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

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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Big Data in Economic Prosperity

Big Data, if utilized properly, is believed to become the historic driver of progress. It plays an important role in the fields of public security, healthcare, poverty, to name a few. Video surveillance and facial recognition using big data is far more effective than reviewing the footages manually, which can be erroneous. It also helps in avoiding cybersecurity threats. Predictive models using big data can predict for future attacks even before their occurrence. With the application of big data in healthcare sector, there has been a shift from treating illnesses to proactively maintaining our health and taking certain measure for preventive care. It plays an immense role in the education sector as well. By understanding the needs of each district, it gives schools the opportunity to build innovative educational techniques. Big data solves urban transportation problem by enabling government agencies develop alternate routes to ease traffic. It helps in alleviating the dangers of food scarcity. It is time to embrace big data as it opens up opportunities to encourage economic prosperity. Read more at: https://datafloq.com/read/5-applications-big-data-in-government/65

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Why Reputation Management matters so much?

Reputation management deals with maintaining and controlling a group’s, an individual’s or a brand’s reputation. The reviews that the customers give on a brand greatly impact how search engines and prospective customers make decisions about that brand everyday. Every business tries to take its reputation management strategy to a next level. Sentiment analysis determines the nature of response to a particular product or brand- if its positive, negative or neutral. It helps to analyze customer feedback or how they feel towards a brand. Besides, competitive intelligence is the key to winning and maintaining business. Understanding one brand’s performance relative to others is necessary. Generating new reviews help businesses to stand out which in turn helps in maintaining the reputation. This can be done by simply asking customers at checkout to write a review if they are willing to. However asking for reviews too often is not a good idea as this can annoy the customers. Hence businesses must learn to create an effective review response. Read more at: https://www.thedrum.com/industryinsights/2019/06/25/how-take-your-reputation-management-strategy-the-next-level

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Visual Data in Decision Making

With every passing day, data and not instincts, are used for the expanding of business. Data is the new gold, as it helps in determining trend, offering better customer experience, responding better to market demands. However, given the data size is so big, Data Visualization is opted for, making the interpretations easier. The major reasons that data visualization is crucial are: • Data visualizations amplify a story with pictures and visuals. • Data visualizations makes difficult data comprehensible. • Data visualizations help in decision analysis. Read more at: https://www.experfy.com/blog/the-value-of-visual-data-in-decision-making

 

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Financial Analysis in Businesses

Financial analysis s beneficial for businesses in the following ways: • Cutting costs: Financial data relating to investments and cash flows are analysed. • Making investments: Financial analysis helps in predicting the returns from investments, thereby enabling the companies to go for profitable investments only. • Forecasting the future: The future of the company can also be forecasted. • Following business trends: Financial analysis relies upon the current business trends and success rates of businesses in the sector. Such analysis helps in recovering faster in case the market suddenly drops. • Management: Financial management is also tracked by the financial analysts which helps in increasing efficiency overtime. Read more at: https://bigdataanalyticsnews.com/big-data-improve-ecommerce-for-businesses-customers/

 

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Predictive Analysis

Predictive modelling software is known for training the model with the dataset with known results to predict outcomes for the new data. The two common types of predictive models are, classification model (example, predict outcome when a component fails) and regression models (predicts a number). The benefits of predictive analysis are: • Improved production efficiency: It allows for effective inventory forecasting, production rates for meeting demand, and the like. • Improved Decision making: It identifies patterns and trends for the data, enabling easy decision making. • Enhanced risk reduction: Predictive analysis, as the name suggests, enables prediction about the future. This is most helpful for a firm to save it from the upcoming risks. • Enhanced fraud detection: Being aware of the trend, a change becomes helpful in detection of fraud. • Targeted, personalized marketing campaigns: Predictive analysis helps in knowing the structure of the market and helps in closely targeting and personalizing marketing campaigns to attract customers. Read more at: https://blogs.opentext.com/predictive-analytics/

 

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Big Data’s contribution in eCommerce

Before the introduction of Big Data, only calculated guesses were made by the companies to optimize pricing and forecast demand. Big Data has contributed big time in facilitating eCommerce activities. Some of the ways are: • Predicting trends: This helps in determining the trend, and the type of customers they will face in near future, and keep the inventory accordingly. • Pricing optimization: It helps in calculating the competitors’ position and make decisions about the set of products. • Demand forecast: Studying the data, the expected time of high or low sales can be predicted. • Flexible pricing policy: Prices can be changed time to time depending upon the concerned factors. Big Data provides the data required by managers for expanding the business, taking into consideration every possible factor. Read more at: https://bigdataanalyticsnews.com/big-data-improve-ecommerce-for-businesses-customers/

 

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Machine Learning and Deep Learning

Machine Learning and Deep Learning both uses the algorithms fed into them. While in the first, the algorithm needs to be told how to make accurate prediction, in the latter, the algorithms are fed via neural networks, making the operation similar to a human brain and involving lower chances of mistakes as compared to Machine Learning. While Machine Learning gives result for a numerical and text field, Deep Learning also enables face, voice and handwriting recognition. Also, with new data fed into the system, the accuracy rates by Deep Learning are much more than by Machine Learning. Although Deep Learning is anyday better than Machine Learning, Machine Learning plays a vital role in the existing economy. Read more at https://www.analyticsindiamag.com/understanding-difference-deep-learning-machine-learning/

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Utilization of AI to refine the entertainment marketing strategies

Merging entertainment with data is a well-known concept. The marketers and content-creators have always focused on strategies that will resonate with the audiences and keep them engaged. Over the past few years, there has been an evolution of AI in the marketing strategies of content creators, brands, networks, etc. AI uses the deep learning algorithms that can digest, asses and contextualize unstructured data quickly to derive actionable insights. AI can analyze millions of pieces of content at a time, with the help of deep learning which is undoubtedly beneficial for the content creators and marketers. Deep learning helps in predicting whether a campaign will be successful even before it starts. Thus marketers are increasingly turning to deep learning algorithms to make better sense of the contents. Read more at: https://www.thedrum.com/industryinsights/2019/04/03/the-evolution-ai-entertainment-marketing

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The era of Influencer Marketing

Over the last few years, industries have become more sophisticated due to the increasing demands of influencer marketing. Many companies are planning to increase their influencer marketing investments to develop long-term partnerships with creative influencers on social media. Influencer marketing is nothing but a hybrid of old and new marketing tools, taking the idea of celebrity endorsement and placing it into a modern day marketing campaign. However the Influencer marketing faces a lot of challenges. Due to inconsistency in data, companies tend to jump from one influence marketing provider to another. However, we can expect to see an evolution in gathering and usage of data in the coming years. Brands are increasingly becoming more educated and competent. Marketers and brands want tools that gather all metrics relevant to value generation and tools that can track the performance from multiple social media platforms. Those providers that fail to deliver these requirements often fall behind as the market evolves. Moreover influencer industry is built on trust and authenticity, so the rise of fake followers erode business and consumer trust. Hence in order to protect the customers, the influencers must take a stand against the rise of fake followers. Read more at: https://www.thedrum.com/industryinsights/2018/12/21/why-we-need-get-little-smarter-about-influencer-marketing

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Criminals making felonious use of Artificial Intelligence

Cyber criminals, also known as hackers, use computer systems to access business and personal information for malicious purposes. There is no doubt that criminals are the most creative people in the world and the development of Artificial Intelligence has only made them stronger. Since the tutorials and tools for its development is widely available in the public domain, AIs use for attacking purpose is hugely unrestrained. Machine learning poisoning is one of the ways for criminals to circumvent the effectiveness of AI. Today we live in a world of chatbots. Most people do not realize how much personal information AI-driven bots may know about them, which makes them easy prey for experienced cybercriminals. Moreover criminals could harness machine learning technology to sift through huge quantities of stolen records of individuals to create more targeted phishing emails. However the biggest fear remains in the fact that the fully unstoppable AI creature will seize the world one day. Thus AI security abuse must be prevented and humans being much better than machines must understand its cause and effect. Read more at: https://resources.infosecinstitute.com/criminals-can-exploit-ai/#gref

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Eliminating Gender Pay Gap Through Data Analysis

Gender pay gap in the workplace is one of the most relevant issues which can be solved by data driven decision making. On taking a closer look, it has been found that gaps in pay structure arise from unconscious biases and strategies that benefit one gender more than the other.  Research shows that there is no connection between the fairness of a raise and the effect of that raise on the gender pay gap, making it a complex issue. This can be solved with Data analysis and visualization. Rather than raising the salary of every female employee by the same percentage, building algorithms using companies’ data is more effective to eliminate the gap. This approach also brings the manifestations of unconscious bias in the pay structure to the forefront. Thus, data driven solutions can test salary decisions before making them thereby closing the gap.

Read more at: https://insidebigdata.com/2019/06/26/addressing-demographic-pay-gaps-with-data-driven-solutions/

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Chatbots: Key To Customer Service Optimization

With the advancement of data science, Chatbots are on the rise. Chatbots optimize customer service as they prod intelligent conversations between a human and an automaton. Chatbots, driven by machine learning, constantly collect new data from their interactions with customers to deliver improved experiences. It has been estimated that in 2020 over 85% of all customer service interactions will deploy Chatbots. They can act as virtual advisors and look into customer issues, can decipher typos made by interlocutor, provide speedy responses with 24/7 assistance to customers, deliver help in the banking industry, etc. Chatbots being the employees that can work without taking any rest, are useful in the insurance industry, Facebook Messenger and health service as well. Developments of Machine Learning algorithms and technology behind Big Data are the pillars of Chatbots.

Read more at: https://www.smartdatacollective.com/big-data-leads-to-impressive-array-of-chatbots-in-customer-service/

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Big Data And Gmail Security

   With the idea of improving Gmail security, Google has adopted new big data security standards. Nonetheless, users should incorporate big data to secure their Gmail login. So far, Gmail is not the most secure email servers in the market as it wants our emails and details which act as data. Big data enhances Gmail security by prioritizing advancements in cybersecurity and malware protection and making two step verification more reliable. As Gmail users, we should upgrade our browser every time we are notified about it and use a sophisticated password which should be a unique combination of characters, letters and numbers. Hence it’s a blessing for us that Big data has started looking into Gmail security concerns.

Read more at: https://www.smartdatacollective.com/4-brilliant-ways-to-use-big-data-to-boost-gmail-security/

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Big Data: Solution for Shipping Industries

Having entered the era of Data Analytics, lately it has been realized that even the shipping industry is driven by Big Data. Companies track KPIs (Key Performance Indicators) to measure its performance and spot areas that need improvement. KPIs can be applied to shipping logistics as well and this monitoring can be done easily by Big Data. Shipping Damage is a vital metric which Big Data tracks during transit as it’s important to identify and curb damages of shipments. Big Data helps monitor Shipping Time and identifies the causes of delayed deliveries. It also ensures that customers’ expectations are met by tracking Inventory Accumulation. Owing to its versatile nature, Big Data can also monitor Shipping Costs. Thus given the profound impact that Big Data has on shipping, it’s important that shipping companies incorporate data analytics to save themselves from higher costs and other damages.   

Read more at: https://www.smartdatacollective.com/data-analytics-optimizes-shipping-through-kpi-tracking/

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Predictive Analytics helping retailers in customer retention

Predictive Analytics make predictions about the future events based on current data. These days retailers use it to determine the future needs of the customers. The biggest challenge faced by retailers today is customer retention. Predictive Analytics is bringing about a huge change in the retail experience altogether. Eminent retailers like Amazon is using predictive analytics to make customer recommendations based on purchasing history. Furthermore, development in Artificial Intelligence and machine learning is boosting its use. These developments in turn are helping the retailers gain a competitive edge. However tech giants like Google, Microsoft tend to keep the cutting-edge innovations for them. The new and innovative predictive analytics must level the playing field for small medium industries. Read more at: https://channels.theinnovationenterprise.com/articles/retail-reaping-rewards-predictive-analysis

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Big Data: Key to Creating Powerful Instagram Stories

With benefits like tracking the response of Instagram users to different Instagram stories, delivering better content, deeper understanding of the ROI of various time slots, etc. Instagram marketers can use Big Data to attract more followers and drive more sales. The feature of Instagram stories help increase these numbers backed by big data. Appealing content for Instagram stories can be created by first determining the goal of the story. Next, a great concept story needs to be spun to convey the benefits of the brand’s products to Instagram users. After building the concept, the story outline needs to be created which should then be sketched visually. Application of these steps and proper incorporation of Big Data should keep Instagram marketers from buying followers without causing any hindrance in the achievement of their goals.

Read more at: https://www.smartdatacollective.com/big-data-for-instagram-using-data-to-perfect-instagram-storyboard/

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Revolutionizing Indian Agriculture with digital farming tools

Since the emergence of Green Revolution in the 1960s, Indian policies have focused on ensuring national food security. However the small and the marginal farmers suffered a lot as the Green Revolution policies failed to increase their income. This has resulted in an increase in farm indebtedness and distress leading to farmer suicides all over the country. Over the last few decades Indian agriculture has seen massive technological developments and opportunities. The recent policies focus on increasing farmer incomes through digital innovations to give the farmers greater access to technology, risk management, finance and markets. Digital innovations enable the farmers to have credits at lesser risk and cost. Digital extension services provide real-time advice to farmers .Among the innumerable obstacles faced by Indian farmers, some include distance from the market, dependence on moneylenders for access to capital, huge cost of transportation, etc. Digital solutions are seen as a way to overcome these obstacles by providing farmers transparency and unmediated market access. Thus digital innovations are greatly helpful in improving farmers’ livelihoods and Indian Agriculture as a whole by making it more market-oriented. Read more at: https://www.thehindubusinessline.com/opinion/the-digital-route-to-transforming-farm-sector/article26006290.ece

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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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ARE EMPLOYEES THE WEAKEST LINK IN COMPANY’S SECURITY CHAIN?

Irrespective of the business size, employees are always on the front lines of the company’s cybersecurity. Based on studies, it has been concluded that two out of three cybersecurity incidents occur because of employee negligence. Employees can be the company’s best defense or its biggest liability. Therefore, cybersecurity training is very important. Employees tend to reuse the same password everywhere. This is a risky practice making it easier for sensitive information to go in the wrong hands. Company Policies must ensure that employees change their passwords every few months. Moreover employees should be taught to spot phishing attempts, including spelling and grammatical errors, suspicious attachments, request for personal information, free offers etc. Sometimes just visiting a site can infect the entire network. In order to maintain security, employees should be taught to hover over the links before clicking on them to see the actual URL; if it looks suspicious it would be best to avoid it. Thus as we see, untrained employees can be the biggest cybersecurity threat. Hence companies should shore up their security systems, starting right from its employees. Read more at: https://channels.theinnovationenterprise.com/articles/employees-cybersecurity-threat

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