APAC leads when it comes to mobile-first markets, but developing a cross-device marketing strategy should not rely solely on this channel.

It’s not the first time you’ve heard it: when it comes to a true “mobile-first” mentality, Asia Pacific is leading the world.

A new study has revealed China has a grand total of 1.29 billion mobile users as of May 2015 — the highest of any country worldwide, with India in second with 960 million mobile users.  

In these markets, the force of mobile is hard to ignore, and retailers have started taking action accordingly.

China’s Alibaba has been pushing retailers to list only on their mobile platform and not on the desktop site. India’s leading fashion retail portal Myntra, shut down its desktop site entirely to go with a “mobile app only” strategy.


In Myntra’s case, consumers who go to its website on desktops are redirected to physically pick up their smartphones and download the mobile app instead. As a result of this change, Myntra lost 10 percent of its sales in the first week.  

The Trouble With Cross-Device

The phrase “mobile is king” is trending in the region and across the globe, but still a mobile-only strategy like Myntra’s is considered risky and extreme.

Some companies, like Carousell in Singapore, choose to start with a mobile app, but then still work on developing a desktop shop, suggesting that personal computers are still considered to be a valuable consumer touch-point by many e-commerce sites.

The prevalence of shopping across smartphones, tablets and desktops makes cross-device marketing paramount, yet connecting the customer across these device types—and more—is still a struggle for marketers. That said, if marketers fail to master cross-device, they could be leaving a substantial amount of money on the table. 

A Loaded Term

(Read more...) theory, the term “cross-device marketing” is self-explanatory: to follow a user across devices with the intention of driving a conversion on one or more devices. In practice, however, things are a little more complicated than that.

Consumers that use multiple devices in reality present marketers with a mess of fragmented and incompatible data that remain locked in the respective platforms. Desktop and some mobile web use cookie-based tracking, while mobile apps use other forms of identification like IDFA.

Cross-device marketing aims to bridge the data across these platforms and identify when two devices belong to one user.

In this quest to paint a complete picture of the customer journey, innovative tracking methods that use unique user identification and probabilistic algorithms are born.

Just look to the Verizon-AOL acquisition to see evidence of the trend: Verizon holds 1.5 billion users’ worth of phone numbers, email addresses, browsing histories, and physical mailing addresses; they want AOL’s programmatic technology to transform all that into a base for cross-device identification.

Marketing that is driven by a simplified notion of cross-device is pointless.

There is a real challenge to be recognized in pairing multiple forms of IDs across devices that belong to a single individual.

But precise matching of devices could be extremely valuable, as it empowers marketers with tools like cross-device targeting, sequential messaging and ad frequency capping, which prevents fatigue.

How Best to Match

Experts are generally split into two schools of thought: deterministic matching versus probabilistic matching.

Deterministic players include giants like Google and Facebook, who use their own form of user ID to match devices that are logged in with the same ID.

At this time, they tend to yield the highest percentage of correct matches, but scale will always be limited to the size of the user base.

This is sometimes referred to as “walled garden” identification, and brands are forced to integrate with these giants to access the benefits.

It is likely that we will soon see more companies in Asia with a wealth of user data follow in the footsteps of Verizon and build their own walled gardens.

Probabilistic matching on the other hand, has no limits to scale in theory since it does not rely on one form of user ID; in exchange, precision suffers. One study showed that probabilistic matching could achieve over 90 percent accuracy in matching devices. But in the context of probabilistic matching, the definition of accuracy includes the number of times the algorithms correctly predicted when two devices match as well as the correct predictions of when two devices do not match.

It should not be confused with precision, which is the percentage of true matches out of the number of predicted matches.

Finally, there is an emerging group of cross-device matchers who support a combination of both methods, depending on marketing objectives—e.g. is scale or precision more needed?

Overall, the status quo shows a fierce need for more innovation to bridge the ecosystems and to have a more cohesive standard of tracking devices.

Measurements Need to Keep Up

We’ve been discussing the difficulties of cross-device tracking so far. Turns out, cross-device measurement is not in a pretty place, either.

Let’s use the illustration below as an example.


A shopper browses on multiple platforms before completing her purchase finally on a desktop.

Many marketing attribution models today might still indicate that the purchase came organically from the desktop site, which means that everything that happened before the purchase is disregarded.

This is known as the last-click attribution model—a model that is both imperfect and dated, yet strangely popular. Using the example above, the risk with last-click attribution is that marketers may focus more of future budgets on the desktop, and neglect the other assisting touch-points on mobile that led the customer to the purchase.

The multi-device phenomenon has been happening for some time now, and measurement needs to evolve beyond the last-click to keep up.

*Homepage image via Shutterstock. 

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