How Kong Turned Buried Campaign Data Into a Monday Morning Signal with Quill

Kong builds the world's most adopted API gateway, powering connectivity for APIs, AI, and microservices at enterprise scale.

Their marketing team is now one of the first to build with AirOps Quill: putting an AI agent to work on the kind of recurring analysis that usually lives in spreadsheets and gets done when someone has time (which, for most marketing teams, is usually a perpetual "later").

Kong's AI search team already uses AirOps to drive visibility in AI search. Jell wanted to stretch the boundaries and see how Quill could support growth beyond search: starting with the operational work that powers lifecycle marketing.

These are early days for agentic marketing, and Kong is helping define what it looks like when an agent handles the operational work so the team can focus on decisions. The results so far are just the beginning.

The gap between having data and acting on it

Kong's lifecycle marketing team sends a high volume of email campaigns across product launches, events, and nurture programs. The data from those campaigns exists, but making sense of it was a manual process.

"I used to piece together campaign performance across multiple exports and dashboards," said Jell Khongkraphan, Sr Manager of Lifecycle Marketing at Kong. Every week meant pulling Marketo data, opening spreadsheets, and trying to spot patterns across hundreds of rows. By the time the analysis was done, the next send was already out the door.

The question wasn't whether the data was available. It was whether the team could get to the signal fast enough to act on it.

A Playbook that reads email performance and posts a weekly digest to Slack

The Engagement Signal Playbook is the first component of APEX (Always-on Pipeline Education Experience), a larger internal system Jell is building for Kong's lifecycle marketing program. It pulls weekly email link performance data from a Google Sheet, analyzes click patterns across every campaign, and posts a formatted digest to Slack every Monday morning.

Quill runs the analysis autonomously but knows when to check in: when it flags an anomaly or a campaign that needs a closer look, the team gets a clear signal before any recommendation goes out.

The Playbook's core job is separating meaningful engagement from noise. In email marketing, raw click numbers can be misleading: a campaign might show healthy click volume, but if most of those clicks are landing on social media icons in the footer rather than the actual CTA, those numbers are inflating engagement without driving pipeline action.

That's the exact blind spot that makes campaign-level click data unreliable. The Playbook breaks this down automatically, flagging which campaigns are driving intentional action and which ones need work.

Jell went from idea to a working Playbook in a single session. "The part that surprised me was how quickly I could go from idea to a working draft," she said. "I had a real use case, real data, and a running Playbook in one session."

First run already surfacing actionable findings

The Playbook is still in early testing, but the first manual run delivered output the team could act on immediately.

It identified Kong's API Management Webinar and the API + AI Summit emails as the strongest performers in the current dataset: the campaigns where a high share of clicks were going to the intended call-to-action rather than footer links. It also flagged several programs on the other end of the spectrum, where nearly all click activity was on social footer icons, a clear signal those emails need stronger CTAs or repositioned content.

"Before Playbooks, we exported campaign data manually, opened a spreadsheet, and tried to find the signal buried in hundreds of rows," Jell said. "Now, the signal finds us."

The goal from here is straightforward: catch underperforming campaigns before the next send, not after. And as the Playbook runs weekly, the team builds a baseline tied to the engagement metrics they care about most, making trend-spotting automatic rather than something that depends on someone having the time to dig in. Quill becomes a core part of how the team operates: not a tool they go to, but an execution lead that delivers the signal on schedule.

What's next

The Engagement Signal Playbook is just the first piece of APEX. Jell's next builds are cadence risk monitoring, to flag when send frequency is hurting engagement, and audience segment health, to surface which segments are going cold before they drop off entirely.

Jell sees Playbooks as a new layer between data and decisions. "The biggest thing for marketing teams isn't just automation," she said. "It's that Playbooks make the analysis legible. The output is plain English that tells you what to do."

Ready for an AI agent that actually moves your metrics? Meet Quill.