91% of senior corporate marketers believe successful brands use customer data to drive marketing decisions. But with more complex technologies, more devices to target across and more data to manage, it’s easier said than done. From ensuring data accuracy to streamlining data flow and measuring success meaningfully, the challenges for senior marketers are mounting fast.
For many marketers, obstacles to success can be overcome by rethinking and simplifying data strategies. So find below several methods that bring some sense to the madness of marketing data management.
1. Taking the needles out of the haystack
There’s a lot of information out there, so marketers tend to hoard as much as possible in the hope that it’ll be easier to glean consumer insights from huge, often fragmented data sets. But not all data is created equal. By indiscriminately collecting information from every touchpoint, the quest for valuable information ultimately becomes a case of searching for needles in a haystack. Rather than be waylaid by every acronym from CTRs to CPAs, marketers should focus on where real value lies: unit sales.
Marketers today are under pressure to demonstrate their data wizardry and creative genius, but their ultimate goal remains the same as that of any other department: revenue. To reach it, there must be an intimate working relationship between sales and marketing on every level from data sharing to department collaboration. Plus, analytics can take some of the pressure off. By accurately establishing causation – not correlation – between unit sales and marketing activities, analytics set out exactly what works and what doesn’t, without distraction from meaningless metrics.
2. Making data revenue-friendly
First-party data is second to none for consumer insights and cost-effectiveness, but it’s often hampered by problems of accuracy and scale. To ensure high accuracy, marketers must rigorously compare their first-party data with other sources and cull what does not match up. But this results in less data, when first-party data volume is often limited to begin with. Strategies like lookalike audience segments – which identify target audiences then incorporate segments with similar traits – can compensate for this, and scale reach; creating a larger, higher-performing audience, which leads to better results and greater ROI.
Another threat to efficiency is the slow flow of data through the martech stack. This latency can, in turn, result in high ad frequency or missed opportunities, either turning prospects off or failing to reach them. But consumer intent is so short-lived that marketers cannot afford delay. They must demand that their tech partners’ are able to engage with their chosen audiences across all devices in real time, with controlled frequency. The translation of data between website cookies, mobile apps, and CTV should be quick, and frequency management at the true user level, not just device level, should be easy. Tech partners should allow marketers to action data with the precision and speed they need to make the most of every sales opportunity with minimum budget wastage.
3. Ensuring accurate analysis and attribution
Sales define marketing success: every marketing activity must be linked to revenue generation and unit sales. But consumer experiences happen across so many devices that attribution is hard to track. Marketers need a holistic view of each individual’s path to purchase, and this only possible with robust data analytics and cross-device insights to unify measurement across channels.
Attribution models should connect the dots across devices from interest to intent and conversion. They should show which activities need pruning, and which merit more investment. These insights should then act as the playbook, if not Bible, for future campaigns, steering marketing efforts towards greater success and more intelligent budgeting.
The value of data for marketers has created an entire ecosystem, but somewhere along the way we lost sight of what information is inseparable from marketing efforts, and what is purely distraction. Data management, as a result, is an unnecessarily difficult task, which puts efficiency and revenue at risk.
But data science strategies can clear up the confusion. Honing in on unit sales, data lets marketers establish causal links between marketing and revenue, and establish the most intelligent way to invest. Activating the customer data that matters efficiently makes way for nimble and accurate campaigns that reach the right audiences at the right time. Mapping every customer’s cross-device path to purchase helps marketers understand how to bring the company closer to business goals. Ultimately, bringing some method back to today’s data madness will make sure future marketing investments boost revenue and growth.
