Technical Reporting & Commercial Controlling
In the first part, I explained why the pious wish for marketing automation often fails in practice. The reason for this is the four challenges that are crucial for successful marketing automation and are usually not adequately met:
1. The quality of customer data
2. Technical reporting
3. Commercial controlling
4. Empathy for the customer
After the point Quality of Customer Data , the points Technical Reporting and Commercial Controlling follow .
2. Technical Reporting
Digital processes are invisible to the eye – therefore technical reporting is required
The unpleasant thing about digital processes is that you can't iceland phone number data see them. In the analogue world, the results of the production line in the printing or letter shop can be checked at random by visual inspection. In the digital world, however, everything happens much faster and you don't see bits flowing. If a printing press has a paper jam or the letter shop folds the mailings incorrectly, you can see (and hear) it immediately.
If the mail server queue is clogged or the JavaScript for banner delivery is not working properly, such an error can remain undetected for hours or even days.
Code Data Magnifier
While automated campaigns usually have a reporting system for marketing, the technical side is often neglected. However, for the reasons mentioned above, ongoing technical monitoring of workflows with notifications of problems and errors is important. This technical reporting must, for example, warn of resource bottlenecks, point out unavailable data sources or raise the alarm when processes run amok (via browser window, email, SMS or push notification).
Test data sets check the functionality of marketing automation
Lead Management
In addition, test data sets for the defined personas should of course be available in the database, which can be used to check whether the marketing campaign really achieves the desired results. And the more result variants a campaign can produce, the more test data sets must be used to cover all of the campaign's decision paths. Automated database queries are ideal for checking the results as end-to-end tests. These compare the results for the test data sets with the planned values and provide information in the event of errors.
This effort may be unnecessary for simple campaigns such as a birthday shopping voucher, but complex workflows such as reactivation campaigns with multi-stage trigger chains and sophisticated business rules for incentives cannot be reliably managed without automating the tests.