Power BI Desktop: The Playground for Creation

Free Desktop Application: This is the environment where you will create, model, and visualize your data.

Flexibility: It allows you to connect to multiple data sources, transform data, create interactive reports, and personalized dashboards.

Power BI Service: Sharing and Collaboration

Cloud Platform: This is where you publish and share reports created in Power BI Desktop.

The different stages involved in sales analysis

                    flowchart TB
                      subgraph **Collecte des données**
                          A(Ventes en magasin)
                          B(Ventes en ligne)
                          C(Retours)
                      end
                  
                      subgraph **Analyse exploratoire**
                          D(Analyse par produit)
                          E(Analyse par région)
                          F(Analyse par saison)
                      end
                  
                      subgraph **Modélisation**
                          G(Régression linéaire)
                          H(Forêts aléatoires)
                      end
                  
                      A --> B --> C --> D
                      D --> E --> F
                      F --> G
                      F --> H
                      G --> I(Visualisation)
                      H --> I
                      I --> J(Prise de décision)
                  

Data collection is a crucial step to obtain meaningful results. To start this phase:

Define the data to be collected: - Customer demographic data: Age, gender, geographic location. - Product data: Categories, sub-categories, prices, margins, suppliers. - Sales data: Total sales amount, number of transactions, best-selling products, peak periods. - Customer data: Purchase frequency, average basket, retention rate. - Sales channel data: In-store sales, online sales, telephone sales.

A diagram representing the company's database structure

                    erDiagram
                      PRODUITS {
                          string ID_Produit PK "Primary Key"
                          string Nom_Produit
                          string ID_Categorie FK "Foreign Key"
                          float Prix_Achat
                          float Prix_Vente
                          float Marge
                          string ID_Fournisseur FK "Foreign Key"
                      }
      
                      CATEGORIE {
                          string ID_Categorie PK "Primary Key"
                          string Libelle_Categorie
                      }
      
                      CLIENTS {
                          string ID_Client PK "Primary Key"
                          string Nom
                          string Prenom
                          string Adresse
                          string Email
                          string Telephone
                          date Date_Naissance
                          string Carte_Fidelite
                          float Note_Satisfaction
                          float Panier_moyen
                      }
      
                      COMMANDES {
                          string ID_Commande PK "Primary Key"
                          string ID_Client FK "Foreign Key"
                          date Date_Commande
                          float Montant_Total
                          string Mode_Paiement
                      }
      
                      LIGNES_COMMANDE {
                          string ID_Ligne_Commande PK "Primary Key"
                          string ID_Commande FK "Foreign Key"
                          string ID_Produit FK "Foreign Key"
                          int Quantite_Commandee
                          float Prix_Unitaire
                      }
      
                      VENTES {
                          string ID_Vente PK "Primary Key"
                          string ID_Produit FK "Foreign Key"
                          date Date_Vente
                          int Quantite_Vendue
                          float Montant_Total
                      }
      
                      FOURNISSEURS {
                          string ID_Fournisseur PK "Primary Key"
                          string Nom_Fournisseur
                          string Categorie_Produit
                          string Adresse
                          string Ville
                          string Code_Postal
                          string Localisation
                      }
      
                      STOCKS {
                          string ID_Stock PK "Primary Key"
                          string ID_Produit FK "Foreign Key"
                          int Quantite_En_Stock
                          date Date_Mise_A_Jour
                      }
      
                      PRODUITS ||--o{ CATEGORIE : "Reference"
                      PRODUITS ||--o{ FOURNISSEURS : "Reference"
                      CLIENTS ||--o{ COMMANDES : "Reference"
                      COMMANDES ||--o{ LIGNES_COMMANDE : "Contient"
                      PRODUITS ||--o{ LIGNES_COMMANDE : "Reference"
                      PRODUITS ||--o{ VENTES : "Reference"
                      PRODUITS ||--o{ STOCKS : "Reference"
                  

Data files in Excel format

Removing duplicates, correcting errors.

During this data cleaning phase, we use Microsoft Excel to remove duplicates using the REMOVE.DUPLICATES function, format dates to DD/MM/YYYY, capitalize texts, and correct input errors, such as numeric values entered as text.

Identifying trends, anomalies, correlations.

Exploratory analysis is a crucial phase in our project, where we will understand the data structure, identify patterns, discover relationships, and formulate hypotheses.

Understanding the data: Familiarize with the data, verify its quality, and detect potential issues (missing values, inconsistencies).

Identifying trends: Highlights general trends, time evolutions, activity peaks and troughs.

Discovering relationships: Identify correlations between different variables, for example, between sales and seasonality, or between product type and customer profile.

Formulating hypotheses: By observing the data, we can formulate hypotheses that will be tested more rigorously later.

Creating predictive models (to forecast future sales).

In this phase, we will develop mathematical and statistical models to predict future sales trends using historical data.

Creating charts and dashboards to present results.

We will transform our analyzed data into interactive charts, diagrams, and dashboards that allow for quick and intuitive understanding of the insights obtained.

Using insights to optimize business strategies.

Based on the analyses and visualizations performed, we will formulate concrete strategic recommendations to improve the company's performance.