Introduction
In today's competitive market, understanding revenue trends and making accurate forecasts is crucial for businesses to plan their strategies effectively. In this analysis, we will delve into the quarterly revenue data of a major sportswear brands, Adidas (in contrast with Nike data). Using historical data, we will employee an LSTM (Long Short-Term Memory) model, a type of recurrent neural network, to predict future revenue trends.
We will install tensorflow and keras two libraries that allow us to access the LTSM model we will be using.
We will also be importing pandas and numpy to parse our initial data. sklearn another machine learning library to prep the data for the LTSM modeling. Visualization libraries matplotlib and seaborn will also be imported so that we can visualize our results and garner actionable insights for future success.
Insights to Actions:
Strategic Planning: Use the forecasted data to align production and inventory management with expected demand.
Marketing Initiatives: Identify regions with slower growth and consider targeted marketing campaigns to boost sales.
Budget Allocation: Allocate resources and budget based on the regions and products that are expected to perform well in the upcoming quarters.
Competitive Analysis: Compare the forecasted growth with competitors to identify potential market share opportunities.
Next Steps Ideas:
Expand the Dataset: Incorporate more data points, possibly from previous years, to refine our model's accuracy.
Incorporate External Factors: Consider external factors like marketing campaigns, product launches, or global events that might influence sales.
Model Optimization: Experiment with different neural network architectures or other forecasting models to improve prediction accuracy.
Regional Analysis: Dive deeper into regional sales data to identify specific markets that are underperforming or overperforming.