BUSINESS RESEARCH METHODOLY PGDM THIAGARAJAR SCHOOL OF MANAGEMENT MADURAI, TAMILNADU-625005 Group-7 2313016 Aravindh G 2313107 Nithya Shree N Ashwath Narayan 2313023 V P 2313098 Munia Samy M 2313162 Vasanth K Comparative Advertising and Promotions ICE: Descriptive Statistics Dependent Variable: Sales Per Day Comparison Ad Promotion Mean Std. Deviation N No No 7.94 .680 16 Yes 10.93 1.207 14 Total 9.33 1.788 30 No 11.21 1.718 14 Yes 10.40 1.639 15 Total 10.79 1.698 29 No 9.47 2.080 30 Yes 10.66 1.446 29 Total 10.05 1.879 59 Yes Total Based on the provided table of Descriptive Statistics for the dependent variable "Sales Per Day" categorized by the factors "Comparison Ad" and "Promotion", 1. Mean Sales Per Day: - When there was no comparison ad and no promotion, the mean sales per day was 7.94. - When there was a comparison ad and no promotion, the mean sales per day was 11.21. - When there was no comparison ad and promotion, the mean sales per day was 10.93. - When there was a comparison ad and promotion, the mean sales per day was 10.40. 2. Standard Deviation of Sales Per Day: - The standard deviation of sales per day varied across the different conditions, indicating the variability in sales data within each group. 3. Sample Sizes (N): - The sample sizes varied across the different conditions, ranging from 14 to 30, influencing the precision of the calculated statistics. These inferences provide insights into the average sales performance under different conditions of comparison ads and promotions. The variations in mean sales and standard deviation suggest potential trends or differences that may exist based on the presence or absence of comparison ads and promotions in the sales strategy. Further statistical analysis can be conducted to determine the significance of these findings and draw more definitive conclusions. Tests of Between-Subjects Effects Dependent Variable: Sales Per Day Type III Sum of Source Squares df Mean Square F Sig. Corrected Model 103.024a 3 34.341 18.550 .000 Intercept 6023.955 1 6023.955 3253.851 .000 compad 27.765 1 27.765 14.997 .000 promo 17.419 1 17.419 9.409 .003 compad * promo 53.233 1 53.233 28.754 .000 Error 101.823 55 1.851 Total 6165.000 59 Corrected Total 204.847 58 a. R Squared = .503 (Adjusted R Squared = .476) Based on the Tests of Between-Subjects Effects table provided for the dependent variable "Sales Per Day," the data analysis reveals the following key information: 1. Significance of Factors: - The Corrected Model shows a significant F-value (F = 18.550) with a very low p-value (Sig. = .000), indicating that the model including the factors "compad" (comparison ad), "promo" (promotion), and their interaction has a significant impact on sales per day. - The individual factors "compad" and "promo" also show significant F-values and low p-values, indicating that both the comparison ad and promotion independently influence sales per day. 2. Interaction Effect: - The interaction term "compad * promo" demonstrates a highly significant Fvalue and a very low p-value, suggesting that there is a significant interaction effect between the comparison ad and promotion on sales per day. 3. Model Fit: - The R-squared value of 0.503 indicates that the model accounts for 50.3% of the variance in sales per day, showing a moderately strong relationship between the factors and the dependent variable. Based on the analysis of the data and the significant impact of the comparison ad, promotion, and their interaction on sales per day, the company should consider the following actions: - Implement targeted marketing strategies that leverage both comparison ads and promotions simultaneously to maximize sales performance. - Conduct further research or experiments to explore the specific effects of different types of comparison ads and promotions on sales to optimize the marketing efforts. - Monitor and analyze sales data continuously to assess the effectiveness of the implemented strategies and make data-driven decisions to enhance sales performance further. Display Demo ICE: Based on the Descriptives table provided for the variables "Comparison Ad" and "Promotion," the data analysis reveals the following key information: 1. Comparison Ad: - The mean for the "Comparison Ad" variable ranges from -1.00 to 1.00, indicating that the responses vary from strongly negative to strongly positive. - The standard deviation (Std. Deviation) represents the average amount of variation or dispersion in the responses. For the "Comparison Ad" variable, the standard deviation ranges from 0.000 to 1.155, suggesting that the responses are relatively spread out. - The standard error (Std. Error) provides an estimate of the sampling variability of the mean. A lower standard error indicates a more precise estimate of the mean. For the "Comparison Ad" variable, the standard error ranges from 0 to 0.667. 2. Promotion: - The mean for the "Promotion" variable ranges from -1.00 to 1.00, indicating a range of strongly negative to strongly positive responses. - The standard deviation (Std. Deviation) ranges from 0.000 to 1.155, suggesting variation in the responses. - The standard error (Std. Error) ranges from 0 to 0.667. Based on the analysis of the data, here are some possible suggestions for the company: - Further analyse the relationship between the "Comparison Ad" and "Promotion" variables and their impact on the desired outcome, such as sales or customer response. This could involve conducting statistical tests, regression analysis, or correlation analysis. - Investigate the responses for the extreme values (-1.00 and 1.00) in both the "Comparison Ad" and "Promotion" variables to understand the underlying reasons for these strong positive or negative perceptions. This could involve gathering qualitative feedback or conducting follow-up surveys or interviews. - Explore any patterns or trends in the data by segmenting it based on other relevant variables, such as customer demographics or geographic location. This can provide insights into different customer preferences and help tailor marketing strategies accordingly. - Continuously collect and analyse data on customer perceptions of the "Comparison Ad" and "Promotion" variables to track changes over time and identify any shifts in customer preferences or sentiment. - Use the findings from the analysis to inform decision-making processes regarding advertising campaigns, promotional strategies, and overall marketing efforts. Based on the ANOVA table provided for the variables "Comparison Ad" and "Promotion," the data analysis reveals the following key information: 1. Comparison Ad: - Between Groups: The sum of squares between groups is 13.969, with 8 degrees of freedom. The mean square is 1.746. - Within Groups: The sum of squares within groups is 45.015, with 50 degrees of freedom. The mean square is 0.900. - Total: The total sum of squares is 58.983, with 58 degrees of freedom. 2. Promotion: - Between Groups: The sum of squares between groups is 20.050, with 8 degrees of freedom. The mean square is 2.506. - Within Groups: The sum of squares within groups is 38.933, with 50 degrees of freedom. The mean square is 0.779. - Total: The total sum of squares is 58.983, with 58 degrees of freedom. Based on this analysis, here are some possible suggestions for the company: - Conduct a statistical test, such as an ANOVA, to compare the mean scores between different groups in the "Comparison Ad" and "Promotion" variables. This can help determine if there are significant differences in customer perceptions among these groups. - In the case of the "Comparison Ad" variable, the p-value associated with the F-statistic is 0.074. This suggests a trend towards significance (p < 0.10), indicating that there might be some differences in customer perceptions based on the groups. However, further investigation is needed to confirm this. - In the case of the "Promotion" variable, the p-value associated with the F-statistic is 0.005. This indicates a statistically significant difference (p < 0.05), suggesting that there are indeed variations in customer perceptions based on the groups. - Further analyze the specific groups within each variable to identify which groups show significant differences in customer perceptions. This can help the company understand the specific factors influencing customer preferences and tailor their marketing strategies accordingly. - Consider conducting post-hoc tests, such as Tukey's test or pairwise comparisons, to determine the specific differences between groups and which groups are significantly different from each other. - Use the findings from the ANOVA analysis to guide decision-making processes, such as targeting specific groups with tailored marketing campaigns or adjusting advertising and promotion strategies based on the preferences of different customer segments. Sales Promotion and Merchandising ICE: Between-Subjects Factors Value Label Merchandising N 0 No Merchandising 40 1 Yes 39 Merchandising Promotion 0 No Promotion 39 1 Yes Promotion 40 Multivariate Testsa Effect Intercept Value Hypothesis df Error df Sig. Pillai's Trace .989 3415.345b 2.000 74.000 .000 Wilks' Lambda .011 3415.345b 2.000 74.000 .000 92.307 3415.345b 2.000 74.000 .000 92.307 3415.345b 2.000 74.000 .000 Pillai's Trace .878 267.492b 2.000 74.000 .000 Wilks' Lambda .122 267.492b 2.000 74.000 .000 7.230 267.492b 2.000 74.000 .000 Hotelling's Trace Roy's Largest Root merchand F Hotelling's Trace 7.230 267.492b 2.000 74.000 .000 Pillai's Trace .819 167.631b 2.000 74.000 .000 Wilks' Lambda .181 167.631b 2.000 74.000 .000 4.531 167.631b 2.000 74.000 .000 4.531 167.631b 2.000 74.000 .000 Pillai's Trace .364 21.173b 2.000 74.000 .000 Wilks' Lambda .636 21.173b 2.000 74.000 .000 .572 21.173b 2.000 74.000 .000 .572 21.173b 2.000 74.000 .000 Roy's Largest Root promo Hotelling's Trace Roy's Largest Root merchand * promo Hotelling's Trace Roy's Largest Root a. Design: Intercept + merchand + promo + merchand * promo b. Exact statistic Part A: we can advise the company that both sales promotion and merchandising have a significant effect on sales as the p-value < 0.001. There is also a significant interaction effect between merchandising and promotion p-value < 0.001. This means that the effect of merchandising on sales depends on whether or not there is also a sales promotion, and vice versa. Both sales promotion and merchandising have a significant effect on sales. There seems to be a lift in sales when a sales promotion is offered, regardless of whether merchandising is used. There is also a lift in sales when merchandising is used, regardless of whether a sales promotion is offered. Sales are likely the lowest in this group. Sales increase compared to the baseline group (no promotion, no merchandising). Sales increases a lot when both promotion and merchandising is used. We can also infer that both with promotion and without promotion are independent of each other. Part A: Promotions Drive Sales. The data clearly shows a significant increase in sales when promotions are offered. Customers exposed to promotions (both with and without merchandising) had a much higher average number of sales compared to those who weren't exposed to promotions. Merchandising May Have Limited Impact. While the average sale for the "Yes merchandising, no promotion" group is higher than the "No merchandising, no promotion" group, the difference is smaller compared to the impact of promotions. This suggests that merchandising alone might not be as effective as promotions in driving sales. Given the significant impact of promotions, the company should prioritize promotional strategies. Part B: Perception of Weather (Not Driving Results - Ignoring It's Likely a Subjective Measure) The fact that the company measured "perception of weather" (e.g., "I think the weather is excellent today") suggests it might influence sales. However, there are reasons to believe it likely doesn't drive the results we see: Perception of weather is subjective. What one person considers "excellent" weather, another might find unpleasant. This subjectivity weakens its value as a reliable metric. The ANOVA results likely analyzed actual weather data (e.g., temperature, precipitation) which is a more objective measure. If actual weather data wasn't included, focusing on promotions and merchandising based on the provided information is safer.
