. While historical patterns indicate consumers turn to private-label products in challenging economic times, the current scenario of inflation and economic uncertainty suggests private brands should outpace name brands. However, only modest gains have been observed in private labels share of the consumer wallet (IRI, 2022). In this context, our client, one of Australias leading retailers, is dedicated to understanding consumer preferences and refining its product offerings. Private-label store brands are integral to the clients product portfolio across categories like groceries, household items, and personal care products. Why research is needed: Our client is interested in gaining a deeper understanding of how consumers perceive and interact with private-label store brands. As the client aims to strengthen its market position in Australia, the research intends to unearth insights that will guide decision-making, enhance product offerings, and foster stronger connections between the retailer and its customers. General information about the retailer: The retailer is a prominent supermarket chain known for its diverse range of products and a commitment to delivering quality and value to its customers. Private-label store brands are an integral part of the retailers dedication to meeting the diverse needs of its customer base. Step 2: Evaluate the datasets (made available after you have completed Assessment 2) and develop three to four specific marketing research objectives guided by the research brief and the data.Step 3: Analyse the data collection methods, including sampling and questionnaire, along with their limitations and errors. Step 4: Analyse and summarise key descriptive statistics like mean, median, mode and standard deviation from the survey data for chosen variables. Step 5: Use at least two bivariate statistical methods, such as correlation, cross-tabulation, chi-square, test of difference or t-test, to explore relationships and patterns in the data. Step 6: Create and explain informative data visualisations such as charts and graphs to highlight key data insights. Aim for five to eight findings. Add these visualisations as supporting information to your descriptive and bivariate statistics analysis. Describe the significance of each visualisation and explain how it contributes to the overall understanding of the dataset. Step 7: Provide clear, actionable recommendations to the client based on your analysis, considering potential implementation challenges. Ensure recommendations are specific and aligned with the research objectives, supported by the data.