If your organization uses mobile to generate revenue or engage customers, your 2020 strategy conversations have likely included meetings around how to capitalize on machine learning (ML). You’re not alone. Last year, global spending on artificial intelligence systems, such as those that use machine learning, reached $37.5 billion, according to a report by the International Data Corp.

Machine learning identifies anomalies and patterns that ultimately optimize the user experience. If your technology conversations have stalled at the brainstorming or ideation phase, consider why. If you don’t have a clear answer, you’re not alone there either.

“Strategic decision makers across all industries are now grappling with the question of how to effectively proceed with their AI journey,” says Marianne D’Aquila, research manager, IDC Customer Insights & Analysis.

Despite questions around how to proceed, organizations know they need to invest in ML for mobile before current competitors, and those waiting in the wings, figure out how to profit from it first. Considering the speed at which machine learning is being adopted and spreading, and its potential to quickly help companies on multiple fronts, the time for execution and implementation is now.

Three key areas make ML for mobile urgent and important right now:

Reason 1. Increased app security

“Facial recognition” ($4.7 billion, 6.0%) and “fraud detection and finance” ($3.1 billion, 3.9%) were among the top five categories of AI global investment in 2019, according to the AI Index 2019 Annual Report (an independent initiative at Stanford University’s Human-Centered Artificial Intelligence Institute).

It’s not surprising. From TikTok’s recent security flaws to Target’s $18.5 million settlement, app vulnerabilities and potential data breaches are breaking news, and there are few signs of a slowdown. While the short-term financial impact can hurt, the long-term cost of losing the trust of customers and partners can be even more painful.

Companies that receive users’ personal information (e.g., passwords, billing addresses, answers to security questions) for processes such as app authentication or making purchases must continually optimize how the data is used. Through machine learning and automating parts of the process, you can identify anomalies faster, allowing you to see patterns and manage potential weaknesses more quickly.

Operationally, ML can detect and staunch security issues related to data inside your company, such as logistics or pricing anomalies, that could be a drain on resources. For example, if one of your products is selling faster than usual via a shopping app, it could be related to a pricing error. Do you really want that $450 device on sale for $4.50?

The mobile application landscape is comprised of a wide variety of operation system versions, devices and software systems. This creates a much greater number of attack surfaces that attackers can target. (A first step to optimizing security is risk evaluation and awareness. Contact Fusion to hear more.)

Reason 2. Increased mobile privacy

It could be argued that the recent news cycle around privacy indicates a real desire for clarity, if not outright skepticism. In more than 3,600 global news articles on ethics and AI from mid-2018 to mid-2019, the dominant topics were “framework and guidelines on the ethical use of AI, data privacy, the use of face recognition, algorithm bias, and the role of big tech.”

You’ve heard about Russia’s role in the 2016 election and the use of personal information for ad targeting. These sorts of debacles haven’t led consumers to give up on digital. Instead, they are demanding more privacy oversight and are being more cautious about the apps they use.

Privacy concerns are complementary to security issues. While security comprises keeping personal data from hackers, trolls, or criminals, privacy is more related to keeping personal data in a person’s own hands, away from any individuals or organizations that don’t need to be privy to it.

For example, if you use an activity tracking app to record runs, you might appreciate a note when you hit a milestone: “You had a personal record today!” Machine learning makes it possible for the mobile app to directly detect this activity and send a congratulatory message without any human intervention. There’s no need for a stranger to know you clocked a fast 10K.

Machine leaning on the edge further increases privacy by eliminating the need for data to be sent to the cloud. When ML on the edge is in place, individualized data never leaves the device, keeping the user’s personal information in their own hands at all times. Amazon, Alexa, and Google Home employ ML on the edge, as some functions are offloaded to a device while others have to go to the cloud. In addition to supporting privacy, the reduced travel time for data makes these apps and devices faster.

Reason 3. Personalized customer experiences

Consumers expect their demographic, behavioral, and other personal data to be secure and private, while they also want increasing levels of personalization. Delivering on these demands can be a delicate, real-time balancing act for companies, but machine learning helps make it possible to juggle data acquisition with protection and those prickly questions around how to use the data to everyone’s advantage.

But is there a clear business case to pursue personalization? According to a 2019 Salesforce report, the answer is yes, as 75% of 8,000 consumers and business buyers surveyed expect companies to use new technologies to create better experiences.

Machine learning for mobile enables you to make user-experience headway on several fronts. First, it can help you build a baseline of customer app usage. Once you have that baseline, you can see patterns in user behavior. Next, particular behaviors or deviations from the baseline can trigger delivery of a relevant coupon, suggested product to explore, or a reminder to revisit an abandoned shopping cart. Even more sophisticated, ML can serve up colors, screen layouts, and language that appeal most to a particular user.

And with machine learning, the reactions are in real time. The more your user engages with your mobile app, the more refined and personalized the experience becomes. Through machine learning, your brand becomes more closely aligned with the customer experience that your customer desires.

Getting started can feel uncomfortable at first, but at Fusion, we’ve found that organizations often have low-hanging fruit ripe to benefit from machine learning for mobile. You just need to be able to see and then act on those opportunities. Working alongside you on this journey should be people who understand data science and machine learning, and who can uncover weaknesses to target. Now is the time to move forward on machine learning for mobile initiatives.

Current market conditions indicate a shortage of professionals in machine learning and data science. Fusion fills this gap. If you’re interested in hearing more about machine learning for mobile, let us connect you with one of our experts.