Strategic Analysis for Children 4 Tomorrow Clothing Company Case Study

Introduction - Stakeholder Mapping and Online Sales Decision Matrix

This report analyzes the four tasks regarding the company children 4 Tomorrow that is belong to the north of England. This company is a clothes manufacturer and distributor for children. In task 1 identify the potential stakeholder of the project and create the stakeholder map to identify stakeholders. Here using ‘Mendelow’s matrix” is to classify the various identify stakeholders. In task 2, analyze the decision-making process that is presented in the module. Weights based on an understanding of the scenario and produce the decision matrix in task 2. In task 3, the company wants to open a new store and consider different formats for its store, which is involved four possible sizes. In task 4, calculate the “regression method” of the social media advertising, analyze the correlation of the two variables and also analyze the descriptive statistics. So overall report is essential to complete the four tasks.

Task 1: Analysis of stakeholder

1. Identify the potential stakeholder of the project

1) Children 4 Tomorrow – As the primary subject of the project, the company is responsible for the impact of its work in the local community and will benefit from any positive results that come from the project’s findings.

2) Local Community – This project will directly affect the community as it is the primary source of raw materials and customers for Children 4 Tomorrow (Robinson, 2020). Their opinion and feedback on the project results will be important to consider.

3) Local Suppliers – As the primary source of raw materials, the local suppliers will be affected by the project’s findings as they provide the resources to Children 4 Tomorrow (Schroeder, et al. 2023).

4) Local Authorities – The local authorities will be interested in the project’s outcomes as they are responsible for the welfare of their citizens and are involved in the project with Children 4 Tomorrow to help those in need.

5) Local Schools – The local schools will be affected by the project’s findings as they are the primary recipients of the uniforms provided by Children 4 Tomorrow.

6) The Government – The government will be interested in the project’s results as it impacts the welfare of its citizens and how it addresses the needs of those in poverty.

7) Other Charitable Organizations – Other charitable organizations may be interested in the project’s results as it could provide insight into how to better serve those in need.

8) Customers – The customers of Children 4 Tomorrow will be interested in the project’s results as they will be affected by any changes that the company may make based on the project’s findings.

9) Media – The media will be interested in the project as it may provide interesting stories to cover and will help to spread awareness about the work Children 4 Tomorrow is doing in the local community.

10) Investors – Investors will be interested in the project’s results as it will affect the company’s financial performance and reputation.

2. Stakeholder map for the identified stakeholders

Figure 1: Stakeholder map

3. Use Mendelow’s matrix by various identify stakeholders

Figure 2: Mendelow’s matrix

4. Discuss Mendelow’s matrix by various identify stakeholders and Stakeholder map for the identified stakeholders

In 1991 “Mendelow’s Matrix” was established to scrutinize stakeholders by measuring their power and interest. Mendelow’s matrix depends on high interest and power that manages closely, high interest with low power that is to inform, low interest with high power that is to satisfy, and low interest with low power which manages low priority.

Task 2: Decision-making (developing online sales)

This task is about finding the problem and opportunity for the children 4 tomorrow. They have different types of choices in respect of online selling their products (Blankespoor, et al. 2023). The main problem is to find out which will be better for the online selling of its products. There are mainly four approaches with five criteria.

The main objective is to find out which will be a better option for doing online selling

The above criteria are made for which one online source is best for “children 4 Tomorrow” marketing revenue throughout the global market. With respect to decision-making process criteria activities on the problem or opportunities, setting objectives, setting and weighting decision criteria, and comparing and choosing alternatives (Mai, et al. 2023). For the level of the developing alternative E-commerce scores, respectively 5,4,3,5 and 4; mail order on developing alternatives is respectively 5,3,2,2 and 1; Mobile apps are helping developing stages as 3,2,1,3 and 2; finally the modern marketplace is not a better contribution for providing enough market business 2,1,4,1,3.

So finally company will have to choose the best option for selling online their business module and here in an alternative way, E-commerce is helping a lot with a total weighted score of 105, and some kind of mobile apps are helping with a weighted value of 65 (Kaiser, et al. 2023); then mail order has been a little bit contribution for enlarging popularity of “Children 4 Tomorrow” then it might be helped by modern mark place with 55.

Task 3: Decision analysis (Size of shops)

Before analyzing the decision analysis, here need to write down the pre-conditions which are already given in the description:

1. In the below table we are finally concluded that if demand for items respectively 50,300, 800, and 1500, then the change is possible for the mini stores, small stores, medium stores, and large stores for dependent two variables, one is the type of store by size and second is the number of items sold per store (Sutcher, et al. 2023). The formula for required sales revenue in respective demand is = (the average sales price per item)*(demand items/ type of the number of items hold per store).

2. Next, in the below table, it has been concluded that after excel calculation on operational cost. Similarly if demand for items respectively 50, 300, 800, and 1500, then the change is possible for the mini stores, small stores, medium stores, and large stores for dependent two variables one is the type of store by size and the second is the number of items sold per store (Subramanian, et al. 2023). The operation cost formula is given = (total cost for individual type of store by size* demand items on per specific stores)/ type of the number of items sold per store. For every demanded storehouse their specific operational cost value will be different.

The above table shows the operational costs of different store sizes in terms of demand. The higher the demand, the higher the operational costs for each store size (Nigrini, 2020). For example, a mini store will cost £1000 for a demand of 50, £6000 for a demand of 300, £16000 for a demand of 800, and £30000 for a demand of 1500. Similarly, a small store will cost £850 for a demand of 50, £5100 for a demand of 300, £13600 for a demand of 800, and £25500 for a demand of 1500. Medium and Large stores follow the same pattern.

Overall, the table shows that the operational costs increase as the demand increases, regardless of the size of the store (Lieder, and Griffiths, 2020). This suggests that the bigger the store, the more costly it is to operate in terms of demand.

3.

The table shows the profits made by different store sizes for different levels of demand. For each store size, there is an increase in profit as the demand increases (Vogel, 2020). For example, a mini store makes £15 in profit when the demand is 50, £90 when the demand is 300, £240 when the demand is 800, and £450 when the demand is 1500. Similarly, a small store makes £3.3 in profit when the demand is 50, £19.8 when the demand is 300, £52.8 when the demand is 800, and £99 when the demand is 1500 (Nenonen, et al. 2023). Finally, a large store makes £1 in profit when the demand is 50, £6 when the demand is 300, £16 when the demand is 800, and £30 when the demand is 1500.

4.

In the single chart table is about to make each store size option and profit margin. Here are the store size along with the x-axis and profit going on the y-axis in the corresponding question number 3 answer (Ichsan, et al. 2023). Practically here the mini store is claiming more profit value than others stores revenue function and the large type of store is not involving itself in profit margin enough.

5.

The table shows the expected profits for different sizes of the store. The mini store had the highest expected profit of 159, followed by the small store at 69.96, the medium store at 28.62, and the large store with the lowest expected profit of 5.3 (Hosaka, 2019). This indicates that the larger the store, the lower the expected profit.

Task 4: Analysis of advertising

A.1: Calculate the descriptive statistics of traditional advertising and social media advertising

Table 1: Descriptive analysis of traditional advertising

Table 2: Descriptive analysis of social media advertising

A.2: Here calculate the descriptive statistics of social media advertising and traditional advertising. “Descriptive statistics” is to describe the feature of the data set by creating summaries about the data samples (Lusiana, et al. 2023). There are the following four main descriptive analyses such as “frequency measures”, “central tendency measures”, variation measure and “position measures” which means ranking measures. But this project analyzes the “central tendency” by this measure to find the mean, median, and mode of both advertisements. Here the value of the mean, median, and mode value of traditional advertising are 50.33333333, 50, and 62 respectively. The “Standard Deviation”, Sample Variance, and Standard Error values of traditional advertising are 16.42819875, 269.8857143, and 2.738033126 respectively. In traditional advertising, the total count is 36. Here the value of the mean, median, and mode values of social media advertising is 89.97222222, 89, and 92 respectively (Kieso, et al. 2023). The “Standard Deviation”, Sample Variance, and Standard Error values of social media advertising are 14.75802176, 217.7992063, and 2.459670294 respectively. In social media advertising, the total count is 36.

A.3: Histogram of social media advertising and traditional advertising

Figure 3: Histogram of traditional advertising

Figure 4: Histogram of social media advertising

A.4: Here discuss the histogram of social media advertising and traditional advertising. It indicates the probability and also indicates the distribution of the data value. Here is the histogram of social media advertising bell-shaped this chart generally presents the normal distribution (Jackson, et al. 2023). And the histogram of traditional advertising is the J- shaped which means the distribution of the probability in a rough shape of the J letter and the distribution trend has to some observation at the one end and a large number at the middle.

B.1: “Calculate the total spend in advertising (traditional plus social media) and the total number of buyers”

Table 3: Calculate the total spend and no of buyers in traditional and social media advertising

This table shows the total spending on traditional and social advertising as well as the total number of buyers in traditional and social advertising. It appears that there was a significant amount of money invested in both traditional and social advertising, as the total spending was nearly half a million dollars (Baker, et al. 2023). Furthermore, there were over 5000 buyers involved in traditional and social advertising. This suggests that both traditional and social advertising were successful in generating interest and purchases from consumers.

Figure 5: statistical analysis

B.2: “Correlation” of two variables

Table 4: Correlation of traditional advertising

Table 5: Correlation of social media advertising

B.3: The table shows the correlation between advertising spend (£) and the number of buyers. The correlation coefficient is 0.264182775, which indicates that there is a weak positive relationship between the two variables (Hoseinzade, and Haratizadeh, 2019). This means that as the amount of money spent on advertising increases, the number of buyers also increases, but not in a significant way (Ichsan, et al. 2023). Overall, the correlation between traditional advertising spend and the number of buyers is weak. This suggests that traditional advertising may not be the most effective way to increase the number of buyers. And the table shows a correlation between social media advertising spend and the number of buyers. The correlation coefficient is 0.799232436, which is a strong positive correlation (Blankespoor, et al. 2023). This means that as social media advertising spending increases, the number of buyers also increases.

C.1: “Amount spent on traditional advertising and number of buyers”

Table 5: “Spent on traditional advertising and number of buyers”

C.2: “Amount spent on social media advertising and the number of buyers”

Table 6: “Spent on social media advertising and a number of buyers”

C.3: The above table indicates that total spending on traditional advertising was $242,890 and the total number of buyers in traditional advertising was 1,812 (Hair, et al. 2023). This suggests that traditional advertising is still a popular marketing method, as it has managed to reach a large number of potential customers and generate a significant amount of revenue (Syverson, 2019). Additionally, the number of buyers may not represent the total reach of the campaign, so it is important to consider other metrics such as impressions or clicks when evaluating the effectiveness of traditional advertising.

D.1: Calculate regression

Table 6: Regression of social media advertising

The table above shows the results of a regression analysis. The “multiple R-value” of 0.7999 indicates that there is a strong positive “correlation between the two variables” being studied. “The R-squared” value of 0.639 indicates that 63.9% of the “variation in the dependent variables” has to be described by the non-dependent variable (Fridson,. and Alvarez, 2022). The adjusted R-squared value of 0.628 indicates that the model still has some “room for improvement” (Akomea-Frimpong, et al. 2023). Finally, the “number of observations” used in the given data is 35. The ANOVA table shows the “sum of squares (SS)”, “the mean squares (MS)”, “the F-value”, and the significance F for “Regression and Residual”, where SS for Regression is 24667787.07, and MS is 24667787, the F-value is 58.63638, and the Significance F is 8.14639E-09. The SS for Residual is 13882796.53, and the MS is 420690.8. The total SS is 38550583.6 (Akter, et al. 2023). This table gives that the regression model is specific and explains a significant amount of variance in the data.

D.2: Here calculate the company Children 4 tomorrow spend on social media advertising in order to 200 new buyers. So if the 200 new buyers are in the social media advertising then the company would have to spend 14021.8 when the number of buyers is 101.

Conclusion and recommendation

After the overall discussion of the report is an analysis of the four tasks of the company Children 4 Tomorrow. E-commerce is significantly assisting with a total weighted score of 105, and some types of mobile apps are also assisting with a weighted value of 65. Mail order has made a small contribution to the growth of "Children 4 Tomorrow" in terms of popularity, and modern mark places with a weighted value of 55 may also be helpful. The mini shop size should be chosen by the company of "Children 4 Tomorrow". In traditional advertising, spending should be focused on.

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