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Analyzing the Impact of Promotion, Awareness, and Advertising on Shampoo Sales
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Contents
Introduction ................................................................................................................................... 3
Marketing Problem Definition..................................................................................................... 4
Data and Methodology ................................................................................................................. 5
Method One ............................................................................................................................... 5
Method Two ............................................................................................................................... 6
SCAN*PRO Model .................................................................................................................... 7
Results and Analysis ..................................................................................................................... 8
Conclusion ................................................................................................................................... 15
References ..................................................................................................................................... 16
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Analyzing the Impact of Promotion, Awareness, and Advertising on Shampoo Sales
Introduction
Any business must have marketing, which depends on many variables to succeed.
Companies can create effective marketing strategies by recognizing these elements and
comprehending how they affect sales. The recent development of sophisticated analytical tools
that can assist businesses in understanding the connection between marketing initiatives and
sales is mainly due to the availability of vast amounts of data. The main topic of this report is the
use of neural networks to determine the effects of various marketing factors on sales. The
information uses 192 observations from a retail company as the basis for this study's data. The
dataset has ten columns: namely, category, brand, price, sales, consideration, liking, promotion,
and awareness.
The category field is the product, which is shampoo. Its goal is to examine the
connection between marketing efforts and sales and pinpoint the significant marketing elements
that impact sales. Neural networks will be the primary analytical tool used in this report to
identify the key marketing elements that influence sales. The retail company will be able to
improve sales performance and develop more effective marketing strategies thanks to the study's
findings. The report is organized as shown below. The research question is clearly and
pertinently described in the following section, defining the marketing problem. The third section
describes this investigation's data and research methodology, including the feature selection, data
cleaning, and neural network model. The fourth section presents the analysis findings and
discusses their ramifications. The report ends with a summary of the significant discoveries and
recommendations for additional studies.
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Marketing Problem Definition
The marketing problem or opportunity is to identify the marketing factors that contribute
most to retail sales growth: promotion, advertising, and awareness. The objective is to
comprehend how these three factors affect sales and determine which one has the most
significant impact. Understanding the effects of various marketing factors on sales is essential for
businesses to develop strategies to boost revenue and keep a competitive edge in the fiercely
competitive retail sector. Although many marketing variables can affect sales, this study
concentrates on three crucial variables: promotion, advertising, and awareness. While advertising
entails using various media to advertise a product or service to potential customers, promotion
refers to using targeted incentives and tactics to persuade consumers to purchase a product or
service. The recognition or familiarity of potential customers with a product or service is called
awareness.
While each factor contributes to sales, businesses must determine which aspect
significantly influences allocating resources efficiently. For instance, if the promotion
significantly impacts sales, a company may want to increase its investment there to boost
revenue growth. The business may want to concentrate more on promotional campaigns that use
advertising to reach potential customers, however, if advertising has a more significant impact.
Companies can create more effective marketing techniques that maximize their return on
investment (ROI) and increase revenue by comprehending the relative influence of these three
marketing factors on sales. Additionally, as consumer behavior changes, knowing which
marketing strategies work best can help companies adjust to shifting market conditions and
maintain their competitiveness in the long run.
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Data and Methodology
Method One
The method assists in forecasting sales when the business increases each factor by five
units. It predicts sales value when the enterprise increases the value of marketing strategies from
their highest values—the analysis combines information on advertising, promotion, and
awareness with sales data for a shampoo product. The "shampoo (with descriptions).xls data.csv" CSV file containing the public dataset was used to obtain the data. The project
imported the data into R and put it in a shampoo data frame using the read.csv function. The
report extracted variables sales, promotion, advertising, price, and awareness from the dataset.
The project used a neural network to model how sales relate to the input variables of
"promotion," "advertising," and "awareness." The project developed a function called
"neural_predict" to train the neural network and generate predictions. The input and output
variables were normalized before neural network training—the process of normalization scales
numerical data within a predetermined range. One way to standardize data in R is to use the scale
() function, which does so by dividing the data by the standard deviation and subtracting the
mean. Utilizing the min-max normalization, which scales the data to a range between 0 and 1, is
an additional option. This process aids in preventing the neural network from being more
affected by higher values of some variables.
The normalized data was divided into a training set, which made up 70% of the total, and
a testing set, which made up 30% of the total, randomly. While the testing set was used to assess
the neural network's performance, the training set was used to train the neural network. The
project built and trained the neural network in R using the "neuralnet" package. The neural
network had an input layer with three nodes (for the three input variables), a hidden layer with
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two nodes, and an output node (for the output variable, sales). The analysis used the default
learning algorithm (backpropagation) and activation function (logistic) from the "neuralnet"
package. By feeding the trained neural network fresh values for the promotion, advertising, and
awareness input variables, the project was able to make predictions. The project used the seq
function to generate a sequence of values for the input variables ranging from the original
dataset's highest importance to the highest value plus 100. The project then passed these values
to the neural predict function, which returned predicted sales figures. The project made three
plots to show the connection between sales and each input variable. The plots were organized in
a 2x2 grid using the par function.
Method Two
The method is crucial in determining which marketing strategy impacts sales most
significantly. Consequently, the business can allocate more resources to it to increase sales. The
"neuralnet" package in R was used to create the neural network model. The dataset was divided
into training and testing sets before constructing the model. The model was trained using the
training set, and its performance was assessed using the testing set. 80% of the data was used for
training, and the remaining 20% was used for testing. Awareness, promotion, and advertising
were the input variables, and sales were the output. The variable output sales were defined as a
linear combination of the three input variables in a formula developed to describe the model.
The project used a neural network model with two hidden layers, with four neurons in the
first and two in the second layers. The model used the parameter' linear to guarantee that the
model's output is a linear combination of the input. The training set was then used to train the
neural network model.
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The testing set was then used to make predictions using the model. The project calculated
the root mean squared error (RMSE) To assess the model's effectiveness. The RMSE estimates
the discrepancy between the actual values and the predicted values. A lower RMSE indicates a
better model performance—the sales figures. The variable importance scores of the input
variables were then calculated using the "VIP package to determine which input variables were
crucial for forecasting sales numbers.
SCAN*PRO Model
In this project, neural networks are the primary method of analysis. Neural networks are a
type of machine learning algorithm that can learn to spot patterns in data and make predictions
based on those patterns. They are frequently used for predictive modeling, natural language
processing, and image and speech recognition. SCAN*PRO
A statistical modeling method called SCANPRO is popularly used in marketing research
to examine how marketing factors affect consumer brand preference. SCANPRO is an acronym
for "Scanner Panel Analysis with Store-Level data and Product-Level data." The SCAN*PRO
model is a type of multinomial logit model that calculates the likelihood that a consumer will
select a specific brand from a list of options using data from scanner panels. The model accounts
for both the product's attributes, such as its cost and features, and the consumer's attributes, such
as their demographics and purchasing habits.
The project estimates a SCAN*PRO model of shampoo sales specified below to assess
the impacts of various marketing strategies such as promotion, awareness, and advertising on the
product sales.
(1) 𝐶𝑜𝑛𝑠𝑡𝑎𝑛𝑡 ∗ (𝑃𝑟𝑜𝑚𝑜𝑡𝑖𝑜𝑛)𝑃𝑅𝑂𝑀𝑂𝑇𝐼𝑂𝑁 ∗ (𝐴𝑤𝑎𝑟𝑒𝑛𝑒𝑠𝑠)𝐴𝑊𝐴𝑅𝐸𝑁𝐸𝑆𝑆
∗ (𝐴𝑑𝑣𝑒𝑟𝑡𝑖𝑠𝑖𝑛𝑔) 𝐴𝐷𝑉𝐸𝑅𝑇𝐼𝑆𝑁𝐺
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Since Equation (1) is a multiplicative model, the project transforms equation (1) by
finding both sides logarithm of the model. Subsequently, the analysis estimates a linear model
using simple statistical method such as ordinary least squares (OLS). When estimating a linear
regression model, we always get a consistent set of model parameters as opposed to nonlinear
optimization techniques. Furthermore, when looking for a global minimum in the solution space,
nonlinear optimizations can be extremely sensitive to your choice of starting value and step
width. Consequently, it is advised to convert the model into a linear one. Specifically, the
transformed model is:
(2) 𝐿𝑜𝑔(𝑆𝑎𝑙𝑒𝑠)
= 𝐶𝑜𝑛𝑠𝑡𝑎𝑛𝑡 ′ + 𝑃𝑅𝑂𝑀𝑂𝑇𝐼𝑂𝑁 ∗ 𝐿𝑜𝑔(𝑃𝑟𝑜𝑚𝑜𝑡𝑖𝑜𝑛) + 𝐴𝑊𝐴𝑅𝐸𝑁𝐸𝑆𝑆
∗ 𝐿𝑜𝑔(𝐴𝑤𝑎𝑟𝑒𝑛𝑒𝑠𝑠) + 𝐴𝐷𝑉𝐸𝑅𝑇𝐼𝑆𝑁𝐺 ∗ 𝐿𝑜𝑔(𝐴𝑑𝑣𝑒𝑟𝑡𝑖𝑠𝑖𝑛𝑔)
The project first log-transforming the model variables and estimated a linear model using
𝒍𝒎 function in R.
Results and Analysis
The business can use the marketing strategies like promotion, awareness, and advertising
to increase the sales of a good or service. Even though they are connected and can be used in
conjunction, they are associated with various meanings and approaches. The term "promotion"
describes a business's multiple strategies to interact with its clients and persuade them to
purchase its goods or services, including advertising, public relations, personal selling, sales
promotions, and direct marketing. The ultimate objective of promotion is to boost sales and
cultivate consumer brand loyalty.
On the other hand, the level of knowledge that customers have about a good or service is
referred to as awareness. It is the extent to which prospective customers know the product or
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service and comprehend its advantages. Advertising, public relations, social media, and other
marketing tactics can all be used to raise awareness. The paid promotion of a good or service
through different media channels is called advertisement, a subset of promotion. Television
commercials, print ads, online ads, billboards, and more are all examples of advertising. The
main objectives of advertising are to raise brand awareness, spark interest in the good or service,
and ultimately increase sales.
The SCAN*PRO model produces the results in the figure below:
Figure 1: SCAN*PRO Model Results
The project made the below interpretations from the results shown in the figure above:
The promotion elasticity for shampoo is 0.32693, implying that one percent
increase in promotion of the product raises its sales by 0.32693 percent. Essentially, an elasticity
having absolute value greater than one indicates that the item sales is vulnerable to change or
quite elastic. In contrast, the findings imply that promotion can be verry efficient and suitable in
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boosting sales. Moreover, the business should also consider brand image in making promotion
decisions. The company should not drastically lower the price of shampoo, especially, if it is a
premium product.
The elasticity for awareness index of the shampoo is 0.76359, indicating that one
percent increase in the business shampoo awareness will raise sales by 0.76359 percent.
The elasticity for advertising index is 0.04380, showing that one percent increase
in advertising index will increases sales by 0.04380 percent.
The project substituted the model estimates into Equation (1) above, and wrote the
formula for shampoo sales prediction as follows:
(3) Log (Sales)= 4.1972+0.3269∗Log (Promotion)+ 0.7636∗Log (Awareness)+0.0438
∗Log (\ Advertising)
From, the above interpretation and based on the SCAN*PRO results, shampoo awareness
is the marketing strategies that has the most significant effect on sales.
Method 1 produces the following Figure 2 representing the predicted values of sales and
the figures that the project generated by sequentially increasing the highest observations from
each variable. All the plots show that the sales increase with the increase in the marketing
strategies. Promotion, advertising, and awareness are all practical and show that they raise sales
when businesses invest more resources. Additionally, figure 2 shows the forecasted sales values,
and it gives the enterprise insights on how much their return on investment (ROI) on each
marketing strategy.
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Figure 2: Predicted Sales Values against marketing strategies values.
Method 2 significantly determines the impacts of promotion, awareness, and advertising
on sales. The “VIP” function in R produced the following results recorded in Table 1:
Variable
Impact
Promotion
58156
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Awareness
108884
Advertising
53557
The findings demonstrate the significance of each variable in predicting shampoo sales.
The variable with the highest impact is " awareness," which has a value of 108884, followed by "
promotion," which has a value of 58156, and "advertising," which has a value of 53557,
suggesting that awareness has the most significant impact on sales, followed by promotion and
advertising. The company can use this information to create its marketing strategy. They can
concentrate more on awareness because they have the most significant impact on sales. They
may also consider stepping up their efforts to raise awareness and advertise to increase sales
further.
Additionally, the findings show how effective neural networks are at deciphering intricate
relationships between variables. Other techniques might be unable to find nonlinear relationships
between variables, but neural networks can. In the case of marketing data, where the
relationships between variables can be complicated and nonlinear, this feature is beneficial.
Below is the visualization of the Method 2 findings:
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Figure 3: Impacts of Various Marketing Strategies
Moreover, figure 4 below shows the model used in producing Figure 3 above. It offers
that the analysis is used for hidden layers to ensure an accurate estimate.
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Figure 4: Neural Network Model
The neural network model above shows that the awareness has the most significant value
of inputs, indicated by the outputs of the first layer. The variable contributes enormously to sales
as compared to other features. The results and analysis above show that awareness has the most
significant impact on sales. Five units increase in advertising will result in a rise in sales; thus,
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the business will sell more when they allocate more resources to awareness than other shampoo
marketing techniques.
Conclusion
In conclusion, this study aimed to investigate how marketing techniques affected the sales
of a shampoo product. With promotion, awareness, and advertising as the input variables and
sales as the output variable, neural network models were used to analyze the data. According to
the findings, awareness was crucial in predicting sales, followed by promotion and advertising.
Additional factors may affect sales, such as seasonality, competition, and product quality, and
this study only looked at these three marketing strategies. The analysis could be expanded in
future studies to consider these factors and examine how they affect sales.
To ensure that potential customers are aware of the product and its advantages, it is also
critical to continue to prioritize brand awareness development and advertising. In general, the
study's findings offer insightful information about how well marketing plans work to increase
sales of a shampoo product. Companies can better understand consumer behavior and make datadriven decisions to enhance their marketing efforts by utilizing the power of neural networks.
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