by Randall CraigFiled in: Associations, Blog, Data, Make It Happen Tipsheet, MarketingTagged as: CRM, Marketing Automation
Marketing has progressed significantly from the days of direct mail addressed to “occupant”.
Or has it?
For the first time ever there are low cost tools (Marketo, Pardot, Eloqua, and ActiveCampaign, amongst others) that allow any organization to do sophisticated marketing automation/database marketing.
There is an eerie parallel between marketing automation today, and desktop publishing in the mid-late 1980’s. Back then, the newfound ability to choose between so many fonts/sizes/styles gave the writer incredible power when it came to formatting and layout. Some of what was produced was brilliant – but most was a disaster. Today, marketers have been given similar superpowers: some superpowers are being used brilliantly, and some… not so much so. (And some marketers don’t even know what these marketing automation superpowers actually are.)
While organizations will eventually develop the knowledge and skills to incorporate marketing automation into their plans, they will never be successful unless they pay attention to the definitely unsexy foundation: their actual data. Consider the following ten questions:
Great data drives great marketing decisions, improved conversion, and is the rocket fuel powering technologies such as marketing automation. This week, strengthen this foundation by improving how you answer these ten questions. No whiz-bang tool will prevent human marketing errors from occurring – but having accurate data will solve the most egregious ones.
Marketing Insight #1: Only once the data quality issue has been addressed, can you use more sophisticated tools, such as Machine Learning and AI, to derive more nuanced insights and even better marketing outcomes.
Marketing Insight #2: Machine Learning is only as good as the underlying data on which it is trained. If the data is bad, then all of the ML/AI outcomes will also be bad: everything from poorer conversion rates, to bad management decisions, to racial bias.
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