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Retailers Can Master Mobile Using Machine Learning

As the modern consumer becomes a multi-channel shopper, the modern retailer has been forced to follow suit. Mobile commerce or “m-commerce” is now a crucial point-of-sale for a retailer hoping to cover the entire consumer shopping experience. Machine learning systems have been expanding to mobile in many ways, but none that provide large-scale results like the ability to collect consumer data and utilize it to make targeted, personalized campaigns.

The mobile transaction is already retail’s next step, and the data reflects it. Companies like Starbucks are showing large upward trends in mobile transactions. Other retailers should take note because this trend seems to be more widespread than just in the case of the coffee giant. A recent study showed that 29% of smartphone owners say they’re likely to use mobile payment apps over the next 90 days. This may not sound very substantial, but considering a 1-point increase since the previous survey in September and a 5-point increase in the past six months, the growth is eye-opening. Mobile appears to be more than a temporary shopping trend, and more likely a progression of modern consumerism.

The increase in mobile transactions is supported by technological developments in the mobile market. Mobile point-of-sale terminals are now being set-up to capitalize on the number of transactions occurring on smartphones. A recent UK-based study says that in the next four years, these terminals will handle 20% of the value of all retail transactions. We’re talking massive dollar amounts if you do the math. Growing infrastructure to support mobile retail is a sign of commitment to the growth of the channel.

While Tech has been playing catch-up, the mobile user has been showing growth and potential for some time now. As a channel heavily focused around personalization of the user experience, retailers are in prime position to use machine learning. The rise of smartphone usage is also huge for retailers because the amount of mobile data has ramped up exponentially. Along with improved personalization, there’s a massive amount of data available that can help retailers more accurately target each consumer. This isn’t strictly specific to mobile, as e-commerce and traditional retail are often used simultaneously by consumers looking to make a purchase.

There’s a market full of retailers trying to perfect mobile consumerism. According to a 2016 survey, 72 percent of respondents still planned to shop in-store regardless of whether they also used mobile. As such, in-store retail is still relevant, and retailers should attempt to implement effective cross-channel campaigns. Correctly implementing cross-channel strategies alleviates the focus of being perfect on mobile. Instead, retailers should concentrate on being efficient with their data and creating the best strategy to meet the needs of their consumers. For example, a customer looking to buy a new TV isn’t as susceptible to promotional upselling as a makeup shopper might be.

One way to do this – use machine learning systems to customize a campaign or promotion to the exact type of consumer target. Mobile applications like navigation, fitness, and Google Now are already using machine learning, and there’s an easy transition to retail.

Robert Chin, Data Scientist at Rubikloud shares his thoughts about why mobile data is such an important part of the overall consumer picture:

“The number of people using mobile day-to-day is massive. These devices allow us to have a sense of who a person is. The unfortunate reality is, we interact with our phone more than our other half. For marketers, though, this is huge. Accessing the data coming from mobile is like unlocking the full potential of the consumer. Ultimately, it’s a way to understand you more and personalize content to you.”

Not only does mobile data offer marketers another channel to target their customer, but it’s also one that is more personalized and more likely to create meaningful impressions. Machine learning has opened up the opportunity for improved data decision-making, but it’s also shifted to being hosted on mobile devices. Compact neural networks can now perform machine learning algorithms on the device. Developers have also tinkered the tech so that machine learning processes are possible without an internet connection. Considering today’s app-crazy mobile users, the potential here is enormous.

For retailers, it’s time to take your mobile marketing strategy to the next level. Machine learning presents an easy transition to make this happen. The mobile platform offers an extensive amount of consumer data, so it’s a crucial place for retailers to be. Marketing to these consumers at a higher efficiency rate will aid retailers in the transition towards capturing what looks to be the future of retail.



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