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日期：2024-11-10 12:00

A3. Advertising Content Audit – Individual Report [CLO1, CLO2, CLO3, CLO4]

Due: Sunday (November 17th) 11.55 PM / Weighting: 40% / Length: Max 2,000 words (+/- 10%)

Description

This assessment provides you with the opportunity to undertake an advertising content audit to evaluate whether a company’s current advertising strategy is proper and to provide recommendations for further improvements.

You are asked to evaluate the consistency of beauty and cosmetics company mamaearth’s Instagram posts (https://www.instagram.com/mamaearth.in/) with its overall positioning objectives. In addition, by analyzing Instagram content and social media engagement data (e.g., the number of likes, comments), you can evaluate whether the theme related tothe company's positioning is more successful than other themes in generating engagement.

Details

1) The focal company is mamaearth.in which is an Indian beauty, cosmetics and personal care brand. Identify the company’s positioning or value proposition statement (or suitable summary) and provide it (with relevant citation) onthe title page of your report. (Note: there are many companies who use the name “mamaearth” worldwide. We are investigating the company with the instagram account as given above)

Follow the below steps to complete your task:

2) Consistent Advertising with Positioning: We want to identify relevant themes across posted images. For the purpose of this assignment, we will assume the relevant main themes are reasonably consistent (but not identical) with the main themes introduced in the Bodyshop case study (Lecture Week 8); Product, People, Baby, Other, About Positioning.

3) You have been provided with the “mamaearth” instagramimages, PB metadata for the images and data extracted from Google Vision (Ocr, label, multiple objects).

Main Folder – Assignment 3_T3_24  Link:

Images sub-folder – 1619 mamaearth Instagram post images;  

Extracted Vision sub-folder – label_data.xlsx, multiple_objects_data.xlsx, ocr_data.xlsx

PB metadata - mamaearth_PB_T3_24_ Ass3(1619).xlsx

Initially, you will need to;

(i) Construct OpenCV data using the images (2.3 Image processing using OpenCV)

(ii) Process the captions/description in the PB metadata file, to obtain Valence_overall and length_descriptionand add them to PB metadata file. (4.2 Text Analysis Variables – will help).

Since the relevant themes are somewhat consistent with the Bodyshop case, you have some base for constructing a dictionary for the main themes. You can add to this base or modify, by sorting detected features from Google Vision according to their usage frequency and adding any items which are specific to mamaearth (possibly deleting items which are specific to the Bodyshop), if required. (This may be more relevant for the “About Positioning” theme since the positioning of the Bodyshop may vary from the positioning of mamaearth) Using the constructed dictionary, determine which themes are present in the posted mamaearth images.

(Do some preliminary googlevision processing with the multiple_objects, label and ocr googlevision data – 5.2 Construct Variables from Googlevision will help)

From the preliminary googlevision processing, construct your data dictionary for the 5 key themes (use the bodyshopdictionary as a base) which you will then use in 8.1 Main Category_Variables_googlevision notebook.

4) Report at least two posts (screenshots of images and text caption) about each theme. Also, state how many posts (frequency and percentage) are related to each theme. Based on this usage frequency, discuss what you suggest the company’s advertising content strategy is. In addition, evaluate whether you feel the frequency of the positioning-related theme is proper. Why? Or why not?

5) Advertising Effectiveness: (Evaluate whether the positioning-related theme is more successful compared to other popular themes in social media engagement by doing the following tasks):

Construct the regression data set which contains OpenCV, PB metadata and theme categories as dummy variables (also include relevant control variables: (1) text length in the caption, (2) text sentiment in the caption, (3) OCR text length within an image, (4) OCR text sentiment within an image, and (5) Posting time dummies: Year, Month-of-Year (January, …, December), Day-of-Week (Monday, ..., Sunday), Time-of-day (Morning, Afternoon, Evening, Night).

Report summary statistics (count/frequency, mean, median, minimum, maximum) in a Table for each of the X and Y (like count, comment count) variables.

For Y equal to “like count” use normal OLS regression (considering multicollinearity) on either like count or Ln (like count+ 1) or both.

Report the result of your model (s) in a Table (columns: X variables, coefficients, p-value, and VIF). Interpret the results focusing on the significance and relative importance of the separate themes in impacting viewer engagement.

For Y equal to “comment count” use normal OLS, Poisson and Negative Binomial regression.

Explain your choice of candidate regression models. For comment count report the regression result of the best model in a Table (columns: X variables, coefficients, p-value, and VIF). Interpret the results focusing on the significance and relative importance of the separate themes in impacting viewer engagement.

Effective Ad Content Exploration: Now, you are required to break down the positioning-related theme into several (max 5) sub-categories (See the examples in class materials). For example, there may be sub-themes based on “Organic” or “Sustainable” which represent how the organization wants consumers to view the brand. You can refer to the mission statement and your overview of the organization’s positioning to provide potential sub-categories (sub-themes). Provide your rationale for this sub-categorization (ensure it is specific to mamaearthand its positioning).

Report at least two posts, the number of posts, and summary statistics for each sub-category.

For Y equal to “like count” use normal OLS regression (considering multicollinearity) on either like count or Ln (like count+ 1) or both.

Report the result of your model (s) in a Table (columns: X variables, coefficients, p-value, and VIF). Interpret the results focusing on the significance and relative importance of the separate positioning sub-themes in impacting viewer engagement.

For Y equal to “comment count” use normal OLS, Poisson and Negative Binomial regression.

Explain your choice of candidate regression models. For comment count report the regression result of the best model in a Table (columns: X variables, coefficients, p-value, and VIF). Interpret the results focusing on the significance and relative importance of the separate positioning sub-themes in impacting viewer engagement.

Construct your data dictionary for the 3 to 5 sub-positioning themes (use the Positioning dictionary as a base and sub-divide into 3 to 5 sub-categories) which you will then use in 8.2 Sub Category_Variables_googlevisionnotebook.

Merge all the data (OpenCV, PB metadata, Main category data (from 8.1), Sub-category data (from 8.2) into one regression ready file (8.3 Merge_All_Variables_Regression_data)

Use the regression file which will come from 8.3 to do the various regressions. Use 8.4 Regression_models with main & sub-category variables (T3_24) to conduct the regressions. (You may need to run this notebook several times (modifying the code) to obtain the required regressions for each part)

6) Conclusion: Provide conclusions on which themes the company is focussed. Is their advertising content consistent with the company’s positioning? Which positioning (sub) themes are successful? Considering all of the above, recommend what the company needs to keep doing or improvements it needs to make to enhance engagement and consistency with positioning.

In completing this task, apply appropriate data analytics and consider the concepts introduced in class. Your report should not exceed the word limit, excluding the title page, relevant images, tables or charts.    

Title page (1 page) includes (1) Company & Positioning statement, (2) Word count, (3) An executive summary (One paragraph) of your report, (4) Course name, tutorial session and group, tutor’s name, (5) Your first and last name & zID

Reference: (If any) Cite academic papers, newspaper articles, blogs, or industry reports using Endnote. Use APA (American Psychological Association) style in-text citations and a reference list at the end. https://student.unsw.edu.au/apa

Format: Use word file (.doc), 16px, 1.5 lines spacing, at least 62.5px margins on all sides.

Submission instructions

Submit your report to Turnitin via Moodle to the Reportsubmissions folder.

1) .doc contains your report. File name: Tutorial_Group_Firstand Last Name_A3.doc” (e.g., W12_1_Con Korkofingas_zXXXX_A3.doc)

Submit other supporting files (data, image, paper and code) to the Other Files submission folder.

2) .xlsx file contains the dataset on which you run yourregressions.

3) .ipynb contains all relevant code to get the results in your report. Make a zip file by combining all colab files.

4) .xlsx files – other relevant excel files (dictionary, summary statistics etc, relevant base files)

● For each missing file among the above (2) to (4), -1 mark

Marking Criteria

Your assignment will be marked based on the following marking criteria:

1. Analysis: Quality of advertising image data analytics

2. Evaluation and Recommendations: Quality of evaluation and recommendations

3. Written Presentation: Quality of written report

For further information, see the below marking rubric.

Marking Rubric for Assessment 3: Advertising Content Audit – Individual Report

Criteria % Fail   Pass   Credit   Distinction   High Distinction  

Analysis

Quality of advertising image data analytics 40% Analysis of advertising image data does not meet the required standard.

Sufficient identification of popular advertising themes. Mostly appropriate analysis of advertising data but may not include all of the criteria, i.e. categorisation, variable construction, and/or statistical testing.

Appropriate identification of popular advertising themes, including one related to the company’s positioning statement; good and mostly appropriate analysis, categorisation, variable construction, and statistical testing of advertising data. Excellent and accurate identification of popular advertising themes, including one related to the company’s positioning statement; effective and proper analysis, categorisation, variable construction and statistical testing of advertising data. Excellent and highly accurate identification of popular advertising themes, including one related to the company’s positioning statement; highly effective and proper analysis, categorisation, variable construction and statistical testing of advertising data.

Evaluation & Recommendations

Quality of evaluation and recommendations 40% Evaluation and recommendation do not meet the required standard. Social media advertising data is sufficiently evaluated with some conclusions and an opinion provided. Recommendations are provided. Social media advertising data is accurately evaluated to draw conclusions, provide an opinion, and determine actions. Recommendations are mostly appropriate and justified with some evidence from data and course concepts. Social media advertising data is accurately evaluated to draw conclusions, provide an opinion, and determine actions. Recommendations are appropriate and justified with specific evidence from data, course concepts and scholarly papers. Social media advertising data is accurately and meaningfully evaluated to draw conclusions, provide an opinion, and determine actions. Recommendations are highly appropriate, actionable, justified with specific evidence from data, course concepts, scholarly papers, and industry articles and offer new insight.

Written Presentation

Quality of written report 20% Report lacks clear structure. Written English is below the required standard.

Report provides a mostlyappropriate structure with distinguishable paragraphs. Written English is appropriate to the task but has spelling, referencing and/or grammatical errors Report is clearly structured with good transitions and paragraphs. Good use of written English, which is appropriate to the task and has few spelling, referencing and/or grammatical errors. Report mostly adhered to the prescribed word count and conventions. Report is clearly structured with excellent transitions. Above standard use of written English language, which is professional and appropriate to the task with minimal spelling, referencing and/or grammatical errors. Report adheres to the prescribed word count and conventions. Report is clearly and logically structured with excellent transitions and paragraphs. Excellent use of written English language, which is professional and appropriate to the task and has minimal spelling, referencing, and/or grammatical errors. Report adheres to the prescribed word count and conventions.