Multi-Objective Statistical Planning in Digital Marketing at Digikala

Abstract

In the rapidly evolving landscape of e-commerce, strategic decision-making is crucial for companies like Digikala. A multi-objective statistical planning approach can enhance digital marketing initiatives by enabling data-driven decisions. Six key decision variables are essential for this analysis: customer acquisition cost (CAC), average order value (AOV), conversion rate, customer lifetime value (CLV), website traffic, and customer engagement metrics (e.g., click-through rates). To operationalize this framework, three potential and measurable objectives can be established: increasing customer retention by 20% over the next year, boosting the conversion rate by 15% within six months, and reducing customer acquisition costs by 10% in the forthcoming quarter. These objectives not only align with organizational goals but also cater to the competitive dynamics of the online retail market. Employing advanced statistical methodologies, such as regression analysis and optimization techniques, enables Digikala to glean insights from historical data. This aids in determining trade-offs among the decision variables, ultimately refining marketing strategies. As a result, a structured approach to statistical planning will strengthen Digikala's position in the market and drive sustainable growth through informed decision-making. This concise essay encompasses the requested components, focusing on the implications of multi-objective statistical planning in digital marketing for Digikala.