Unconventional uses of big data and predictive analysis

Unconventional uses of big data and predictive analysis

? In this article, will? Kelly will share with us some unconventional uses of big data and predictive analysis in many industries.

We have been exposed to many articles about the conventions, challenges, popular thinking concepts and business models of big data and predictive analysis. However, in addition to the fear, suspicion, uncertainty and hype about the concept of big data, some enterprises have begun to use big data analysis technology in some unconventional fields.

Mining data tracking in open-pit mining

First, let's take a look at how Hitachi Data System (HDS) uses big data and predictive analysis to support some heavy industry applications in large-scale construction, mining, transportation and other industries. When I was with Michael, their vice president of product planning. Sea; Sarah, Senior Director of Software Product Marketing? Gardner; And assim, Senior Vice President of Global Marketing? When Zahir communicated, they gave me an overview of how big data and predictive analysis work on heavy mining equipment.

This article was written by Sarah? Gardner's book Hitachi Data Machine: Open-pit Mine Data Mining focuses on how Hitachi uses big data to support its open-pit mine data mining machinery. I'm not talking about data mining, but about underground mining of minerals. Gardner's article illustrates some extreme examples: for example, data machine tools promote big data and predictive analysis, thus helping to complete business tasks that many of us think are unconventional.

Some people in the big data industry regard the application of big data and predictive analysis in heavy industry equipment and transportation systems as a major factor for the growth of the whole big data application field in the future.

Improving the customer experience of e-commerce

Although the competition in the retail store industry has been very fierce, some of the same problems have begun to extend to the field of e-commerce. A startup company called Bloomreach aims to use big data to improve the customer experience of e-commerce. This is based on the customer's search habits to provide customer-specific pages, rather than modifying the user experience of the entire website. Bloomreach's technology focuses on discovering content by analyzing product requirements.

Rajdedatta, CEO of Bloomreach, told me how the company uses big data technology to enhance the customer experience of e-commerce. Their technicians are at the back end of large-scale e-commerce websites, enabling them to customize new product landing pages according to the best match of customer search conditions, while ensuring a strong customer experience.

Although it seems normal to use big data applications as part of e-commerce and customer experience. But it points out three development directions for us. The first development direction is that big data will challenge content strategists, information architects and designers in the e-commerce world. The second development direction is that the fluency of big data will become an important requirement for future e-commerce talents. The third and perhaps most important development direction is that the big data technology at the back end of e-commerce websites will become a necessary technology to attract customers' attention in search and online competitive sales.

Application analysis behind cash register and call center

Perhaps the most famous application field of big data is tracking customer behavior. The big data and forecast analysis of Hitachi commercial microscope is suitable for analyzing the other side of customers by applying the technology to the cash registers of large customer service centers and retail stores.

The commercial microscope captures the so-called "emotional moment", and uses sensors to analyze the voice of customers accepting telephone customer service, or counts the passenger flow through the customer's credit card consumption, and knows which customer service staff the customer communicates with in the call center through the work card.

In the retail environment, the commercial microscope can study the passenger flow and then return the data to help optimize the layout of the retail environment.

Big data can track customers' behaviors according to their interactions, thus providing enterprises with actionable information, providing customers with the best service and winning business competition.

Implementing the dynamic price of NFL tickets

Most fans of Redskins football team who live in Washington like me are very familiar with people's complaints and complaints about ticket pricing during the football season. Fans in other regions also love and hate the pricing of tickets for their local NFL teams every season. The NFL is using FICO's big data and predictive analysis methods to determine and implement dynamic ticket pricing strategies.

Using big data and predictive analysis methods to implement dynamic pricing may know consumers better than we do. However, FICO and NFL have just started to use the case study project stage. A big data and forecast analysis project of this scale can only be put into practice after collecting customer needs and other consumer needs for a period of time.

Improve the retention rate of advanced subscribers in enterprises

Today, the paid subscription market faces more challenges. Because canceling subscriptions is the first step for users to cut their budgets when the time is right. The startup ScoutAnalytics is applying big data and predictive analysis to help companies including Software as a Service (SaaS), information services and digital media improve user retention.

ScoutAnalytics claims that its revenue in helping enterprises improve the retention rate of quality users has increased by 10%, reaching 15%. It can be used as a data center, associated with sales quotas, and help sales teams get more recurring income.

abstract

In this article, we will show you how big data and predictive analysis become the basic technologies of non-traditional applications across multiple industries. Although the frequent use of big data and predictive analysis is still a challenge, these unconventional special application technologies show us a better future for individuals and enterprises. Online living is bound to become a part of a larger business platform today and in the future.

The above is the unconventional use of big data and predictive analysis shared by Bian Xiao. For more information, you can pay attention to Global Ivy and share more dry goods.