Marketing Automation: Four Challenges
Posted: Thu Jan 30, 2025 10:20 am
Prerequisites for Successful Marketing Automation
Marketing automation , i.e. the automation of marketing campaign management , is currently considered the holy grail for many marketing managers.
The solution providers' advertising suggests that all marketing processes can be completely automated and that marketers can sit back and relax. Only now and then a campaign may need to be adjusted slightly because a key figure is in danger of developing outside the defined corridor.
Marketing Automation with the Campaign Manager
Otherwise, there is enough time to fill the statistics for nigeria number dataset management with the increasing lead, customer and sales figures.
And in principle it is true: Due to the increasing digitalization of communication, the software-controlled implementation of lead generation campaigns, marketing trigger chains or automated follow-up actions is technically possible.
By using digital communication channels such as your own website, online advertising, email, SMS, push notifications or Facebook, customers and interested parties can be reached from different directions. At the same time, the use of these channels can be measured on a personal basis, so that the marketer immediately knows whether and how his marketing message is being received.
But the brave new world of marketing automation often fails in practice due to many small but important details. These can be summarized in the following four challenges:
The quality of customer data
Technical Reporting
Commercial Controlling
empathy for the customer
1. The quality of customer data
The success of marketing automation depends on the quality of customer data.
(For simplicity, the data of contacts, leads and customers are referred to as customer data below.)
The basic prerequisite for implementing marketing automation is that the data for campaign control is available. If this data is not complete , current and correct , the campaign is at best a waste of marketing budget and at worst a hindrance to sales.
completeness
gears
To achieve the goal of completeness , it is necessary that the necessary data is available. The challenge: The data required for a campaign is usually not available centrally, but is distributed across various marketing systems such as CRM, online shop, email marketing and BI tools. And of course, each system works with its own data model, which means that marketing has to deal with a heterogeneous database landscape.
For cross-channel planning and control of customer communication, the integration of digital marketing systems is therefore essential, i.e. the data must first be brought together - namely where it is needed for targeted customer communication. The prerequisite for implementing this networking is database know-how and the corresponding interfaces for automated data exchange.
But the data must not only be in the right place , it must also be in a format that can be further processed. The lowest common denominator is often the CSV file, but even for this simple file format there are dozens of variants for practical implementation, because essential parameters such as character sets, date format, separators or text recognition characters are not standardized. If the data is more complex (e.g. with hierarchical relationships), you quickly end up with the XML format, which is even less standardized and therefore requires even more detailed agreements between the data source and target.
Marketing automation , i.e. the automation of marketing campaign management , is currently considered the holy grail for many marketing managers.
The solution providers' advertising suggests that all marketing processes can be completely automated and that marketers can sit back and relax. Only now and then a campaign may need to be adjusted slightly because a key figure is in danger of developing outside the defined corridor.
Marketing Automation with the Campaign Manager
Otherwise, there is enough time to fill the statistics for nigeria number dataset management with the increasing lead, customer and sales figures.
And in principle it is true: Due to the increasing digitalization of communication, the software-controlled implementation of lead generation campaigns, marketing trigger chains or automated follow-up actions is technically possible.
By using digital communication channels such as your own website, online advertising, email, SMS, push notifications or Facebook, customers and interested parties can be reached from different directions. At the same time, the use of these channels can be measured on a personal basis, so that the marketer immediately knows whether and how his marketing message is being received.
But the brave new world of marketing automation often fails in practice due to many small but important details. These can be summarized in the following four challenges:
The quality of customer data
Technical Reporting
Commercial Controlling
empathy for the customer
1. The quality of customer data
The success of marketing automation depends on the quality of customer data.
(For simplicity, the data of contacts, leads and customers are referred to as customer data below.)
The basic prerequisite for implementing marketing automation is that the data for campaign control is available. If this data is not complete , current and correct , the campaign is at best a waste of marketing budget and at worst a hindrance to sales.
completeness
gears
To achieve the goal of completeness , it is necessary that the necessary data is available. The challenge: The data required for a campaign is usually not available centrally, but is distributed across various marketing systems such as CRM, online shop, email marketing and BI tools. And of course, each system works with its own data model, which means that marketing has to deal with a heterogeneous database landscape.
For cross-channel planning and control of customer communication, the integration of digital marketing systems is therefore essential, i.e. the data must first be brought together - namely where it is needed for targeted customer communication. The prerequisite for implementing this networking is database know-how and the corresponding interfaces for automated data exchange.
But the data must not only be in the right place , it must also be in a format that can be further processed. The lowest common denominator is often the CSV file, but even for this simple file format there are dozens of variants for practical implementation, because essential parameters such as character sets, date format, separators or text recognition characters are not standardized. If the data is more complex (e.g. with hierarchical relationships), you quickly end up with the XML format, which is even less standardized and therefore requires even more detailed agreements between the data source and target.