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SigmaWay Blog

SigmaWay Blog tries to aggregate original and third party content for the site users. It caters to articles on Process Improvement, Lean Six Sigma, Analytics, Market Intelligence, Training ,IT Services and industries which SigmaWay caters to

Predictive Analytics in Social Media Marketing: How Machine Learning Predicts User Behavior

At present, it is becoming less and less challenging to identify and target specific audience segments more effectively, optimize performance of social media ads and to create personalized content due to the emergence of Predictive Analytics. Predictive analytics, driven by Machine Learning (ML), allows digital marketers to predict future trends and user behavior, make smarter decisions and improve ad performance.

What is Predictive Analytics?

Predictive Analytics uses current or historical data and predicts plausible future trends, events and patterns. Such models have been in use for quite some time now, such as predicting the sales of tickets for a movie, understanding future staffing needs of a hospital or even forecasting a business’s financials at the end of an upcoming quarter. However, today this practice has evolved from simple manual predictive analysis to complex ML systems that are faster, way more effective and can be implemented on a much larger scale.
ML, a type of AI, uses algorithms to enhance prediction accuracy by analyzing data and making informed judgments. ML algorithms analyze datasets to find patterns and characteristics among users. In the context of social media marketing, this helps marketers to segment their audience accurately and effectively. It can also be used to customize ad content based on individual user preferences. Through analyzing data, it can predict which ads are likely to give the highest ROI.
According to the
Crowdfire website, 57% of businesses that used machine learning to improve customer experience notice a 100% boost in customer loyalty, over 100% rise in brand awareness, 70% improvement in fraud detection and 28% increase in acquiring new customers. Therefore, using ML and AI, offers great benefits to a business in leading to higher growth, increasing loyalty and enhancing market position.
Every business wants to be a part of the AI movement, especially implementing in all business systems at the earliest, but do not do so as they have no idea where to begin
. We can help you with that! Learn more and consult us today!

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Machine learning and the future of beauty industry

Machine learning is progressively transforming the way we work, live and interact. It is effectively applied in almost all sectors with beauty industry being no exception. Machine learning can help the beauty industry in several ways. It is expected that computer vision would help recognize facial features, analyze the data obtained and come up with a prediction or conclusion about the appearance. At present, data scientists are working on AI systems that have the ability to understand human face. If it works out, we no longer require to physically test out new looks and products. Data analysis will lead to better cosmetics. Leveraging data means better, long-lasting formulas. Nowadays, startups and industry leaders are offering machine-based advice on finding one’s personal style. For instance, Sephora and Mira uses worldwide tests and computer vision helping customers choose the perfect combination of foundation, complexion, etc. Some businesses like Olay have developed applications to determine skin needs of customers and come up with personalized products. Thus Artificial Intelligence with its machine learning and computer vision can go a long way in ensuring customer satisfaction. Read more at: https://medium.com/sciforce/machine-learning-changing-the-beauty-industry-ab3a2fa0aaf

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MACHINE LEARNING OPTIMIZING CUSTOMER EXPERIENCE

Machine learning applies artificial intelligence to automatically learn and improve from experience without being explicitly programmed. Machine learning applications are generally applied to those areas which involve processing lots of a data; a field where humans aren’t well-equipped. Machine learning applies discovered insights in ways that can optimize the customer experience. Chatbots provide effective solutions by stimulating an interaction with a customer service representative or resolving simple inquiries. Machine learning helps chatbots learn when to give specific responses, from where to gather necessary information and most importantly when they should hand off a conversation to a human agent. Virtual assistants, with the help of machine learning focus on specific areas where they can provide assistance to the customers. In order to continually optimize, customer service needs measurable analytics. Machine learning can help add a predictive element to support analytics. Thus machine learning helps in delivering better customer experiences. Read more at: https://www.business2community.com/brandviews/zendesk/how-is-machine-learning-being-used-in-customer-service-02215814

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Omnipresence of AI

From food to clothing, to real estate, and to everything; now AI and machine learning is everywhere. They can recognize foods, value of real estates, things in your images and many more. They are really good at whatever it does. There have been varieties in application of AI and it will be interesting to integrate all those stuffs into one general intelligence.

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The Relationship They Share: AI, ML, DL

Artificial Intelligence, Machine Learning and Deep Learning are now the most exploring topics for any techie. In spite of enough differentiation between these terms, they are often used interchangeably. To put an end to this confusion one could say that ML and DL are nothing but cousins of AI. 

Artificial intelligence (AI) is an area of computer science that emphasizes the creation of intelligent machines that work and react like humans. Many applications of AI are being seen and used today. From voice-powered personal assistants like Siri and Alexa to self-driving cars and many more are applications of AI.

On the other hand, Machine learning is an artificial intelligence (AI) that is discipline geared toward the technological development of human knowledge. Machine learning allows computers to handle new situations via analysis, self-training, observation and experience.

Whereas, deep learning is a subset of machine learning which is a collection of algorithms used in ML to build and train neutral networks and act as decision making nodes.

So, though AL, ML and DL are interrelated but in this vast field of technology they all stand on their own and using them interchangeably would not be justice.

Read more at: https://towardsdatascience.com/cousins-of-artificial-intelligence-dda4edc27b55

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TWITTER TAKING HELP OF MACHINE LEARNING

Twitter will now be shaking hands with a machine-learning startup that specializes in working with images, to deliver better video and picture content to expand its Machine Learning and AI parts. According to the sources, nearly $150 million is invested in this machine learning startup. A team of engineers will help Twitter by letting the users explore new experiences and share them. Read more at: http://economictimes.indiatimes.com/small-biz/startups/twitter-allows-users-to-share-140-second-videos/articleshow/52848116.cms

 

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Exploring the era of machine learning

Machine learning can be used in our daily lives such as filtering the spams in our mailbox or for banks judging the credibility of customers before issuing credit cards to them. We can even deposit checks through our phone. Machine learning models train itself, gather inputs and generate output. Machine learning tools are used as a part of business intelligence. Through Natural Language Processing (NLP) machine learning can comprehend speech or written words and generate graphs and figures. It can correct any anomalies in business and find out when the demand for your product is high. The more inputs one feeds in faster it learns. Read more at: https://www.sisense.com/blog/beyond-hype-machine-learning-unlocking-power-bi/

 

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