For every company, Sales is the ultimate revenue generator which takes care of all costs and expenses. While Sales may be achieved easily or in some cases in a very difficult way, analysis of the Sale that has materialized is very important.
What is Sales analysis?
As the name suggests, sales analysis involves analyzing the sales made by a company over a period of time. Many companies have a monthly sales analysis , a quarterly sales analysis or an annual sales analysis. A regular sales analysis helps the company understand where they are performing better and where they need to improve. Sales analysis also allows the company to make proper budget allocations so that the profit is maximized.
Let us see how an actual sales analysis is performed.
Any Business Intelligence project begins with the understanding of the client demands. A client had sent a Business Request in form of an email stating their requirements and demands. Below is the Business Request received from the customer. I have changed the name due to non disclosure agreement issues and retracted the client's name.
Business Requirement Document and User Stories
Once a Business Request document is received, the first step is to determine the exact demands of the customer. Usually a one-on-one meeting with the designated person helps us to map the client's mind and get on our journey.
After analyzing the Business Request document, a Business Requirement Document is created mentioning the Reporter name, the value of change required, the necessary systems to be incorporated and other relevant info.
Based on the request that was made from the client, the following user stories were defined to fulfill delivery and ensure that acceptance criteria’s were maintained throughout the project.
|Sr No.||Role||Request / Demand||User value||Acceptance Criteria|
|1||Sales Manager||To get a dashboard overview of internet sales||Can follow better which customers and products sells the best||A Power BI dashboard which updates data once a day|
|2||Sales Manager||A detailed overview of Internet Sales per Customers||Can follow up my customers that buys the most and who we can sell more to||A Power BI dashboard which allows me to filter data for each customer|
|3||Sales Manager||A detailed overview of Internet Sales per Products||Can follow up my Products that sells the most||A Power BI dashboard which allows me to filter data for each Product|
|4||Sales Manager||A dashboard overview of internet sales||Follow sales over time against budget||A Power Bi dashboard with graphs and KPIs comparing against budget|
With a defined Business Demand overview and a proper user story table we move towards the Data Transformation and Cleansing part.
Data Cleansing and Transformation
The client has provided the backup sales database which contained combination of fact as well as dimension table data. The data provided was from 2010, however since we are required to analyze only the last year and the current year data we will first perform two steps.
1. Update the database.
2. Filter out the data for the two required years.
To create the necessary data model for doing analysis and fulfilling the business needs defined in the user stories we need to extract only the required tables from the entire dataset. Also in order to ease the analysis and dashboarding process, only the most important fields from the required tables are retained in the final transformed data. We have chosen the Sales Fact Table, the Customer Details Table (Dimension table), Product details table (Dimension table) and Calendar table (Dimension table). Along with these, the client had also provided the budget data in an excel sheet which also forms the part of a fact table.
Below are the SQL statements for cleansing and transforming necessary data.
-- Cleansed DIM_Date Table -- SELECT [DateKey], [FullDateAlternateKey] AS Date, --[DayNumberOfWeek], [EnglishDayNameOfWeek] AS Day, --[SpanishDayNameOfWeek], --[FrenchDayNameOfWeek], --[DayNumberOfMonth], --[DayNumberOfYear], --[WeekNumberOfYear], [EnglishMonthName] AS Month, Left([EnglishMonthName], 3) AS MonthShort, -- Useful for front end date navigation and front end graphs. --[SpanishMonthName], --[FrenchMonthName], [MonthNumberOfYear] AS MonthNo, [CalendarQuarter] AS Quarter, [CalendarYear] AS Year --[CalendarSemester], --[FiscalQuarter], --[FiscalYear], --[FiscalSemester] FROM [AdventureWorksDW2019].[dbo].[DimDate] WHERE CalendarYear >= 2019
-- Cleansed DIM_Customers Table -- SELECT c.customerkey AS CustomerKey, -- ,[GeographyKey] -- ,[CustomerAlternateKey] -- ,[Title] c.firstname AS [First Name], -- ,[MiddleName] c.lastname AS [Last Name], c.firstname + ' ' + lastname AS [Full Name], -- Combined First and Last Name -- ,[NameStyle] -- ,[BirthDate] -- ,[MaritalStatus] -- ,[Suffix] CASE c.gender WHEN 'M' THEN 'Male' WHEN 'F' THEN 'Female' END AS Gender, -- ,[EmailAddress] -- ,[YearlyIncome] -- ,[TotalChildren] -- ,[NumberChildrenAtHome] -- ,[EnglishEducation] -- ,[SpanishEducation] -- ,[FrenchEducation] -- ,[EnglishOccupation] -- ,[SpanishOccupation] -- ,[FrenchOccupation] -- ,[HouseOwnerFlag] -- ,[NumberCarsOwned] -- ,[AddressLine1] -- ,[AddressLine2] -- ,[Phone] c.datefirstpurchase AS DateFirstPurchase, -- ,[CommuteDistance] g.city AS [Customer City] -- Joined in Customer City from Geography Table FROM [AdventureWorksDW2019].[dbo].[DimCustomer] as c LEFT JOIN dbo.dimgeography AS g ON g.geographykey = c.geographykey ORDER BY CustomerKey ASC -- Ordered List by CustomerKey
-- Cleansed DIM_Products Table -- SELECT p.[ProductKey], p.[ProductAlternateKey] AS ProductItemCode, -- ,[ProductSubcategoryKey], -- ,[WeightUnitMeasureCode] -- ,[SizeUnitMeasureCode] p.[EnglishProductName] AS [Product Name], ps.EnglishProductSubcategoryName AS [Sub Category], -- Joined in from Sub Category Table pc.EnglishProductCategoryName AS [Product Category], -- Joined in from Category Table -- ,[SpanishProductName] -- ,[FrenchProductName] -- ,[StandardCost] -- ,[FinishedGoodsFlag] p.[Color] AS [Product Color], -- ,[SafetyStockLevel] -- ,[ReorderPoint] -- ,[ListPrice] p.[Size] AS [Product Size], -- ,[SizeRange] -- ,[Weight] -- ,[DaysToManufacture] p.[ProductLine] AS [Product Line], -- ,[DealerPrice] -- ,[Class] -- ,[Style] p.[ModelName] AS [Product Model Name], -- ,[LargePhoto] p.[EnglishDescription] AS [Product Description], -- ,[FrenchDescription] -- ,[ChineseDescription] -- ,[ArabicDescription] -- ,[HebrewDescription] -- ,[ThaiDescription] -- ,[GermanDescription] -- ,[JapaneseDescription] -- ,[TurkishDescription] -- ,[StartDate], -- ,[EndDate], ISNULL (p.Status, 'Outdated') AS [Product Status] FROM [AdventureWorksDW2019].[dbo].[DimProduct] as p LEFT JOIN dbo.DimProductSubcategory AS ps ON ps.ProductSubcategoryKey = p.ProductSubcategoryKey LEFT JOIN dbo.DimProductCategory AS pc ON ps.ProductCategoryKey = pc.ProductCategoryKey order by p.ProductKey asc
-- Cleansed FACT_InternetSales Table -- SELECT [ProductKey], [OrderDateKey], [DueDateKey], [ShipDateKey], [CustomerKey], -- ,[PromotionKey] -- ,[CurrencyKey] -- ,[SalesTerritoryKey] [SalesOrderNumber], -- [SalesOrderLineNumber], -- ,[RevisionNumber] -- ,[OrderQuantity], -- ,[UnitPrice], -- ,[ExtendedAmount] -- ,[UnitPriceDiscountPct] -- ,[DiscountAmount] -- ,[ProductStandardCost] -- ,[TotalProductCost] [SalesAmount] -- ,[TaxAmt] -- ,[Freight] -- ,[CarrierTrackingNumber] -- ,[CustomerPONumber] -- ,[OrderDate] -- ,[DueDate] -- ,[ShipDate] FROM [AdventureWorksDW2019].[dbo].[FactInternetSales] WHERE LEFT (OrderDateKey, 4) >= YEAR(GETDATE()) -2 -- Ensures we always only bring two years of date from extraction. ORDER BY OrderDateKey ASC
Now that we have transformed the required dataset using the above SQL statements, we can export the generated tables to a CSV file for the final modelling and dashboarding. This is achieved by using the Import/Export wizard of SQL Server Management Studio.
Data Modelling is the process of analyzing the data objects and their relationship to the other objects. It is used to analyze the data requirements that are required for the business processes.
In order to achieve this we will load our datasets to Microsoft Power BI Desktop. Since the initial cleansing and transformation is already done using SQL querying statements we can skip the Power Query Editor and directly begin our modelling part.
Once the dataset is loaded in Power BI Desktop, we have to create the schema diagram using the star schema mechanism. The primary keys of the dimension table are connected to the corresponding foreign key of the Fact Sales table on a one to many relationship as shown below.
Once the data modelling is done we have to create certain measures which would allow us to better depict the affect on sales by different customer and products overtime as per the client's demands. The three measures once calculated are moved to a newly created standalone table called the measure table. The three measures calculate are:
1. Total Sales over the specified time period.
2. Total Budget of the specified time period.
3. Total profit made
With the three calculated measures along with the fact and dimension tables we now move on to designing the dashboard
Sales Management Dashboard
As per the client's demands and based on the acceptance criteria we have created three separate dashboard namely Sales Overview, Customer Details and Product Details. These dashboard together form the Sales Analysis and Budget Management Report.
1. Sales Overview
The Sales overview dashboard is an interactive real-time sales analysis dashboard fulfilling the first business request of migration from static reports to interactive reports. It displays the KPI of Sales vs Budget allowing for proper visualization of the profits made. As the customer needed to view the effects overtime , we have included the Year as well as month filters.
The Sales overview also shows the Sales based on Product category as the client has mentioned that they have specialized Sales representative for each sub category and wanted to analyze the performance of their sales employees.
The Dashboard also shows the Top 10 Customers and Products based on the sales, which were also part of the Business demand overview. In order to facilitate the analysis of sales in different region we have included a map visual displaying the sales amount as circles on specific locations with radius proportionality to sales amount.
2. Customer Details
The customer details dashboard contains visuals depicting the more specific details of sales based on the customer i.e. the Top 10 customer based on sales, purchase by the top 10 customer over time (monthly analysis) as well as a comparison of the budget allocated and the revenue generated.
3. Product Details
The Product details dashboard contains visual the more specific details of sales based on the product i.e. Top 10 products based on sales, sale of the top 10 product over time as well as a comparison of the budget allocated and the revenue generated.
Once the final report is prepared, we publish the dashboard to Power BI service and schedule automated data refresh based on the client's specification.
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