The Empowering Influence of Machine Learning on Mobile App Development

Generally, every other business churning out cutting-edge mobile apps, as this arena has truly become overloaded. So how do customers choose amongst this abundance of apps and how do businesses retain their share of loyal customers? This is a challenging scenario. There are primarily two aspects targeted with the launch of a mobile app. Retaining existing customers and attracting as many new customers as possible. Thereby, Businesses are expected to strike the right balance between these two dimensions to attain their ultimate goal. To cope with the soaring expectations and ever-changing demands, app developers have begun to increasingly adopt artificial intelligence and machine learning in particular. It empowers developers to build intelligent apps which can sense the pulse of customers and provide a hassle-free, delightful customer experience.

Read on to know more…

What is machine learning?

Machine learning, as the term refers, it is learning. Learning of customer behavior, shopping trends, demands, likes and dislikes. This kind of exhaustive information backs organizations to better comprehend their customer base and modify their services/products based on specific customer requirements. More than anything, it comes with a one-of-its-kind customer service which is personalized and tailor-made based on the requisite of every customer. It supports intuitive and intellectual apps which understand customers and react based on this interpretation rather than simply prompting mundane responses.

Areas of application:

The impact of machine learning:

1.Provides personalized experience:

Machine learning helps obtain critical details about customers which include:

• Who are the target audience?
• What kind of a budget do they majorly look for?
• What are their needs, if anything specific?
• Are there any specific areas of improvement the app needs to focus on?
• Particular phrases or keywords used for searches on mobile apps

Equipped with this data, businesses are able to categorize their customer base and cater to their needs in a better way. All this information also paves way for an interactive communication with customers through the mobile apps.

2.Improving search results:

When apps begin to understand the needs of their customers with machine learning, they are able to throw out better results in response to customer searches. The results meet their requirements and are able to serve them appropriately. Machine learning algorithms obtain information about customers from their queries and prioritize results for every customer. Cognitive technology categorizes content like videos, documents, FAQs into a knowledge base which is helpful in providing the best-suited responses for search queries.

3.Providing recommendations based on user behavior:

Machine learning tracks user behavior like their purchase history, budget, product preferences and so on. Based on this data, apps are able to provide relevant recommendations. Such recommendations consider multiple aspects like gender, age, geographical location, search requests made often, app usage patterns and so on. For example, if there is a finding that the number of female users is more for an app, they could focus on retaining the interests of this crowd along with working on why has there been a dip in the male group of users. Machine learning helps businesses work smarter by taking instantaneous decisions.

4.Improved authentication and controlled fraudulence:

Machine learning does take charge of app security and authentication through fingerprint recognition, wallet management, image recognition, shipping cost estimation, logistics optimization, business intelligence to name a few. While traditional apps can only resist threats, machine learning can protect apps from unidentified threats and attacks in real-time. With machine learning apps can allow their customers to log into websites the most secure way.

5.Other traits:

Other aspects which machine learning supports is features like weather forecasting, filters which help in animating photos, videos and so on. It also helps them in evaluating property, evangelizing augmented reality and helps in making apt purchase decisions.

How are developers benefitted from machine learning?

Developers could explore and utilize the unsurpassed capabilities of machine learning which are:

• Predictive analysis:

Developers can use loads of data for market analysis and intuitive business predictions. There is ample information about customer patterns and upcoming trends but there is a gap in between with regard to opportunities for organizations. This gap is filled by machine learning which backs developers aggregate customer tendencies in real time and obtain impulsive insights.

• Security of apps:

Developers can implement machine learning to enhance the security of their apps. This can be implemented in real-time when there are ongoing changes in the input. Machine learning is competent to block spammers and secure apps from threats without explicit programming instructions.

• Optical character recognition:

Machine learning apps enable conceiving OCR capabilities. This allows developers to skip few variations in the original algorithm. Such missing variations are identified by the OCR application.

Machine learning also supports natural language processing apps. It reduces the time taken to develop it, update it and also slashes down the need to fine-tune different algorithm elements.

According to a survey by Forrester Research, Artificial intelligence can contribute immensely in code generation. If an AI software system be given a requirement in natural language, it would be able to generate the relevant code-with its own idea and implementation.

Therefore, Other than this machine language also supports technologies like augmented reality, and virtual reality for the benefit of the developers.

Few of the predominant apps using machine learning are:

• Netflix
• Tinder
• Oval Money
• Snapchat
• Google Maps
• Impromp Do
• Dango

Wrap up:

Thus, Machine learning has a plethora of out-of-the-box capabilities which need to be identified appropriately and implemented based on business requirements for the best outcomes.