Extracting clusters from aggregate panel data: A market segmentation study

• We propose a method that finds homogeneous trajectories in aggregate panel data.
• The SQP algorithm is used to maximize the log-likelihood function.
• The p-values are computed from the pseudo-inverse Hessian matrix.
• A case study shows the application of the method in market segmentation.

This paper introduces a new application of the Sequential Quadratic Programing (SQP) algorithm to the context of clustering aggregate panel data. The optimization applies the SQP method in parameter estimation. The method is illustrated on synthetic and empirical data sets. Distinct models are estimated and compared with varying numbers of clusters, explanatory variables, and data aggregation.Results show a good performance of the SQP algorithm for synthetic and empirical data sets. Synthetic data sets were simulated assuming two segments and two covariates, and the correlation between the two covariates was controlled in three scenarios: ρ = 0.00 (no correlation), ρ = 0.25 (weak correlation), and ρ = 0.50 (moderate correlation). The SQP algorithm identifies the correct number of segments for these three scenarios based on all information criteria (AIC, AIC3, and BIC) and retrieves the unobserved heterogeneity in preferences. The empirical case study applies the SQP algorithm to consumer purchase data to find market segments. Results for the empirical data set can provide insights for retail category managers because they are able to compute the impact on the marginal shares caused by a change in the average price of one brand or product.