A few days back, I was at Changi Airport to catch a flight and something interesting caught my eye. There was this guy staring at a large billboard of a luxury brand and punching something on his smartphone. He’d look up, and then go back to his handheld. I couldn’t resist from finding out more. It turned out that the brand was encouraging people to download their app and/or call their support center for more information.
This is an interesting example of a user wanting to engage with a brand after getting exposed to an ad (exactly why the ad was there in the first place). The challenge here though is the transition from the offline to the online channel. And this problem gets further complicated as users start to spend more time on their smartphones and tablets to “find” or “do” more. If the brands could reinforce their messaging onto the handheld devices at the “right” time, it would make the follow up seamless and hence impact conversion and measurement.
Traditional media channels have been an integral part of consumption behaviour and will continue to be. No surprise then that they have been the primary channels for brand outreach. However, as the Millward Brown AdReaction Study 2014 indicates, in most of the Asian markets like Japan, Australia, India, Indonesia, Philippines the time spent on smartphones exceeds that spent on laptops and TV. People are using the handheld devices to search, connect, read, buy, sell, track, measure, learn and do so much more. Hence, the question we’re asking ourselves is: How do we connect the offline world with the online to make the engagement and path to purchase more seamless? We look at it from two different perspectives:
Convergence of messaging
Most of the brand clients we’ve been working with, have a clear strategy around offline channels especially Out-of-Home media. In the OOH media, the location plays a critical role in defining the target audience, viewership and hence pricing. This made us think: if a user is exposed to the messaging through OOH media in a certain location, what If we could leverage our proprietary location technology to re-enforce the same messaging onto his handheld whenever he came close to the billboard? We ran a few tests across markets. For instance, in Singapore, a luxury cosmetics brand had a strong messaging on certain strategic OOH locations. We built a mobile campaign targeting the relevant demography in those exact locations. So when a user is in the vicinity of any of the billboards, she’s also exposed to the same message on her smartphone. What’s more, upon clicking on the message, there could be multiple actions pre-defined by the client (call to find out more, follow on social media, download a coupon, download an app etc). Think of the earlier example I pointed out and how the experience is changed if the messaging gets re-enforced onto the device.
There are other angles to it as well. The technology enables us to show different messages and different calls to action depending on which billboard or area you’re close to. Also, we could measure down to the unique users who were exposed to the ad on their phones in each of the areas. This could, in a sense, give us an idea of the footfalls across each of the OOH units as well. The results for such integrated campaigns showed a 130% lift in engagement against a normal mobile campaign. Think re-enforcement. Think integration.
Offline data to understand user behaviour
The amazing upgrade in the capabilities of smartphones in the past few years has posed challenges and opportunities to the mobile advertising landscape. This has also manifested itself in the amount of data getting generated by every single user. At AdNear, our focus has been on using location data generated by these devices to understand user behaviour and predict intent.
The traditional way of predicting behaviour has largely been based on identifying content consumption patterns (site visits, app usage, video views etc). And this is where mobile offers a unique opportunity – it ‘s the only media channel that provides us access to location data at scale. Imagine a location graph of an individual user as a function of time. I see a mobile device regularly at a particular location, say X, between 9am and 6pm on weekdays and at say Y, between 10pm-6am. Now, if we could overlay this information with offline data from multiple sources (census data, point-of-interest data, survey data collected by offline channels), this could give me a probabilistic estimate of the individual’s demography, ethnicity, affluence, shopping behaviour and a lot more. Also, how often the user digresses from base location (Y) and work location (X) could tell us a lot more about other traits. If the digression dwell time is at kids’ play school, we’re likely looking at parents, whereas if this happens to be a different city, we’re looking at travel behaviour, which could be either that of a frequent business traveler (weekday travel, higher frequency, shorter trips) or a leisure traveler (weekends, lower frequency, longer trips).
The interesting thing here is that brand’s own research data could also be baked into the location graph to derive relevant audience segments. For instance, if an ice-cream brand’s internal research suggests that most of the buyers happen to be college-going students, this is a data point that could be overlayed to build custom segment (young demography seen on weekdays in college and university campuses).
The Big Picture
The larger point here is that in an increasingly converging media world, offline media channels and offline data sources can be used more effectively in the digital marketing spectrum and also, mobile needs to be looked at in alignment with other media channels and not in isolation. We’d love to hear any thoughts/challenges and happy to share more if you’re curious.