Driving Product Innovation with Data Insights: An Interview with Jonathon Ben-Haim from SundaySky

In today's data-rich business landscape, product leaders must be savvy at leveraging a wide range of inputs to guide their roadmap decisions and deliver outsize value to customers. We had the pleasure of speaking with Jonathon Ben-Haim, VP of Product Management, at SundaySky, a leading video platform that enables organizations to create, integrate and distribute personalized video content at scale. Our conversation explored Jonathon's approach to gathering and actioning customer insights, the frameworks he uses to prioritize feature development, and how SundaySky itself is harnessing data to power more relevant, engaging customer experiences. Here are the key takeaways from our discussion:

Q: Jonathon, can you share an overview of your role at SundaySky and where you spend the majority of your time as a product leader?

Jonathon: As the head of product at SundaySky, I oversee our product management and product design functions. My primary focus is on developing our product strategy and roadmap, which involves a deep analysis of both qualitative and quantitative customer inputs.

On the quantitative side, I'm constantly digging into our product usage and adoption data to understand how customers are engaging with our various capabilities, where they're finding value, and where they might be hitting friction points. 

But I also spend a significant amount of my time talking directly with customers - understanding their use cases, their pain points, what's working well for them in our platform and where they see opportunities for us to enhance the experience. Those conversations are a goldmine of insights that I'm always working to synthesize and feed into our prioritization process.

Given the cutting-edge nature of our platform and its intersection with a lot of emerging AI capabilities, I'm also often engaged with our customers' AI governance teams to understand their questions, concerns and requirements around things like data privacy, model transparency and algorithmic bias. I take those learnings back to our own product and engineering teams to ensure we're at the forefront of responsible, ethical AI practices.

Q: With so many potential inputs and demands on your roadmap, how do you ultimately decide what gets prioritized and when? What's the framework you use to make those tough tradeoffs?

Jonathon: We use a scoring system based on four key dimensions for every feature or capability we're considering: user impact, market differentiation, alignment to company objectives, and customer satisfaction. 

On the user impact front, we look at how many of our current customers are asking for something, and how frequently this pain point or need is arising in their workflows. Is it an issue they're encountering every time they use our platform, or is it more of an occasional annoyance?

From a market perspective, we evaluate whether a given capability is table stakes in our competitive set, or if it has the potential to truly differentiate us and cement our leadership position. We'll also consider if investing in a particular area could help us win back customers we've previously lost, or open up new market segments where this functionality is a must-have.

Alignment to company objectives is all about ensuring our product investments are advancing our overarching strategy and goals. So for example, if expanding our distribution capabilities is a key company priority, we'll place a higher weight on feature requests that enhance our ability to get personalized video content into more channels and touchpoints.

Finally, on the satisfaction side, we look at how a potential change could move the needle on key experience metrics like NPS or CSAT. Would closing this gap dramatically reduce frustration and churn risk? Would it turn passive users into promoters? 

Once we've scored each item against those criteria, we look at our available engineering capacity for the quarter and align on the mix of projects that will deliver the greatest impact across those dimensions. But it's not just about the raw scores - we also factor in the level of effort required and the expected time to value. We're always striving to find that optimal balance of quick wins and longer-term, more foundational bets.

Q: It sounds like you have a very rich set of data sources feeding into your prioritization process. Can you share a bit about the tools and team structures you have in place to stay on top of all those inputs?

Jonathon: Absolutely. From an analytics perspective, we rely heavily on tools to capture granular data on how users are interacting with every aspect of our platform. We instrument every feature release with tags that allow us to understand not just what customers are doing, but the context around those actions. What were they doing immediately before and after they used a particular capability? What outcomes are they achieving as a result?

We also have robust tools for collecting and organizing qualitative feedback. Users can submit feature requests or bug reports directly within our platform, and we use a combination of in-house and vendor-provided AI to automatically categorize and cluster that feedback to surface the most common themes and pain points. We also have an embedded voting mechanism so we can quickly gauge the relative demand and urgency of ideas coming through that channel.

Of course, all of that only yields insights if you have dedicated resources going in and making sense of it on a regular basis. Analytics can't just be an afterthought for product managers who already have their hands full. So we have a centralized product ops function that's responsible for analyzing all of that feedback data and producing monthly reports that summarize the top requests, the distribution across different customer segments and cohorts, and the trends over time. That's a key input into our quarterly roadmapping process.

We also have a team of what we call "enablement" specialists who work closely with customers to drive adoption, provide training, and troubleshoot issues. They're on the front lines every day seeing where customers are getting stuck or where product gaps exist. So we're constantly syncing with them to get that unfiltered, qualitative feedback to pressure test our data and ensure there's nothing we're missing.

Additionally, that tight coupling between product data and the customer experience is so critical, and it's something we're really passionate about at SundaySky. Not only do we use those insights to inform our own product development, but we also enable our customers to harness their product data to deliver hyper-relevant video experiences to their end users.

So for example, we have customers like Okta who will use product adoption data to trigger personalized onboarding videos that highlight the specific features a given user hasn't explored yet, and walk them through the key steps to get value from those capabilities. Or we'll create end-of-year recap videos that surface insights about a customer's usage patterns and the milestones they've achieved. 

It's all about leveraging data to create a more tailored, actionable experience that meets users where they are in their journey and helps them level up. That's really the power of bringing together product analytics and rich media like video.

Finally, what's really exciting is that with the emergence of AI and large language models, we're now able to automate a lot of that video personalization based on individual user data. So the same way we're using AI to process and extract insights from unstructured customer feedback, we can also use it to dynamically generate highly customized video narratives at scale. It's a huge unlock in terms of being able to deliver the right message to the right user at the right time.

Conclusion

Our conversation with Jonathon highlighted the immense opportunity for product leaders to harness data to not only build better products, but to fundamentally reshape the customer experience. By combining quantitative usage metrics with qualitative feedback, and leveraging emerging technologies like AI and personalized video, companies can create a flywheel of continuous improvement that deepens engagement, loyalty and lifetime value.

Some key principles stood out:

• Instrument your product to capture not just what users are doing, but the context and outcomes surrounding those actions
• Invest in dedicated ops resources to analyze and socialize those insights cross-functionally on a regular cadence 
• Develop a clear set of prioritization criteria that balance user needs, market demands, and strategic objectives
• Look for opportunities to close the loop with customers by turning product data into hyper-relevant, actionable communication 
• Stay at the forefront of AI and other technologies that can help you extract insights from unstructured data and automate personalized experiences at scale

As SundaySky continues on its mission to help businesses unlock the power of video to engage customers and drive growth, Jonathon and his team's commitment to data-driven, customer-centric product development offers a powerful model for any product leader looking to deliver outsized impact. By maintaining a tireless focus on understanding and anticipating user needs - and harnessing cutting-edge tools to execute on those insights with speed and precision - they are well-positioned to define the next frontier of data-driven customer experiences.

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