Machine learning and data science provide app developers with business intelligence that can steer them to better quality prospects for their products, which may ultimately lead to more revenue.

Around the world, millions of entrepreneurs create apps and music, post their work to popular app sites, and then hope for sales. Few developers and musicians have the ability to make their apps stand out, and to find those people who are most likely to pay for what they have developed.

“The app market has the potential to grow to 100 billion dollars by 2020, whether it is through ads or purchases,” said Prabhjot Singh, co-founder and president of Pyze, which provides business intelligence that helps app developers monetize their apps. Singh points out that there is a monumental divide between those app developers who are extremely successful, making over one million dollars a day in revenue, and the vast majority of app developers, who have difficulty making money.

“Out of three million apps that are available over the internet, less than 50 percent of the developers make over $500 per month,” Singh observed.

A major reason for the failure to monetize is that many app developers lack data and data intelligence that can steer them to better quality prospects for their products.

“Those companies that can build big data and analytics pipelines to learn about how their apps are being used and who uses them are in the best position to build a community of ‘sticky users’ who will continually use their apps,” said Singh. “They link into media outlets like Facebook and LinkedIn to retarget users. This is in sharp contrast to a couple of guys in a garage who develop an app, launch it, and just hope that it gets noticed and does well, which is where most app publishers are.”

Singh bases his data on comprehensive research that he and others conducted more than two years ago. “We talked with hundreds of app publishers of all sizes,” he said, “and we found that most were really frustrated. They wanted to learn more about their users and potential users, but the problem was that these are mobile apps used by mobile users, and mobile analytics wasn’t scaling particularly well to give these developers the types of information they were looking for.”

The problem was being able to adequately capture information about millions of users and to then ferret out key data points that could tell you meaningful things about these users and how to engage them. Traditional marketing segmentation approaches don’t work well with this type of problem.

“We felt that we could solve the problem by approaching it with machine learning and data science that would automatically cluster groups of users into segments, then further personalizing the engagement between the apps and the individual users,” said Dickey Singh, cofounder and CEO of Pyze.

The clustering of users into segments begins with looking at how different classes of users are using an app. Machine learning coupled with analytics algorithms then seek to dissect these users and their usage habits to identify different patterns of app use. From here, an app can develop personalized messages for users based upon how they use the app. Both non-profit and for-profit uses of the app can also be identified. This enables app developers to more clearly see who is using their apps in a premium, pay-for mode, and where they should be investing their efforts to further monetize their products.

“In an analysis like this, an app developer might see that only 20 to 30 percent of persons who download the app actually use it,” said Dickey Singh. “At the same time, the app developer has visibility of those users who are not only using the app, but who are paying for the right to use its most advanced features. These are the people that the developer can build a lasting and profitable engagement with.”

Prabhjot Singh said that in a study of 12 companies using the product, the companies are building their engagement with customers by 35% and their revenues by 20%. This kind of lift can help level playing fields between garage designers and large app companies. It can also improve engagement rates for large enterprises that are not primarily in the app business, but that want to use apps to improve its relationship with customers.

The best news of all

The analytics technologies coming online to help in the mobile space are more advanced than the simplistic login reports that only tell you who is logging in from where and when. There is real potential for companies and entrepreneurs to develop a more sophisticated understanding of their customers, including what users are willing to pay for.

Mary E. Shacklett is president of Transworld Data, a technology research and market development firm. Prior to founding the company, Mary was Senior Vice President of Marketing and Technology at TCCU, Inc., a financial services firm; Vice President of Product Research and Software Development for Summit Information Systems, a computer software company; and Vice President of Strategic Planning and Technology at FSI International, a multinational manufacturing company in the semiconductor industry. Mary is a keynote speaker and has more than 1,000 articles, research studies, and technology publications in print.