The wave of digital transformation (DX) is sweeping the world. Among these, what managers are paying attention to is the possibility of management innovation through the use of data.
So, how exactly can data be used to advance management innovation?
That’s a difficult question. For such people, in this article, we will introduce how data utilization contributes to management innovation through actual examples.
How To Utilize Data
Analyzing the data
The process of converting to a specific management strategy
By understanding the overall picture of data utilization, you can determine which phase is the bottleneck for your company. By understanding this, you can reconsider how to improve your competitiveness. This is precisely the kind of “management innovation” that modern managers should pursue.
This article will allow you to look at data from a new perspective and bring innovation to your management.
The necessity of utilizing data analysis in business
The modern business environment is truly the “age of data.” Data analysis is becoming increasingly important in corporate management’s strategic planning and decision-making processes. Using data such as customer behaviour, market trends, and competitor activity, you can make accurate and timely decisions based on this information. This has become essential for companies to maintain competitive advantage and achieve management innovation.
Why Data Utilization Is Necessary For Business
First, data analytics makes companies’ business operations more efficient. For example, by analyzing customer data, it is possible to understand purchasing behaviour and consumer preferences, and based on this information, optimal marketing strategies can be formulated. Additionally, by utilizing inventory and logistics data, it is possible to reduce unnecessary inventory and improve the efficiency of the supply chain.
Relationship between management innovation and data utilization
Utilizing data is also the key to companies achieving management innovation.
Developing new business models
Exploring new markets
Insights based on such data are directly linked to solving these management issues. Utilizing data also makes the decision-making process within an organization more efficient and faster, which is a great help in driving management innovation.
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Specific Examples Of Business Impact Due To Data Analysis
Information obtained from data analysis can significantly improve the accuracy of business decisions.
Email newsletter open rate
Number of webinars attended
Sales value-added rate
Fixed asset turnover rate
It becomes possible to understand such things as concrete numbers and formulate strategies based on these data.
Furthermore, by utilizing the collected and analyzed data,
Drafting a business plan
Necessary system development
Verification of implementation status
It also contributes to solving various management issues
4 Success Stories Using Data Analysis
Data analysis can be a powerful tool for driving business innovation. Leading companies use data to innovate their business models and improve performance. This heading describes successful examples of management innovation through data utilization.
- coca cola
We will introduce case studies of four companies. Through these examples, you can understand the effectiveness of data analysis and apply it to your business.
Coca-Cola Company: Data-driven Management Progress
The Coca-Cola Company is achieving sophistication in its marketing through collecting and analyzing consumer behaviour data. They focus on social media, sales, and consumer behaviour data. Combining this information makes it possible to capture customer preferences and purchasing trends in real-time and reflect them in product development and advertising strategies. Social media data is extremely useful in coming up with new product ideas and determining the direction of campaigns, as it allows us to understand consumer sentiment and opinions directly.
Furthermore, such data analysis also contributes to the formulation of management strategies. Data makes it possible to accurately understand market trends and competitive situations and make quick decisions. Coca-Cola’s success can be attributed to its corporate structure, which allows it to collect and analyze data smoothly and take action based on that data.
Netflix: Content Development Using Data
A giant movie and drama distribution service, Netflix has succeeded with a data-driven content strategy that analyses viewer behaviour data. Netflix collects and examines viewers’ viewing history, ratings, viewing times, viewing interruption and restart points, and even the type of viewing device. This data reveals viewer preferences, viewing behaviour patterns, and even new trends.
Based on such detailed data analysis, Netflix develops original content. For example, we plan and produce original content based on information such as what genres and themes are popular with viewers and which cast members will attract viewers’ attention. We also use viewer-specific data to provide personalized video recommendations and increase viewer engagement.
Netflix’s success has been achieved by collecting large amounts of data and incorporating that data into its business strategy.
Case study 3:
Amazon’s personalization strategy and data utilization
As an online marketplace leader, Amazon’s success is primarily driven by the use of data. They collect a wide variety of data, such as customer purchase history, browsing history, search history, click account, etc., and analyze it using AI and machine learning technology. We make personalized product recommendations tailored to each customer based on the results. This makes it easier for consumers to discover products that match their interests and needs and allows Amazon to achieve high sales efficiency.
Seven-Eleven’s Inventory Management Reform And Data Utilization
Convenience store chain 7-Eleven is known for its advanced inventory management using POS data. They analyze in detail the POS data obtained from each store (sales time and quantity of each product, etc.) and predict the sales pattern of each product. This allows us to assess demand for each store and deliver the appropriate amount of needed products, reducing unnecessary inventory and minimizing loss.