With the emergence of Machine Learning, outsourcing software companies have to transform themselves to adapt to the trendy, creative, evolutionary, and usage of mobile applications.
The ultimate goal of every company is to generate new customers and retain the older ones with their mobile applications. It is not always the case, but mobile apps are a still basis for meeting users’ needs promptly. Indeed, apps are nowadays the most popular way through which companies showcase their process and grant discretion to customers. The apps now do not play the mere role of collecting data but also managing to address the need of a specific user’s needs simultaneously in the way that the users feel to be understood.
Prior to moving to the next sections, there are a few key terms that we should share the same understanding.
- Machine learning is the process of training computer system to recognize the patterns normally used by a human so that the computer system could “think” and “understand” in a similar way to that of human’s
- Artificial Intelligence makes computer capable enough to take over some tasks that usually requires the interaction of human. Examples are speech recognition or language translation.
- A neural network is comprised of interconnection different networks or nodes or artificial neurons. The output of one neuron acts as input for the next neuron in the network.
- Deep Learning is a process of programming that make a neuron understand and solve a certain problem.
- Linear Regression is, in a simple way, a linear relationship between single input variables.
- Logistic Regression is to model a binomial outcome with one or more explanatory variables.
In what way ML can help?
ML can help recognize users and categorize them following to their needs from the mobile apps. ML can also help collect information and decide how the app should look like for different users. To obtain that target, ML should go through the following steps:
- Determine the target customers
- Determine their possible purchase
- File their requirements
- Find out the particular issues that the app could resolve
- Select the appropriate “voice” of language for target customers on the app.
Some example apps that apply ML could be Uber, Grab, Optimize Fitness. The common formula for their app success involves categorizing their customers, then approach them in the right way by using suitable content and language and then satisfying their customers’ need by providing real-time communication and interaction.
- Change in the searching process:
Machine Learning with its good customers’ insight would then help optimize the search tool of the apps. The search results would be more precise and relevant to the customer’s intent. Besides, with the intention of mobile app developers, the search results could also include closely relevant information such as article, documents, videos, or FAQ to make users more informative about their intended purchase. They, therefore, would be more interested and retain longer on apps. And the conversion rate would be more promising.
- Requires in Product Suggestions and User Behaviors
With deeper understanding for ML about customer demographics and data collected from search history, the app now could well monitor app users’ routine. It even can base on customers’ buying history to suggest an appropriate service or product or send interaction email to get in touch with passive customers.
- Enhances Security
Security measurements are reinforced and made more convenient with AI features. With in-app technologies as face recognition, cost estimation, wallet management, and logistics tracking, customers now would have much better buying experience on their app in a much more secure manner. With just few touches on their fingertips, the product they want to buy could be at their desired location within a few hours.
- Predict trends
With the integration of ML, the possibilities on mobile apps now seem to be endless. App users now could make their photo into animation using the app with facial recognition tech and intelligent filters. Or the app could also provide the customer with virtual tours of the real estate properties that they intended to buy. This would save a huge amount of time and effort in travelling and visiting the real spots.
Through the experiences of users, the apps then could make statistic report on the trend and the interest of users. The company hence will have an appropriate approach or solutions to catch up with newly emerge ones.
How ML and AI were deployed by developers in renovating mobile apps?
As mentioned, the application ability of ML is tremendous. It is the role of developers to make this trendy technology into innovative products, in this case, mobile apps. Below are some suggestions that could be taken into consideration.
- Integrate ML with AI
- Apply ML in big data analysis and prediction.
- Help enhance security measurements
- Use Optical character recognition (OCR) from machine learning to skip some possible steps of the original algorithm.
- Employ ML for natural language processing (NLP) apps to reduce the time updating and fine-tuning various algorithm elements.
Followings are the most popular examples apps using machine learning:
- Oval Money
- Google Maps
ML could help transform mobile apps with enough features of personalization, better secure, more efficiency, and more entertainment. The possibility of ML in app development is countless and it is the role of the mobile app developers to make it into innovation.
The New Year is coming, why don’t you think of developing a mobile app with the integration of AI and ML for your own company. Drop us a few lines, and our dedicated team at TP&P Technology will be with you shortly.