.svg)
.svg){kind=icon}
Data Processing: efficient data management in marketing
Over the years, modern companies have integrated tools and services into their workflow to improve performance within the company and get more out of the product or service. Today, in the next step of optimization, many are faced with the problem of how to make tools previously integrated into the process and data silos that have arisen jointly usable.
We explain to you why data processing is worthwhile and how you can best implement it!
What are data silos?
Data silos, also often referred to as insular solutions, are collections of information (data) owned by one department that other departments in the same company cannot access easily or only to a limited extent. The individual departments, such as finance, administration, human resources or marketing, usually store the information, i.e. data, they need for their work in separate locations. This is promoted by the separate work processes and a closed corporate structure. At first glance, these data collections don't seem to be a problem, but they become one when corporate processes change and departments need to collaborate more. Then data silos make it difficult to collaborate and share data between departments. One problem is often data quality due to inconsistencies between stored data from different departments (e.g., different addresses for the same person). In addition, it is difficult for executives to get a holistic view of corporate data when data is siloed.
Improved data management with tightly integrated data processing methods helps companies gain a better overview, enabling them to take the next step in optimization.
Goals of Data Processing
The daily volume of data within tools and processes in the company is incredibly large and hardly manageable for the individual person or department. In the future, the amount of data will continue to grow. Data processing is part of every business and leveraging optimized data offers many benefits. Potential risks and opportunities to increase financial success, for example, can be tracked down, analyzed and processed through data processing alone.
Integrated tools and software also benefit from optimized data processing and adapted data management. By seamlessly linking underlying databases, processes and interfaces, the potential can be fully exploited. The result is, for example, an improvement in the customer experience.
What is Data Processing?
"Data Processing" is the collection of data and its processing into the desired and usable form. This is nothing more than data processing performed either manually or automatically in a predefined sequence of operations. In the past, processing was done manually. However, this was very time-consuming and error-prone. Today, digital processes enable automatic and time-saving processing and a correct result.
Why is data processing worthwhile?
Creation of a foundation for data analytics
Faster cross-platform processes
Performance boost through synergies
Overview for management of departments and dependencies
Automation of data warehousing
Well-functioning marketing is based on a good understanding of the customer base or target group and product data.
These include the following factors:
- Site-specific offers and product information
- Demographic information such as age or location information
- Preferences and purchasing power of customers from online stores
- Information obtained from voucher promotions or loyalty programs
A high processing speed of this information, in order to be able to react quickly and in a personalized way to interactions with customers when needed, is of crucial importance in order to use the information to its full extent. This is a clear advantage over manual cross-departmental analysis processes that require more time.
However, fast processing alone will not lead to success. To be effective, retailers need a unified customer data platform that responds to the activities of a single customer across all channels.
Apart from that, data processing methods help in the following areas:
- Conversion tracking: determine how and in what way ads or marketing techniques lead to sales
- Real-time segmentation: active segmentation of customers during their customer journey
- Budgeting: maintain an overview of advertising expenditures and automatic adherence to set budgets
An improved "Data Process" is almost always accompanied by the revision or renewal of the already existing system architecture. For each specific use case, there needs to be an individually tailored solution and a system that fits the requirements of the respective company.
What is the best way to implement data processing?
Learn more about Data Driven Marketing now
Analysis of the actual state
Definition of the goals
Stakeholder requirements analysis of the result
Prototype implementation
Adaptation and expansion through iterative development cycles
Our conclusion
Companies are confronted with an ever-increasing amount of data. Big data can quickly become confusing, but it offers many opportunities.
Want to use your data with analytics tools to gain actionable insights that drive your business forward? Whether you want to analyze customer data to make your marketing more customer-centric or collect data from multiple sources to build predictive models, whatever form you want to use the data in, it requires knowledge, time, and a high level of diligence. Unfortunately, it's often not enough to simply enter data into a business intelligence system and then expect high-quality results.
To successfully implement data processing methods, don't just follow the trends. Instead, focus on where they add the most value to your business and where they are needed most.
Whether it's intelligent portal solutions, PIM and MAM solutions, or the integration of analytics, automation, or marketing evaluations, we'll be happy to advise you from the first step all the way through to support during implementation and operation. Benefit from
our experience!
Learn more about customer centricity now
Find out more