Analytics, Modelling and Forecasting
Digital advertising is a critical growth engine in today’s competitive e-commerce landscape. Many businesses allocate substantial budgets to ad platforms like Google, Facebook, and TikTok, however they might not always avoid underperformance and waste capital due to inefficient ad spend and unreliable ROAS estimates.
Seasonal decomposition is a tool that breaks down data into three components: trend, seasonality and residual. Trend represents the long term direction or movement in the data over time. Seasonality represents the repeating short-term patterns or cycles that occur at regular intervals.
Businesses can take advantage of Artificial Intelligence and Machine Learning to study consumer behaviour which is useful to businesses to tailor their products, services and marketing strategies. Individuals can select, purchase, use and dispose the goods
Quality of data has a vital role in predictive analytics that shapes the accuracy, reliability and utility of its outcomes. Data quality dimensions encompass accuracy, completeness, consistency, timeliness, reliability, and relevance. Common challenges in ensuring high data quality include human errors in manual data handling
Real-world data representing the time dimension can be used to forecast future scenarios using time series forecasting methods. Autoregression, moving average, autoregressive moving average and automated machine learning (automated ML)
