machine learning Archives - Page 2 of 2 - Winklix

The era of generic service is diminishing . Customer now a days are more willing to get custom tailored offers as per their specific demand . In fact in recent studies it has been proven that almost 50 % of customers switch their brands if company is not able to meet their specific sets of needs and almost 57% shares their data with companies that send personalised offers .

It is because of digital transformation and technological advancement that has opened up many new doors for vendors which aids in attracting and retaining of customers . But there is a huge difference in fact and reality , in short you will never be able to fulfil needs of your targeted audience with mobile app that does not contain any advance technology . Machine Learning (ML) is one such cognitive technology that has ability to create algorithms and understand human in a way that can assist them in completion of tasks and even entertain them .

So Machine Learning (ML) is technology which can be implanted in mobile app to make it more user friendly , thereby giving more user experience , customer loyalty and thereby aids in building consistent omnichannel experience .

Let’s now look at how machine learning can enrich your experience :

Personalised Experience

With machine learning you can redirect machine to learn and adopt continuously . It has algorithms which redirects analysis of various sources of information which can either be acquired from social media , credit ratings and more which later on given pop recommendation to customers devices .
In addition to above Machine Learning can help you classify users interest , collect information of users and can also guide you how your app should look alike . Machine Learning can be used to learn :

Who your customers are
What they want
What are their affordability power
What are their hobbies , interest and pain points
What they are specking about your products

On the basis of all the information collected above , machine learning can actually help you in structuring as well as classifying your customer into groups . As a result you can deliver content relevant to them on the basis of information collected and hence convey the impression that your app is really talking to them .

Advance Search

Machine learning helps you building search more intuitive and less burdensome for your customers as they will deliver results on the basis of their most recent searches . Machine learning algorithms helps learning from customers queries and thereby showcase the result which most matters to them . Due to its cognitive in nature , it helps grouping articles , videos , FAQs and documents to provider smarter result and immediate answers to their solutions .
Once the data is collected , machine learning utilised that data to helps customers perform searches , search histories and typical actions with ease . In addition to it , you can also upgrade your mobile app with voice search and spelling corrections .
Reddit is making use of ML which aids them in improving overall search performance for hundred of millions of community members .

User Behaviour Prediction

Marketers get detailed data about user behaviour by analysis of data collected on the basis of age , gender , location , search request , frequency of app usage and so on . Marketers then make use of data collected to facilitate customers as per their interest as well as increasing overall effectiveness of your app and your marketing efforts . For instance say on the basis of data collected , you have found that females under age of 30 are more using your app in comparison with male , then you may either find ways to attache male audience or move your target entirely on women audience .
Machine learning can also facilitate you with even creating of individual recommendation to boost customer engagement as well as time spent on your app . Have you even been browsing on Amazon ? If yes then you must have experience that Amazon suggest on the basis of machine learning algorithms about your likes and dislikes . In addition to it , almost 80 % of TV shows watched on Netflix are the result of their suggestion system based on machine learning algorithms .

Showing Advertisement On The Basis Of Interest

One of the hardest part to deal with when it comes to advertisement is showcasing right ads to right audience . Thanks to Machine Learning technology which aids advertisers to showcase advertisement to right people more accurately .
With Machine learning you can even avoid showcasing advertisement in respect to items that has just been brought by customer and thereby showcasing ads to customers who are more likely interest inn buying products or services . This technology will not only helps you saving your time and money , but will also help you with improving brand’s reputation .
Coca Cola is great example for making use of this technology on social media advertisement . Company make use of image recognition technology for identification of people who have posted their product images . This has helped Coca Cola know about the situation when customers talk about their product and what can be the best way to showcase them advertisement . Ads designed by way of machine learning algorithms has greater changes of getting clicks in respect to other targeted ads .

Security Improvement

Video , audio and voice recognition makes it easier for customers to add on extra layer of security to their mobile apps by secure app authentication . It is smart decision for any kind of mobile app .
Machine learning algorithms can also help you in detecting and banning suspicious activities . Traditional technology on one hand can only help you with knowing of threat , machine learning mythology can help you protect your customers with identification of previously unidentified malware attacks on real time basis .
Banking and financial companies are also making use of machine learning to inspect previous transactions of customers , borrowing history which helps in determining their credit rating .

Also Read : What Is Machine Learning & Its Applications ?

User Engagement

Machine learning has superpower which offers solid customer support and range of specific features and entertainment which given customer a reason to use your app on day to day basis .

Support

Both Amazon and Facebook is making use of Machine Learning technology for user engagement in a way to handle their request intelligently . Machine learning technology has capability of analysing large sets of data and make decision in real time .
Some people have habit of not making calls or writing long emails until and unless somebody responds . Many companies now a days are implementing machine learning to build conversational UX or Virtual assistant often known as AI chatbots .

Entertainment

Beyond AI chatbots which can handle customer request even at 3 am , thereby are various other machine learning entertainment tools for customers . Take for instance say , Erwin is bot that lives in Facebook messenger which helps users to solve complicated puzzle by sending them clue if they struck somewhere .
Snapchat is using AR and ML to let customers revamp their pictures using funny filters . Their face is detected by camera and AR helps adding filters on their face .

Valuable Features

Machine learning also supports real time speech translation . So if your target is on international customers as well , then ML can facilitate you with making successful communication within your app without any need of third party online translators .
We can take example of Airbnb wherein more than 60 % of their booking are done by users in different languages . They are making use of cloud translations API which helps them translating listing , reviews and even conversion between its users . Azar , a chat app is using Cloud Speech API as well as cloud Translation API to translate audio between matches .
Another great example of machine learning application is Realtor.com , which is real estate listing which use Vision API to facilitate people to take pictures for sales sign and get immediate information about property .

Conclusion

Machine learning has great super power to play with , which has ability transform your mobile app development with new technologies .

In past few years , we had been experienced many companies who finds ways to incorporate artificial intelligence (AI) in they systems . As being one of the emerging technology , AI exploration has bought about sub concepts . AI as a technology which concept is machine learning in which computers has ability to learn without being programmed for it .

Machine Learning

As this article is about Machine Learning (ML) , the question that arises in everyone mind is what exactly is machine learning (ML) ? In simple terms ML is known to be subset of AI wherein computer algorithms are being used which has ability to learn automatically from data and information , even without being programmed for it .
So computers learn data and information on the basis of algorithms which aids them to build predictive models on the basis of observation . ML is being used for prediction and not perfection which are good enough to be useful .
Nothing in the tech world is simple and the same exist with machine learning . ML concept totally depends on how much data is being provided using supervised learning , unsupervised learning and other types of learning . Their type of learning is based on the information it’s fed labeled or not .

But the question arises how will an ML enabled device undergoes training ? The amount of training initially depends on how much data system is providing initially . ML concept can’t do anything without presence of data , but how much data is to be initially provided to the system ? Before going any further about why to use ML and other stuff , lets get into details about what exactly they are .

Supervised Learning

Supervised learning is one of the process of ML wherein we can say system is provided with data (x) by defined algorithms and in respective of data x output (y) is also labeled . Due to correct labelling of input and output data , system gains capability to recognise pattern in data with algorithms . This enable system to do future predictive analytics on the basis of correct labels . The reason why it is beneficial is it is being used to predict outcomes based on future inputs of data without any human interface .

Unsupervised Learning

In unsupervised learning , although data is getting fed into the system but outputs are not labeled like in supervised learning . In this data is being observed and determined as per the predefined patterns , and data pattern is no where recognised . These system of ML concept is being used in social media platform wherein your friend who had been graduated from X university and have taken degree in Calculus , and you are being recommended on social media platform on the basis of your interest to follow this friend on the basis of demographic data .

Reinforcement Training

Reinforcement training is very similar to unsupervised training . In this as the system is not labeled , so the system is left to create its own pattern . The reason why reinforcement is being categorised as different from unsupervised training is that when correct output is produced , the system is guided that the output is correct , thereby allowing system to explore full range of possibilities . If you are one of those Spotify user , you make thumbs up and thumbs down on the songs you like and dislike , and at this point they suggest you songs on the basis of music taste you are having by using concept of reinforcement training in ML .

Machine Learning Application

Well , have you have thought of why machine learning is on high phase ? Well the answer being it is next generation solution in achieving concept of artificial intelligence , and is one of the most achieved things for developers . ML gives apps superpower to improve on the basis of users inputs , without intervention of developers . As it has ability to auto learn , it also saves the time of developers and helps enhancement of user experience by suggesting user what they want . When we talk about machine learning , there are two significant concept , the very first being image processing and the second one being predictive analysis .

Image Processing

Image processing is being commonly used application of ML , which we all have experienced . Supervised learning concept is being used to recognise objects in image . In this we give turning to machine to recognise what we desire . For instance a machine is being trained to identify set of labels in image and once machine takes on the image will labels different objects in the image and provide their input accordingly . Apple’s Face ID feature is perfect explorer of this technology wherein apple tired to initially recognise your face from different angle and later on can analyse your face stored in data to perform multiple task

Predictive Analysis

The most popular category of ML is predictive analysis , which makes use of historical data to predict or recommend your future data . No matters whether you are using Android phone or an iPhone , your phone now a days suggest you text which you are writing . That is predictive analysis of work . They usually recognises pattern of the words you use and then provide for suggestions for future responses .
Predictive analysis is being used across all industries to recommend customers what they desire of . E-commerce uses this technology to suggest buyer with commonly purchased item , wherein social media used this to recommend people whom to follow .

Machine learning is a very huge concept which has not been blasted in the market so well till now . Right from social media recommendation to recognition of your face , it is being used everywhere which has impacted our life in positive way . At the same time developers are also gaining better understanding of what users behaviour are and are able to deliver the things accordingly . ML also facilitates users with sense of next level security . In case you are willing to take your product to next level , task with one of Winklix product manager to identify ML and suggest best solution for you .

Tag: machine learning

How Machine Learning Can Help Revamping Mobile App