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In this project, a 'bike purchase' dataset is presented.
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My aim is to clean,transform,analyze and visualize the dataset inorder to draw insights that will guide the bike seller in making informed marketing decisons hence improving his/her bike sales.
1). Identify which demographic of the identifiers purchased bikes more compared to their counterparts.
- This will grant the bike seller insight on which demographic to market more to in order to maximize bike sales.
2). Identify which demographics are underexplored and have a potential to purchase bikes at a high rate.
- This will guide the bike seller in diverting marketing targets from demographics that are least interested in bike purchase to demographics that have a greater potential to purchase more bikes.
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The dataset is obtained from the open source plattform 'Kaggle' and is accessible to the general public for use.
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At a glance,I oIbserved that the dataset contains 106 identifiers and 12 variables.
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The variables are namely: Marital status,Gender,Income,Children,Education,Occupation,Home owner,Cars,Commute,Region,Age,Purchased bike.
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NB:Variables, in this case, are the attributes of identifiers that may have influenced their decision of whether or not to purchase a bike.
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The first step in data analysis is often data cleaning.In this case,I proceeded to clean the dataset after data overview.
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Copy and paste the dataset into a new excel sheet,namely,'working sheet'.The process of data cleaning and data transformation takes place on the working sheet.This allows for retention of the raw data in its original form at the original excel sheet.
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The first step in data cleaning is removing any duplicates from the data set.Method: Select whole dataset(ctrl+A) > data> remove duplicates> data has headers.In the dataset,26 duplicates exist (and are eliminated) and 1000 unique values remain.
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Check each column for any inconsistancies or in accurracies.
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In this case,no column contained any inconsistecy or inaccurracy.Thus I procceded to transforming our dataset.
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Check each column for any data type,data value or data format transformation required.
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In 'Marital status' column,replace 'M' data values with 'Married' and also replace 'S' data values with 'Single'.This is because other users may not be familiar with the given abbreviations.It is best to use univerasally known words. Proccess: select column and use the find and replace function available at the home tab.
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In 'Gender' column,replace 'M' data values with 'Male' and also replace 'F' data values with 'Female'.This is because other users may not be familiar with the given abbrevations.It is best to use commonly known words. Process : select column and use the find and replace function available at the home tab.
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Convert the 'Income' column to currency form. Process: >select column >number > currency. In addittion,make the column data values only whole numbers.
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In the 'Commute' column, replace the '10+ miles' data values with 'More than 10 miles'.Use the find and replace function.This transformation will enable efficient sorting of the data in the column as excel will now recognize the 'More than 10 miles' data values as the greatest in the column.
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Since the 'Age column' contains alot of distinct data values,it is best to create a new column namely 'Age bracket' , this will ensure that our visualization later on is not crowded. Process: >insert column(right of 'age' column).
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Group the distinct age data values into ranges: 'Old' is when x>54,'Middle age' is when x>=31 and 'Adolescent' is when x<31.Use the nested IF function to transform the data in 'Age' column i.e =IF(L2>54,"Old",IF(L2>31,"Middle age",IF(L2<31,"Adolescent","Invalid))).Write your code on the 'Age bracket' column first cell,then fill the rest of the column cells with the formula.Thus a new column with only three distict values will have been created thus making visualization easier.
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Since the other remaining columns did not require any transformation,I proceeded to analyze the clean and transformed dataset.
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Create a new work sheet namely,'pivot tables'.I perform all my analysis on this work sheet.Copy all data from the 'working sheet' and paste it onto this new sheet.
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In my analysis,I answer to the following:
- Does the level of income of an identifier make a difference in their decision of whether or not to make a bike purchase?
- Does the commute distance of an identifier make a difference in their decision of whether or not to make a bike purchase?
- Which age group is more interested in bike purchase?
- Create a pivot table 1 that compares the average income of males versus the average income of females that did or did not purchase a bike.
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Process: >insert pivot table>select dataset from working sheet>rows:Gender>columns:Purchased bike>values:Average of income.
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From my analysis,I noted that:
1. Bikes were purchased by identifiers that have an average higher income compared to the non-purchasers.This is in both male and female genders.
2. The average income of the male identifiers is higher compared to the average income of the female identifiers.
3. In addittion,the average income of the male identifiers that did not purchase a bike is still higher than the average income of female identifiers that purchased bikes.
- Create a pivot table 2 that compares the commute distance of an identifier with thier decision to purchase a bike.
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Process: >insert pivot table>select dataset from working sheet>rows:Commute distance>columns:purchased bike>values:count of purchased bike.Sort commute distance in ascending order.
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From my analysis,I noted that:
1. The largest target market in line with the commute distance attribute lies in the '0-1 miles commute distance' identifiers.This is because this category contains the highest number of identifiers.
2. As the commute distance increases, the size of the target market decreases.
3. As the commute distance increases,the amount of bike purchases decreases.
- Create a pivot table 3 that compares age brackets versus thier bike purchase.
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Process: >insert pivot table>select dataset from working sheet>rows:Age Brackets>columns:Purchased bike>values:Count of purchased bike.
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From my analysis,I noted that :
1. The largest target market in line with the age bracket attribute is the 'middle age bracket'(31-49 years).This is because this age bracket contains the highest number of identifiers.
2. The 'middle age bracket'(31-49 years) has significantly higher bike purchases compared to the 'old'(54 and above years) and 'adolescent'( 30 and below years) age brackets.
- After performing data analysis on the dataset and creating 3 pivot tables,I proceeded to visualizing my findings.
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Create a new work sheet namely 'Dashboard'.We shall showcase our data visualization charts on the 'Dashboard' sheet.Remove grid lines from the worksheet and add a header (merge,centre and colour fill the top sheet cells) namely 'BIKE SALES DASHBOARD'.
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From each pivot table created, create corresponding pivot chart.
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For pivot table 1, create a bar chart.For pivot table 2,create a line graph.For pivot table 3,create a bar graph.
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Resize the charts to look presentable.
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Add slicers to the pivort charts i.e Marital Status,Region,Education,Home Owner,Children,Occupation,Cars slicers.Process:Select pivot chart 1>pivot chart tools>analyze>insert slicer>select categories>then add connections to all pivot charts.
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After data analysis and visualization, we can now proceed to deliver reccommendations to the bike seller.
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From insights obtained from analyzing the data set,I set to give reccommendations that will guide the bike seller in conducting targeted marketing which will in turn lead to increased bikes sales.
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My reccommendations are as follows:
1). Target identifiers with higher income levels when marketing the bikes.As from our insights,higher income earners purchase bikes more compared to thier counterparts.
2). When marketing to identifiers with the same average income level, prioritize the female gender in comparision to the male gender.This is because from our analysis,we observe that at the same avarage income level,female identifiers are more likely to make a bike purchase as oppossed to their male counterparts.
3). Prioritize identifiers in the 0-5 miles commute distance with top priority to those in the 0-1 miles commute distance during marketing.From our insights,we observe that identifiers in this range are more in number and have a tendancy to purchase bikes.
4). Middle age bracket identifiers should also be accorded target market priority.It is evident that they carry the highest number of identifiers as well as the highest number of bike purchases in terms of age bracket.