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Retail analytics - Improvising pricing strategy using markup/markdown

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In this project, retail data was cleaned in the ETL process using Microsoft SQL Server 2013 and the pricing strategies formulated were visualized on Tableau dashboards.

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Retail analytics - Improvising pricing strategy using markup/markdown

  1. 1. PRICING STARTEGY FOR RETALERS. EVERY PENNY MATTERS….. Jahnavi Gangula Reddy - 50125205 Komal Hanamasagar - 50123141 Smitha Mysore Lokesh - 50124016
  2. 2. 1 | P a g e CONTENTS Introduction..................................................................................................................................... 2 Project Plan..................................................................................................................................... 3 Budget & Timeline...................................................................................................................... 3 Roles and Responsibility ............................................................................................................. 4 Project Development....................................................................................................................... 5 Need Analysis.............................................................................................................................. 5 Success Criteria ........................................................................................................................... 6 Goals............................................................................................................................................ 6 Bus Matrix................................................................................................................................... 7 Stakeholder matrix....................................................................................................................... 8 Key Performance Indicator.......................................................................................................... 8 Dimensions.................................................................................................................................. 9 Fact Tables:................................................................................................................................ 10 Dimensional Model for the project............................................................................................ 11 ETL Process.................................................................................................................................. 12 ETL for our systems: ................................................................................................................. 13 SQL Server Integration Services:.............................................................................................. 14 Visualization for Business Intelligence ........................................................................................ 24 Inventory.................................................................................................................................... 24 Inventory Dashboard ................................................................................................................. 29 Sales........................................................................................................................................... 30 Sales Dashboard ........................................................................................................................ 34 Conclusion .................................................................................................................................... 35
  3. 3. 2 | P a g e INTRODUCTION The retail services industry provides an openly competitive environment that fosters strong business operations and spurs innovations that increase efficiency and reliability. Numerous opportunities for growth exist in the U.S. retail market for retail providers of all sizes, including individual direct marketers or direct sellers, small- to medium-sized franchise unit owners, and large “big-box” store operators. New distribution companies are opening stores and units daily to serve a large, affluent consumer base. It is for this reason that they often involve pricing strategy. Price Markup is a strategy used by the retail industry to introduce percentage increase in price to increase the profit margin of highly demanded products and Price Markdown is to introduce discount on products with an aim to get rid of slow moving products. These strategies may not always lead to high profitability, however the need is to optimize the pricing strategies for effective output. Our project aims to design data warehouse design for retail industry and help to achieve maximum profit, by strategically optimizing the markup and markdown. Price markup and markdowns are among few challenges that retail industry needs to balance to stay ahead in the competitive market. Too frequent markdowns to assist in fast movement of products may lead lack in goods selling at their actual price and also create reluctance in customer towards the good. On the other hand frequent markup to increase profit may lead to loss of customer and decrease the demand for the product. Therefore, it’s important for the retail industry to optimize their pricing strategy. We this project we aim to design a data warehouse to address the above issues in pricing and suggest methodologies to develop and implement optimum pricing for retail industry.
  4. 4. 3 | P a g e PROJECT PLAN BUDGET & TIMELINE The budget for the one-time cost is estimated to be $140,870. The division of expenses is as shown in the pie chart One Time Cost 1. Hardware 2. DBMS 3. Data warehouse Environment 4. Termination Analysis 5. Transformation Environment 6. Software requirements. Figure 1: Shows the percentage of cost distribution involved in building retail data warehouse Hardware 33% DBMS 19% Data warehouse Environment 15% Terminal Analysis 15% Transformation Environment 11% Software requirements. 7% ONE TIME COST
  5. 5. 4 | P a g e Figure 2: Shows timeline for the project ROLES AND RESPONSIBILITY Role Primary Responsibility Requirement Analysis Team Data warehouse Designer Komal Hanamasagar ETL Designer Jahnavi Reddy Dashboard Designer Smitha Mysore Lokesh
  6. 6. 5 | P a g e PROJECT DEVELOPMENT NEED ANALYSIS Need analysis was carried out through extensive case study of pricing strategy of various retail outlets. Although no formal survey was carried, we did try our best to understand the various challenges faced by the retail industry in terms of price markup and markdown and also challenges faced by retail industry to implement mark up and mark down effectively. During the process of our analysis we have come up with following question, which most of the retail industry owner would like to know about. Our business intelligence application which helps to answer following questions and assist stakeholder to come up with optimum pricing.
  7. 7. 6 | P a g e SUCCESS CRITERIA The success of our project implementation is measured by on meeting following objectives: 1. Flexibility in analysis and reporting. 2. Able to answer most of the vital questions on pricing strategy. 3. Help business implements most optimal pricing strategy. GOALS Our project goals are as follows. 1. To implement data warehouses, that provides a comprehensive analysis of pricing strategy, that assist retail industry to diligently undertake markup and markdown. 2. To implement interactive and user friendly dashboard, that provides a complete overview of markups and markdowns to take business decisions.
  8. 8. 7 | P a g e BUS MATRIX. Business Process Dimensions Customer Product Store Markdown Markup Vendor Date Product Sales × × × × × × Inventory Management × × × Customer Returns × × × × Vendor Returns × × × × Product Sales: Defines the total sales generated by different products purchased by different customers at different location Inventory Management: Defines the number of days the product is in the inventory Customer Returns: Products returned by the customer. Vendor Returns: Products returned back to a particular vendor from a retail store.
  9. 9. 8 | P a g e STAKEHOLDER MATRIX Business Process Business Functions Marketing Sales Finance Operations Product Sales × × × Inventory Management × Customer Returns × × Vendor Returns × KEY PERFORMANCE INDICATOR Sl.No Key Performance Indicator Stakeholders 1. Inventory to sales ratio Managers, Sales Representatives 2. % sales increase to markdown %ratio Managers, Executives 3. % sales increase to markup %ratio Managers , Executives 4 Low selling products Managers, Sales Representatives 5 High selling products Managers, Sales Representatives 6 Product sales by store Managers 7 Product sales by Product line Manager 8 Product markdown to product returns ratio Managers , Executives 9 Product markup to product returns ratio Managers , Executives
  10. 10. 9 | P a g e DIMENSIONS Sl.No Dimensions Description 1 Customer Dimension This dimension will help to identify highest and least customer segment who have contributed to the revenue. This will help to mark up and mark down the price to increase the revenue 2. Product Dimension This is an important dimension as mark up and mark down will be applied to the product price 3 Store Dimension This dimension is used to determine which stores have reported highest sales and lowest sales and help decide mark up/down accordingly. 4 Markdown/up Dimension This helps to determine the percentage of markup/down for each product. 5 Date Dimension This dimension is to accommodate different types of dates. Sl.No Business Process Grain 1 Product Sales One row for each product sold. 2. Inventory Management One row for each product in the inventory. 3. Vendor Management One row for each product purchased or returned to the vendor. 4. Customer Return One row for each product returned by the customer.
  11. 11. 10 | P a g e FACT TABLES: Sl.No Fact Tables Description 1. Sales Fact The fact will be used to hold the sales data. Each row in the sales fact table will help in deciding the markup/down strategy. 2. Inventory Fact This fact will be used to hold the data on the products in the inventory. The quantities of product available in the inventory will be the key measure to determine the mark up and mark down price.
  12. 12. 11 | P a g e DIMENSIONAL MODEL FOR THE PROJECT. Figure 3: Dimensional Model for Retailers Inventory Fact Inventory ID Store ID Product ID Inventory Date On hand quantity Store Store_ID_Key Store_Name Store_Location Product Prod_ID_Key Prod_Descr Prod_Brand Prod_Line Prod_Type Prod_Size Prod_Expiry_Date Seasonal Sales Fact Sales_ID_Key Cust_ID_Key Store_ID_Key Date_Key Markdown-up_ID_Key Prod_ID_Key Sales_Qty Reg_Unit_SP Discount Price Net_Unit_SP Total_Price Customer Cust_ID_Key Cust_Name Cust_Location Date Date_Key Date DayoftheWeek CalenderMonth CalenderYear Holiday Markdown/Up Markdown-up_ID_Key Start_Date End_Date Markdown- up_ID_Interval MD Perc
  13. 13. 12 | P a g e ETL PROCESS Every day large amount of data is generated. Data management has become concerned for businesses as proper management of data helps in gaining market insights, product needs, customer needs etc. It is a long term process that must be done methodically. In order to consolidate the data from different source to one place data warehouse is used. Before this data is readily consumed, it must be checked for validity, anonymity and structure. The different ways in which data can be used for analysis are:  Generate multiple reports and consolidate to one single report.  Fetch data from different systems/application and load into a data warehouse and generate reports as per the requirement. To achieve this ETL tool play a significant role to extract data from different system, cleanse and transform that and load that into the target system. Now a days as all companies are going in the global environment and doing lots of acquisition, there are lots of demand coming in the field of data migration and data integration. An ETL tool plays a significant role in the data migration and data integration fields.
  14. 14. 13 | P a g e ETL FOR OUR SYSTEMS: In this project, we begin with extraction of data of a retail store from different sources. Since, the data were dirty and inconsistent, it had to be cleansed and formatted to an appropriate format. The cleansed data were loaded into the Staging table using ETL tools called SQL Server Integration Services. Microsoft SQL Server Integration Services (SSIS) is a platform for building high performance data integration solutions. Staging tables store date that is extracted from the source system. All the changes happen on a replica of the source systems to avoid affecting
  15. 15. 14 | P a g e Figure 4: shows the interface of on Microsoft SQL Server Integration Services (SSIS) From Staging to Datamart an incremental load containing only the new changes is done. This optimizes the load performance of a data warehouse. These operations were done with Microsoft SQL Server Integration Services (SSIS). SQL SERVER INTEGRATION SERVICES: ETL process is carried out using control flows and data flows. Data flow is a type of control flow that allows you to extract data from an external source which would flow through a number of transformations such as sorting, lookup, filtering, merging etc. With other data and converting data types and finally store the result at, a destination table in a data warehouse.
  16. 16. 15 | P a g e Figure 5. Shows creation of a Data Flow Task Initially, data is extracted from the external data source, a CSV file in our case and a raw copy of the data is stored in the staging tables. The same column names as the source file are maintained. Filtering of data is done to avoid redundancy, which leads to extra space in the staging tables. The Data Flow task encapsulates the data flow engine that moves data between sources and destinations, and lets the user transform, clean, and modify data as it is moved. Addition of a Data Flow task to a package control flow makes it possible for the package to extract, transform, and load data .A data flow consists of at least one data flow component, but it is typically a set of connected data flow components: sources that extract data; transformations that modify, route, or summarize data; and destinations that load data.. At run time, the Data Flow task builds an execution plan from the data flow, and the data flow engine executes the plan. You can create a Data Flow task that has no data flow, but the task executes only if it includes at least one data flow.
  17. 17. 16 | P a g e Figure 6: shows the creation of OLE DB Destination for the data source extracted The Flat File source reads data from a text file. The text file can be in delimited, fixed width, or mixed format.  Delimited format uses column and row delimiters to define columns and rows.  Fixed width format uses width to define columns and rows. This format also includes a character for padding fields to their maximum width. A Flat File connection manager enables a package to access data in a flat file. For example, the Flat File source and destination can use Flat File connection managers to extract and load data. OLE DB (Object Linking and Embedding, Database, sometimes written as OLEDB or OLE-DB), an API designed by Microsoft, allows accessing data from a variety of sources in a uniform manner. An OLE DB connection manager enables a package to connect to a data source by using an OLE DB provider. For example, an OLE DB connection manager that
  18. 18. 17 | P a g e connects to SQL Server can use the Microsoft OLE DB Provider for SQL Server. Implemented using the Component Object Model (COM); it is otherwise unrelated to OLE. Figure 7: Shows importing a table from flat file into a table The data displayed indicates that all the entries in the excel sheet would be then imported and stored as dimension table in the Microsoft SQL Server. The connection manager as shown in the figure below establishes a connection with the database and this is where the flow of data into the tables in the database happens. The OLE DB connection manager can be configured in the following ways:  Provide a specific connection string configured to meet the requirements of the selected provider.  Depending on the provider, include the name of the data source to connect to.  Provide security credentials as appropriate for the selected provider.
  19. 19. 18 | P a g e  Indicate whether the connection that is created from the connection manager is retained at run time. Figure 8.Connection Manager Configurations The below snapshot shows creation of a dimension table customer in the Database “RetailDataWarehouse”.Define all the constraints such as the primary constraint and the uniqueness constraint.
  20. 20. 19 | P a g e Figure 9. Customer Dimension creation Run the build configuration for a proper transfer of data from flat files into OLE DB.The snapshot below shows a successful execution of the import job. This is repeated for all the dimension and fact tables. In the fact tables the facts are generated using Look up match on these dimension tables and data in the sales table in the flat file.
  21. 21. 20 | P a g e Figure 10. Running the data flow build in SSIS Generation of Fact table
  22. 22. 21 | P a g e Other Dimension tables Product Dimension Table Figure 11. Generation of Product Dimension Table
  23. 23. 22 | P a g e Vendor Dimension Table Figure 12. Creation of Vendor Dimension Table Once all the facts and tables are generated and populated the star schema is built by connecting the primary keys with all of the dimension tables with the foreign keys of both the fact tables. The star schema is a highly Denormalized with a lot of records to support ad hoc and complex querying of data present in all the tables. We have two fact tables the sales and the inventory fact table.The star schema for each of the this fact table is as shown below. The entire data warehouse is highly integrated , subject oriented and not updatable.
  24. 24. 23 | P a g e Inventory fact table – schema diagram (as in SSIS) Figure 13:Inventory Star Schema Sales Fact table schema diagram Figure 14. Inventory Star schema
  25. 25. 24 | P a g e VISUALIZATION FOR BUSINESS INTELLIGENCE Business Intelligence helps make informed decisions from historical data. Retail industry works with big data. Hence, it is vital to be able to make sense out of this data. In this project, we are using Tableau’s analytical depth and visualization capabilities to suggest pricing strategies using markup and markdown of fast and slow moving goods. INVENTORY In this project, inventory has been identified as one of the most important KPIs. In order to effectively manage inventory to improve pricing strategies, we identify slow moving goods i.e. goods that stay longer than expected in the inventory and fast moving goods i.e. goods that quickly move to retailers for sale and stay only briefly in the inventory. The figure below shows all the slow moving goods in the inventory. Figure 15: shows the slow moving goods in the inventory
  26. 26. 25 | P a g e Similarly, we identfy fast moving goods that stay briefly in the nventory and generate sales quickly when sold at the retailers. Figure 16 :shows the fast moving goods in the inventory When analyzing the slow moving goods, we must analyze the sales generated by these goods to understand their role in the overall pricing strategy that contributes to the sales and profits of the retailer. The figure shows the sales generated by the 10 slowest goods in the inventory. Though it is expected to contribute very less to the overall sales, it is evident that some exceptions exist that contribute a sizeable amount to the sales. These products must not be marked down. It could lead to losses.
  27. 27. 26 | P a g e Figure 17 :shows the sales generated by slow moving goods When analyzing the fast moving goods, we must analyze the sales generated by these goods to understand their role in the overall pricing strategy that contributes to the sales and profits of the retailer. The figure shows the sales generated by the 10 fastest goods in the inventory. These products must not be marked up. It could help improve the sales and eventually the profits made by the retailers.
  28. 28. 27 | P a g e Figure 18 :shows the sales generated by the fastest moving goods It is important to identify the locations that sell the slow moving goods so that retailers can make decisions on how these products must be shelfed in the stores in order to improve their sales. They could give discounts and promotions on these goods to improve their sales. In the figure below it is evident that East region sell most of the slowest moving goods. Figure 19: shows the locations that sell slow moving goods
  29. 29. 28 | P a g e We have identified the locations that sell the fast moving goods so that retailers can make more profits on these products to improve their sales. They could give discounts and promotions on these goods to improve their sales. In the figure we can identify all the fastest selling products n each region. Figure 20 :shows the fastest moving products in each region The geographical heat map shows all the locations that sell fast and slow moving goods. These locations must improvise their pricing startegies by using product price markup and markdown to have positive effects on the sales and profits of all the retailers.
  30. 30. 29 | P a g e Figure 21: shows the locations of retailers that must improvise their pricing strategies INVENTORY DASHBOARD Figure 22 :shows the inventory dashboard to improvise pricing strategies
  31. 31. 30 | P a g e The figure above shows all the KPIs of inventory management that help identify slow moving goods, fast moving goods, the sales generated by slow and fast moving goods and identifying any exceptions, locations that sell these goods which need to improvise their sales strategies. SALES In this project, inventory has been identified as one of the most important KPIs. In order to effectively manage inventory to improve pricing strategies, we identify slow moving goods i.e. goods that stay longer than expected in the inventory and fast moving goods i.e. goods that quickly move to retailers for sale and stay only briefly in the inventory. The figure below shows all the slow moving goods in the inventory. Figure 23: shows the most profitable product categories The figure below shows the sales generated in different regions – Central, East, West, and South for each customer segment – Consumer, Corporate, Home Office and Small Business. It is
  32. 32. 31 | P a g e evident that Corporate customer segment contribute most to the sales generated. This is the sales generated before pricing startegies are applied. The sales peak at $125,000. Figure 24 :shows the sales generated by each customer segment by regions On identifying the slow moving and fast moving goods and applying pricing strategies like markup and markdown the sales were generated. It shows a significant improvement in the sales. The sales generated has closed the $140,000 remark.
  33. 33. 32 | P a g e Figure 25: shows the sales after pricing strategies were applied The locations where sales were mediocre it showed improvements like Florida, New York, and Washington.
  34. 34. 33 | P a g e Figure 26: shows locations where pricing strategies improved sales The pricing strategies applied had a positive effect on the sales and improved it significantly. Figure 27: shows the sales before products were marked up
  35. 35. 34 | P a g e Figure 28: shows the sales generated after products were marked up SALES DASHBOARD Figure 29: shows the Sales dashboard The figure above shows all the KPIs of sales that help compare the sales of slow moving goods, fast moving goods and their positive effect on improving it. The slow moving goods showed
  36. 36. 35 | P a g e improved sales and profits. Locations that showed low sales showed drastic improvements namely Florida, New York and Washington. In this way we can conclude that mark up and mark down as pricing startegies can help retailers improve their inventory management and improve their sales and profits. CONCLUSION In this project, we successfully designed and implemented a data warehouse that helps create optimal mark up and mark down for retailers. During the course of the implementaion, we learnt a lot about answering curical business question to make informaed decisions, understood the realword applicationof the concepts learnt in class and successfully carried out ETL operations. Tableau helped us visualize the solutions to our problem statement in am initutive manner. The most challenging tak was to formulate KPIs that would improve the overall business startegy of a retailer. Hence, using Data Warehouse and inititutive dashbaord managers and executives and visualize their problem areas and formulate startegies to overcome them to increae profitability and process improvement.

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