Linear Regression Model for Measuring Relationship between Purchase Intention and Product Price

Marketing Analytics
Reports

Contents
Questions..........................................................................................................................................3
1) How can we use the linear regression model to measure the relationship between
customers’ purchase intention and product price? What are the limitations for the linear
regression model for this task? If possible, what would you propose to mitigate these
limitations...............................................................................................................................3
3) What is the intuitive rationale of the segmentation analysis in the marketing analytics
approach?................................................................................................................................4
REFERENCES................................................................................................................................6

Questions
1) How can we use the linear regression model to measure the relationship between customers’
purchase intention and product price? What are the limitations for the linear regression
model for this task? If possible, what would you propose to mitigate these limitations
The linear regression model is being used to predict the relationship among two variables
or among two factors. The factors for which the prediction is made are known as dependent
variable while on the basis it is predicted it becomes the independent variable. This model is
often used by the marketer for determining the prices of the products by establishing relationship
with the purchase intention of the customers. For the marketer it is crucial to identify the factors
that affect the buying behaviour of the customers and the value that they are getting from their
products because the customers are willing to pay more for the products that offers them better
value. As the name suggest, the linear regression is directly associated with the mathematical
implication for determining the value of one dependent variable for the other. It is dependent
upon the linear equation which is given below:
Y= a + bX
Where, y is considered to be the dependent variable, b is the slope of the line and a, is the
intercept, X is the explanatory variable. For determining the prices of the products the customers
purchase intention must be identified along with their relation among the two. To support this,
the data regarding the factors that are considered by the customer while making purchase will be
gathered. On the basis of the past data it will be identified that to what extend the product offered
by the company satisfied the need of the customer or to which category it belongs to such as
necessity goods, comfort or luxury good or habitual good because the intention of buying is
directly related to it. With the identification of the type of products the need of the product for
the customer can be identified and this will facilitate them to understand the reactions of the
customer while making purchase in context of prices of the product. For example, to determine
the relationship between the intention of buying and prices of the chocolate with the help of
linear regression, with equation y= a + bX it can be identified that the demand for the chocolate
is constant but if the prices of the chocolate decreases the demand will increase and if the prices
will increase the demand may not decrease than the existing level. This is because of the
intention of buying the chocolate which can be to satisfy the need to it or to use it as gift but with