Mechanism for generating business value from big data utilization cases

 

Generating business has become mainstream in the business scene. By analyzing and verifying a huge amount of data, you can obtain all kinds of suggestive information.

Rather than relying on personal experience and intuition, if we identify issues based on the solid facts of data and implement improvement measures, we will be able to reach our goals in a short period of time.

Big data can be used in various ways, such as optimizing campaign measures and ad distribution, and renovating websites. Data utilization is making a major contribution to creating new value and predicting the future, and many companies are already enjoying its benefits.

we introduce case studies of companies that have created new value using big data. Learn business tips from successful companies.

1. Cross-selling based on customer behavior analysis (cross-selling)

Cross-selling is a method of increasing the unit price per customer by proposing other products and services in addition to the products and services that the customer is considering purchasing.

Cross-selling is based on purchasing behavioral data such as customer information, transaction history, and purchase process. By analyzing vast amounts of purchasing behavior data, you can predict user preferences and deliver useful information for comparison. All of these are personalized, so it's a big feature that we can make the best suggestions for each and every user.

Cross-selling can also be used for “Generating business”. Community marketing is a measure to promote community utilization based on customer social behavior analysis.

For example, by gathering people who are interested in the same brand or product and analyzing their thoughts, hobbies, judgment criteria, preferences, etc., we can set clear targets. By formulating and implementing marketing measures aimed at the set target, it will lead to an improvement in the purchase rate and purchase unit price.

In this way, cross-selling can realize more effective marketing measures, but customer purchase behavior data is essential for cross-selling.

There are many ways to obtain customer purchase behavior data. Examples include social media reviews, customer transaction data, your own e-commerce sales channels, and customer behavior data stored in corporate communities.

2. Product design based on customer reviews

Customer reviews have great potential value. Information that is useful for improving product design, pricing, operational efficiency, customer service, etc. is accumulated, and it is being used in the marketing field.

By analyzing word-of-mouth information, companies can improve the functionality, service content, and support system of their products and services, and build products and services that are customer-oriented.

3. DSP advertising based on data analysis

DSP advertising is an approach method for advertisers who want to increase the cost-effectiveness of advertising. We target customers who have purchased products in the past and users who behave similarly to the users who requested materials, and deliver advertisements at the optimal timing.

In addition, the advertisement is optimized in real time according to the timing, number of times, and time when the advertisement is clicked, helping to improve the click-through rate. By analyzing performance data and repeating verification and improvement, we can maximize cost-effectiveness.

4. Trend forecasting and viral marketing

By analyzing keywords that are trending on social media and search engines, it is possible to predict trends. If trends can be predicted, it can also be used for viral marketing, in which companies spread their products and services to an unspecified number of people using word of mouth.

5. Product pricing based on data analysis

As it is said that "price setting is management", product pricing is an important decision-making process that affects a company's sales and profits.

Data analysis and testing are required to reasonably set product prices. Specifically, after researching and categorizing customer reactions to product pricing, we interview groups with different reactions to measure price tolerance. Appropriate product pricing becomes possible through data analysis, rather than relying on past experience and intuition.

6. Service churn rate prediction (churn analysis)

Reducing churn is a key issue for any SaaS tool that incorporates subscription plans. If you can accurately predict churn customers through "churn analysis", an analytical method for predicting churn customers, you will be able to efficiently reduce the number of churn customers.

At that time, we collect customer satisfaction and word-of-mouth data for products and services through customer behavior data analysis. Segment users by churn rate based on them. Then, we will implement measures for each segment and continue to verify the results.

In this way, by analyzing the correlation between the churn rate and the results of measures for each segment, it is possible to predict service churn.

7. Analysis of external conditions based on market trend data

Analyze the external situation based on market trend data such as competitors' sales performance data on EC sites and people's emotions (happiness) from social media. Analysis results can be used to predict changes in the external environment, formulate measures and management strategies that the company should take in the future, and consider and implement marketing measures.

Especially in today's world, where the times change rapidly, it is a shortcut to success to read the trends of the times with data, start small, and grow what has gone well.

8. Product life cycle management based on IOT data analysis

Product Life cycle Management P L M is a collective process of necessary information in all phases, from planning, design, development, procurement, production preparation, production, sales, disposal, and recycling, just like a person's life. It refers to management and utilization.

Sensors, wearable devices, video capture, augmented reality (AR) and other IoT technologies enable real-time collection and analysis of product lifecycle information. Furthermore, in recent years, IT tools called PLM systems have emerged to efficiently create and manage the data required in each phase.

Based on the data entered and created in the PLM system, it is possible to analyze the correlation between all phases of the product life cycle and the balance of payments (income and costs).

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What is RPA? Impact with RPA

 

In recent years, due to the serious declining birthrate and the impact of corona, many companies are actively promoting work style reforms. Among them, many companies have paid a lot of attention to the introduction of RPA tools, which are task automation using software robots. While the introduction of RPA tools can solve the problem of labor shortage to some extent, it can also improve work speed, work quality and working environment.

In this article, we will introduce what “RPA” is, how to choose an RPA tool, points to be aware of when introducing it, and 8 recommended RPA tools. Let us help you choose the right tool for your company.

What is RPA?

RPA is an abbreviation for "Robotic Process Automation". Simply put, it is to automate the simple and repetitive manual work. For example, you could let a robot do all the work, from data collection to integration and input. Currently, the combination of RPA and AI has greatly expanded the range of processes that support work automation.

Impact with RPA

Banking, financial services, and insurance are not the only industries that have implemented RPA tools. As it promotes business automation, it has a great impact on industries and industrial fields such as manufacturing, telecommunications, aviation, oil and gas, retail, and analytics.

Many industries and industries are moving to introduce smart RPA automation tools into their business processes.

Before RPA tools appeared, companies' "automation tools developed in-house" processed business processes with simple batch processing, Excel automation, macros, etc. They lacked scalability and reliability, but they also have a proven track record of improving employee productivity.

Choosing the right RPA tool is of utmost importance before developing an RPA automation robot . This is because it directly affects the efficiency and automation effects of introducing RPA.

Gartner research predicts that global RPA software spending will reach $2.4 billion by 2022. The RPA market is expected to reach approximately $8.75 billion by 2024. Also, by the end of 2022, it is said that 80% of large and major companies will introduce RPA.

However, it is very difficult for companies to choose the most appropriate RPA tool for their business.

This article introduces how to choose an RPA tool for your business, the current top-level RPA tools, and a comparison of their main features.

How to choose an RPA tool

In order to successfully introduce RPA, it is necessary to work on improving business processes through workflows such as process/operation automation and machine learning. It's also important to choose the right RPA solution that fits your company's needs and resources.

With a clear understanding of business objectives and process transformation, we can develop a list of specific requirements in addition to the initial research. This greatly narrows your options and makes it easier to choose the right tool for your needs.

Common requirements include:

Cost (development, maintenance, etc.)

Standardization of unstructured data

Customized RPA development possibilities

Feasibility of Core Functionality

Possibility to layer tasks and workflows into automated processes

It is also important to note the future scalability of RPA. When possible, choosing an RPA platform that meets your current and future needs can reduce the time it takes to identify, deploy, and ensure coexistence with your digital infrastructure for additional solutions. . In addition, it ensures the scalability of the tool.

Things to consider before introducing RPA tools

In general, RPA can perform a wide variety of tasks that previously required human oversight or direct involvement. However, current RPA tools don't provide the specific capabilities your business needs.

The most important thing to consider is how relevant a solution is to your business needs . As an example, if machine learning, cognitive automation, or intelligent automation are your core needs, you should check to see if your candidate provider can provide such tools and software.

Support and customer support services are also important.

Automation software must consider issues such as data privacy, management and integration.

Will there be one administrator responsible for piloting and testing the automation?

Are there specific repetitive tasks that are critical to your current operations and should not be reviewed or transformed yet?

If you have cooperating companies that actively support you, you can proceed with the introduction process smoothly , and you will have good conditions for successful RPA introduction.

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What is SNS? How it use SNS

SNS (Social Networking Service), which many people use today, has a great influence on our lives.

Many people use it to facilitate communication with friends, connect with people with similar interests, and obtain new ideas and information. According to data from the Ministry of Internal Affairs and Communications, more than 70% of people in Japan use SNS, and the average is over 80% among people in their teens to fifties.

Reference: FY2020 survey report on information communication media usage time and information behavior｜Ministry of Internal Affairs and Communications

In recent years, companies and governments have been paying attention to the use of such SNS data for market research and marketing activities. In order to analyze and process SNS data, the original data must be collected. But how can we collect the vast amount of SNS data?

Therefore, in this article, we will introduce five "SNS information automatic collection tools" that are useful for collecting SNS data. If you are in charge of marketing for your company, please refer to it.

What can be done by collecting SNS information?

Data extracted from SNS is undoubtedly a dynamic dataset on human behavior. SNS data is a treasure trove of value. For social scientists and business experts, collecting social media information opens up new opportunities for understanding societies, organizations and individuals, and allows us to explore all the hidden value in data.

In fact, many companies and marketers are already using SNS information. According to a paper titled [SNS Analytics: An Exploration of Techniques, Tools and Platforms], published by researchers in London, social media data analytics can be used to detect brand perceptions, improve customer service, marketing strategies, and even fraudulent activity . It is said that it is used for

In addition, the following are examples of the use of SNS information in the age of big data.

Customer sentiment measurement

After collecting customer reviews (word-of-mouth) from SNS, you can analyze customer sentiment by measuring ratings, purchasing background, and expectations for specific topics or products. This allows us to understand overall customer satisfaction and customer loyalty, as well as gain deeper insight into the marketing initiatives we have implemented.

 Market segmentation

Market segmentation is a method of classifying customers with similar characteristics and behavioral patterns and executing optimal measures for each group.

The reason why market segmentation is necessary is that ``the motivation to purchase products and services is the maintenance and expansion of lifestyle habits''. In order to maintain their lifestyle habits and lifestyles, they may regularly repeat or purchase products and services that meet their needs.

Also, when you expand your lifestyle, such as when your family grows or you start a new hobby, you increase the number of products and services you use. In this way, since consumption is closely related to lifestyles, it is considered efficient to implement optimal measures for each group with similar characteristics and behavioral patterns.

Online monitoring

Online monitoring is to monitor whether rights such as company brands and copyrights are being used without permission, and to investigate whether scandals such as internal leaks by company employees or SNS flares have occurred.

In recent years, EC transactions through the Internet have increased dramatically, and counterfeit and fraudulent products are increasing more than ever. For example, there is no end to the damage caused by consumers searching for the brand they want using SEO keywords and being led to a counterfeit product sales site, mistaking it for a genuine product and purchasing counterfeit products.

In addition, troubles originating from SNS and the Internet are increasing, such as inappropriate videos posted by employees, leakage of internal information, and flames caused by extreme posts. Since the flame spreads at once, the speed of the initial response is important. Online monitoring is useful for daily risk assessment.

Market research

Market research refers to the correct understanding of market conditions through data for product development and marketing strategy planning. Today, it is indispensable for formulating business strategies and considering marketing measures. This is because, in today's fast-paced world of change, there is a high possibility that we will continue to take wrong measures based on past successful experiences and "on-site intuition."

Therefore, it is required to promote product development and measures based on quantitative and qualitative data. SNS data is useful for grasping consumer needs because it is possible to understand consumer voices and what services have "likes".

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How to Automate Data Entry in Google Sheets?

Are you manually copying and pasting data on your website to organize it into tables and lists? This manual process may be the easiest method when the amount of data is small. However, the larger the amount of data, the more time-consuming and labor-intensive it becomes.

If you have programming knowledge and skills, you can automatically extract it by web scraping using Python etc. But if you don't have programming knowledge, how can you efficiently retrieve data on the web?

Therefore, in this article, we will introduce two methods for acquiring web data that anyone can easily use.

1. How to Automate Data Entry in Google Sheets

2. How to scrape data with no-code tools

Each step is explained in an easy-to-understand manner, so let's try it right away!

What is web scraping

Web scraping is a computer software technique that automatically extracts certain information from websites. By using web scraping, you can search specific websites and databases on the Internet and automatically extract any data from a large amount of data.

In order to perform web scraping, it is necessary to create a scraper by programming such as Python or Ruby. However, it is not easy to learn programming from inexperienced. That's where spreadsheet functions and web scraping tools come in handy.

Automate data entry with Google Sheets

Here, I will introduce the procedure for building a simple web crawler that utilizes the "IMPORTXML function" of Google Sheets. The data collection site " Steam Spy " is used  as a data acquisition source .

Step 1 : Open a new Google Sheets .

Step 2: Open “ Steam Spy ” in your Chrome browser , right-click on the page and select “Verify” from the menu

Then the source code will be displayed, so click the "arrow icon" (red frame at the bottom left of the screen) to enable the selector.  

With selectors enabled, place the cursor where required and the corresponding information will be displayed in the Validation Panel. Select "PRICE" here.

Step 3: Paste the Steam Spy URL into your spreadsheet. Here we specify cell A2.

Step 4: Get the Xpath

This time, we will use the "IMPORTXML function" to automatically acquire the price data. The IMPORTXML function is a function that allows you to specify necessary information from a website and automatically output that information to a spreadsheet.

First, copy the Xpath that will be the element. Xpath is a language that specifies a specific part from a document conforming to the markup language XML. If you want to know more about Xpath, please see the following articles.

To get the Xpath, click "Pricing Info > Copy > Copy XPath".

If you paste it in a spreadsheet, you can see that the Xpath "//*[@id=”trendinggames”]/tbody/tr[1]/td[4]" has been obtained.

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6 recommended automatic data entry tools for beginners!

 Six recommended automate data entry and be freed from troublesome data entry work" Have you ever felt like this? Many people are not good at repeating monotonous tasks such as transcribing paper documents to a computer, typing in data, and copying and pasting from websites .

In such a case, a data auto-entry tool is recommended. The automatic data entry tool automates the data entry work that was previously performed manually by a program simply by performing simple operations and settings.

Data autofill tools free up time in your day so you can devote time to more value-added work or side hustle. In particular, cloud-based tools are reasonably priced, so they can be easily introduced by individuals and small teams.

In this article, we will introduce recommended data auto-fill tools for each scene. Please take a look if you are worried about the efficiency of time.

1. What is a data autofill tool?

A data auto-entry tool is a tool that automatically processes input work that is usually done manually by a program. There have been tools for automatic processing such as "RPA tool" and " Excel VBA", but it was difficult for beginners to handle because advanced programming was required to set automatic processing. It was a high degree.

The automatic data entry tool is characterized by the ability to create workflows mainly through drag-and-drop operations, so even those without programming knowledge can easily handle it.

Depending on the type of data auto-entry tool, the range of support is also diverse. For example, there are "web scraping tools" that automatically collect only necessary information from websites around the world , and "voice data input tools" that automatically transcribe voice data into text .

By using it according to the purpose and application, it will lead to shortening the work time that was previously spent, and the free time can be spent on high value-added work.

2. Why data auto-fill tools are recommended

Data auto-entry tools can be used in any situation. In particular, those who do not have knowledge or experience in IT should actively use it. There are three specific reasons.

No need to write programming code

Many data autofill tools require no-code programming. Settings can be made mainly by intuitive operations such as mouse drag and drop.

The data auto-entry tool has parts prepared in advance, and you can automate your work by combining them like a puzzle.

Simple automation tools can be created in minutes, and settings can be easily changed after creation.

Easy to handle even for beginners

The automatic data entry tool is characterized by being easy to handle even for those who are not good at programming knowledge and experience and operating IT tools. Since it does not require specialized knowledge or skills, it is possible to automate the necessary tasks on an individual or team basis without requesting an information system department.

Reduce data entry errors and mistakes

Manual data entry is prone to typos and erroneous entries due to human error. By providing a check function after input, such mistakes can be prevented to some extent, but it is not easy to eliminate them, and the more time and effort is spent on checking, the lower the productivity.

By using the data auto-entry tool, the program will automatically process it, so errors such as incorrect data entry and mistyping will not occur as long as the settings are correct in advance.

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4 recommended tools for big data analysis

 Data analysis refers to "an effort to find useful information from a large amount of data and use it for improvement." In the era of big data, the analysis and utilization of data is becoming more and more important.

Incorporating data analysis into business has many benefits, such as speeding up decision-making, identifying issues, and coming up with new measures.

The use of IT tools is indispensable for collecting huge amounts of data and proceeding with quick analysis. In recent years, as data analysis has attracted attention, various analysis tools have appeared. Therefore, this time, we will introduce 29 tools that are useful for data analysis by purpose.

4 web data collection tools

1. Octoparse

Octoparse is a powerful web scraping tool that can easily extract data on any website . Equipped with an automatic web page recognition function, when you enter the URL of the acquisition destination, the data is automatically detected and you can easily extract the data without programming knowledge.

In addition, we provide a number of web scraping templates to make scraping easier. Just by entering parameters (target site URL, search keywords, etc.), data will be extracted more and more. Acquired data can also be saved in formats such as CSV, Excel, or databases.

 

We also provide a cloud-based platform, so you can extract data periodically, and the acquired data is stored in the cloud and can be accessed at any time. No hardware maintenance is required, so you don't have to worry about network interruptions.

It is one of the most recommended data collection tools because you can use all the functions necessary for web scraping for free .

2. Content Grabber

Content Graber is web crawler software for businesses. You can create a standalone web crawling agent. Content data such as text and images can be extracted from almost any website, and the extracted data can be saved as structured data in most databases such as Excel, XML, CSV, etc.

In addition, it provides many powerful script editing, interface debugging, so you can build a more advanced web scraping process. Users can use C# or VB.NET to debug and write scripts to build crawling processes. For example, Content Grabber integrates with Visual Studio 2013 to support the most powerful script editing, debugging, and unit testing for advanced, sophisticated and customized crawlers based on your specific needs.

As a point of caution, while advanced editing is possible, it is difficult for people without programming skills to handle.

3. Import.io

Import.Io is a scraping tool that extracts data just by entering a URL. It's a cloud-based service, so there's no need to install any software. Just start Import.Io and enter the URL of the target web page, then it will automatically determine the data location and extract the information.

In addition, there are scraping apps available for each OS, such as Windows, Mac, and Linux. After scraping is completed, data can be exported in CSV/JSON format. In addition, you can set regular execution of crawling work such as weekly, daily, and hourly.

4. Parsehub

Data analysis refers to "an effort to find useful information from a large amount of data and use it for improvement." In the era of big data, the analysis and utilization of data is becoming more and more important.

Incorporating data analysis into business has many benefits, such as speeding up decision-making, identifying issues, and coming up with new measures.

The use of IT tools is indispensable for collecting huge amounts of data and proceeding with quick analysis. In recent years, as data analysis has attracted attention, various analysis tools have appeared. Therefore, this time, we will introduce 29 tools that are useful for data analysis by purpose.

 

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What is a BI tool? Thorough comparison of 5 recommended BI tools

BI tools are tools used for organizing information warehouses, visualizing and analyzing huge amounts of data, and so on. By utilizing BI tools , it is possible to launch corporate marketing measures and improve operational efficiency based on huge amounts of data.

In this article, we will introduce 26 popular BI tools in Japan and overseas. We also briefly summarize the features of each tool, so we hope that it will serve as a reference when introducing BI tools.

What is a BI (Business Intelligence) tool?

BI is an abbreviation of "Business Intelligence", and refers to a method that collects, accumulates, analyzes, and reports all kinds of data that exists within a company or organization, and is useful for decision-making in management.

BI technology provides future predictions based on past performance and current conditions in management. Key features of BI include:

data analysis

data mining

Business Process Management

predictive analytics

normative analysis

Therefore, BI is often equated with "business analysis" or "data analysis".

[Domestic] 5 Recommended BI Tools

This time, we used the scraping tool Octoparse to obtain information on popular BI tools from word-of-mouth sites in Japan and overseas. Here, we will introduce the characteristics of the BI tools developed and operated by domestic companies.

1.MotionBoard / WingArk 1st Co., Ltd.

MotionBoard is a BI dashboard that aggregates and visualizes all kinds of data within an organization and supports decision-making for effective improvement measures.

By using more than 30 types of rich charts, anyone can easily analyze data and lead to the next action, even if you do not have expertise in data analysis. We support the construction of data-driven organizations.

Furthermore, by linking APIs with external tools such as Slack and Salesforce, various functions such as scheduled delivery and conditional alerts can be used, realizing the construction of a data-driven organizational structure.

2. GoodData / Samurai Inc.

GoodData is a BI tool that automates data linkage, processing, visualization, and sharing, and streamlines report creation work. It is equipped with an all-in-one system from data collection to sharing, and thanks to the excellent UI/UX dashboard, even those who are new to data analysis can use it with intuitive operations.

In addition, the number of users is unlimited and the monthly fee is reasonable at 40,000 yen, so it is the best tool for building a data-driven organizational system.

3.Data Knowledge / Cross UIS Co., Ltd.

Data Knowledge is a knowledge-sharing BI tool that enables data utilization by all employees. It has been used as a domestic BI tool for more than 30 years, and we continue to improve convenience, such as improving usability and operability and adding new functions, reflecting the voices of many companies that have introduced it.

In addition to promoting "visualization of management" with various analysis reports, we support all employees in improving their analysis level by using the analysis know-how sharing function.

4. LaKeel BI / LaKeel Co., Ltd.

Lakel BI is a self-service BI tool that provides one-stop data aggregation, integration, analysis, and visualization necessary for data analysis. There are plenty of useful templates, so not only those who are not good at handling IT tools, but also those who are new to data analysis can operate intuitively. Furthermore, it is a big feature that you can receive full support unique to domestic companies.

5. Actionista! / Justsystem Co., Ltd.

Actionista! is a BI tool that can be operated only with a web browser without using any programming coding. With intuitive operations on the dashboard, you can complete one-stop data collection, analysis, and report creation within your company.

Because it is a client-free server license, all employees can use it with one license contract. In addition, as a purely domestic BI tool, the manufacturer consistently handles development, sales, and support, so it is a solution with high customer satisfaction, with 90% of the companies that have introduced it continuing to use it.

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