How to Collect Data in Your Company?

Data collection is a key process that enables companies to make informed decisions, optimize operations, and better understand customer needs. In this article, we’ll discuss the most effective data collection methods for your company, along with their advantages and practical applications.

Why is it Worth Collecting Data in a Company?

Data collection is the foundation of effective company management. Without the right information, it's difficult to make accurate decisions, predict market changes, or improve customer relations.

Data helps better understand what customers expect, which products sell best, and how to optimize company processes. It can be used to build marketing strategies, plan resources, or improve customer service.

Example: Imagine you run an online store. Sales data analysis shows that customers are more likely to buy discounted products in the middle of the week. Based on this insight, you can plan additional promotions, which will ultimately lead to increased revenue and customer loyalty.

If you’re able to understand and properly use this information, you can stay ahead of the competition, better serve customers, and grow your business in a direction that aligns with market needs. In the following sections, we’ll show you how to effectively collect data using different methods.

Surveys and Questionnaires

How to Design Effective Surveys?

Surveys are one of the simplest and most popular tools for collecting data. To be effective, it’s important to:

• clearly define the survey’s goal (e.g., to gather feedback on a new product),
• ask specific, not overly long questions,

• offer multiple-choice answers to simplify analysis.

You can use tools like Google Forms, Typeform, or specialized CRM platforms to automate the process of collecting responses and analyzing results.

Example: Imagine you run a coffee shop chain. A survey shows that customers appreciate the quality of coffee but suggest increasing the number of seating places. Based on this information, you can decide to renovate the interior.

Worth reading: "4 e-commerce data analysis tools: Optimize sales and increase conversions"

Direct Observation

Observing online customers involves analyzing how users behave on a website or in an app. This tool helps understand what catches their attention, what challenges they face, and which parts of the purchasing process can be improved.

In a digital environment, observation is conducted using advanced analytics tools like Hotjar or Google Analytics. These platforms allow tracking real-time user interactions, analyzing website traffic, traffic sources, and conversions. It’s crucial to collect data that enables quick identification of problems, such as difficulty finding a product or a high cart abandonment rate.

Example: An online store analyzes which product pages attract the most user interest. With this data, the company can optimize product presentation through better descriptions or more attractive images. If the analysis shows some products are often ignored, it’s worth considering changing their placement or improving their information to increase visibility and appeal.

Online observation data can also reveal broader trends, such as shopping preferences at different times of day or days of the week. For instance, if analysis shows users spend the most time near promotions, the company can implement cross-sell strategies by placing complementary products in these sections. In e-commerce, observation may also reveal that long page loading times discourage users – in such cases, improving site speed becomes a priority.

Effective online observation is not just about analyzing numbers but also drawing practical conclusions that can be quickly implemented to improve user experience. This enables continuous improvement of customer interaction, resulting in increased satisfaction and loyalty.

Why Invest in Online Customer Observation?

Investing in online customer observation brings many benefits. It primarily allows for a better understanding of user needs and preferences, which enables more precise offer adjustments. Optimizing the purchasing process based on collected data leads to higher conversion and sales rates. Additionally, having detailed analytical data supports better business decisions regarding product and service development, helping maintain a competitive advantage in the market.

Integrating online customer observation into the company’s analytical strategy opens new optimization possibilities both on the website and in mobile apps. As a result, businesses can respond more effectively to user needs, increase engagement, and build lasting customer relationships.

Transactional Data Analysis

Transactional data is one of the most valuable sources of customer information because it reflects their actual actions—not just declarations or intentions. By analyzing transactions, you can learn about the most frequently purchased products, shopping cart values, or customer preferences during different periods.

Example: Analyzing the most frequently purchased products can help identify so-called bestsellers—products that attract the most customers. This allows for better inventory and promotion planning. Knowledge of cart values is key when planning upsell or cross-sell strategies. If the average cart value is low, it’s worth offering product bundles at attractive prices or free delivery above a certain order value.

Collecting data about peak shopping times helps manage resources more efficiently, both in physical and online stores. In e-commerce, you can use this information to schedule ad campaigns during peak hours, increasing conversion chances.

Data on preferred payment methods can indicate which options are worth developing to improve customer convenience. If customers more often use mobile payments, introducing options like BLIK or Google Pay can increase completed transactions. In physical stores, the popularity of contactless payments may suggest the need to modernize payment terminals, improving service and shortening queues.

A key part of transactional data analysis is integrating it with other systems, such as loyalty programs or marketing personalization tools. This enables the creation of more detailed customer profiles and highly targeted actions. Properly conducted transactional data analysis not only saves resources but also increases customer satisfaction.

Focus Groups

Focus groups are one of the more advanced data collection methods that allow for direct contact with potential or current customers. This method involves organizing a small-group discussion where participants share their insights on a specific topic. A moderator leads the conversation and ensures all important aspects are covered.

Focus groups work best when a company wants to understand its customers’ deeper motivations. For example, during testing of a new product prototype, participants can point out both its strengths and potential issues that could affect the buying decision. These types of qualitative data are hard to obtain through surveys or transaction analysis, making focus groups a valuable complement to a data collection strategy.

In company data collection, focus groups can be used to verify marketing ideas, assess the appeal of an ad campaign, or analyze how customers perceive the brand. For example, in the food industry, a group of customers can be invited to test new product flavors and provide detailed feedback. In the tech sector, focus groups can help evaluate a new app’s interface before it’s released to the market.

Although organizing focus groups involves certain costs—renting space, participant and moderator compensation—the benefits from the obtained data often outweigh the expenses. The company gains not only specific suggestions but also a deeper understanding of customer expectations, enabling better product and service alignment with their needs. As a result, business decisions become more accurate and based on real insights.

Using IoT and AI Technologies

How Does the Internet of Things Support Data Collection?

The Internet of Things (IoT) refers to a network of connected devices that can automatically collect, transmit, and analyze data. IoT is revolutionizing how processes are monitored and how information is obtained in real time. Sensors, cameras, tracking devices—these are just some examples of IoT technologies commonly used in industry, logistics, and commerce.

Example: In logistics, sensors mounted in vehicles can monitor routes, delivery times, or fuel consumption. This data helps optimize routes and minimize operational costs. In warehouses, IoT can support monitoring storage conditions such as temperature and humidity, which is crucial in the food or pharmaceutical industries.

Artificial Intelligence (AI) in Data Analysis

AI is the next stage of the technological revolution, allowing companies not only to collect but also to perform advanced data analysis. Machine learning algorithms can identify patterns and relationships in data that may be invisible to the human eye.

In e-commerce, AI can analyze customer behavior on a website and deliver personalized product recommendations, increasing conversions and customer satisfaction. For instance, if a user frequently browses sports equipment, the system can automatically suggest promotions on similar items.

AI is also used in customer service. AI-powered chatbots can answer customer questions in real time, reducing the workload on support teams. Thanks to natural language processing (NLP), they can recognize user intentions and tailor responses to their needs.

When combined with IoT, AI can support companies in creating predictive business models. For example, in manufacturing, data collected by IoT devices can be analyzed by AI to predict machine failures and minimize downtime.

Integrating IoT and AI allows companies to reach a higher level of automation and efficiency. Thanks to these technologies, data becomes not only an analysis tool but also a basis for real-time decision-making, giving a significant competitive advantage.

Summary

Data collection is an essential element of operations for modern companies that want to stay competitive. The methods discussed—from surveys and observation to transactional data analysis and the use of technologies like IoT and AI—allow for effective collection and analysis of information that translates into real business benefits.

Each of these methods brings unique value and can be tailored to the specific nature of the business. The key to success is choosing the right tools, regularly analyzing collected data, and being ready to implement changes in response to customer needs.

By consciously using data, your company will not only better understand its customers but also gain the tools to respond faster to changing market conditions. As a result, it will become more innovative, efficient, and competitive.