"Data-Driven Thinking" is written by members of the media community and contains fresh ideas on the digital revolution in media.
Today’s column is written by Michael Wilhite, senior vice president of data strategy at 84.51°.
Measuring the effectiveness of digital advertising depends less on absolute performance and more on the benchmark.
Current standards suggest that the bar is low. For most applications, even the simplest level of targeted digital advertising will outperform historical traditional channels and media.
Because of the growing fragmentation of media channels, crude targeting that helps us reach a “likelier” audience still returns dividends. Collaborative filtering using browsing and basket cues through retail channels still works. Retargeting in the diverse web browsing space will continue to show a positive return, even as consumers show more fatigue to intrusive ads. And geofenced targeting will drive spontaneous relevancy to the mobile channel and grow in coming years.
Is this good enough? The better question might be: Do we have a choice?
High Stakes For Effective Targeting
Most of the limitation is that targeting algorithms require scaled data assets linked to consumers so we can personalize.
Functional assets, including buying behavior, search history and browsing history, are readily available. The emergence of mobile device data also provides large-scale contextual data that can drive relevancy to the moment. These data assets drive effective digital targeting.
How much would our standards change if we started benchmarking not against the effectiveness of our historical levers, but against the increasingly higher expectations of consumers?
With today’s marketplace and emerging media channels presenting so many disruptive forces, it is important to strive for a higher standard to remain relevant. Consumers have more options than ever for where to shop and what to buy. They expect brands to truly know them.
Brands that succeed make a real connection with each consumer. Retailers that succeed will see greater share capture and higher return on capital. Brands that do it well can fuel growth and extend their reach. Both see protected long-term loyalty that translates to advocacy and protects their futures.
How do we capture the data to really get to know consumers beyond that functional level? We must dig deeper into the underlying needs and desires of the consumer to truly create something that is differentiated and special. If we don’t, others will.
Thick data, traditionally captured through rich, immersive observational research, is the best path. When marketers understand consumers’ life needs, they can offer solutions. This is building a different level of confidence so that we understand what consumers want, even if they don’t.
Yes, we want this rich data, but we also want a digital ecosystem that will serve up the most relevant experience customized to each user. This is our conundrum for digital targeting: We have more functional data linked to people, but we want the richer “thick data” to be scaled.
Science can scale existing data assets to infer and validate, rather than waiting until the perfect data exists. Most importantly, we can leverage the sample of thick data sources to frame the data and science work we are doing at scale.
Aspirational, Inspirational And Emotional Data
Compelling examples in the digital marketplace provide a glimpse into a future where aspirational, inspirational and emotional data sources can drive a next-level connection.
For instance, Google’s ecosystem of expanded and connected life data connected to individual users can support targeting to aspirational needs. Consumers who have adopted most of the Google platform, including Android mobile, Gmail, calendar, search, Home and Play, provide scaled data on a full view of their lives. When applying advanced science to similar big-data assets, we can proactively target needs without the consumer asking.
Meanwhile, Pinterest has such a compelling value equation for consumers that its very existence is designed to facilitate inspirational needs. Whether users are pinning in their boards or snapping photos on their phones through a Pinterest lens, they are providing data that is a direct indication of what they want. Creating compelling platforms that enable consumers to share their desires at scale provides the targeting map for brands to follow.
Finally, we should all expect that technology will solve most of the important data gaps that exist – even something as elusive as understanding if brands are connecting emotionally with consumers. My new iPhone X uses facial recognition technology for security. Is it too much of a reach to think that Apple will leverage those same cameras to measure my emotional state in real time, thanks to startup technology it acquired with last year’s purchase of Emotient?
Our future looks like this when richer thick data assets become big data. We must start by recognizing that we cannot rest on the efficiency of current functional targeting in digital media because consumers expect us to know them better.