Lesson 05 Three Course Requirements
Download as PPT, PDF1 like676 views
This document outlines the requirements and scoring criteria for a course assignment involving an AdWords campaign competition. Students must create a Pre-Campaign Strategy document of up to 4 pages describing their client and campaign strategy. They must also create a Post-Campaign Summary of up to 8 pages with an Executive Summary, Industry Component analyzing campaign results, and Learning Component reflecting on what was learned. The campaign will be scored based on both campaign performance metrics and the quality of the written reports. Dr. Jansen recommends students start working on the written reports immediately to have time to address all requirements.
![Three Course Requirements and What You Should Do About Them Jim Jansen College of Information Sciences and Technology The Pennsylvania State University [email_address]](https://image.slidesharecdn.com/lesson05threecourserequirements-091206065045-phpapp02/85/Lesson-05-Three-Course-Requirements-1-320.jpg)
![Thank you! (reminder to do your daily logs) Jim Jansen College of Information Sciences and Technology The Pennsylvania State University [email_address]](https://image.slidesharecdn.com/lesson05threecourserequirements-091206065045-phpapp02/85/Lesson-05-Three-Course-Requirements-31-320.jpg)
Lesson 05 Three Course Requirements
- 1. Three Course Requirements and What You Should Do About Them Jim Jansen College of Information Sciences and Technology The Pennsylvania State University [email_address]
- 2. The Challenge Two written components Pre-Campaign Strategy Post-Campaign Summary One computed component Campaign Statistics (what statistics were used for scoring?) This is a course, so major goal is the learning objectives, but … … why not do your best?
- 3. The Scoring Algorithm Did not want to just look at performance Final algorithm incorporates 30 different signals in 5 categories Performance Account structure Tool and feature usage Advertiser savviness Budget management The Campaign Statistics algorithm will determine the top 50 accounts in each region. Independent Global Academic Panel will review the Pre-Campaign Strategy and Post-Campaign Summary reports of the top five teams in each region to determine the regional and global winners. It takes great Campaign Statistics to make the regional top five, and then great written reports to win.
- 4. Campaign Statistics Account structure : An excellent account structure mirrors the structure of the client’s Website campaign grouped by product lines each campaign contains multiple Ad Groups each Ad Group contains ad texts/variations specific to these subsections and a targeted and specific keyword list
- 5. Campaign Statistics Optimization techniques how well you implement the optimization techniques and best practices how you optimized the Google network to your advantage (e.g. how effectively you used the content network)
- 6. Campaign Statistics Account activity & reporting Review and change your approach over time to ensure you get the most from your campaigns Use the Report Center and take actions as a result
- 7. Campaign Statistics Performance & budget effectively use your budget across your keywords throughout the competition edit the cost-per-click of individual keywords during campaign
- 8. Campaign Statistics Relevance Achieve a strong click-through rate Create relevant and effective advertising Revisit your campaigns and change where necessary
- 9. Written Report Format In English Use 12-point Times font 2.54cm page margins A4 paper (standard layout) Left-justification 1.5 line spacing
- 10. Pre-Campaign Strategy Maximum four pages Communication and Readability Pre-Campaign Strategy - Two components Client Overview AdWords Strategy
- 11. Client Overview (Pre-Campaign Strategy) About two pages Provides a brief overview of the client and their marketing Serves as a foundation for the proposed AdWords Strategy.
- 12. Client Overview Part 01 (Pre-Campaign Strategy) Client profile (a few sentences including some of the following) Name, location Sales and number of employees Goods and services offered Key online marketing personnel Age of the company URL Website age, website management Company presence and sales via online and offline channels Other relevant information
- 13. Client Overview Part 02 (Pre-Campaign Strategy) Market analysis (a couple of paragraphs including some of the following) Current and potential customers Current and potential competitors Overview of the industry (key characteristics, competitive/saturated/mature) Projected and historical online spend for the industry Market position/specialties Unique selling points of the goods/services offered Seasonality of their goods/services or seasonality that the company has identified Other relevant market information
- 14. Client Overview Part 03 (Pre-Campaign Strategy) Current marketing (a couple of paragraphs including some of the following) Website uses, e.g. sales, customer service Website strengths and weaknesses Website visibility, such as Google PageRank, incoming links, a few keyword search results, online advertising, and offline promotion of the url. If available, summary information from Google Analytics or other third party web tracking software Email campaigns Offline advertising Other online or offline marketing
- 15. Client Overview Part 04 (Pre-Campaign Strategy) Conclusion on how the Ad Words campaign should align with the client’s business (a few sentences)
- 16. Ad Words Strategy (Pre-Campaign Strategy) About two pages including sample ads and keywords Based on an analysis of the client, their Website and their marketing, teams should craft an appropriate AdWords Strategy and metrics for their campaign.
- 17. Ad Words Strategy (Pre-Campaign Strategy) The Proposed AdWords Strategy should include: Number of Ad Groups and the focus for each Ad Group Keywords and negative keywords Text for at least two AdWords versions for some Ad Groups Daily and weekly plans for spending their campaign budget Network(s) for their AdWords ads Target audience settings Ad Serving options Keyword Bidding Geotargeting Goals for impressions, clicks, CPC and CTR Proposed success metrics Other relevant information
- 18. Communication and readability (Pre-Campaign Strategy) The Pre-Campaign Strategy should have a logical flow be easy to follow use proper English no grammatical mistakes
- 19. Post-Campaign Summary Maximum eight pages The Post-Campaign Summary has three components: Executive Summary Industry Component Learning Component Also includes Communication and Readability and relevant use of Tables, Figures and Charts
- 20. Executive Summary (Post-Campaign Summary ) One page, stand alone document highlights four factors: 1. Campaign Overview – a basic review of the project by introducing the campaign goals and operational details. 2. Key results – discuss the overall campaign performance as well as the performance of each AdGroup. Specifically, you will want to reference each AdGroup as well as the overall campaign. This section should provide a brief overview of the key metrics. 3. Conclusion – a clear synthesis of the content of the report and key items. This is your chance to tie together the entire package and focus the client’s attention on the most important project aspects. 4. Future Online Marketing Recommendations – simple, actionable and well-justified advice on what your client should do in the future with respect to online marketing. Develop the Executive Summary after you generate all other content, as it summarizes and will overlap with your content in the Industry Component.
- 21. Industry Component Part 01 (Post-Campaign Summary ) Maximum four pages This is the team’s chance to share the results with their client and expand upon the Executive Summary. The ideal approach is to write the Industry Component first and then summarize this content for the Executive Summary. You would include most if not all of your Charts, Tables and Figures in your Industry Component and cover the following areas:
- 22. Industry Component Part 02 (Post-Campaign Summary ) 1. Introduction – Overview the Industry Component section and introduce it’s core content. 2. Campaign Overview: Review the major campaign goals (strategic goals as well as metrics: CTR, CPC, and Impressions, etc.) set prior to the project and discuss your general strategies for approaching each goal. Operational details (campaign dates, money spent, ad groups used). Review the basic schedule and cost structure you followed, your methods for monitoring the account, etc.
- 23. Industry Component Part 03 (Post-Campaign Summary ) 3. Evolution of Your Campaign Strategy: What were the major changes you made during the campaign and what led to these changes? How did these changes affect your campaign? 4. Key Results – Summarize your results based on three weeks of data, such as: Overall performance of the campaign and individual ad groups. Discussions of performance of the initial campaign and changes in performance following your optimization efforts. Discuss the keyword combinations that were effective and ineffective. Highlight your success stories and make quick, but clear references to the failures you experienced.
- 24. Industry Component Part 03 (Post-Campaign Summary ) When discussing performance, refer to the metrics such as: Impressions Clicks Click Through Rate Average Cost per Keyword Total Cost of Campaign Other metrics provided by the client, such as conversions
- 25. Industry Component Part 04 (Post-Campaign Summary ) 5. Conclusions Synthesize the Industry Component Tie together the entire package and focus the client’s attention on the key project aspects Repackage all the information from the data section to display your practical lessons learned to the client. The goal is to develop a great transition that summarizes the critical results and starts to link these results to the future recommendations in the next section. 6. Future Recommendations – Provide simple actionable and well-justified advice on what your client should do in the future with respect to online marketing.
- 26. Learning Component Part 01 (Post-Campaign Summary ) Maximum three pages. The teams’ reflection on what they learned. Should cover four points: 1. Learning objectives and outcomes – what did the team hope to learn? How well did the team meet their learning expectations? What else did they learn? What key outcomes will the team remember? What were the expected and unexpected outcomes from participating in the Challenge? 2. Group dynamics – what problems did the team encounter and more importantly, how did they overcome these problems? What were some of the expected and unexpected outcomes from working as a group?
- 27. Learning Component Part 01 (Post-Campaign Summary ) 3. Client dynamics – what problems did they encounter and as importantly, how did they overcome these problems? What were some of the expected and unexpected outcomes from working with the client? 4. Future recommendations – what would they do differently in the future to improve their campaign strategy, learning experience, group dynamics and client dynamics?
- 28. Post-Campaign Summary Part 03 Communication and readability - The Post-Campaign Summary should have: a logical flow be easy to follow use proper English no grammatical mistakes
- 29. Post-Campaign Summary Part 03 Charts, Tables and Figures Teams should intersperse relevant charts, tables, figures to illustrate their results. In addition, teams should label and refer to the charts, tables and figures in the body of the report.
- 30. Dr. Jansen’s Recommendations Start with the end in mind ! Know what the criteria are! Campaign – review pages 18 – 20 Student Text Written Reports – review pages 14 – 18 Student Text Appoint Team Editor – start addressing the requirements that you can now ! Why wait? (i.e., start the reports today!) For remainder of lesson Continue locking in client Review Challenge criteria Develop plan for addressing written reports
- 31. Thank you! (reminder to do your daily logs) Jim Jansen College of Information Sciences and Technology The Pennsylvania State University [email_address]
Editor's Notes
- #4: So what did we actually decide on? We didn’t want to solely look at performance so we ended up incorporating 30 different signals within 5 distinct categories. Performance, as discussed earlier. Account structure Tool and feature usage Advertiser savviness Budget management To come up w/ the final list, we worked closely with focus groups consisting of AdWords Optimizers, Managers and Engineers and determined the ideal mix of signals, thresholds and weights based on their feedback. We then validated and refined the initial algorithm using Beta competition groups that started before the actual challenge. And based on qualitative rankings of those accounts, we fine-tuned and measured the performance of the algorithm.
- #5: So what did we actually decide on? We didn’t want to solely look at performance so we ended up incorporating 30 different signals within 5 distinct categories. Performance, as discussed earlier. Account structure Tool and feature usage Advertiser savviness Budget management To come up w/ the final list, we worked closely with focus groups consisting of AdWords Optimizers, Managers and Engineers and determined the ideal mix of signals, thresholds and weights based on their feedback. We then validated and refined the initial algorithm using Beta competition groups that started before the actual challenge. And based on qualitative rankings of those accounts, we fine-tuned and measured the performance of the algorithm.
- #6: So what did we actually decide on? We didn’t want to solely look at performance so we ended up incorporating 30 different signals within 5 distinct categories. Performance, as discussed earlier. Account structure Tool and feature usage Advertiser savviness Budget management To come up w/ the final list, we worked closely with focus groups consisting of AdWords Optimizers, Managers and Engineers and determined the ideal mix of signals, thresholds and weights based on their feedback. We then validated and refined the initial algorithm using Beta competition groups that started before the actual challenge. And based on qualitative rankings of those accounts, we fine-tuned and measured the performance of the algorithm.
- #7: So what did we actually decide on? We didn’t want to solely look at performance so we ended up incorporating 30 different signals within 5 distinct categories. Performance, as discussed earlier. Account structure Tool and feature usage Advertiser savviness Budget management To come up w/ the final list, we worked closely with focus groups consisting of AdWords Optimizers, Managers and Engineers and determined the ideal mix of signals, thresholds and weights based on their feedback. We then validated and refined the initial algorithm using Beta competition groups that started before the actual challenge. And based on qualitative rankings of those accounts, we fine-tuned and measured the performance of the algorithm.
- #8: So what did we actually decide on? We didn’t want to solely look at performance so we ended up incorporating 30 different signals within 5 distinct categories. Performance, as discussed earlier. Account structure Tool and feature usage Advertiser savviness Budget management To come up w/ the final list, we worked closely with focus groups consisting of AdWords Optimizers, Managers and Engineers and determined the ideal mix of signals, thresholds and weights based on their feedback. We then validated and refined the initial algorithm using Beta competition groups that started before the actual challenge. And based on qualitative rankings of those accounts, we fine-tuned and measured the performance of the algorithm.
- #9: So what did we actually decide on? We didn’t want to solely look at performance so we ended up incorporating 30 different signals within 5 distinct categories. Performance, as discussed earlier. Account structure Tool and feature usage Advertiser savviness Budget management To come up w/ the final list, we worked closely with focus groups consisting of AdWords Optimizers, Managers and Engineers and determined the ideal mix of signals, thresholds and weights based on their feedback. We then validated and refined the initial algorithm using Beta competition groups that started before the actual challenge. And based on qualitative rankings of those accounts, we fine-tuned and measured the performance of the algorithm.
- #10: So what did we actually decide on? We didn’t want to solely look at performance so we ended up incorporating 30 different signals within 5 distinct categories. Performance, as discussed earlier. Account structure Tool and feature usage Advertiser savviness Budget management To come up w/ the final list, we worked closely with focus groups consisting of AdWords Optimizers, Managers and Engineers and determined the ideal mix of signals, thresholds and weights based on their feedback. We then validated and refined the initial algorithm using Beta competition groups that started before the actual challenge. And based on qualitative rankings of those accounts, we fine-tuned and measured the performance of the algorithm.
- #11: So what did we actually decide on? We didn’t want to solely look at performance so we ended up incorporating 30 different signals within 5 distinct categories. Performance, as discussed earlier. Account structure Tool and feature usage Advertiser savviness Budget management To come up w/ the final list, we worked closely with focus groups consisting of AdWords Optimizers, Managers and Engineers and determined the ideal mix of signals, thresholds and weights based on their feedback. We then validated and refined the initial algorithm using Beta competition groups that started before the actual challenge. And based on qualitative rankings of those accounts, we fine-tuned and measured the performance of the algorithm.
- #12: So what did we actually decide on? We didn’t want to solely look at performance so we ended up incorporating 30 different signals within 5 distinct categories. Performance, as discussed earlier. Account structure Tool and feature usage Advertiser savviness Budget management To come up w/ the final list, we worked closely with focus groups consisting of AdWords Optimizers, Managers and Engineers and determined the ideal mix of signals, thresholds and weights based on their feedback. We then validated and refined the initial algorithm using Beta competition groups that started before the actual challenge. And based on qualitative rankings of those accounts, we fine-tuned and measured the performance of the algorithm.
- #13: So what did we actually decide on? We didn’t want to solely look at performance so we ended up incorporating 30 different signals within 5 distinct categories. Performance, as discussed earlier. Account structure Tool and feature usage Advertiser savviness Budget management To come up w/ the final list, we worked closely with focus groups consisting of AdWords Optimizers, Managers and Engineers and determined the ideal mix of signals, thresholds and weights based on their feedback. We then validated and refined the initial algorithm using Beta competition groups that started before the actual challenge. And based on qualitative rankings of those accounts, we fine-tuned and measured the performance of the algorithm.
- #14: So what did we actually decide on? We didn’t want to solely look at performance so we ended up incorporating 30 different signals within 5 distinct categories. Performance, as discussed earlier. Account structure Tool and feature usage Advertiser savviness Budget management To come up w/ the final list, we worked closely with focus groups consisting of AdWords Optimizers, Managers and Engineers and determined the ideal mix of signals, thresholds and weights based on their feedback. We then validated and refined the initial algorithm using Beta competition groups that started before the actual challenge. And based on qualitative rankings of those accounts, we fine-tuned and measured the performance of the algorithm.
- #15: So what did we actually decide on? We didn’t want to solely look at performance so we ended up incorporating 30 different signals within 5 distinct categories. Performance, as discussed earlier. Account structure Tool and feature usage Advertiser savviness Budget management To come up w/ the final list, we worked closely with focus groups consisting of AdWords Optimizers, Managers and Engineers and determined the ideal mix of signals, thresholds and weights based on their feedback. We then validated and refined the initial algorithm using Beta competition groups that started before the actual challenge. And based on qualitative rankings of those accounts, we fine-tuned and measured the performance of the algorithm.
- #16: So what did we actually decide on? We didn’t want to solely look at performance so we ended up incorporating 30 different signals within 5 distinct categories. Performance, as discussed earlier. Account structure Tool and feature usage Advertiser savviness Budget management To come up w/ the final list, we worked closely with focus groups consisting of AdWords Optimizers, Managers and Engineers and determined the ideal mix of signals, thresholds and weights based on their feedback. We then validated and refined the initial algorithm using Beta competition groups that started before the actual challenge. And based on qualitative rankings of those accounts, we fine-tuned and measured the performance of the algorithm.
- #17: So what did we actually decide on? We didn’t want to solely look at performance so we ended up incorporating 30 different signals within 5 distinct categories. Performance, as discussed earlier. Account structure Tool and feature usage Advertiser savviness Budget management To come up w/ the final list, we worked closely with focus groups consisting of AdWords Optimizers, Managers and Engineers and determined the ideal mix of signals, thresholds and weights based on their feedback. We then validated and refined the initial algorithm using Beta competition groups that started before the actual challenge. And based on qualitative rankings of those accounts, we fine-tuned and measured the performance of the algorithm.
- #18: So what did we actually decide on? We didn’t want to solely look at performance so we ended up incorporating 30 different signals within 5 distinct categories. Performance, as discussed earlier. Account structure Tool and feature usage Advertiser savviness Budget management To come up w/ the final list, we worked closely with focus groups consisting of AdWords Optimizers, Managers and Engineers and determined the ideal mix of signals, thresholds and weights based on their feedback. We then validated and refined the initial algorithm using Beta competition groups that started before the actual challenge. And based on qualitative rankings of those accounts, we fine-tuned and measured the performance of the algorithm.
- #19: So what did we actually decide on? We didn’t want to solely look at performance so we ended up incorporating 30 different signals within 5 distinct categories. Performance, as discussed earlier. Account structure Tool and feature usage Advertiser savviness Budget management To come up w/ the final list, we worked closely with focus groups consisting of AdWords Optimizers, Managers and Engineers and determined the ideal mix of signals, thresholds and weights based on their feedback. We then validated and refined the initial algorithm using Beta competition groups that started before the actual challenge. And based on qualitative rankings of those accounts, we fine-tuned and measured the performance of the algorithm.
- #20: So what did we actually decide on? We didn’t want to solely look at performance so we ended up incorporating 30 different signals within 5 distinct categories. Performance, as discussed earlier. Account structure Tool and feature usage Advertiser savviness Budget management To come up w/ the final list, we worked closely with focus groups consisting of AdWords Optimizers, Managers and Engineers and determined the ideal mix of signals, thresholds and weights based on their feedback. We then validated and refined the initial algorithm using Beta competition groups that started before the actual challenge. And based on qualitative rankings of those accounts, we fine-tuned and measured the performance of the algorithm.
- #21: So what did we actually decide on? We didn’t want to solely look at performance so we ended up incorporating 30 different signals within 5 distinct categories. Performance, as discussed earlier. Account structure Tool and feature usage Advertiser savviness Budget management To come up w/ the final list, we worked closely with focus groups consisting of AdWords Optimizers, Managers and Engineers and determined the ideal mix of signals, thresholds and weights based on their feedback. We then validated and refined the initial algorithm using Beta competition groups that started before the actual challenge. And based on qualitative rankings of those accounts, we fine-tuned and measured the performance of the algorithm.
- #22: So what did we actually decide on? We didn’t want to solely look at performance so we ended up incorporating 30 different signals within 5 distinct categories. Performance, as discussed earlier. Account structure Tool and feature usage Advertiser savviness Budget management To come up w/ the final list, we worked closely with focus groups consisting of AdWords Optimizers, Managers and Engineers and determined the ideal mix of signals, thresholds and weights based on their feedback. We then validated and refined the initial algorithm using Beta competition groups that started before the actual challenge. And based on qualitative rankings of those accounts, we fine-tuned and measured the performance of the algorithm.
- #23: So what did we actually decide on? We didn’t want to solely look at performance so we ended up incorporating 30 different signals within 5 distinct categories. Performance, as discussed earlier. Account structure Tool and feature usage Advertiser savviness Budget management To come up w/ the final list, we worked closely with focus groups consisting of AdWords Optimizers, Managers and Engineers and determined the ideal mix of signals, thresholds and weights based on their feedback. We then validated and refined the initial algorithm using Beta competition groups that started before the actual challenge. And based on qualitative rankings of those accounts, we fine-tuned and measured the performance of the algorithm.
- #24: So what did we actually decide on? We didn’t want to solely look at performance so we ended up incorporating 30 different signals within 5 distinct categories. Performance, as discussed earlier. Account structure Tool and feature usage Advertiser savviness Budget management To come up w/ the final list, we worked closely with focus groups consisting of AdWords Optimizers, Managers and Engineers and determined the ideal mix of signals, thresholds and weights based on their feedback. We then validated and refined the initial algorithm using Beta competition groups that started before the actual challenge. And based on qualitative rankings of those accounts, we fine-tuned and measured the performance of the algorithm.
- #25: So what did we actually decide on? We didn’t want to solely look at performance so we ended up incorporating 30 different signals within 5 distinct categories. Performance, as discussed earlier. Account structure Tool and feature usage Advertiser savviness Budget management To come up w/ the final list, we worked closely with focus groups consisting of AdWords Optimizers, Managers and Engineers and determined the ideal mix of signals, thresholds and weights based on their feedback. We then validated and refined the initial algorithm using Beta competition groups that started before the actual challenge. And based on qualitative rankings of those accounts, we fine-tuned and measured the performance of the algorithm.
- #26: So what did we actually decide on? We didn’t want to solely look at performance so we ended up incorporating 30 different signals within 5 distinct categories. Performance, as discussed earlier. Account structure Tool and feature usage Advertiser savviness Budget management To come up w/ the final list, we worked closely with focus groups consisting of AdWords Optimizers, Managers and Engineers and determined the ideal mix of signals, thresholds and weights based on their feedback. We then validated and refined the initial algorithm using Beta competition groups that started before the actual challenge. And based on qualitative rankings of those accounts, we fine-tuned and measured the performance of the algorithm.
- #27: So what did we actually decide on? We didn’t want to solely look at performance so we ended up incorporating 30 different signals within 5 distinct categories. Performance, as discussed earlier. Account structure Tool and feature usage Advertiser savviness Budget management To come up w/ the final list, we worked closely with focus groups consisting of AdWords Optimizers, Managers and Engineers and determined the ideal mix of signals, thresholds and weights based on their feedback. We then validated and refined the initial algorithm using Beta competition groups that started before the actual challenge. And based on qualitative rankings of those accounts, we fine-tuned and measured the performance of the algorithm.
- #28: So what did we actually decide on? We didn’t want to solely look at performance so we ended up incorporating 30 different signals within 5 distinct categories. Performance, as discussed earlier. Account structure Tool and feature usage Advertiser savviness Budget management To come up w/ the final list, we worked closely with focus groups consisting of AdWords Optimizers, Managers and Engineers and determined the ideal mix of signals, thresholds and weights based on their feedback. We then validated and refined the initial algorithm using Beta competition groups that started before the actual challenge. And based on qualitative rankings of those accounts, we fine-tuned and measured the performance of the algorithm.
- #29: So what did we actually decide on? We didn’t want to solely look at performance so we ended up incorporating 30 different signals within 5 distinct categories. Performance, as discussed earlier. Account structure Tool and feature usage Advertiser savviness Budget management To come up w/ the final list, we worked closely with focus groups consisting of AdWords Optimizers, Managers and Engineers and determined the ideal mix of signals, thresholds and weights based on their feedback. We then validated and refined the initial algorithm using Beta competition groups that started before the actual challenge. And based on qualitative rankings of those accounts, we fine-tuned and measured the performance of the algorithm.
- #30: So what did we actually decide on? We didn’t want to solely look at performance so we ended up incorporating 30 different signals within 5 distinct categories. Performance, as discussed earlier. Account structure Tool and feature usage Advertiser savviness Budget management To come up w/ the final list, we worked closely with focus groups consisting of AdWords Optimizers, Managers and Engineers and determined the ideal mix of signals, thresholds and weights based on their feedback. We then validated and refined the initial algorithm using Beta competition groups that started before the actual challenge. And based on qualitative rankings of those accounts, we fine-tuned and measured the performance of the algorithm.