Today’s column is written by Jared Belsky, president at 360i.
Technology advancements, such as beacons, DMPs, attribution platforms and geotargeting, have spawned larger volumes of more precise data, creating opportunities to truly understand how various channels are working. That’s put measurement and analytics on the rise, both in strategic importance and dollars spent, with this share of marketing budgets expected to nearly double to 12% by 2018, according to a recent Duke survey of 300 chief marketing officers.
This has left too many marketers paralyzed, generally for two reasons. First, they’re afraid of what they don’t know and aren’t sure how to start fixing that. Second, digital marketing analytics isn’t keeping up with digital marketing, and the pursuit of perfection has marketers leaving opportunities on the table.
The result is marketing that may not be as efficient or effective as it could be, along with investment in the wrong tools and products.
Getting familiar with data and analytics is imperative for a modern marketer, and it can be done with a purposeful analytics learning agenda and framework for analyzing and testing measurement tools. This enables a proactive data strategy that gets smarter over time and drives business outcomes.
The Measurement Blueprint
Every organization plans ahead for tech, finance, data and R&D, but few do so for measurement. The same forward-leaning approach needs to be applied to a two- to three-year blueprint for more rigorous measurement. Getting C-suite buy-in on a multiyear plan that includes a testing budget gives marketers the time and resources to enact a learning agenda and deploy the right measurement tools and platforms, instead of flying from test to test. This not only allows marketers to find and validate the right partners, but also to develop their measurement technology ecosystem.
The blueprint should provide clarity on brand objectives and goals before jumping into tactics. Many organizations rush to test a measurement or attribution system without clarity on what marketing riddle they’re trying to solve. If, for example, the objective is to gauge the impact of digital on a physical store, the tactics supporting that learning objective might be more aligned with beacon experimentation or CRM testing than attribution testing.
A Culture Of Data Curiosity
It’s possible to develop a culture that allows for bleeding-edge investment by allocating a percentage of the media and analytics budget to testing – roughly 5% of total media. This will help marketers stay ahead and identify potential new data and analytics sources in a lower-risk way.
That test budget can be used to challenge the team to identify and explore three new ways to measure direct impact in the next six months, without relying solely on old models. Marketers should consider trying something bite-sized yet meaningful. For example, a retail or travel brand that already has robust customer email lists might consider building segments into Facebook using CRM ingestion tools and remarketing to those groups, or prospecting off look-alike models. This lets brands stretch their CRM- or data-management platform-type muscles without having to make major investments, and it increases segmentation knowledge to inform future opportunities.
Other tests may include a Datalogix study to understand offline impact from social ads or a multipath level analysis between search and display to understand the most profitable paths to purchase.
Having this budget also lets marketers play in important channels where robust data and measurement aren’t yet available. For example, although mobile marketing is one of the most promising areas for building brands, nearly half of marketers are underfunded in this space, because the tracking tools aren’t as developed as those for measuring the ROI of desktop.
Getting The Creative Team Platform-Informed And Data-Enthused
There’s great data available to help creatives develop more effective work. For example, if a brand is creating video assets, data can help the creative team understand the implications of the skippable barrier in YouTube, the opt-in audio nature of Facebook ads or the sequential storytelling opportunities in paid social. A creative might focus on breaking through in the all-important first five seconds when an ad must grab viewers’ attention before being skipped. And a data-fluent creative will know that he or she can look at video-viewing data on a quartile level to understand precisely where viewing drop-off occurs so videos can be iterated to better capture user attention.
This data-driven approach also gives creatives quick feedback about how the campaign is performing so they can optimize accordingly. The ability to launch multiple pieces of video content and know within 24 hours that one video’s completion rate is outperforming another will open up faster, more productive feedback between marketers and agencies.
Staff For Modern Analytics
A modern analytics staff, whether it is in-house or at a third-party partner, should include four core positions. The first is a database expert to help manipulate data and set up an analytics database that marketers can own. Financial clients, in particular, should invest in this position, because their ability to organize data and share it effectively with agencies and publishers to optimize is critical.
The second core position is a business intelligence expert who is fluent with data access and visualization software packages, such as Tableau, to help advance how to visualize and draw value from endless reams of data. This person helps plate the data in a meaningful way for an organization to make decisions based on it.
Don’t overlook a statistician who knows and loves forecasting, SQL, R and modeling and will crunch data to get value out of it and inform better marketing decisions. This person will help with forecasting, diminishing-returns analysis and complicated multivariant testing on creative or media placements.
Finally, it’s vital to employ a developer with API expertise who will use data to develop unique solutions to business challenges. For example, this person can help marketers determine which weather API to test and how to connect it to Google to inform weather-based media bidding strategies or find traffic patterns to adjust mobile offers in real time.
By embracing an analytical culture of learning and experimenting with new ways of doing things, marketers will get closer to more effective measurement for their organizations and the industry.