Google Analytics and Air France Campaign Analysis Homework

Digital Marketing (MBA 723.01) Homework Assignment 2 Due Date: December 08 Names of Group Members: A. Google Analytics Exercise Follow the instruction in the following URL to link your Google analytics account to the Google Analytics Demo account. https://support.google.com/analytics/answer/6367342?hl=en%20 Complete the following exercises using data from the Google Demo account (Google Merchandize store: Master view). 1. Explore the Google Demo Dashboard and download data for the following table from the relevant section (time period for data: last one month). Once the data is downloaded, plot a pie chart on share of different channels in driving traffic to the website (2 points) . Users Direct Organic Search Cross-Network Unassiged Referral Email Organic Social Paid Search Affiliates Organic Video Organic Shopping Display Paid Video

B. Paid Search ROI Analysis: Air France Case The objective of this part of the assignment is to analyze the Campaign Strategy for Google US Campaigns that Air France is running. As part of the assignment the following tasks need to be accomplished. Dataset for the case is available in the HBP course pack. 1. Use the Pivot Function of the spreadsheet to generate summary statistics related to for the each of the following campaigns for Air France run on Google US network . The variables for summary statistics are given below (2 points) . Campaigns Average of Probability of Booking (TCR x CTR) Avg. CPC (=Click Charges /Clicks) Total Cost Average of Engine Click Thru % (CTR) Transaction Conversion % (TCR) Total Volume of Bookings Business Class French Destinations Geo Targeted Boston Geo Targeted Chicago Geo Targeted DC Geo Targeted Detroit Geo Targeted Houston Geo Targeted Los Angeles Geo Targeted New York Geo Targeted Philadelphia Geo Targeted San Francisco Geo Targeted Seattle Outside Western Europe Paris & France Terms Western Europe Destinations Note that you will have to delete the rows “Air France Brand & French Destinations, Air France Branded, Air France Global Campaign, Geo Targeted Miami, Google Yearlong 2006, Unassigned” when copying the table from pivot table to a separate spreadsheet since these are outliers.

2. Air France needs to optimize its investment across the above campaigns and needs to know how to reallocate its investment. Therefore, as part of this assignment, analyze the campaigns on basis of their average CPC, average probability of booking and current level of investment using the Bubble scatter plot with avg. CPC as the y-axis, average probability of booking as x-axis and size of the bubbles as the total cost of campaigns. Based on the graphical plot, provide insights on what investment strategy to take with regard to at least four of the campaigns (3 points) . - Based on the scatter plot data above the most lucrative campaign to run is the Geo Targeted New York on low total cost and high average possibility of booking. The remaining 3 best campaigns to run would be Geo Targeted San Francisco, Geo Targeted Houston and Geo Targeted DC respectfully.

Your preview ends here

Eager to read complete document? Join bartleby learn and gain access to the full version

Access to all documents
Unlimited textbook solutions
24/7 expert homework help

1. Air France needs to optimize each of the above campaigns. To do so it needs to know which campaigns need improvement in search side copy vs. which ones need improvements in website/landing page site copy. This can be done by creating a bubble scatter plot with Transaction conversion rate on y-axis, click through rate on x-axis and total volume of booking as the size of the bubbles. Based on the graphical plot, provide insights on what search copy vs. landing page copy strategy to take with regard to at least four of the campaigns (3 points) . - Based on the scatter plot data above Geo Targeted Detroit and Geo Targeted Seattle would benefit on more search copy strategy based on the low click through rate and high transaction conversion whereas Geo Targeted Houston and Geo Targeted Philadelphia would benefit from a landing page copy strategy due to their low transaction conversion and high click through.

C. Online Experiments: Rocket fuel case Read the Rocket Fuel case and analyze the dataset associated with the case to answer the following questions. The description of variables in the CSV file data set is in Exhibit 2 of the case. 1. Was the advertising campaign effective? Did additional consumers convert as a result of the ad campaign? Did the test meet the requirements of randomization across the treatment and control groups? (carry out the relevant analysis, e.g. t-test etc., in R and provide codes for the analysis along with brief comments explaining the rationale for the analysis) (4 points) - The campaign was effective. The test did meet the requirements the groups based on the p- value of less than 0.05 which suggests that there was a statistically significant difference between the mean conversion rate of the treatment and control groups. The campaign can then be seen as effective because the test group exposed to the ad had an increase in conversion rate. 2. Was the campaign profitable? In particular, how much more money did TaskaBella make by running the campaign (excluding the advertising costs)? What was the cost of the ad campaign? Calculate the ROI of the campaign and determine its profitability (no analysis on R needed for this question) (4 points) . To calculate if the campaign was profitable we must determine the ROI. - ROI is calculated as (Net return on Investment/ Cost of Investment) x 100 We must calculate the Net return on Investment first from the data given: - Net Return on Investment is calculated as Total Revenue less Total Cost o Total Revenue is calculated as Profit per Converted User ($40) x Users (564,577) x Conversions (per user) (0.7692%) which equals $173,719 o Total Cost is calculated as CPM (Cost per thousand impressions) ($9) for both the test and control groups which equals $131,374.64 o Therefore the Net Return on Investment is $173,719 - $131,374.64 = $42,344.36 - ROI is then equal to ($42,344.36/$131,374.64) x 100 = 32.2318% - Based on the ROI of 32.23% we can conclude that the campaign was profitable

D. Text Mining Carry out a word cloud analysis for the review text in the Hotel review data (2 points) . - install.packages("rvest") - library(rvest) - url <- "https://www.amazon.com/Acer-A515-46-R14K-Quad-Core-Processor-Backlit/product-reviews/B08VKNVDDR/ ref=cm_cr_arp_d_paging_btm_next_2?ie=UTF8&reviewerType=all_reviews&pageNumber=1" - reviews <- read_html(url) %>% - html_nodes(".a-section") %>% - html_text() - install.packages("wordcloud") - library(wordcloud) - install.packages("tm") - install.packages("slam") - wordcloud(reviews, scale = c(2, 0.5), max.words = 100, random.order = FALSE, rot.per = 0.35, colors = brewer.pal(8, "Dark2")) - This suggests the reviews for the product are mostly positive.

Your preview ends here

Eager to read complete document? Join bartleby learn and gain access to the full version

Access to all documents
Unlimited textbook solutions
24/7 expert homework help

MBA 723.01 Digital Marketing HW 2

Related Documents