Data as a Fuel. How to use data in organization with statistics.

Nothing reflects quality as well as real numbers. However, to make proper analysis, each individual company tries to tame data using databases for this purpose and ERP systems. Marketing specialists primarily use information about users and try to implement this in future campaign strategy. The processed information about each individual product's type preferences, price sensitivity, channels of access and the best distribution methods can be simply managed and transformed into material for reports.
All modern companies use data for profit. Data are facts in a different form as a number, short phrase, observation or full of sentences description of things. Data in a business environment should be prepared accurately to construct structured information as a fundamental item for larger systems during business processes. Information refers to understanding those facts in a specific context and making any logical sense.
Data as an input does not directly help in decision making process, but with right tools could collectively produce information to serve make concrete decisions. Good understanding of terms between employees or in noncommittal conversation can be useful for lucid communication. Organization concentrates efforts on collecting, organizing, maintaining and using data to have prosperous and effective Information Management Systems which will be reflected in sales results.
In order to create profitable Information Management Systems organizations heavily rely on leadership, operations, processes, data and technology.

The data can be stored in the main memory in the CPU of office machines but considering technological development, tremendous amount of data, security, limitations of the company’s infrastructure and proper data handling a better option would be to transfer all data to the cloud.

Databases concept.

Putting effort into the integrity of the developed data, for example, analysing only the purchase from a given sales season, impacts the report's quality and clarity. Hence, everything starts with a good part of the intentional — the structure of the database and the extensional part, that is, the content of the database.

Databases aligned for data types categorize data but to get the best out of them for example relational databases need fully normalized tables.

Normalization in database practical matter provides structure in databases in a way that can't express redundant information.

In a nutshell normalized tables frees you from a problems. Normalized tables are easier to understand, enhance and extend at the same time protect from insertion, update and deletion anomalies.

The selection of the analysed data and the macro and microeconomic perspective define the type of report, its structure and purpose. Main source for investors choice are existing data from publicly available market analysis reports which presents the place of company in the market in relation to its value and in comparison, to its biggest competitors. In the good reports there is a question of stability of supply and demand, analysis of price formation by internal factors such as intense employee turnover which increases the potential costs for the company and external factors, for example, the current tightening of economic relations because of armed conflicts in the world. The market absorption clarifies if production does not keep up with the supply over the demand of consumers, which for a perceptive market player gives a clear argument for the purchase of its shares.
Traditional sales reports often fall short of providing actionable insights due to their reliance on basic descriptive statistics. Powerful statistical technique that can transform raw sales data into meaningful segments, enabling businesses to make more informed decisions and tailor their strategies effectively is cluster analysis.

Statistical guru - Cluster Analysis. What is it?

Cluster analysis, also known as clustering, is a machine learning technique used to group a set of objects in such a way that objects in the same group (or cluster) are more similar to each other than to those in other groups. It is widely used in various fields, including marketing, biology, image processing, and, importantly, sales analytics. The primary goal of cluster analysis is to identify natural groupings within data, which can then be analyzed to uncover patterns and trends that might not be apparent through traditional analysis methods. It can benfit in customer segmentation which is marketing tool for all companies regardless of the size of the company. However, in large companies group of customers could be similar in some respects. Cluster analysis can segment customers based on various attributes such as purchasing behavior, demographics, and preferences. This allows businesses to tailor marketing strategies to different customer segments, enhancing customer satisfaction and loyalty. A retail company can use clustering to identify high-value customers who frequently purchase premium products and tailor exclusive promotions for this group.

Product Segmentation and Data Preparation

Product segmentation which is categorization of products into groups or segments based on list of criteria (for ex. price, volume, ratings), helps businesses in tailoring their marketing strategies, managing inventory efficiently, and developing new products that meet the needs of different market segments. To effectively perform product segmentation, it's crucial to gather comprehensive data from various sources such as sales databases, customer feedback systems, inventory management systems, and marketing platforms.
All data should includes relevant attributes like product ID, product name, category, sales volume, price, customer ratings, and any other features that might be useful for segmentation. Data preparation is a critical step in product segmentation, ensuring that the data is clean, standardized, and ready for analysis. By analyzing sales data, products can be grouped into clusters. This helps in inventory management, targeted marketing, and product development. Retailer can cluster products into high-demand and low-demand categories to optimize stock levels and marketing efforts.

Other use of cluster analysis can overcomethe problems of the company with identifying high-performing regions, as well as areas needing improvement. Diagnosis which regions have the highest sales performance better allocate company's resources. What's more, stores can use clustering to find that customers who buy their products often also purchase certain types of items, leading to strategic product placement and promotions.

Types of Clustering Algorithms:

• K-means Clustering: This is the most commonly used clustering technique. It partitions data into K clusters, where each data point belongs to the cluster with the nearest mean.

• Hierarchical Clustering: This method builds a hierarchy of clusters either through a bottom-up approach (agglomerative) or a top-down approach (divisive).

• DBSCAN (Density-Based Spatial Clustering of Applications with Noise): This algorithm groups together points that are closely packed together, marking points that are alone in low-density regions as outliers.

• Gaussian Mixture Models (GMM): This method assumes that the data points are generated from a mixture of several Gaussian distributions with unknown parameters.

Cluster analysis is a powerful tool that can significantly enhance the quality and effectiveness of sales reports. By uncovering hidden patterns and segments within sales data, businesses can make more informed decisions, tailor their strategies to specific customer needs, and ultimately drive growth and profitability. As the business landscape continues to evolve, leveraging advanced analytical techniques like cluster analysis will be key to staying competitive and achieving long-term success.