Machine Learning Approach to Sales Demand Forecasting

 Machine Learning Approach to Sales Demand Forecasting

During these uncertain times, businesses are operating in unfamiliar grounds and are obliged to rely on rapid shifts in customer behaviour. 

Behaviour may be influenced by new experience, market economic conditions, inflation, political unrest etc. Machine learning techniques can help with demand or sales order forecasting.


ML Approach
Data Collection-> Exploratory Data Analysis->Feature Engineering->Modelling->Hyperparameter Tuning->Deployment

The most critical part for demand forecasting is data and feature engineering.

Data Integration Ideas:-
- Link sales demand to order management data
- Link CRM opportunities with transactional sales data
- Link customer and product data to expected demand
- Outlier treatment and data cleaning/enrichment

Feature Engineering Ideas:-
- Sales opportunity representation based on historical sales
- Transactions forecasting
- Historical product sales

Feature Selection Ideas:-
- Rank features based on their relevance.
- Adding the most important features and checking model performance each time until the ideal cut-off is achieved.
- Using a wrapper method to eliminate noise/redundancy.

ML Algorithms:-
- ARIMA/SARIMA
- Linear Regression
- XGBoost
- K-Nearest Neighbors Regression
- Random Forest
- Long Short-Term Memory (LSTM)
- Ensemble model depending on the business need - NLP to understand customer sentiment from emails, classification model to predict if customer will order and churn and finally forecasting order dates, quantity etc.