December 14, 2017 - By Ginna Hall, Senior Writer, Visual IQ
Marketing has grown increasingly complex. You’re using more channels than ever before to reach, engage and convert your target consumers, and they expect a relevant, coordinated experience.
Yet many marketers are still using siloed, outdated and inaccurate techniques to measure their efforts. Do you really understand if (or how) your marketing is working?
Download our new ebrief How to Stop Making Excuses for Poor Measurement
To optimize experiences and results, marketers need an actionable understanding of what’s influencing consumers across the entire consumer journey. Knowing what touches are important and how they work together is the foundation of multi-touch attribution (MTA).
At its core, multi-touch attribution is a way to allocate fractional credit to the marketing touchpoints along the consumer journey that influenced a lead, conversion or other desired outcome. More advanced approaches can use these insights to plan and optimize future campaigns more efficiently and effectively, as well as those already in flight.
As a CMO, VP or marketing director, you may already know adopting a multi-touch attribution approach is essential to remain competitive. It’s the only way to know what’s working and what’s not and, more importantly, where you should be spending more (or less).
But it’s easy to come up with reasons for not upgrading your measurement practices – it’s disruptive, it’s expensive, it’s hard, and so on. Many companies have one of four common excuses for using poor measurement practices:
1. I can’t add another platform. My martech ecosystem is already too fragmented.
2. We already have customer journey analytics.
3. Upgrading our measurement approach will interrupt the workflow.
4. Upgrading our measurement will be too expensive.
As our year-end gift to you, we've put together an ebrief that shows you how overcome each of these excuses and get the marketing attribution and the insights you need. Download our new ebrief here: How to Stop Making Excuses for Poor Measurement.
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