H1 = Alternate Hypothesis
On executing the ANOVA model the ANOVA co-efficient (F) was calculated as 7.558 and the p-value was calculated as 0.011. Since, the F-value was greater than the p-value the Null hypothesis was rejected.
This confirmed our theory that the means of the online-ad sales revenue for Google and Meta are un-equal according to the ANOVA results. This also partially indicates that the online-ad sales revenue might be affected based on the platform.
A survey is also conducted to analyse customer behaviour towards online advertisements. A total of 59 individuals took the survey Table 1 summarizes the different categories of people who took the survey.
Table 1
Gender | Count |
Male | 44 |
Female | 15 |
Other | 0 |
Figure 8 tabulates the summary of the gender, age and profession of the individuals.
Figure 9 visualizes if the individuals prefer online shopping or online according to gender.
Figure 10. visualizes if the individuals prefer online shopping or online according to age group.
Figure 11 summarizes the count of individuals, belonging to different age groups and gender, who think that online ads provide better deal on products and who think that they do not.
Figure 12 is a pie chart visualizing how long have the individuals been shopping online.
Figure 13 depicts an average of how influential online-ads are on platform like Google, Meta, Instagram etc. It also depicts an average of how influential these online ads are during festival seasons. The scale was between 1–10.
Figure 14 is a pie chart of which platform do the individuals think has the most influential online-ads.
Figure 15 is a donut-chart of which medium of online-ads do individuals think is most effective.
Figure 16 heat-map shows which category of items do individuals spend on most depending upon their age and gender.
Figure 17 condenses how often do individuals, based on gender, tend to click on online-ads displayed to them.
Figure 18 is a pie chart visualizing if the individuals think the online-ads displayed to them are random, targeted or unknown.
Figure 19 heat-map summarizes how much do individuals spend on online shopping on an average, based on gender, age and profession.
Figure 20 shows if the individuals think that the covid-19 lockdown has increased their tendency to buy products online.