Probability Of Booking

Transcription

1 Axis Title Web Social Analytics Air France Assignment 1 Spring 216 Shuhua Zhu Assignment 1 Question 1: CTR TCR NET REVEAVE. COSROA AVE. REV PROB COUNT MAX 2.% 9.% $549,524 $ % % 82% MIN.%.% -$8,726 $. -1% 34% % MEAN 11.14%.57% $866 $ % 12426% % STD The first observation is that the standard deviation of some data is quite large such as net revenue and average revenue per click. Next, the probability of booking has a quite low average of %, which is not a good sign. Average revenue per booking only has 368 observations while our total sample size is 451, meaning that many data has a total number of booking of, which is also not a positive sign. Probability Of Booking 9.% 8.% 7.% 6.% 5.% 4.% 3.% 2.% 1.%.% -1.% y = -9E-7x +.27 R² = Axis Title

2 Axis Title Web Social Analytics Air France Assignment 1 Spring Probability of booking 111,65 223,3 334,95 446,6 558,25 From this scatter plot and histogram, we can see that the probability of booking is largely equal to. $6, $5, $4, net revenue $3, $2, $1, $ -$1, y = x R² = Axis Title

3 net revenue ,726 12, , , , ,524 The net revenue data shows that much of the revenue falls into the to range and that a lot of the data are around ROA , , , , ,249

4 Axis Title Web Social Analytics Air France Assignment 1 Spring 216 4% 35% 3% 25% 2% 15% 1% ROA 5% % -5% 1 2 y = -.58x R² = Axis Title The ROA analysis also shows that it is more likely to be. Question 2.a average revenue per booking sum of click charge sum of net average cost per total volume prob of cost/book publisher name revenue click of bookings ROA booking ing google global %.4% google us %.6% msn global %.8% msn us %.6% overture global %.1% overture us %.% yahoo us %.29% grand total %.6% bubble chart %.%.5%.1%.15%.2%.25%.3%.35%

5 b. Based on the bubble chart, we can see a positive correlation between the probability of booking and average cost per click. There is no significant relationship of bubble size, that is, the sum of click charges. Group 1: high booking prob. & low CPC: Yahoo US, MSN global Group 2: low booking prob. & low CPC: Overture Global Group 3: high booking prob. & high CPC: Google US, MSN US Group 4: low booking prob. & high CPC: Google global Question 3: From the data we can know that geographically based on campaigns are sensitive to specific cities, therefore the destinations are in France. And this data does not only give us insight into which regions frequent which French cities, but also the data should be further used to implement dynamic pricing. Pricing based on the cities for which the search took place. The best campaigns have a campaign category of Geo Targeted San Francisco and Air France Branded. Other successful cities include New York, Miami, DC, Detroit and etc. the keyword combinations are France, airline, cheap, ticket. The match type for all of the campaigns are broad. The bid strategy Position 5-1 bid strategy is the most successful one. Question 4: SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted R Standard E Observation 451 ANOVA df SS MS F ignificance F Regression E E Residual E Total E+9 Coefficientstandard Erro t Stat P-value Lower 95% Upper 95%Lower 95.%Upper 95.% Intercept Search Eng Avg. Cost p E Clicks Trans. Con Impression E E

6 I have chosen the independent variables: search engine, avg. cost, clicks, TCR, and impressions because they have higher correlations with total cost. However, the p value of search engine and TCR is not significant, so I omitted those two variables. Then I chose the following regression: SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted R Standard E Observation 451 ANOVA df SS MS F ignificance F Regression E+9 2.7E Residual E Total E+9 Coefficientstandard Erro t Stat P-value Lower 95% Upper 95%Lower 95.%Upper 95.% Intercept E Avg. Cost p E Clicks Impression E E The final regression is total cost= avg. cost+1.488clicks+.789impressions. This makes sense because all of the variables should increase the total cost, so the coefficients all have positive values. The final regression says that one dollar of increase in avg. cost increases total cost by Increasing the number of clicks by 1 increases the total cost by 1.4 dollars. Increasing the number of impressions by 1 increases the total cost by.789 dollars. Although this amount is small, this variable is significant. Question 5: Kayak trans. Conv. Rate=total volume of bookings/number of clicks=7%. Average publisher TCR is calculated using the average function in Excel for the column of TCR for all the publishers. The calculated number is 56.93%. Kayak CPC is calculated by media cost divided by the number of clicks=1.26. Average Publisher CPC=1.89, calculated by taking the average of all the publishers in the given data sheet. The cost per click for Kayak is lower than the average, which is a good sign, suggesting that Kayak should be used. However, the transaction conversion rate is rather low for Kayak compared to the industry average. So I suggest that overall, we do not use Kayak even though it is cheaper.

7 Question 6: The best campaign For MSN US (AirFrance Brand & French Destinations): ROA = 137.4%. AirFrance flights are shown on top via the relevant of results in the table. MSN US (Business Class): ROA = -1% (Geo Targeted Seattle) = -1% Their ROA and net revenue are negative, so they should be improved.