Many companies have to rethink and reposition themselves as a result of Corona. Especially at this time, it can’t hurt to spend more money on digitization. This starts in the area of technology, continues in the area of business processes and ends with employees. Nearly 51% of companies are willing to spend money on digitization because of the pandemic, many in hopes of increasing revenue or efficiency. Much of what companies thought they knew about their customers is changing because of the current situation. With everyday shopping options gone, buying behavior is shifting, and so is data analysis. As a result, companies need to resupply themselves with their customers’ data and, more importantly, learn how to use that data as a tool for themselves. Many companies have already recognized this, because as “The Adex” mentioned, 2020 is the year of data. Read more in the following text.
1.What is BIG DATA?
There are, in simplified terms, 3 components into which Big Data can be divided. Important here is the data size, i.e. the volume of data. Also, the speed at which the data is read counts. Since this happens several times a day, you end up with a lot of data.
Based on these three factors, two additional parameters have been derived over time. These are the credibility of the data and the benefit that results from the data for a company and its decision makers. The subsequent benefit for the company is determined by the four preceding factors.
2. Data analysis
2.1 Descriptive Analytics
Descriptive data analysis provides information about what happened in the past, but it does not provide information about why it happened. This is precisely why it is important to use other methods to get the best possible picture of the overall situation.
2.2 Diagnostic Analytics
This method is the counterpart to descriptive analytic and can explain why something happened by analyzing data from the past and comparing it with other data. The advantage here is that data are compared that do not necessarily belong together. By bringing them together and comparing them anyway, it is possible to identify and address deep-seated business problems.
2.3 Predicitve Analytics
This type of data analysis is not necessarily the most reliable, but it does bring a glimpse into the future. What do I know about my customer’s future buying behavior? Will I be able to retain him? The accuracy of this analysis depends primarily on the quality of the data, as well as the performance of the algorithm.
2.4 Prescriptive Analytics
This is specifically more about problem solving. It wants to avoid future problems and identify trends. It is the most modern type of data analysis because it uses past data as well as external data. An example of this would be repeat purchases, where customer data and buyer history is used to see what the likelihood of a repeat purchase is.
3. Big Data in marketing
Although this data is enormously important for a company, it is of little use on its own if it has not yet been evaluated.
These evaluations can provide marketing staff with insights into which contents of the sales process are particularly important. In this way, they can expand these even further and optimize errors. How did the customer become aware of our website? For how many times is he buying the product? Which target group do we address exactly?
Consequently, how to improve investments in customer relationship management (CRM) systems.
Customer value analytics, for example, can be used to identify profitable customers. This allows the company to decide whether to optimize this customer relationship, in the best case, or not to continue with it. It is also important to know whether to invest in them or in improving systems. By optimizing customer service or by improving returns processing. Big Data is so important because it’s not just the ordering process that’s being analyzed by data. It is also the identity and interests of the customer that the company then has at its disposal. Processes that take place before and after the purchase can also flow into this data.
This all sounds very simple, but it is important to know that without the right question, numbers remain just numbers. You need a precise intention and a precise formulation of a use case to convert numbers into data. The task of a marketer is to ask the right question to get exactly the answer you want. In the end, it comes down to looking at the results in context and drawing profitable conclusions for the company.