Amazon Marketplace Analytics Dashboard
In a super competitive eCommerce industry, there are many competitors offering similar products and services and can be a challenge to set yourself apart.
The solution offers a thorough competitor analysis and helps answer the following questions:
- What products your competitors are offering, and how much they’re selling it for.
- What platforms are they using to connect with customers?
- How are they generating leads?
- Do they have any promotions going on?
Understanding the Requirements (Discovery Phase)
This step begins with a series of requirements meetings with key stakeholders in the organization. These include sales, marketing, supply-chain, finance, merchandising, and human resources. To start these discussions, we create a set of prototype dashboards to elicit feedback and demonstrate some of the capabilities that Power BI can provide:
- Sales & Margins: Provide sales and margins. Include trends, sales per SKU, store sales, Sales and Margin, Market Share vs. Competitors. Has benefits like real-time insight into sales and margin performance of individual Manufacturers and brands.
- Marketing: Provides deliver key metrics for each website on web page views, sessions, web users, web search, Social Media engagement, campaign performance, coupon/promotion analysis, marketing spends vs. budget. Has benefits like ability to visualize the impact of marketing activities and campaigns on website and in-store
Mapping the Requirements to Data Entities and Sources
The next step is to develop an entity model based on the requirements gathered in the previous step.
Begin by modelling the data to define the data objects and their relationships. In contrast to a relational database, the goal of the data modeling is to use two types of data tables; fact tables and dimension tables. Fact tables contain the data corresponding to a particular business process. Dimension tables contain the descriptive attributes related to each instance of the data. Fact tables and dimension tables are related to each other.
After creating the data model and entities, we need to map the relationships between the entities. This modeling can be done within the Power BI Desktop tool or in SQL Server. Build the data schema, including the tables and their relationships. Each data entity needs to be traced and its associated data attributed to a data source.
Loading the Data
Power BI allows you to connect to a relational data source in a few different ways. This includes Import, DirectQuery, and through a Live Connection when utilizing SQL Server Analysis Services.
Creating Calculations and Measures
With Power BI, DAX (Data Analysis Expressions) can be used to create formulas and calculations that you can use to make columns, measures, and even your own tables to represent specific data. With larger datasets, DirectQuery will require that the creation of the calculated columns and measures be in the data source.
Below are some sophisticated columns that might be created:
- Market Share
- Competitor market Sales, share and units sold
- Current Year Results
- Previous Year and Previous Period Results
- Year-Over-Year Comparisons
- Rolling 52-week trend
- Sales by SKUs
- Marketing Campaign Performance
Developing Reports and Dashboards
After the necessary calculated columns and measures are developed, the last step is to create the reports and dashboards that will deliver key insights to the organization.
Optimization: The dashboard is optimized by incorporating DAX best practices and has efficient query load time.
Infographic: Usage of infographic elements to make the dashboard highly visual and interesting.
Anomalies: Anomalies on each section would help understand the insights faster and thus helps in quicker decision making