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Efficient data management: concentrated feed for marketing automation

Posted by Aioma | 02-Jul-2020 08:00:00

Customer communication without data - it's like a car without wheels. It goes without saying that not only the amount of data but also the quality of the available information must be of the highest standard if the customer approach via marketing automation is to lead to the desired results. Both, however - data volume and data quality - require professional management systems in order to keep the information available under control at all times and use it in the best possible way.

Data management - the struggle for overview

At first glance, everything seems to counteract an efficient use of data: Costly IT projects, cumbersome structures, space- and time-consuming software technologies, and the comprehensive guidelines for data governance and compliance - all of these are certainly important and useful, but they stand in the way of lean data use trimmed for performance and speed.

The systemic problem of effective data management can be illustrated in this way: The basic elements of the database are designed in such a way that they hinder fast, targeted and high-quality customer communication. Good data management is designed to overcome these frictional losses, not forgetting the consideration of the additional requirements of the GDPR.

Data management as a booster for marketing automation

The automation of marketing processes is geared towards speed and accuracy. If the underlying data is not prepared accordingly, this strategy will come to nothing. Data must be managed centrally, enriched with new data sets and the resulting data stock used as a basis for decisions on upcoming marketing measures - all within an automatically operating system based on artificial intelligence.

Marketing automation is therefore big data management via self-acting processes. The database used for this must meet the high requirements of such an autonomous system in order not to hinder it or even bring it to a standstill. Data management for marketing automation processes is therefore subject to special challenges.

Data management: These are the advantages

To counter a common misconception: data management is not primarily an IT project. The integration of appropriate applications is certainly a step in the right direction - but that alone is by no means enough.

Rather, it is a matter of perceiving the role that data plays in the overall context. Treating them like crown jewels is certainly the right way to go, or, to put it in a technical term: Data as an asset. This means: data are valuable units and must be handled as such.

There are various principles that describe the treatment of data as an asset. They should serve as a basis for building an efficient data management system, especially in the context of marketing automation:

Data Quality

If data is to be used in connection with Marketing Automation, certain qualitative standards are indispensable. The data must be complete, error-free, accurate and up-to-date. To ensure this, it is necessary to have a constant overview of what data is stored where and for what applications it is intended.

StandardsQuality standards can be defined on this basis. They are based on the principle: You cannot manage what you cannot measure. An essential element of efficient data management is therefore to check the data stock - and here especially the new additions - for all criteria for the desired quality level, and to do so regularly and in detail.

Controls of this kind are vital in order to check the processes through which data is generated for their efficiency and functionality. In this way, weak points can be located and eliminated. At the same time, it also fulfils the requirements of the GDPR to ensure that personal data is up-to-date and correct.

Data Transparency

Transparency of the existing data stock should be ensured both externally and internally. In external relations, this concerns in particular the owners of the data, i.e. the persons from whom the data originates. On request, information must be available on how the data is used and what information about the owner of the data it contains.

Transparenz

Internally, it is mainly a matter of informing all the departments involved about the quality and structure of the data stock. On this basis, other units can decide whether the use of the data is appropriate for them.

Contrary to widespread scepticism about the requirements of the GDPR, the proactive handling of the data also offers significant advantages over its owners. If customers are informed of the current status of the data stored, they can actively participate in improving the quality of the data, for example by notifying us of a new address or bank details.

Competence and responsibility

Data quality cannot be optimized if no one is responsible for it. Good data management requires the clear allocation of responsibilities for the data stock. This is not only about providing the required quality, but also about complying with all provisions of the GDPR.

VerantwortungAccountability, i.e. the accountability for the data stock, thus describes the responsibilities in connection with its administration and maintenance. The term ownership, which is often used in this context, is misleading and should be avoided if possible. It implies that the data belong to the company, which has been clearly refuted since the introduction of the GDPR at the latest.

Regardless of how the data came into the possession of the company - even by purchase - the data remain the sole property of the persons to whom they refer. The company only has a right of use for the data for a specific purpose.

Data networking

Old structures, especially from the time before the advent of marketing automation, often rely on isolated solutions for data management. The central storage of the entire data stock is often extremely complex and error-prone. For this reason, departments, teams and specialist areas often store and manage the data relevant to them in isolated databases.

DatenvernetzungSince different departments have very different requirements for their master data and this results in very different database structures, this approach makes sense under certain aspects. The disadvantage: Isolated databases make fast and efficient customer communication based on marketing automation almost impossible.

The subsequent centralization of all data in a meta-database would, however, be an extremely complex and lengthy undertaking that would also incur high costs. The synchronization of different areas and the very complex IT projects required for this would often go beyond any reasonable measure - if they are feasible at all.

The means of choice is the intelligent networking of existing units. This enables the individual areas to continue to access their data in the usual way. At the same time, the data is available in higher-level processes for central tasks, as required for customer communication in marketing automation.

Data cataloguing

Target-oriented marketing requires a constant overall view of the customer database, both in terms of structure and purpose.

KatalogisierungIn conventional systems, data management often does not provide this basic information. Even today, customer data as an asset still leads a shadowy existence in many companies, while assets such as inventory or IT equipment are the focus of attention. However, comprehensive cataloguing of the data stock is essential for effective customer communication, quite apart from the requirements of the GDPR.

The strategic key position of data management

Incomplete or inferior data cause damage in various areas. They can lead to rejection reactions by the recipients - a wrong salutation or a misspelled name is enough. Annoyances caused by multiple mailings - caused by duplicate data - can also turn the benefit into damage. All this results in massive damage to the company's image, coupled with a long-term loss of customer confidence.

Professional data management allows Marketing Automation to respond to current customer influences and sensitivities in real time. This allows the strengths of intelligent, automated customer approach to be fully exploited, for example by taking current sensitivities into account: Customers who are critical of the company require a different approach than people with positive attitudes, to name just one example.

Conclusion

Efficient and professionally designed data management is a central requirement for the effective use of marketing automation. In this way, customers and interested parties can be picked up according to their needs and state of mind at the appropriate level of awareness and addressed in a precisely focused manner. A flawless database is also indispensable for further campaigns and activities with the existing customer base.

It is advisable to view the GDPR requirements not as a burden but as an opportunity to involve customers in improving data quality through a proactive approach.

The central installation of the database is often too costly, especially if isolated solutions already exist. In this case, the intelligent networking of existing databases is a good solution.

Topics: Marketing Automation

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