• AI Intro
• LLM (Large Language Models)
• Natural Language Processing (NLP)
• 應用層、 技術底層 (Foundation Model )
• AI 應用領域
• AI for Marketing Automation
• High level intro:
• Application Scenarios that AI can help MA
• 1. Personalized Marketing Campaigns)
• 2. Automated User Segmentation
• 3. Predictive Analytics
• 4. Optimization of Marketing Campaigns
• 5. Conversational User Engagement
• 6. Summarize Complex Information
• Overall Benefits that AI could help with Digital Marketing
• AI 應用範例
• AIGC
• AIGC產品的特性
• AIGC 應用: 電商 聊天 chatbot
• AIGC 應用: Customer Service
• 可以解決的問題
• 具體作法
• AI 未來趨勢
• Wave 2 of AI Revolution
• 用戶 透過 LLM 叫 AI 模型寫程式
• Future of AI by OpenAI CEO Sam Altman
• No-code & Composability
• 2nd-order Effect of Generative AI
• 李開復的看法
• 做 AI Application 的小公司該如何維持競爭力

AI Intro

LLM (Large Language Models)

• AI tools that are trained on massive data sets to read, summarize, and translate text, then generate sentences by predicting future words. The more data that the model is trained on the more it is able to learn, remember, and form connections between different words, concepts, and things.

Natural Language Processing (NLP)

• NLP is a subfield of AI that allows machines to understand and interpret human language. In marketing, NLP can be used to analyze customer feedback, social media comments, and customer service interactions to understand sentiment, identify common issues, and improve customer experience.

'Prompt engineering' is one of the hottest jobs in generative AI. Here's how it works.

Prompt engineers are responsible for talking to AI chatbots in creative ways to identify errors and hidden capabilities to ensure safety and accuracy.

www.businessinsider.com

The Ultimate AI Marketing Automation Guide for 2023

Wondering what role AI will play in marketing automation in the coming years? This guide covers everything from how AI is currently being used for automation to how it will impact marketing jobs roles.

influencermarketinghub.com

應用層、 技術底層 (Foundation Model )

References

Foundation Models: The future (still) isn’t happening fast enough

The foundation model stack has evolved so fast (and a tooling layer so rapidly started to form) that more opportunities for founders are emerging.

www.madrona.com

AI 應用領域

AI for Marketing Automation

The Ultimate AI Marketing Automation Guide for 2023

Wondering what role AI will play in marketing automation in the coming years? This guide covers everything from how AI is currently being used for automation to how it will impact marketing jobs roles.

influencermarketinghub.com

High level intro:

• AI marketing automation is the use of artificial intelligence and machine learning algorithms to automate and optimize marketing activities.
• More efficient, more personalized, and ultimately more effective.
• analyze vast amounts of data to identify patterns, predict customer behavior, and make decisions in real-time
• result in higher engagement rates, increased customer satisfaction, and ultimately, better business results

Application Scenarios that AI can help MA

1. Personalized Marketing Campaigns)

1. E.g. Product Recommendation
2. Benefits
1. higher engagement, retention rates, customer loyalty
2. AI can help marketers achieve this level of personalization at scale by analyzing customer data and making tailored recommendations

2. Automated User Segmentation

(incl. scoring, nurturing)

對潛在客戶評分、培育潛在客戶

1. Can do these
1. identify which prospects are most likely to become customers.
2. guide the leads through the sales funnel and convert into a purchase
2. Benefits:
1. help businesses save time and resources while improving the quality of their leads
2. Automated nurturing result in higher conversion rates 還可以帶來更高的轉化率，as leads are effectively guided through the sales funnel until they are ready to make a purchase.

3. Predictive Analytics

(for customer behavior and trends)

1. Do these
1. By analyzing history customer data and behavior, help marketers anticipate customer behavior, needs, and trends before they happen.
1. e.g. businesses can use predictive analytics to determine the best time to launch a new product or service based on historical data.
2. e.g. MA tool to predict and suggest when is the best timing to send out marketing messages to consumers
2. product recommendations to its customers based on their purchase history and browsing behavior.
2. Benefits
1. higher customer satisfaction
2. increased sales.
3. And help marketers anticipate the trends and adjust their strategies accordingly.

4. Optimization of Marketing Campaigns

(incl. channels, ad placements)

1. Do these
1. By analyzing data on the performance of different marketing channels, AI algorithms can identify which channels are most effective and can allocate resources accordingly.
1. E.g. Meta Robyn
1. E.g. businesses can use AI to optimize their ad spend by targeting the right audiences and identifying the most effective ad placements.
2. Benefits
1. This can lead to more cost-efficient marketing campaigns and better business results.

5. Conversational User Engagement

(Chatbots)

1. Do these
1. automate the repetitive tasks
1. answer FAQs
2. Provide product recommendations
2. Benefits
1. More cost efficient . Help marketers save time and money
2. reduce customer service agents’ work load.
3. Increase customer satisfaction

6. Summarize Complex Information

Overall Benefits that AI could help with Digital Marketing

1. Personalization
2. Predictive analytics
3. Cost efficiency
4. Optimization of ROI:
1. According to a study by Accenture, companies that implement AI in marketing see an average increase in return on investment (ROI) of up to 30%.
2. Because AI can help marketers identify new opportunities for growth, optimize ad spend, and improve customer engagement.
5. Higher engagement rate, retention rate, loyalty
6. Higher conversion rate
7. Higher sales
8. by leveraging AI and automating routine tasks, marketers will be able to focus more on strategic planning, creative thinking, and driving innovation.

AI 應用範例

酒店用机器学习，预测哪些客人会放鸽子 | annotated by Alex

微信号 BigDataDigest 功能介绍 普及数据思维，传播数据文化 以下文章来源于HyperAI超神经 ，作者神经小兮 HyperAI超神经 人工智能说明书，了解 AI 的功效与副作用 大数据文摘授权转载在HyperAI超神经作者：神经小兮如今， 各大 OTA（

readwise.io

All Things AI - The Complete Resource Of Artificial Intelligence Tools & Services

World's Largest Resource Of Artificial Intelligence Tools & Services

allthingsai.com

AIGC

AIGC產品的特性

1. 從經驗中挖掘內容。
1. 它能產出的 output 完全取決於 被給予了什麼 input 資料
2. 因為無法自己創造「某些 input 資料」，導致：
1. 不會自己「產生需求、明白要解決什麼問題」，所以無法自己「決定要達成什麼目標」
2. 無法主動決定「現在該做什麼事情」
3. 無法獨立思考，無法判斷事情的真假、對錯、做決定

全球月造訪量前50名的生成式AI產品呈現哪些面貌？誰可能是下一個「大贏家」？ | annotated by Alex

ChatGPT 的出現引領了新的時代方向，不到一年的時間，生成式AI 遍地開花，a16z 透過調查研究，發表了全球造訪量前五十的生成式 AI 產品榜單。

readwise.io

Notes (來自上篇文章)

1. 早期「贏家」已經出現，但大多數產品類別仍然有機會
1. 每個類別中第一名和第二名之間的流量差異。雖然有一些例外（例如陪伴類），但對於大多數類別來說，差距不到兩倍，這意味著頂級公司的造訪量僅是其最接近競爭對手的兩倍（或更少）。在過去六個月中，榜單上的公司平均每月增長率為50％，這一差距並非不可逾越。
2. 過去的5年中，許多消費者應用程式陷入了獲客遊戲中。由於缺乏平臺轉移（例如從PC 端到行動端），很難激發人們對新產品的興趣。獲客成本也在上升，這意味著大多數消費品公司不得不看重一些指標，如生命週期價值和獲客成本。
3. 生成式AI改變了這場遊戲。榜單中的多數公司沒有投放廣告（至少，SimilarWeb可以歸因於此）。透過X.com、Reddit、Discord、電子郵件以及口碑和推薦，獲得大量免費流量，產品增長迅速。排名後四分之一的產品僅有 2% 的流量是付費流量。相比之下，根據 a16z 對 150 種產品的基準測試，非 AI 公司消費者訂閱的付費流量則為 70%。
4. 而且，消費者願意為生成式AI付費。榜單中的90％的公司已經開始賺錢，其中幾乎所有公司都採用訂閱模式。榜單中產品的平均收入為 21 美元/月（對於月度套餐的使用者）——每年收益 252 美元。
5. 瀏覽器是觸達消費者最廣泛基礎的自然起點。許多AI公司團隊較小，不想將其關注點和資源分散到Web、iOS和Android上。因此，該榜單上目前只有15家公司有APP，而且幾乎所有這些公司每月的總流量中，來自APP的流量不到網路流量的 10%。
1. 有3個例外值得注意：專業設計工作室PhotoRoom（估計88％的流量來自APP），陪伴類 CharacterAI（46％的流量來自APP）和語音合成產品Speechify（20％的流量來自APP）。這些公司APP上的使用者參與度（以每位造訪者的會話次數為基礎）高於其網站。
2. 有3個例外值得注意：專業設計工作室PhotoRoom（估計88％的流量來自APP），陪伴類 CharacterAI（46％的流量來自APP）和語音合成產品Speechify（20％的流量來自APP）。這些公司APP上的使用者參與度（以每位造訪者的會話次數為基礎）高於其網站。

AIGC 應用: 電商 聊天 chatbot

Amazon AI 購物聊天機器人 Rufus，能夠根據 Amazon 網站上的產品資訊、客戶評論和社群問答來生成答案，告訴用戶哪個產品符合需求

Amazon announces Rufus, a new generative AI-powered conversational shopping experience

With Rufus, customers are now able to shop alongside a generative AI-powered expert that knows Amazon’s selection inside and out, and can bring it all together with information from across the web to help them make more informed purchase decisions.

www.aboutamazon.com

使用場景範例：

1. 購買耳機時要考慮什麼產品條件？
2. 計劃某種活動時，需要購買什麼？
3. 找出不同產品之間的差異

AIGC 應用: Customer Service

可以解決的問題

1. 適合回答：重複性問題、常見問題
1. E.g. 處理客戶查詢簡單的問題 (e.g. 訂單狀態)
2. 很多無 AI 的 chatbot 已經在做類似的事情，有時難以做到 fully conversation-based
2. 可幫助品牌深入了解：
1. 消費者的意圖
2. 消費者的情緒 (Sentiment analysis)
1. ChatGPT can be trained to understand and classify the sentiment of customer inquiries. By identifying customers' emotions, brands can be more equipped to respond in an appropriate manner.
3. Task Classification
1. 自動將 customer inquiry 打標做分類 (e.g. 配送進度 / 退款退貨 …etc)
4. Provide “personalized support” based on:
1. Customer’s input in the inquiries
2. Customer’s purchase history
5. 預測消費者的下一步行為
1. 消費者還需要什麼別的 support

具體作法

1. 讓 AI 自動回答：重複性問題、常見問題
2. 真人客服 搭配 AI
1. E.g. Text Generation. 預先產生「建議的回答」給 CS Agent ，然後人工審核、做修改，再發給 消費者。確保消費者得到具有正確資訊的回覆
3. Integration with external systems
1. (這將使人工智能超越回答常見問題，真正開始對客戶的賬戶和訂單進行更改。通過整合，人工智能將能夠解決更多的客戶支持問題。)
2. 範例
1. Order management system of e-commerce
2. External CRM
4. 但要提防這些事：
1. 如果消費者知道他們正在與 AI 交互 (而非真人克服)，也許消費者很可能會試圖 “欺騙 / 玩弄” 模型、導致模型以不恰當的方式做出響應

AI 未來趨勢

Wave 2 of AI Revolution

For B2B Generative AI Apps, Is Less More? | Andreessen Horowitz

Rather than writing long text, we will see generative AI converge information in B2B settings. It's an approach that we call SynthAI.

a16z.com

1. To date, generative AI applications have overwhelmingly focused on the divergence of information. That is, they create new content based on a set of instructions.
2. In Wave 2, we believe we will see more applications of AI to converge information.
1. That is, they will show us less content by synthesizing the information available.
2. Aptly, we refer to Wave 2 as synthesis AI (“SynthAI”) to contrast with Wave 1.
3. While Wave 1 has created some value at the application layer, we believe Wave 2 will bring a step function change.
3. When it comes to B2B applications, the objectives are different. Primarily, there is a cost-benefit assessment around time and quality.
1. You either want to be able to generate better quality with the same amount of time, or generate the same quality but faster.
2. This is where the initial translation from B2C to B2B has broken down.
4. As we move into the next wave of generative AI applications, we expect to see a shift in focus from the generation of information to the synthesis of information.
1. In knowledge work, there is huge value in decision-making.
2. Employees are paid to make decisions based on imperfect information, and not necessarily the quantity of content generated to execute or explain these decisions.
3. In many cases, longer is not better, it’s just longer.
5. How can AI improve human decision-making?
1. We believe LLMs will need to focus on synthesis and analysis — SynthAI — that improves the quality and/or speed of decision-making (remember our B2B diagram above), if not make the actual decision itself.
2. The most obvious application here is to summarize high volumes of information that humans could never digest themselves directly.
6. The real value of SynthAI in the future will be in helping humans make better decisions, faster.
1. We are envisioning almost the opposite of the ChatGPT user interface: Instead of writing long-form responses based on a concise prompt, what if we could reverse engineer from massive amounts of data the concise prompt that summarizes it?

用戶 透過 LLM 叫 AI 模型寫程式

前哈佛計算機教授、谷歌工程總監：AI將在3-5年內取代程式設計師 | 區塊鏈 | 鉅亨號 | Anue鉅亨

人類對未知的恐懼，似乎是天生的。這也是每當新技術出現，都會有人對其感到不安的原因。比如最近的生成型AI，就導致很多的美術師擔心被奪走飯碗，甚至出現過大量美術師集體對抗AI作品的現象。 實際上，生成型AI除了畫圖之外，還可以寫代碼，甚至比人類程序員（程式設計師）速度更快、效率更高，而且成本更低。 此前的線上演講中，Fixie聯合創始人兼CEO、前谷歌Chrome移動團隊工程總監Matt Welsh表

hao.cnyes.com

In Fixie, each Agent is a standalone service that combines an LLM with a little bit of code — which can be implemented in any programming language — that understands how to connect to an external system, like a database or an API

Future of AI by OpenAI CEO Sam Altman

(Some key points from the article)

1. In the next decade is that the marginal cost of intelligence and the marginal cost of energy are going to trend rapidly towards zero. That’s going to touch almost everything because these seismic shifts that happen when the whole cost structure of society changes, which happened many times before. And that will quicken the iteration cycles of many things.
2. With the AI tools emerging more and more in the coming future, we human beings will still really care about interaction with other people. “The stuff that people cared about 50,000 years ago is more likely to be the stuff that people care about 100 years from now than 100 years ago.”
3. There could be a number of verticalized AI startups that essentially have adapted a fine-tuned foundation model to industry. We will not be doing prompt engineering in five years time. And the AI-added tools be integrated everywhere. And SA thinks the fundamental user interface will be the natural language (may be as what we see in the ChatGPT today)
4. The AI startups do not start from scratch such as building models from zero. They will take base models that are hugely trained with a gigantic amount of compute and data (maybe by incumbent giant companies), and then they will train on top of those to create the model for each vertical. But they’re doing the 1% of training that really matters for whatever the use case (for their product) is going to be.

No-code & Composability

https://chiefmartec.com/2023/03/holy-two-major-martech-disruptions-just-collided-together-ai-and-composability/

Chat Interface / No-code tools

1. For years, software users had to adapt how we work to the way the software we purchased was built. Now, natural language interfaces like this start to break us free from that imposed structure. Software begins adapting to our asks, instead of us adapting our asks to it.
2. Some companies provide a layer of intent translation between people and software. Instead of navigating an app with a series of mouse clicks, you simply ask the app to do what you want. It’s a faster, more direct path to getting things done.
3. Empower more people to create more software with less or no coding
4. A business user doesn’t have to be bound by a prescribed workflow or user experience invented by a far away product team at a completely different company. They can invent their own.

Composability (可組合性)

1. Different software components “can be selected and assembled in various combinations to satisfy specific user requirements
2. Composability dramatically multiplies the number of apps in the world. It unleashes waves of combinational innovation, where different components can be mixed and matched in effectively an infinite number of niche contexts

Examples

1. (ChatGPT’s plugin from Zapier) It’s not hard to imagine that extending across Zapier’s entire portfolio of integrations, enabling more and more sophisticated workflows to be executed, merely by telling ChatGPT what you want to happen. Those workflows could span many different apps in your stack, leveraging data and functionality across any of them. But you won’t need to know or care about those app boundaries. All controlled via natural language.
2. ChatSpot combines multiple different, independent software apps — ChatGPT, HubSpot CRM, DALL-E, and Google Docs — and orchestrates them together. It’s a testament to the phenomenal cosmic power accessible today through API calls in the cloud.

2nd-order Effect of Generative AI

In the article you shared, the term "buyer" refers to the people or organizations that purchase products or services from companies that use generative AI in their marketing and martech¹. The article discusses how generative AI can help companies create more personalized and effective marketing campaigns that can better target potential buyers¹.

My takeaways:

1. Generative AI empowers sellers to generative marketing and sales content faster and less costly. Even though it’s personalized, the huge amount of automated comms/content may make customers choose to ignore the marketing and sales content.
2. Trusted sources (media or individuals) and data will be more valuable than ever.
3. Customers might find new ways to discover and evaluate the content and products that they exactly need. They might use AI agents to do this (those could be the same/similar tools that the sellers generate content to attract/lure the buyers?) And a new kind of marketing objective could be to serve these “buyer-centric AI agents”.

Exploring the 2nd order effects of generative AI in marketing and martech

What an amazing time to be working in marketing and martech! Our entire industry is blossoming like the wildflower 'super bloom' happening in California this spring, fed by the rain, rivers, and sunshine of generative AI, universal cloud data warehouses, and software composability. There's literally gold flowing down from the mountains in California — and figuratively from the rush of AI-powered innovation flooding the Valley. Before I forget: don't miss the opportunity to catch 21

chiefmartec.com

李開復的看法

Nicky Hsieh

www.facebook.com

做 AI Application 的小公司該如何維持競爭力

小公司如何應對 OpenAI 挑戰？

面臨最大的難題是，許多原定的服務還在開發中，但是 OpenAI 的革命性技術已經問世，問題廣度和回應深度都非常夠；已知繼續開發下去很難取得勝利，對於較小的 AI 公司來說，應該如何因應？

medium.com

Summary

1. 承認自己的強項並非燒錢戰
1. 燒了一堆錢做出一個 me-too product，不應該是台灣走的路
2. 使用這些強大的 AI Basemodel (built by 大公司) 已經不是選擇而是必須，否則就會落後於競爭對手。
1. AI 模型的開發從一開始就是基於前人的實驗來進行優化。所以不要因為使用別人的 APIs 而感到不安，這是產業演化和專業分工的必然結果
3. 當 OpenAI 和 Google 的模型推出時，AI 小公司必須針對自己主打的情境立刻使用和測試，並將其納入常規產品開發流程
1. 因為一旦什麼都做，就是直接與這些生成模型競爭。這些模型已經具有很強的通用性，因此通用型絕非小公司的發展方向 (View Highlight)
4. 範例：判斷對話中消費者是否想要購買蘋果，並實時顯示在消費者的螢幕上
1. 需要一個實時的語音轉文字串流，再從對話中提取與購買水果相關的對話，然後將其交給 ChatGPT 來判斷顧客是否想要購買水果
2. 在此過程中，模型對水果買賣要有非常強的理解，而 ChatGPT 的輸入有技術上限
3. 因此，需要找出如何從對話中提取正確的文本，並將其轉換成 ChatGPT 能夠好好回答的格式。
4. 最後，針對這個情境，設計並嘗試適合的 ChatGPT 提示問題，以便獲得相應的輸入/輸出。

About Alex

• Software Product Manager. Work experiences in Taipei, Singapore, and Shanghai.
• Currently based in Taipei City, Taiwan.
• Contact me via: alex.ho.helloworld@gmail.com

Notes & Articles

產品經理、思維策略

經濟、投資

產業筆記

其他

電影筆記

旅行

旅行遊記

旅行建議、推薦景點

台灣

日本

東南亞

歐洲