This document discusses the importance of having a sense of purpose in life. It states that purpose is feeling like you are part of something bigger than yourself and feeling needed. Having purpose creates true happiness. It also discusses Porter's Five Forces model of industry analysis and outlines the key aspects of a business model canvas including value propositions, customer segments, channels, customer relationships, revenue streams, resources, activities, partnerships, cost structure, and provides examples.
This document provides information on various topics related to social media marketing and analytics. It discusses generational traits and how to engage customers on social media. It also covers viral marketing, social media tools and optimization, web analytics, and how businesses are using social media. The document concludes with assignments for students, including creating a testimonial, developing content about Thailand's late king, and forming groups for a final project on branding and marketing a new product or business concept.
This document provides an overview of an upcoming social commerce course. It introduces the instructor and discusses topics that will be covered like e-commerce, digital marketing, social media marketing, and social commerce. A tentative schedule is presented that explores concepts like e-tailors, e-manufacturing, matching sellers and buyers, and increasing online sales. Students will have an assignment to propose a product idea they could potentially sell online by presenting a one-page description and pitching it in a 3-minute presentation.
This document discusses the importance of having a sense of purpose in life. It states that purpose is feeling like you are part of something bigger than yourself and feeling needed. Having purpose creates true happiness. It also discusses Porter's Five Forces model of industry analysis and outlines the key aspects of a business model canvas including value propositions, customer segments, channels, customer relationships, revenue streams, resources, activities, partnerships, cost structure, and provides examples.
This document provides information on various topics related to social media marketing and analytics. It discusses generational traits and how to engage customers on social media. It also covers viral marketing, social media tools and optimization, web analytics, and how businesses are using social media. The document concludes with assignments for students, including creating a testimonial, developing content about Thailand's late king, and forming groups for a final project on branding and marketing a new product or business concept.
This document provides an overview of an upcoming social commerce course. It introduces the instructor and discusses topics that will be covered like e-commerce, digital marketing, social media marketing, and social commerce. A tentative schedule is presented that explores concepts like e-tailors, e-manufacturing, matching sellers and buyers, and increasing online sales. Students will have an assignment to propose a product idea they could potentially sell online by presenting a one-page description and pitching it in a 3-minute presentation.
The document discusses several case studies and applications of data mining including:
1) Customer attrition prediction helped a mobile phone company reduce attrition rates from over 2%/month to under 1.5%/month.
2) Credit risk models used by banks to predict loan defaults enabled proliferation of mortgages and credit cards.
3) Amazon's product recommendations were successful by clustering customers based on products purchased.
4) A case study of MetLife found $30 million in fraudulent insurance claims through data mining of a $50 million consolidated database within companies worldwide to detect fraud like rate evasion faster than manual methods.
This document provides an overview of clustering techniques. It discusses what clustering is, different types of attributes that can be clustered, and major clustering approaches. The major approaches covered are partitioning algorithms, which construct partitions and evaluate them; hierarchical algorithms, which create a hierarchical decomposition; and density-based algorithms, which are based on connectivity and density. Examples of applications are also provided.
This document provides an overview of classification and prediction evaluation techniques. It discusses evaluating models on large and small datasets using techniques like train/test splits, cross-validation, and the bootstrap method. Evaluation measures for binary classification like precision, recall, and accuracy are presented. Visualization techniques like lift charts and ROC curves for comparing model performance are also introduced.
This document discusses various classification and prediction techniques including Naive Bayes classification, regression, and support vector machines (SVM). It covers topics such as Naive Bayes assumptions, dealing with missing data, numeric attributes, and Bayesian belief networks. Statistical modeling approaches like Naive Bayes make independence assumptions between attributes. Regression can be used for numerical prediction problems.
The document discusses classification and prediction using decision trees. It begins by defining classification as predicting categorical labels from data, such as predicting if a loan applicant is "safe" or "risky". Prediction involves predicting continuous or ordered values, such as how much a customer will spend. The document then discusses how decision trees perform classification by recursively splitting the data into purer subsets based on attribute values, with leaf nodes representing class labels. Information gain is used as the splitting criterion to select the attribute that best splits the data. Finally, it notes that attributes with many values can bias decision trees towards overfitting.
This document provides a summary of lecture 5 on association rule mining. It discusses topics like association rule mining, mining single and multilevel association rules, measurements like support and confidence. It provides examples of mining association rules from transactional databases and relational tables. It describes the Apriori algorithm for mining frequent itemsets and generating association rules. It also discusses techniques like FP-tree for overcoming performance issues of Apriori.
This document discusses data mining concepts including data preprocessing and postprocessing. It covers the differences between data mining, machine learning, and statistics. Data mining aims to discover knowledge from data in an automatic or semi-automatic way. Both data mining and machine learning use techniques to generalize from data, but data mining focuses more on gaining knowledge rather than just prediction. Data preprocessing techniques like cleaning, integration, and transformation are used to engineer the input data. Data postprocessing techniques combine multiple models to engineer the output.
The document provides an overview of data warehousing and OLAP technology. It defines a data warehouse as a subject-oriented, integrated collection of historical data used for analysis and decision making. It describes key properties of data warehouses including being subject-oriented, integrated, time-variant, and non-volatile. It also discusses dimensional modeling, data cubes, and OLAP for analyzing aggregated data.
This document provides an introduction and overview of the DBM630: Data Mining and Data Warehousing course. It outlines the course syllabus, textbooks, assessment tasks, schedule, prerequisites, and provides a high-level introduction to data mining and data warehousing concepts including definitions, processes, applications and evolution of database technologies.
The document discusses several case studies and applications of data mining including:
1) Customer attrition prediction helped a mobile phone company reduce attrition rates from over 2%/month to under 1.5%/month.
2) Credit risk models used by banks to predict loan defaults enabled proliferation of mortgages and credit cards.
3) Amazon's product recommendations were successful by clustering customers based on products purchased.
4) A case study of MetLife found $30 million in fraudulent insurance claims through data mining of a $50 million consolidated database within companies worldwide to detect fraud like rate evasion faster than manual methods.
This document provides an overview of clustering techniques. It discusses what clustering is, different types of attributes that can be clustered, and major clustering approaches. The major approaches covered are partitioning algorithms, which construct partitions and evaluate them; hierarchical algorithms, which create a hierarchical decomposition; and density-based algorithms, which are based on connectivity and density. Examples of applications are also provided.
This document provides an overview of classification and prediction evaluation techniques. It discusses evaluating models on large and small datasets using techniques like train/test splits, cross-validation, and the bootstrap method. Evaluation measures for binary classification like precision, recall, and accuracy are presented. Visualization techniques like lift charts and ROC curves for comparing model performance are also introduced.
This document discusses various classification and prediction techniques including Naive Bayes classification, regression, and support vector machines (SVM). It covers topics such as Naive Bayes assumptions, dealing with missing data, numeric attributes, and Bayesian belief networks. Statistical modeling approaches like Naive Bayes make independence assumptions between attributes. Regression can be used for numerical prediction problems.
The document discusses classification and prediction using decision trees. It begins by defining classification as predicting categorical labels from data, such as predicting if a loan applicant is "safe" or "risky". Prediction involves predicting continuous or ordered values, such as how much a customer will spend. The document then discusses how decision trees perform classification by recursively splitting the data into purer subsets based on attribute values, with leaf nodes representing class labels. Information gain is used as the splitting criterion to select the attribute that best splits the data. Finally, it notes that attributes with many values can bias decision trees towards overfitting.
This document provides a summary of lecture 5 on association rule mining. It discusses topics like association rule mining, mining single and multilevel association rules, measurements like support and confidence. It provides examples of mining association rules from transactional databases and relational tables. It describes the Apriori algorithm for mining frequent itemsets and generating association rules. It also discusses techniques like FP-tree for overcoming performance issues of Apriori.
This document discusses data mining concepts including data preprocessing and postprocessing. It covers the differences between data mining, machine learning, and statistics. Data mining aims to discover knowledge from data in an automatic or semi-automatic way. Both data mining and machine learning use techniques to generalize from data, but data mining focuses more on gaining knowledge rather than just prediction. Data preprocessing techniques like cleaning, integration, and transformation are used to engineer the input data. Data postprocessing techniques combine multiple models to engineer the output.
The document provides an overview of data warehousing and OLAP technology. It defines a data warehouse as a subject-oriented, integrated collection of historical data used for analysis and decision making. It describes key properties of data warehouses including being subject-oriented, integrated, time-variant, and non-volatile. It also discusses dimensional modeling, data cubes, and OLAP for analyzing aggregated data.
This document provides an introduction and overview of the DBM630: Data Mining and Data Warehousing course. It outlines the course syllabus, textbooks, assessment tasks, schedule, prerequisites, and provides a high-level introduction to data mining and data warehousing concepts including definitions, processes, applications and evolution of database technologies.
8. What is Marketing 4.0?
Combine online and
offline interaction
between companies
and customers
9.
10.
11.
12. ความหมายของ Social Media (Turban et al, 2012)6
“รูปแบบสื่อออนไลน์และเครื่องมือที่ถูกใช้สำหรับการปฏิสัมพันธ์เชิงสังคมและพูดคุย โดยมี
วัตถุประสงค์เพื่อแลกเปลี่ยนความคิดเห็น ประสบการณ์ และความเข้าใจซึ่งกันและกัน”5
5
Social Media อยู่ในหลายรูปแบบไม่ว่าจะเป็น5
ข้อความ, รูปภาพ, เสียง หรือวีดีโอ5
5
หัวใจสำคัญที่เป็นลักษณะเด่นของ Social Media อยู่ที่5
“ผู้ใช้คือคนธรรมดาทั่วๆ ไป สามารถผลิต ควบคุม ใช้ และจัดการเนื้อหาได้เอง”E
“user-manipulated content”E
13. คุณลักษณะของ Social Media6
1. เป็นสื่อที่เน้นปฏิสัมพันธ์และพูดคุย6
6
ž One-way Communication à Two-way Communication5
ž สร้างกิจกรรมเพื่อให้เกิดการมีส่วนร่วม (Engagement)5
5
14. คุณลักษณะของ Social Media6
2. วัตถุประสงค์เพื่อแลกเปลี่ยนความคิดเห็น ประสบการณ์ และความ
เข้าใจซึ่งกันและกัน6
6
ž คนชอบพูดคุยแลกเปลี่ยนความคิดเห็นและประสบการณ์5
ž Social Media สามารถทำได้ในวงกว้างและติดตามได้ต่อเนื่อง5
5
15.
16. คุณลักษณะของ Social Media6
3. ใครก็เป็นเจ้าของสื่อได้6
เปิดโอกาสให้ผู้ที่มีความชำนาญ แต่ไม่มีโอกาสเข้าถึงหรือใช้สื่อเดิม5
6
“Key Success of Social Media”6
UGC
User Generated Content
ผู้ใช้คือคนธรรมดาทั่วๆ ไป ผลิต ควบคุม ใช้ และจัดการเนื้อหาได้เอง5
เปิดโอกาสให้ผู้ที่มีความชำนาญ แต่ไม่มีโอกาสเข้าถึงหรือใช้สื่อเดิม5
21. ประเภทของ Social Media (I)
ž Blog
— Blogger, Wordpress, TypePad, LiveJournal,
Bloggang
ž Twitter & Microblog
— Plurk, Tumblr, Yammer
ž Social Network
— Facebook, G+, Hi5, Myspace, Linkedin, Plaxo
22. ประเภทของ Social Media (II)
ž Media Sharing
— YouTube, Flickr, Instagram, Slideshare
ž Social News and Bookmarking
— Delicious, StumbleUpon, Digg, Mixx, Pinterest
ž Online Forum
— เว็บไซด์เป็นกระทู้โดยเฉพาะ PANTIP5
— กระทู้เป็นส่วนหนึ่งของเว็บไซด์ Jeban, Siamphone5
23. เป้าหมายการใช้ Social Media เพื่อการตลาด6
1. เพื่อใช้เป็นหน้าร้านสำหรับขายสินค้าหรือบริการ5
2. เพื่อทำให้เกิดการรับรู้ถึงแบรนด์5
3. เพื่อเป็นเครื่องมือในการรับฟังความคิดเห็นและบริการลูกค้า5
4. เพื่อสร้างชุมชน5
5. อื่น ??5
29. Team Challenges
ž Existing Product/Service5
— A3: ลูกโป่ง อรรถรส5
— B4: Pulanla5
— B5: Somsai5
— D5: โซดามะขาม5
— F3: ROYTHAI5
ž New Product/Service5
— B1: Diet Cookie5
— A4: TinyPlant5
— D2: Fiber Ball5
— F1: HousePlants O2 Night5
— F6: Prapatsorn Aroma5
GROUP I: A3 + A4
GROUP II: B4 + F6
GROUP III: B5 + D2
GROUP IV: D5 + B1
GROUP V: F3 + F1
30. Online Marketing Diagnosis
ž Search Research
— Is the brand accessible by search engine?
ž Inspection
— Are you happy with the ROI of your digital
marketing efforts?
— Did you do these?
○ Tweaking website and content with long tail
keywords
○ Sending out email campaign
○ Creating flashy digital experience for your
audience
31. Digital Marketing Diagnosis
ž Review your digital marketing strategy
— Actual results and Desire results
— E.g., one year ago gave more emphasis on Facebook; but time has
changed; need to pay more heed to Instagram
ž Analyze your conversion
— Analyze your audience (Who is buying your stuff?) and target your strategy
according to their particular preference, taste, choice and behavior
ž Social media review
— Satisfied? If not, why strategy isn’t working? Frequency?
Post Inspection (content, image, hashtag, short/long, etc.)
Engagement, Reply, What are missing?
ž Review customer experience
— [Others] Feeling great? User-friendly? Provide more value than
competitors? Convey the brand image?
ž Stuck in one place or exploring possible opportunities?
— Who is your competitors? Check competitors’ current digital strategy.
Pricing, Campaign. Find new way to add value to your potential customers