Customer management strategy & POS data analysis by data scientist, 4P application, issues and solutions

Mistakes:

This article describes the customer management strategy as a data scientist, my profession. Specifically, we aim to improve customer satisfaction, increase sales, and grow business through the collection and analysis of POS data, and 4P (product, price, promotion, location) of marketing. In addition, we will discuss issues in this strategy and their solutions.

Introduction:

In modern business environments, data is an important decision -making. In particular, the data obtained from the POS (POINT OF SALE) system is an important source of information to understand the purchasing and preferences of customers.

● Customer management strategy and its means:

Collect data obtained from POS systems and analyze using statistical methods and machine learning algorithms. As a result, the customer's behavior patterns, preferences, and trends are grasped.

Forming a marketing strategy based on the analysis results. This includes 4P consideration.

Product (Product): Understand what the customer is buying, which products are selling well, and use it for product development and improvement.

Price: Understand which price range of products you buy and optimize the price setting strategy.

Promotion: Analyze which promotion was effective and plan an effective marketing campaign.

Place (Place): Understand where customers are purchasing and optimize stores and online sales strategies.

● Supplies and solutions:

There are the following issues in the data -type decision -making.

(1) Data quality :: Infinite or inaccurate data may lead to incorrect conclusions. To solve this, use methods such as data cleansing and data complement.

(2) Privacy and security :: Handling customer data involves privacy and security issues. In order to solve this, we will take appropriate data protection policies and security measures.

(3) Changed market environment :: Market environment is constantly changing, and past data may not always be effective for predicting the future. In order to solve this, real -time data analysis and quick decision -making are required.

④ Execution and evaluation: Execute the formulated strategy and evaluate the results. This evaluation is collected as a new data and is subject to analysis again. As a result, the strategy is always updated and optimized.

Conclusion:

Data scientists enable data -driven decision -making, contributing to improving customer satisfaction, increasing sales, and growing business. It supports this process and plays an essential role in business success. However, the ability to understand and solve them in the process is also an important skill required by data scientists.