Point-of-Sales Performance Analysis

Abt is a corporate and retail company specialized in the sale and customization of electronic payment services. Established in 1998, Abt Sales has grown from a regional business to one of the leading payment service providers in its sector, known for its exceptional customer service and a wide range of products.

Problem Statement

Abt aims to enhance its operational efficiency, customer satisfaction, and increase revenue generation by analyzing transaction performance data. The key challenges include:

1. Identify active and inactive terminals: Understanding what drives customer satisfaction and terminal usage. Analyse the ratio of performing to nonperforming terminals.
2. Channel Performance: Evaluating the effectiveness of different region point of sales to optimize marketing strategies and resource allocation.
3. Cost Management: Analyzing total costs of utilities such as paper rolls, deployment, and repair cost to identify opportunities for reducing expenses and improving profit margins.
4. Deployment Forecasting: Utilizing order quantities to predict future request and adjust deployment levels accordingly.

Methodology

The analysis was conducted using a combination of Excel and Power BI, leveraging various data analysis techniques:

1. Data Modeling: The raw data was imported into Excel and Power BI, where it was transformed, cleaned, and structured into a coherent data model for analysis.

2. Exploratory Data Analysis (EDA): Extensive EDA was performed to understand the data, identify trends, and uncover hidden patterns. This included the use of pivot tables, charts, and other visualization techniques.

3. Statistical Analysis: Advanced statistical methods, such as regression analysis and trend calculations, were employed to derive meaningful insights from the data.

4. Insights Generation: The findings from the EDA and statistical analysis were synthesized to generate actionable insights that could inform Abt's business strategy.

5. Recommendations Development: Based on the insights, data-driven recommendations were formulated to help Abt optimize its operations, improve profitability, and enhance customer satisfaction.

Tools Used

• Microsoft Excel: For data cleaning, transformation, and basic analysis.
• Power BI: For advanced data visualization, interactive dashboarding, and in-depth analysis.

Key Skills Demonstrated

• Data Modeling: Structuring the data into a coherent, analyzable format.
• Exploratory Data Analysis: Uncovering trends, patterns, and outliers in the data.
• Statistical Analysis: Leveraging advanced statistical techniques to derive meaningful insights.
• Data Visualization: Creating intuitive and informative visualizations to communicate findings.
• Insight Generation: Synthesizing analysis results into actionable business insights.
• Recommendation Development: Translating insights into data-driven recommendations to improve business performance.

Key Insights and Recommendations

Based on the insights generated from the Abt company's data analysis, here are 7 recommendations:

1. Investigate the reasons behind the lower revenue and order costs in January compared to February. Analyze the factors that contributed to the weaker financial performance for the period under review and see if there are any strategies that can be applied to improve performance in the future.

2. Explore the factors behind the lower usage in February compared to January. Analyze customer behavior and preferences to understand how customers can be encouraged to make more purchases via point of sale, especially during traditionally slower months.

3. Continue to focus on and maximize the Wholesale channel, as it generated the highest proportion of revenue. Explore ways to further optimize and enhance the Wholesale channel to maintain high revenue generation.

4. Closely monitor the performance of the ABC retail Stores, as it generated the highest revenue compared to other retail stores. Investigate the reasons behind its success and see how the practices and strategies used in this store can be replicated in others, particularly the underperforming one.

5. Prioritize the Fuel Stations, Wholesale and Distributor channels, as they contributed the most to total performance. Ensure that these channels are well serviced and efficiently managed to maintain customer preferences.

6. Analyze the factors driving the positive correlation between total terminal deployed and total revenue. Identify ways to further increase terminal deployment, as this directly impacts revenue generation.

7. Explore the customer preference for products in Abj region, as they generated the highest revenue through the Wholesale and Distributor channels. Develop strategies to cross-sell and promote other channel usage to effectively maximize revenue.

To Download Abt Sales Power BI Report

POS Performance Analysis