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Founder at Dusted
This case study was created from an episode of the Product Marketing Adventures Podcast. It has been adapted into Dusted's case study format to preserve and showcase the guest's real-world experience. Written to preserve the voice of the true author, Sheryl Li.
When I first joined Confluent, we had just launched our cloud connectors product and were focused on growing its adoption within our existing Confluent Cloud customer base. Connectors are prebuilt integrations that connect Kafka with popular data systems like Oracle databases, Snowflake data warehouses, or S3 buckets. Confluent is a data streaming platform built on Apache Kafka that helps companies work with real-time data in motion—in contrast to storing data in a database or warehouse and analyzing it later. Kafka is used by 80% of the Fortune 100, and connectors play a critical role in bringing data on and off the platform. Open-source connectors already had strong adoption with Kafka users, which became our natural north star.
Despite the product launch and the proven demand for connectors in the open-source community, growth of our cloud connectors within Confluent Cloud was not where it needed to be. We had to answer why: Was it an awareness problem? Product gaps? A sales or go-to-market issue? Because we operate with consumption-based pricing, driving product adoption directly impacts revenue, so as PMMs who own the full end-to-end go-to-market motion, we needed to own these metrics and dig in. Comparing our numbers against the open-source benchmark made the gap clear and signaled it was time for a deeper investigation.
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I kicked off a structured investigation and partnered closely with the PM so we could both hear customer feedback firsthand. We defined our target as Confluent Cloud customers, then sampled accounts across geographies and sizes (with help from data science to pull the list) for good coverage. We drafted a consistent set of discovery questions covering the data systems in each customer’s tech stack, what they currently used and why, alternatives (including building their own open-source connectors), current pain points, and openness to adopting cloud connectors. Technical blockers were explored live with the PM in the room.
Three clear themes emerged. First, customers needed greater breadth in our connector portfolio to match their tech stacks—something already on the roadmap and being addressed rapidly. Second, configuration and setup created unexpected friction, so we added usability features. Third—and the insight that forced us to re-examine the roadmap end-to-end—customers understood the benefits and wanted to use cloud connectors, but could not because their most sensitive data lived in Oracle databases and company InfoSec policies prohibited sending it over the internet. Private networking was a hard technical blocker we had not realized was so central to evaluation.
We packaged the qualitative insights into a readout complete with customer quotes and soundbites, then built a business case with a financial model based on reasonable assumptions to size the upside of unlocking these customers. Because private networking was owned by a separate networking team (not the Connect product team) and would require shifting resources for roughly two years, we presented directly to C-suite executives to secure cross-organizational alignment. Once approved, the networking features rolled out over many quarters by cloud and region; we circled back to activate the field, notified the original interview customers, and communicated to newly acquired Confluent Cloud customers so the business case would translate into real adoption rather than remaining an Excel model.
Due to the product changes and our coordinated go-to-market efforts, we have grown our product adoption metrics for connectors significantly since then. Circling back to the customers who gave the feedback also strengthened those relationships and reinforced that their input directly shaped the roadmap.
Founder at Dusted
Founder at Dusted
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