How to analyze customer data for management decisions

To maintain customer loyalty, an entrepreneur needs to be able to anticipate, analyze their needs and behavior. According to Promodo, 80% of future profits come from only 20% of customers.

Customer data analysis, or as marketers call it, Retention, includes the collection, processing, and interpretation of information about buyers to obtain valuable insights for making important management decisions.

This process is a key element of modern business management as it allows offering customers what they need, increasing satisfaction, and boosting profitability.

What are the methods and stages of collecting and analyzing customer data?

Main stages of customer data analysis

1. Data Collection

At the first stage, companies collect information about their customers. Sources of information can vary:

• Internal data: information from CRM systems and customer accounting programs, online stores, financial reports, purchase histories.

• External data: data from social networks, media and contextual advertising, surveys, marketing research, or analytical reports.

• Digital data: information about customer behavior on websites, mobile apps, or other digital channels.

Internal analytics systems can meet the needs of retail businesses in different formats. For such tasks, accounting programs like Torgsoft are quite suitable.

2. Preparing the Database

After collecting customer information, it must be entered into the accounting system and prepared for analysis. This stage includes:

• Data cleaning: correcting errors and removing information about customers that is inaccurate or outdated.

• Standardization: creating customer cards and entering important data, such as age, date of birth, social status, education, place of residence, contacts, as well as additional information about interaction or important customer characteristics.

• Segmentation: dividing customers into groups based on common features such as industry, discount %, type of loyalty card, or volume of purchases over a period.

In Torgsoft, it's easy to manage customer databases and filter them based on specific attributes, ensuring correct analysis:

3. Data Analysis

At this stage, various methods and tools are used to gain insights. A common method of data analysis, besides internal analytics, is the Google Analytics platform.

It allows collecting and analyzing important marketing metrics for Retention—indicators that reflect the level of customer retention and loyalty:

• Customer Retention Rate (CRR) — shows how effectively a company can retain its customers on a consistent basis over a period of time.

• Churn Rate — shows the percentage of customers who have stopped using your products or services over a specific period of time.

• LTV (Lifetime Value) — evaluates the total profit a business can earn from a single customer over the entire period of interaction.

• ARPU (Average Revenue Per User) — is the average revenue a company receives from one user over a defined period of time.

• Redemption Rate — shows the share of customers who have used offered promotions, discounts, or special offers.

• Repeat Purchase Rate — determines the percentage of customers who made repeat purchases in your business within a defined period.

Additionally, it is important to use various methods of analysis:

• Descriptive method: for example, analyzing sales volumes for a quarter.

• Diagnostic analytics: identifies the reasons behind certain phenomena (e.g., why the number of repeat purchases decreased).

• Predictive analytics: helps predict future customer behavior based on statistical models and machine learning.

• Prescriptive analytics: formulates recommendations on further actions (e.g., which products to offer to a customer).

• Cohort analysis — an important method of marketing analysis that allows analyzing patterns of repeat purchases. Typically, it considers periods between the first and subsequent purchases of customers.

To collect and visualize customer data, specialized software products such as Power BI, Tableau, and AI-based tools are also used.

4. Interpreting the Results

The data collected must be interpreted to make it understandable for management and to form marketing proposals. To do this, the following are created:

• Reports (there are about 50 in Torgsoft): summaries that capture and highlight key analysis results.

• Infographics: data visualization that simplifies the perception of information (graphs, charts, maps,

• Interactive panels, which allow tracking key metrics in real-time.

How to collect external data using different forms

Behavioral data about customers and their preferences can be collected using special forms. These help set up personalized interactions with customers:

Subscription form

These forms are usually used on company websites to collect basic data (name, phone, email). This method is suitable for new customers who still don't have a high level of trust in the brand.

Chatbots

Allow you to collect both basic information (name, phone) and extended data (opinions, interests, customer ratings). They enable gradual (sequential) surveys, often in an interactive form, which increases customer engagement.

Surveys in email newsletters

This method allows obtaining full information about the customer: marital status, birth date, and specific details such as hobbies or customer wishes.

Personal account on the website

In the personal profile, customers can specify their preferences regarding newsletters to avoid excessive messages and reduce the risk of unsubscribing.

Order form

During the order process, the manager can collect both basic data (name, phone, email) and additional information (address, delivery method, customer status — individual or business).

Using Analysis Results in Management Decisions

You can use the results of customer data analysis to make various management decisions:

• Strategic decisions: development of new products, market expansion, brand repositioning.

• Marketing decisions: personalization of offers, planning advertising campaigns, defining target audiences.

• Operational decisions: optimizing inventory, improving logistics, enhancing customer relationship management.

Customer data analysis is an essential feature of successful business management. Using quality analysis allows companies to better understand their customers, make informed management decisions, and achieve strategic goals.