Why your data is import to corporations

Big Data: How important is data in the marketing of the future?

An infinite amount of marketing-relevant information is available on the web: on new products from competitors, on changing customer behavior patterns, all the way to the very latest results from research. Big data tools exist all around the web. One of those service types is known as platform as a service.

With this new flood of data, marketing has changed. In the past, marketers relied on mass media rather than targeted mailings to reach as many customers as possible at once. Today, the focus is on the individual customer. The aim is to understand their behavior and needs as best as possible. Based on this, marketers decide which products and services to offer the customer via which channel. The more precisely you segment your customers, the higher the response rate. This is because precise segmentation is the only way to address customers in a specific way.

This is where the term Big Data comes into play. Read this article to find out what it means and where the potential lies. The aim is to help you assess what role Big Data will play in your strategies in the future.

Big Data - a definition

The term Big Data describes the processing of large, complex and constantly changing amounts of data.

Due to the large amount of data, but also its weakly structured nature, it is becoming increasingly difficult to process this data using conventional, often manual methods. For this reason, the term Big Data often also describes digital technologies that are used to process and analyze this data.

The special nature of "mass data"

The special feature of this data lies in its origin. With Web 2.0, new sources of data have emerged. For example, information about an individual customer can be collected from Internet forums, search engines and social media platforms.

So-called "smart applications" such as fitness wristbands or networked technologies in the home also bring with them a large amount of new data.

Since this technological change has not only changed the quantity of data, but also the type of data is new, the requirements for analysis have changed. Today, everything is done at the click of a button and, if possible, in real time across multiple channels. Data has multiplied, and the time to analyze it is getting shorter and shorter. As a result, Big Data analysis is inevitably embedded in automated processes.

Big Data - a technical challenge

With the tools available to date, analysts are not sufficiently equipped to evaluate this Web 2.0 data. On the one hand, the tools cannot access the unstructured data from a technical perspective. On the other hand, these tools are not designed for semantic evaluations, which play an important role in forum and social media posts. For useful evaluations of semantic data, the tools must be able to handle human language along with the puns, irony, humor and other challenges.

This is where so-called data mining comes in. This group of analytics is based on algorithms and methods that process data in parallel, autonomously and automatically. This is because it is practically impossible for human analysts, even with technical assistance and visualization of the data, to recognize patterns in this volume of data. Thanks to data mining, on the other hand, it is possible to identify significant correlations in large floods of data and use them to derive forecasts for the future.

The potential of Big Data

In addition to the high demands placed on IT systems and the quality of data analyses, big data analytics offers great opportunities. The wealth of information about customers and markets has never been as great as it is today. By analyzing this Big Data, it is possible for a company to identify complex relationships in large amounts of data. It enables more precise forecasts and the targeted and rapid addressing of customers. Companies can adapt their services and products precisely to the needs of their customers and increase their satisfaction many times over.

In his article "How Big Data Analytics Can Improve Your Marketing," Bernhard Marr shows how detailed insights from big data analytics can be used in marketing in a variety of profitable ways:

• Target groups or the ideal customer can be defined more precisely than ever before.
• Customer engagement can be increased by identifying when and where they want to receive which services, products, or information.
• By analyzing purchases in detail, for example with the help of loyalty cards, it is possible to identify which incentives and promotions customers particularly like. From this, measures can be derived to increase loyalty. 
• Thanks to more precise analyses, a company can continuously optimize its marketing activities and increase efficiency.
• Because marketers can measure the performance of marketing activities, they can also optimize the allocation of the budget and thus achieve the best possible ROI.
• Offers can be personalized in real time. For example, remarketing: You search for a product on Amazon. You are then shown matching offers on a wide variety of websites.
• The performance of content marketing can also be increased. On the one hand, marketers get to know their target groups better. On the other hand, they can use Big Data analytics to see which text sections from past publications were well received.
• Through analytics, companies can search the web for mentions of their brand. This enables, for example, comments on individual tweets, posts or other contributions to build customer loyalty. It also allows companies to monitor the activities of their competitors in detail and in real time, and to react if necessary.

Conclusion

Basically, each company must evaluate individually how it wants to deal with Big Data Analytics. However, the power of this new type of analysis should not be underestimated. If all competitors already understand their customers precisely thanks to systematic evaluations, respond specifically to their needs and also react faster than you, sooner or later you will feel this in your sales.