# The Spam Button Is Winning

Page: https://stenobird.com/podcast/finance-tech-brief-by-hackernoon-6365652/the-spam-button-is-winning
Text version: https://stenobird.com/podcast/finance-tech-brief-by-hackernoon-6365652/the-spam-button-is-winning.md
Podcast: [Finance Tech Brief By HackerNoon](https://stenobird.com/podcast/finance-tech-brief-by-hackernoon-6365652)
Published: 2026-05-22T16:01:07+00:00
Episode link: https://share.transistor.fm/s/110bbc1c
Audio file: https://media.transistor.fm/110bbc1c/348ad9d7.mp3
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JSON: https://stenobird.com/v1/public/podcasts/finance-tech-brief-by-hackernoon-6365652/episodes/the-spam-button-is-winning
Duration seconds: 631

## Resource

This story was originally published on HackerNoon at: https://hackernoon.com/the-spam-button-is-winning . Finance has the richest customer data of any industry. Most of it never reaches the tools sending the messages. Here's why — and how to fix it. Check more stories related to finance at: https://hackernoon.com/c/finance . You can also check exclusive content about #fintech , #marketing , #digital-marketing , #hyper-personalization , #data , #data-engineering , #enterprise-data-engineering , #spam-button , and more. This story was written by: @santoshdurgam . Learn more about this writer by checking @santoshdurgam's about page, and for more stories, please visit hackernoon.com . Most enterprise marketing in finance is still running a 2008 playbook — blast a segment, measure opens, repeat. The data to do genuine personalization already exists inside every large institution. The problem is a two-layer infrastructure gap: enriched customer signals never get unified, and even capable martech tools like Eloqua, Salesforce Marketing Cloud, and Klaviyo get fed weekly CSV exports instead of live behavioral profiles. Layer on top of that an unsubscribe model that honors opt-outs on one channel while ignoring them on three others, and a consent architecture that was clearly bolted on after the fact — and you don't have a personalization problem. You have a data engineering problem. Fix the foundation first. Everything else follows.

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## Transcript

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