Marketing Analysis Report
Extra: Report Writing for Data Visualization Example
Introduction
Knowing your customer is a critical component of marketing success. Collecting and analyzing data on the customers allows the company to acquire more insights into its customers. Tailoring customer service to their needs, providing more personalization and build stronger relationships with them. It is possible to streamline this process using data analysis.
This report summarizes findings from an assessment of the customer data collected by the company. The report also discusses the findings with implications for the company.
Customer satisfaction depends on well-marketed campaigns and customer service efforts. Retaining and attracting more customers is central to the company. This can only be achieved through our overall flexibility and personalized marketing campaigns.
Main Questions Discussed
What are the customer profiles?
The customer profiles are the details of individual customers serving as a guide to determine which targets should correspond with certain products. These details could include marital status, age, income, country and even the customer’s dependents. The purpose of identifying this ideal character is to be able to understand and relate to customers you want to develop products for and market too.
What are the product preferences?
Product preferences are essentially products different segments of customers usually spend their money on. This will help us identify the products that seem to be in high demand thus allowing the company to prioritize these products to increase their profits by optimizing the price and volumes of the products.
Dashboard Summary
This dashboard provides a summary on some of the findings presented throughout this document. It gives a superficial view of what the ideal customer profile to briefly introduce what sort of customers the company has been interacting with mostly.
Data Sources and Tools
Data Sources
Where is the data sourced from?
The data can be accessed via this URL: https://www.kaggle.com/jackdaoud/marketing-data (Marketing Analytics).
How was it collected?
The data was collected by Dr. Omar Romero-Hernandez, a Professor at U.C. Berkeley’s Haas School of Business from data provided by ad campaigns.
What is the size of the data set?
The data set contains 2240 observations (customers).
What are the number and types of variables in the data set?
There are 28 variables in the data set. The variables consist of quantitative and categorical data relating to the customer profiles, products, and the ad campaigns.
Tools Used
- R: (Ggplot 2)
- Tableau
Visualization Results
Customer Profiles
Who is the company’s ideal customer? Every company’s marketing department has asked itself this question at least once in the company’s existing lifetime. Successful companies are aware of who their ideal customers are to prioritize them when making decisions and attract other potential customers who fit their ideal customer persona.
It is always good practice to personalize the ideal persona, so a decision has been taken place to name this persona “Antoinette Holt”. This allows the company to form a deeper connection with its customers and not just have them as numbers on a screen.
To start with, one might ask, “How old is Antoinette?” The data collected provides us with the customer birth years. Ages are then derived from the birth years to discover the average age of our customers.
Figure 1.1
In figure 1.1, the data has been plotted on a histogram to determine the average age of customers that make purchases from the company. Antoinette by the average would be deemed to be the age of 52 based on the data.
Figure 1.2
The modal age discovered from the data however was discovered to be 45 years. It would be safe to assume that the ideal customer then would have ages between 45–52 of age. Graphs use the retinal variable colour to distinguish the modal and mean age in both graphs.
Furthermore, another question to be asking is what level of education Antoinette is likely to have completed? From the data, we have been provided the customers and their level of education.
Figure 1.3
According to the data from figure 2.1, it is very likely that our ideal persona Antoinette has at least graduated university with a bachelor’s degree. In this figure, the colour hue of the different education levels allow us to distinguish between them, the retinal variable length also allows seamless identification of the magnitudes of each education level count.
Besides this, we were provided the marital status of each customer so additionally it would be right to ask what the marital status of our ideal customer Antoinette would be.
Figure 1.4
The figure 1.4 conveys to us that most of the customers that make purchases from the company are likely to be married or at least in a relationship. There is also a good chance that the customer could be single but based of the data Antoinette would likely be married or at least in a relationship. Additionally, there are roughly 4 customers who provided no relevant information on their marital status. This could be because some people are rather reluctant to disclose that information.
Moreover, since we have determined Antoinette is likely to be married or at least in a relationship. It would also be useful to find out how much she is earning. Fortunately, the data collected provides us insight to the customer income, so it is possible to give an estimate of what Antoinette is likely to be earning.
Figure 1.5
Figure 1.5 provides a chart showing the box plot of income for the education levels. Having discovered that Antoinette was likely to have held at least a bachelor’s degree. The average income of all levels had to be discovered.
Table 1.1
In Table 1.1, the average income for Graduation category is 48895. The max income for that category is 105471, whereas the minimum income is 1730. Interestingly enough, the Graduation category with the most numbers also has the lowest minimum income among the rest. Inferring from all this, it would be appropriate to use the mean income to estimate what Antoinettes income is likely to be.
Figure 1.6
Aside this, given the number of married people sourced from the customer data it is expected that Antoinette should have at least one child. The data also does seem to confirm that with the histogram reflecting 1 dependent with the highest frequency in Figure 1.6.
Finally, the origin of the customer is also important to know. From the data provided, the customers location is available. Thus, able to determine the country most of the company’s customers reside. This would allow as to ascribe Antoinette with a suitable place of origin to complete our ideal buyer persona.
Figure 1.7
Figure 1.7 shows all the countries that customers reside in. These countries are further distinguished by the colours in the legend. Additionally, on all the countries, the frequency of customers is displayed as labels on their respective maps with Spain boasting the most customers making purchases from the company and Saudi Arabia coming in second. Judging from this Antoinette could be described as either Saudi or Canadian ideally.
Figure 1.8
This K-means cluster also highlights how the customer base could be segmented into three clusters. The retinal variable used here would be area describing how large each segment is.
Product Preferences
Given product preferences, we’re given the amount spent on six different kinds of products. To draw some deeper insights, the charts have been grouped by country to narrow down on which country spends more on different kinds of products.
Figure 2.1
Figure 2.1 shows the products: Wine, Meat and Fruits. Looking at the graph Spain spends significantly higher than other countries across the three products. This might effectively be due to the number of customers residing in Spain.
Figure 2.2
In Figure 2.2, it shows Spain has a low average income compared to the rest. Spaniards do not seem to be spending just because they’re earning a lot of money, but they might genuinely have preference for company products.
Figure 2.3
Figure 2.3 goes on to show the rest of the products and the countries spending the most on them. It appears that the products that have the most amount spent on them appear to be Meat and Wine.
Figure 2.4
Figure 2.4 shows a scatterplot of Amount Spent on Meat vs Wine. A possible reason for both these products having high spends could be because of the positive correlation of these two products emphasizing a moderate relationship between the two.
Conclusion
In conclusion, our ideal customer Antoinette would Ideally be someone between the age of 45–52. Living in Spain with at least a bachelor’s degree and is married or in a relationship of some sort and has at least one child/dependent.
Finally, the product preferences have been discovered to be mostly meat and wine products. As these were the products with the most money spent on them.
Implications of these conclusions
· Customer Profile
Knowing your customer profile provides insight into customers behaviour. This allows the company to personalize marketing campaigns by having a better idea of what to market, when to market and who to market to and produce the best results in customer acquisition and customer retention. Thus, increasing customer experience and customer engagement.
· Product Preferences
Knowing customer product preferences allows the company to know which products to prioritize when budgeting, this also allows companies to work out reorder values and work out economic order quantities to have preferred products in stock all the time and at good costs thus reducing over all costs for the business and increasing product sales as these preferred products would be improved over time.
Bibliography
· Marketing Analytics, viewed 24 November 2021, <https://kaggle.com/jackdaoud/marketing-data>.
