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Posted on Sep 28, 2020
byKaran Ahluwalia (Business Development Executive)

Why will the next generation of Machine Learning powered mobile apps be a game changer

In recent years, there has been a paradigm shift in our lives with the revolution brought in by smartphones and mobile applications. Businesses worldwide are heavily investing on mobility to attract newer audiences and maintain a competitive advantage in the market.

However, with changing market dynamics and the need to create an immersive customer experience, technologies such as Machine Learning (ML) and Artificial Intelligence (AI) are being leveraged to further enhance the power of mobile apps to revolutionize the way apps interact with a user.

Based on the latest report by Allied Market Research, Machine Learning is growing at a CAGR of 39.0% from 2017-2023. The report also suggests that Machine Learning as a service is projected to reach $5,537 Mn by 2023.

Machine learning enabled mobile apps help businesses increase their customer engagement through personalized services and product recommendations. Mobile apps such as Uber, Netflix and Google maps are using machine learning to record, analyze and re-use the data fetched from customer activity to draw conclusions. It also helps them learn automatically and improve the customer experience during program performance.



How Do Enterprises Benefit From ML-enabled Mobile Applications?
  • Customer Segmentation
    Machine learning enabled apps can help enterprises provide personalized services for their customers.
    As a matter of fact, they can utilize data from these apps to analyze customer behavior, which is crucial when it comes to formulating strategies and taking customer feedback.
  • Profitability
    Machine learning being applied on mobile apps can help enterprises identify profitable opportunities with the help of insights based on app usage. For example, based on the information collected about the customer’s spend on shopping on mobile apps, businesses can allure customers by showing them related products, leading to the chance of a bigger sale.
  • Virtual Assistant
    Machine learning-powered chatbots embedded in your mobile app serve as your automated service agent. A fully functional chatbot can communicate seamlessly with a wide customer base and can provide ample amount of information to the user.

How Do Mobile App Developers Benefit From Machine learning?

  • Low Latency
    A successful mobile application demands the latency to be as low as possible. Irrespective of how rich the features are, higher latency of an app can ruin the customer experience completely. Since Machine learning is performed “on-device”, there is an increase in the mobile app’s data processing abilities which reduces the latency next to zero.
  • Cost-effective App Development
    Since Machine learning is applied on the device, app developers benefit from reduced complexity of data processing, that further helps to avoid expensive costs related to cloud services. This also means that a smaller team is needed to develop a ML-enabled mobile app, as the need for cloud infrastructure expertise reduces.
  • Effective App Testing
    Machine learning helps developers and testers derive patterns from operational data. This further helps them in generating a customized module for testing their ML apps which improves test cases and testing in general.
Summing up, the next-gen mobile apps are expected to be more powerful and will soon be a game-changer. Although Artificial Intelligence is yet to be harnessed to its fullest, the human-to-computer collaboration is a promising factor for ML-enabled applications to work more intelligently and drive an intuitive customer experience.

More than a software development house ourselves, Aress is a community dedicated to exploring the emerging intersection of Machine learning and Mobile app development. We continue to develop and enhance our skillsets in building ML-enabled mobile apps so that we can drive better results for our customers. Contact us at info@aress.com to know more.

Category: Analytics, Artificial Intelligence, Big Data and BI

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