Data is an essential element in a successful Marketing Automation strategy. As the adage goes, “good input means good output”. Which translates to “better data quality equals better quality results”.
Think of data as the heart of your Marketing Automation implementation. The heart does not function well when it is weak.
Data not only needs to be clean for it to be useful. It also needs to be correct.
Consider leads driven automation. The leads data is useless if the names of the contacts coming through are incorrect or if they do not come through in the correct format. This will cause issues if you’re running complex automations and relying on the leads data to feed lookalike audiences and run automated media campaigns using machine learning. The same is true for any other data point or implementation.
It does not only apply to the ingested data or output data. It is also pertinent to the schema and structure of the databases, data warehouses, and data pipelines you set up to store and process the data from a backend perspective.
Again, data is of no use if it is stored or processed incorrectly. A Phone Number accidentally being processed and stored as First Name data is useless. It should be obvious, though you would be surprised at how often this error occurs.
Data is typically used in conditional formulas to trigger automation. Which is why the information needs to be precise. If the information is not accurate, then the criteria set up to trigger the automation may never be met.
Example:
1. Setup
You have set up your data pipeline to ingest form data from a web page or an app. You accidentally misconfigure your automation to send form field data to the wrong columns in the database. You have set up an automation trigger based on data you would expect to see in one of the columns in the database (“1. DO THIS when Field Value is X” or “2. DO THIS when data is added to Column X”).
2. Errors
The data from the form is not only processed incorrectly (meaning the data may be formatted incorrectly) but is also inaccurately stored.
Sometimes databases will not have any data stored. This can happen when the database schema does not allow for the storage of unspecified data types.
3. Problem
The automation trigger may do one of two things using the example triggers above. Trigger 1 may not fire at all because it never receives the information it expects. Trigger 2 may fire, but the processed data would be incorrect.
4. Conclusion
You can see how this could be a showstopper for both the Marketing Automation implementation and for the business as a whole. It is a frequent error amongst many, and you would be surprised at how often it is left unattended.
Good input means good output
The above example illustrates why clean data is necessary for Marketing Automation to work effectively. The data needs to be correct right from the beginning if you want to realise the power Marketing Automation has to offer.
You will have to take the time to ensure everything is correct — there are no shortcuts. Not doing your due diligence could result in a mass of errors which may turn out to be very costly.
Implementation is simple
As long as you make sure you’ve done the necessary legwork upfront, then implementing a best-in-class Marketing Automation strategy should be simple. There’s no reason why your business cannot succeed by leveraging Marketing Automation.
It’s one of the best ways to maximise your marketing efforts, bring your business into the ’20s, outperform your competitors, and stay relevant to your customers.
Start now and thank yourself later for having done so.
Reach out to me if you want guidance around best-in-class Marketing Automation strategies for you and your business.
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