big thanks to Meytal for sharing her wisdom and experiences. Now it's your turn - how are you using data to drive your product decisions? What challenges have you faced in building a data-driven culture? Drop us a comment and let's keep the conversation going!
