Big Data Analytics in Marketing

Digital consumers are connected all the time, through their smartphones, tablets, game consoles, and practically all applications, services, and channels accessible through these devices. While moving between devices and channels, they are creating more points of contact with customers in different media, online, offline, owners, third parties, corporate networks, social networks, seats based on seats and furniture. For sellers, this information presents a great opportunity to get better to its consumers. Retailers also have adopted advanced analyses, to provide personalized recommendations to their online consumers such as searching for operations. Telecommunications providers use large data techniques to reduce customer consumption. Retail banks use ample data analysis for fraud prevention.

As the number of customer channels increases, marketers need to ensure that they provide tailored experiences across all channels. All these efforts help provide a highly personalized experience while maximizing the return on your marketing investment. In the long run, marketers can feed these new real-time insights back to the company to influence product development and pricing.

Changes of Big Data to Big Marketing outcomes, several big data applications have shown great potential for improving marketing effectiveness in the field of customer management. The following examples illustrate various applications.

1) Next best action to engage customers

Next Best Action (NBA) Marketing is a customer-centric marketing method that takes into account all potential offers for each customer in real-time and then determines the best offer. The next best action proposal depends on the interests and needs of the customer, as well as the business goals, policies, and regulations of the marketing organization. This is in stark contrast to traditional marketing methods - first create an offer for a product or service, then try to find interested and qualified potential customers.

2) Personalization of online shopping

Nearly 20 years ago, the retail industry changed fundamentally with the advent of online retailers: They used the Internet to expand their market reach while reducing inventory, personnel, and operating costs. Today, it continues to promote shopping by collecting and processing a large amount of data that is characterized by quantity, variety, speed, and complexity, transforming shopping into a personalized experience. Online retailers use powerful big data systems to collect information about user preferences, user browsing and buying behavior, product attributes, geographic shopping locations, inventory levels, active promotions and activities, and any other information that can be digitally captured.

3) Monetizing big data in targeting dynamic advertisement

Data monetization creates opportunities for organizations with important volumes of data to take advantage of information without exploiting or implementing and creating new sources of income. As shown in Figure 3, different forces are converted to create mature conditions for data monetization. The volume and wealth of data are now uniquely accessible to mobile device providers, both in the form of transactions, queries, text messages or tweets, GPS positions, or live video sources, offer a real gold mine of ideas and applications. And even if mobile phones have become the main device through which consumers get their information, these same devices have begun to provide new types of information, including extremely accurate information, in real-time, geolocation information.

4) Machine-to-machine (M2M) analytics acts to improve the product life- cycle management

There was tremendous progress in the sensor technology that enters machines, cars, mobile devices, public service grids, and business networks. This led to the generation of data from Machine to machine (M2M) at an unprecedented speed and in real-time. Companies can use the data emitted by the sensors of a wide variety of applications to analyze and improve the efficiency of production processes, predict device failures, and identify timely times to get new products for the customer. Data can also provide information about product development, customer service, and sales equipment that use information, for example improving product features, increase income and reduce costs.