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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