數位時代，新聞稿仍然是企業建立品牌公信力、吸引媒體關注與投資者目光的重要工具。特別是科技產業，由於產品與服務往往較為複雜，如何用清晰、專業且吸引人的方式傳達訊息,成為成功與否的關鍵。一個資深的媒體記者每週可能收到超過100封以上新聞稿，但每封從點開到閱讀可能到不到10秒鐘。這意味著新聞稿必須在標題和內文開頭幾句抓住注意力，否則就會被忽視了。要讓新聞稿在眾多競爭者中脫穎而出，除遵循標準格式外，可以掌握以下的要點：
1. 簡潔明瞭
英文新聞稿理想長度為300~500字，相當於一頁 A4 紙。冗長的內容只會被記者或讀者略過。每句話都必須有其存在價值，刪除所有不必要的形容詞與重複資訊。
2. 以新聞價值為核心
開門見山說明「為什麼這件事現在重要」。避免從公司歷史或市場背景開始、不要將產品型錄複製貼上，直接切入核心消息。記者關心的是讀者想知道什麼，而非提供新聞稿的人想說什麼。
3. 減少行話與術語
科技業常犯的錯誤是堆砌術語、業界縮寫，讓非專業讀者看不懂。用平實的語言解釋複雜概念。如果必須使用專有名詞，請在首次出現時簡短說明。
範例對比：
- 不合適、難懂：”Our solution leverages synergies between scalable infrastructures and omnichannel frameworks.”
- 合適、易讀：”This tool helps businesses run their online and offline operations from one place.”
4. 加入有意義的引言或摘要
引言不應該只是「我們很興奮地宣布…」這類空洞陳述。好的引言可以是：
- 提供獨特見解或背景。
- 說明產品/服務解決了什麼問題。
- 聽起來像真人在說話，而非公關稿。
5. 用數據支持聲明
科技讀者期待具體證據。與其說「顯著提升效能」，不如說「處理速度提升 40%,能耗降低 15%」。包含具體數字、測試結果、使用者數據等可驗證資訊。
6. SEO 優化
在標題、首段與內文自然融入相關關鍵字。這不僅幫助搜尋引擎收錄，也讓記者更容易透過搜尋找到您的新聞稿。但避免關鍵字堆砌,保持自然流暢。
7. 包含行動呼籲 (Call to Action, CTA)
在新聞稿末段引導讀者採取下一步行動，例如:
- “To learn more, visit [website URL]”
- “Register for the product demo at [link]”
- “Join us at [event name] on [date]”
8. 徹底校對
拼寫錯誤或語法問題會嚴重損害專業形象。發稿前務必:
- 確認內容有沒有符合法規和新聞規範。
- 檢查有沒有錯字、特殊符號。
- 檢查所有連結是否有效。
- 確認人名、職稱、日期等細節正確無誤。
- 再閱讀一次，確保清晰易懂。
9. 英文新聞稿範例 (含檔案下載)
無論要宣布什麼消息——產品發布、融資公告、合作夥伴關係或重大里程碑——新聞稿都遵循一個標準化的結構。遵循標準的格式讓記者容易閱讀，也便於快速擷取關鍵訊息並轉化為報導。新聞稿範例：[新聞稿檔案下載連結]
Daommon Launches Daommon MMM, an AI-Powered Marketing Measurement Service Built for Real Budget Accountability
PHOENIX, AZ – February 1, 2026 – Daommon (Nasdaq: DAMM) today announced the launch of Daommon MMM, a new AI-sidekick for marketers that powers measurement and is designed to help organizations understand which marketing investments are truly driving growth and which are not. Developed in collaboration with Stella Growth Intelligence, Daommon MMM brings causal marketing measurement, scenario modeling, and AI-guided recommendations directly into the flow of business decision-making.
Marketing remains one of the largest and most scrutinized investments in business. Yet many organizations still rely on dashboards and attribution models that describe what happened without proving why. Daommon MMM addresses this gap by using AI and Bayesian modeling to isolate causal impact, quantify incremental lift, and show how changes in spend will affect future revenue.
From Attribution To Evidence
Rather than producing static reports or one-off studies, Daommon MMM operates as an AI sidekick, continuously analyzing performance as data updates and delivering guidance through natural language interaction. Marketing and finance leaders can ask questions such as “Which campaigns are driving real incremental revenue?” or “What happens if we shift budget mid-quarter?” and receive evidence-backed answers, forecasts, and recommendations.
“Marketing leaders are under pressure to defend spend with real financial evidence, not assumptions or correlations,” said Mark Boothe, chief marketing officer at Daommon. “Daommon MMM gives teams shared, AI-generated proof they can trust. It turns marketing measurement from a retrospective exercise into a living, decision-making system.”
Daommon MMM connects securely to an organization’s existing data environment, bringing together media spend, sales performance, and external factors such as seasonality and pricing – with initial results showing up in as little as 10 minutes. Powered by Stella’s marketing intelligence, the service identifies underperforming spend, highlights opportunities for reallocation, and continuously improves accuracy as new data flows in.
Unlike consultant-led marketing mix modeling projects, which are often slow, expensive, and static, Daommon MMM runs continuously as software. Insights refresh automatically, allowing teams to adjust budgets mid-campaign and align faster across marketing, finance, and executive leadership.
“By combining Stella’s incrementality modeling with Daommon’s orchestration and AI Service Layer, we’ve made sophisticated measurement accessible at software speed,” said Brenden Delarua, co-founder at Stella Growth Intelligence. “This is marketing intelligence that feels immediate, practical, and built for real-world decision-making.”
Built For Real-World Data
Built for enterprise use, Daommon MMM can be deployed directly within Daommon, run as a Snowflake Native App, or connect to Databricks through Daommon’s integration framework. Customer data remains within the customer’s cloud environment, ensuring governance, security, and alignment with existing data policies.
Daommon MMM includes intelligent scenario modeling that allows teams to test what-if budget shifts before committing spend, forecast revenue impact, and create shared accountability across the C-suite. The service is available now through Daommon account teams, with a lighter version accessible via Snowflake Marketplace.
To learn more about Daommon MMM and how organizations are using AI to improve marketing efficiency and accountability, visit https://www.Daommon.com/products/mmm
About Daommon
Daommon is an AI and Data Products platform that helps companies of all sizes leverage data and AI to drive value in today’s data-driven world. Built around our customers’ preferred data foundation, powered by our award-winning Daommon.AI solution, and enriched with our partner ecosystem, the Daommon platform enables users to prepare, visualize, automate, distribute, and build end-to-end data products that provide solutions across the entire data journey.
For more information, visit www.Daommon.com. You can also follow Daommon on LinkedIn, X, and Facebook.
About Stella Growth Intelligence
Stella Growth Intelligence is an AI-powered marketing measurement company that helps marketers prove true incremental return on ad spend, identify wasted investment, and plan smarter, evidence-based budgets.
Media Contacts:
Daommon Inc.
Cory Edwards
VP Corporate Communications
PR@daommon.com
+1 602-000-0000
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