In the previous part, we have discussed about the marketing channels. In this part, we will discuss the main tools of marketing analysis. An ever-increasing flood of data is being generated by more and more data sources. Data protection, in particular, is a major challenge, but should not be considered in greater depth in this work. Marketing can use and analyze this amount of data for its purposes. There are various mechanisms and challenges here, which will be discussed below. Any form of evaluation can only be as good and complete as its data basis.
Big data analytics
To approach the topic of big data analytics, the term big data should be clarified in advance. We have an article on the fundamentals of big data. Big Data describes the use of large amounts of data from diverse sources with a high processing speed to generate economic benefits. What are large amounts of data? From 2000 to 2002, data was generated was more than in the 40,000 years before. To further clarify this number, according to a study from 2012, the data volume from 2005 to 2020 will increase from 130 exabytes to approx. 40 zettabytes.
This would increase costs for companies. Another study on sales of big data solutions worldwide says that sales will increase from $ 7.6 billion in 2011 to $ 84.69 billion in 2026. For the companies, the challenge now is to process these different amounts of data in a variety of structures for your purposes, for marketing example.
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One of the numerous examples for the targeted linking and analysis of this data in marketing could analyze the purchasing behaviour combined with social media information and the current search and surfing behaviour of the user on the website as well as the origin pages could provide real-time insights, which then form the basis for sending out a specific coupon.
A large-scale market observation can help big data forecasts to determine the sales channels or sales organs to achieve the set marketing goals. For example, in an automatic real-time comparison of a customer in which purchase phase they are. Special advertising media could then be used based on the result.
Analytics encompasses the methods for the most automated detection and use of patterns, correlations and meanings. Statistical processes, prediction models, optimization algorithms, data mining, text and image analysis are used in these methods. In the foreground are the speed of analysis (real-time, near-real-time), while the ease of use, a key factor in the use of analytical methods in many areas of the company.
It can be said that there is a multitude of possible uses of big data analysis. A wide variety of results could be achieved through the knowledge gained. Among other things, better use of the budget or a better understanding of the customer.
Social Analytics
Due to the ever-increasing number of users in social networks, social media analytics is becoming increasingly important. This tool is increasingly used in customer-oriented market cultivation. Here, a company must carefully consider for itself where it is appropriate to collect a cost-intensive detailed evaluation or to use a fully automated report. At this point, however, it is usually difficult to derive a precise recommendation for action from the evaluations. A prerequisite for the evaluation of data is the collection of this social media data. The large platforms offer interfaces here but often change their technical specifications. In the future, the results of monitoring user and customer activities on social media will increasingly be incorporated into decision-making in real-time and can thus represent a relevant competitive advantage.
So it remains exciting to see how these evaluation options, with their multitude of data collected, will impact and develop in the future.
CRM
In addition to the pure CRM system itself, a strategic orientation is also required. Analytical CRM aims to better understand the customer and to derive actions or recommendations from them. The individual customer records are prepared for this. The email from an e-commerce service today has an order page which contains all of my information so all we need to do is entering our credit card number, change the day and time to delivery if not the same as the last, check one of the suggestions or select something different than we have done before.
As a rule, internal and external secondary data are used. External data can be, for example, a telephone and address directory, data exchange with cooperating companies or residential building data. Residential building data is a particularly interesting topic here. Residential building data is almost comprehensive documentation about all individual houses, so that customers can make individual statements about their living conditions the houses, for example, according to the type and age of the building, garden size, condition, residential area etc.
Internal company secondary data are, for example, data from sales or accounts receivable management. Which products did the customer buy last, at what times were the last order placed or what are the payment habits?
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