Applications of Big Data in E-commerce

There was a point in time when lack of data was an issue. Now, times have changed and it is the overabundance of data that seems to be complicating the matter all the more. Especially in the e-commerce sector, where metrics like GMV, CLV, cart abandonment rate, and AOV are tracked diligently by online retail companies to gauge its performance.

Using the traditional methods to organize, store and study data are no longer feasible. Fortunately, Big Data is going mainstream and offers a range of advantages that will aid the data and analytics needs of the e-commerce companies. Here’s how big data is changing the face of e-commerce:

1. Enhanced customer service experience

The larger your customer base, larger is the data generated and the more you’d need to invest in infrastructure for storing data. This being the traditional method has been leading to poor customer service and unsatisfied customers overall. This can be easily avoided by using big data.

Big data holds the potential to track not just customer information but also maintain their experience records. Companies can then backtrack each customers experience and see what it is that is going wrong repetitively and strive to improve it.

2. Secure payments online

While paying online, big data has a significant role to play. Big data makes paying over the net a more secure and faster process. Big data integrates various payment platforms into one centralized platform. It aids not only the customers but also keeps fraudulent activities at bay.

3. Mobile commerce

Smartphones have turned out to be an extended organ of humanity. An organ so essential that imagining anything over an hour without it would seem impossible. Big data is in favor of mobility simply because anything with an IP address and the ability to transfer and receive data is compatible with Big data. Google also is turning partial towards those sites that are mobile responsive, and we don’t see why it wouldn’t be doing so.

4. VR advancements

When you combine another buzzword in the IT field such as VR and merge it with big data, you get something that holds the potential to reconstruct the future. This merger not only changes the way a consumer would go about with his/her day but it would also revolutionize the way sellers sell things.

Virtual reality would materialize things right in front of your eyes without the dependence on anything remotely human and for that, big data would be of help. Are we looking at the insurgence of Artificial intelligence here as VR and big data score so high on compatibility charts? It is for us to wait for not too long and watch.

Parting thoughts

Seemingly the King Midas of the IT sector, big data is changing to gold whatever it comes in contact with. E-commerce companies are increasingly relying on big data to get that much needed competitive edge and enhance the overall customer service experience that they can deliver.

Legal is a complex vertical with its own unique style of functioning unlike any other vertical. So, let us have a look at how Artificial Intelligence could influence the legal sector.

How Machine Learning can affect Economics

Machine learning has found its way into multiple business applications. It makes computing the process more efficient, cost effective and trustworthy. Machine Learning is no longer a fantasy but a set of authentic business technologies that will help the management take better decisions in the long run. Be it Facebook recommending friends tag based on photos or voice recognition systems like Siri and Cortana, Machine Learning helps us to make smarter and better decisions.

The power of machine learning goes mainstream

Studies show that in 2017, ¼ of the respondent companies used 15% of their IT budget on machine learning and have seen phenomenal ROI emanate from it. These numbers are expected to rise in the future because of fast infusion of machine learning into different and diverse industry verticals.

Machine learning is broadly used in the field of life sciences, healthcare, hospitality, and retail. Interestingly, there is one more vertical that machine learning can impact but it is not that popular yet.

It is the field of Economics!

Research shows that machine learning is yet to revolutionize economics the same way it has done for other fields. But it will majorly expand its possibilities and more economists should start to implement machine learning in their studies.

Want to see how machine learning can impact the field of economics? Read on…

How Machine Learning and Economics come together?

When an economist analyzes economic data, they try to figure out the relationship between two dynamic and seemingly unrelated conditions. For example, an economist might sort through real estate data to figure out how much the size, location, or other factors will determine the price people are willing to pay for a home.

Machine learning will not only help an economist figure out the relationship between the user and external factors but also will use that data to predict on how much that same house is worth and what should be expected from potential buyers.

Machine learning will not directly influence economic research but will help economists with the research data and predications.  Machine learning is primarily useful in collecting new sources of data. For example, economists have already been able to convert satellite data into estimates of economic growth, as well as to measure neighborhood income levels in Boston and New York using Google Street View, Yelp, and Twitter.

Another feature of machine learning is it allows economists to analyze language as data. Algorithms can be used to identify news articles too. It can gauge if the sentiment of the text is negative or positive. Brand marketers can then utilize this information to roll out campaigns accordingly.

Machine learning is also useful for testing out economic theories. Economists are working on the theory that share prices should incorporate the most relevant information, thus making it possible to predict future stock market fluctuations.

To conclude

Although machine learning is a new concept, it has a promising future in the world of economics. Some of the above examples show succinctly why machine learning has the (nearly) perfect answer to some of the most complex queries faced by an economist.

An Introduction to Design Systems, their uses, why we should use them and the nuances surrounding the topic.

How Big Data can help HR with hiring decisions

The Human resources (HR) function is usually not the first thing that comes to mind when we think of big data. That’s because it is still a developing concept and a majority of the companies are still using traditional methods to perform HR related tasks.

Organizations should make the most of big data to find the right level of adaptability to offer work-life balance to its employees as well as to determine the right benefits and bonuses in order to encourage employee loyalty towards its company. It can also be used to increase the quality and usefulness of regular training programs, thus maximizing the use of their human capital.

Big data has proven itself fruitful in many businesses; be it sales, marketing or accounting. Today we explore how big data will impact the HR function:

1. Amplify the quality of new recruits

Hiring the wrong person for the job is probably the worst mistake an HR team can make. With the help of big data, recruiters can be more analytical and strategic when it comes to finding the ideal candidate for the job.

If they get access to online employee resume databases, employment records, social media profiles, tests and other profiles, it will help the recruiter find the best candidate with the highest potential by sorting this information and narrowing down the talent pool.

Take the case of Royal Dutch Shell; they made their employee play specially designed video games in order to analyze the best idea generators in their team. As a result, the team found it easier to recruit employees that had the 6 main qualities the company needed i.e. mind wandering, social intelligence, goal-orientation fluency, implicit learning, task-switching ability, and conscientiousness.

2. Promote better training and employee success rate

Training can be an expensive affair if the overall employee retention is unsuccessful. Big data allows businesses to measure the effectiveness of the training program so they can make better investments when it comes to training and development of their employees. If regular performance evaluations are conducted, the efficiency can be measured via big data and it will help the HR understand the effectiveness of their employee development programs.

Check out IBM’s strategy in this context. Traditionally an outgoing personality has been seen as a key trait, but IBM compared worker surveys and tests with manager assessments and found that the most important characteristic of sales success was actually emotional courage. Successful salespeople may or may not be outgoing, but they do need to be persistent, and not take no for an answer.

3. Prevent employee attrition

Making strategic workforce decisions without data to back them up is like guessing, and it’s an issue that has prevented the HR from making a bigger impact on business outcomes. Workforce analytics is the art and science of connecting data to discover and share insights about your workforce that will lead to better business decisions. In order to reduce employee turnover, HR needs to become more data-driven, looking past simple descriptive analytics and towards more exploratory analytics, predictive analytics.

To conclude

These are a few ways in which big data can help the HR perform at a much more efficient rate. It is an infant concept but once companies truly start implementing it in their recruitment processes, it will yield great results.

How the use of Artificial Intelligence in Education can improve Student Retention at Universities

It is seen that there are scores of university students who enroll in higher education but do not end up obtaining a degree. Since they leave the education midway, they end up not being awarded the degree they had enrolled for. Universities and educational institutions are increasingly looking to technology to address this challenge of student retention. With the help of Artificial Intelligence and Machine Learning, they are seeking a way to improve the retention rates for higher education in countries like US and India.

By analyzing data from forms, educational literature, surveys, and studies, AI can detect the key reasons behind attrition and dropouts. This, in turn, helps the institutions to plug the gap wherever possible and improve their own university rankings by improving the retention rates.

How does Artificial Intelligence in Education work?

A student and an institution exchange volumes of information at every stage of the educational journey – right from initial expression of interest to completion of the programme and awarding of the degree. This helps the AI system to mine data that is of particular importance for tracking retention record of a student.

So, metrics like falling grades or increased absenteeism may provide early indicators of a likely dropout in the future. Once the student advisor or university professor gets an alert of the same, he/ she can counsel the student about ways to overcome the present challenges and continue pursuing the education.

Imagine if the AI system weren’t in place – the institution would’ve never got to discover the likelihood of a student dropping out until it was very late i.e. when the student actually drops out. Rather than taking a reactive stance, AI helps the institution and university to take proactive action and avert attrition from actually happening.

Use Cases in Artificial Intelligence’s impact on Student Retention Rates

There has been an interesting project that saw a public university bring in the benefits of Artificial Intelligence to tackle this very problem of falling student retention. The University of Oklahoma had witnessed a drastic fall in the number of higher education students returning for sophomore year in college. Out of the first-time students who started school in fall 2013, only 64% returned for the second year in fall 2014.

The university worked with IBM (for IBM Watson, its well-known proprietary cognitive computing, and AI system) and analyzed unstructured data like student essays. This helped to assess the tone of language, personality insights, and natural language classification. With this data, the university could better identify potential retention risk students and counsel them before they dropped out.

The outcome of the project was highly positive – from 64.2% in 2014, the retention rate climbed to 86.1% in 2015 and reached 92.1% in 2017.

To sign off

This post shows the exciting potential for Artificial Intelligence to make a truly lasting impact on the education and academics sector. The more universities and institutions embrace AI, the higher will be the likelihood of retention of students into the system.

AI in Logistics Industry

Artificial Intelligence (AI) has become a major topic in almost every business sector. Leaders talk about all kinds of positive impacts that robotics, machine learning, and other AI technologies can make possible. Optimizing these advanced technologies can save time and improve quality as well.

Parcel and logistics is one such industry that has started leveraging AI to influence supply chain and other associated processes.

AI is reshaping almost every procedure of parcel and logistics. Interested to know which areas has it impacted? Read on to know more

1. Automation in productivity

Productivity in logistics doesn’t rely solely on human expertise now. Advanced algorithms and robotics are bringing automation and reducing human errors. Automatic processes provide better quality products in terms of packaging, management and distribution preparation. Plus, they also reduce overall logistics cost for companies.

2. Enhanced delivery models

Automakers and Artificial Intelligence experts are partnering to incorporate best technologies. Logistics and parcel industry can get the best outcome with this partnership. Future presents a chance to incorporate autonomous vehicles, self-driving drones and parcel carriers for delivery. All of these technologies can provide exceptional accuracy and cost-effectiveness to the industry.

3. Efficient route optimization

Most logistics and parcel companies struggle with route optimization. Bad route selection increases fuel consumption and also affects customer satisfaction. Ultimately, it all impacts the cost of delivering parcels.

An AI integrated vehicle can resolve all these problems. Modern self-learning technologies can offer a platform for parcel companies that optimize routes on its own. Technology can find a coordinated and faster route for a delivery. This can reduce about 30 percent of travel distances and also decrease the need for vehicles for about 10 percent.

Machine learning uses data in real-time to provide a dynamic route for delivery teams. It anticipates potential traffic problems, weather conditions, distances and many other factors to decide the most cost-effective routes for delivery.

4. Improved customer experience with chatbots

In recent studies, more than 62% of consumers accepted that they feel comfortable about having a virtual assistant answer their questions. Chatbots can automate and also enhance customer interaction. Both websites and call centers of logistics companies can improve customer experience with immediate and accurate assistance.

5. Delivering intelligent interfaces

Self-learning via Artificial Intelligence is allowing machines to understand vast data related to logistics. Analyzing scenarios in terms of historical data, machines can resolve complex issues related to the supply chain. Machine learning enables them to create intelligent interfaces for automated decision-making.

6. Understanding consumer behavior

Two-way communication is a necessity in parcel and logistics industry. Companies need to know when and why customers need a product. This allows them to understand an overall demand for a product in a particular location. However, manual analysis of consumer behavior seems almost impossible.

On the other hand, technologies are becoming smarter and better in terms of consumer behavior analysis. This is absolutely perfect for the industry, as they can now know why consumers want a product. Anticipating consumer behavior with AI proves much more accurate and efficient. Algorithms take much less time to conduct a predictive analysis and anticipate consumers’ demands.

A rapid growth in AI is ready to empower this industry. Are you ready?!

NLP tools for Artificial Intelligence

There is consistent research going on to improve Artificial Intelligence (AI) so that it can understand the human speech naturally. In computer science, it is called as Natural Language Processing (NLP). In recent years, NLP has gained momentum because of the use of neural networks. With the help of these networks, there has been increased precision in predictions of tasks such as analyzing emotions.

With its advent in the world of computer science, a non-linear model for artificial computation has been created that replicates the neural framework of the brain. In addition, this structure is capable of performing NLP tasks such as visualization, decision-making, prediction, classification, etc.

Artificial Neural Networks that benefit NLP

An artificial neural network combines the use of its adjoined layers, which are input, output and hidden (it may have many layers), to send and receive data from input to the output layer through the hidden layer. While there are many types of artificial neural networks (ANN), the 5 prominent ones are explained in brief below:

1. Multilayer perceptron (MLP)

An MLP has more than one hidden layers. It implements the use of a non-linear model for activating the logistic or hyperbolic tangent function to classify data, which is linearly inseparable otherwise. All nodes in the layer are connected to the nodes following them so that the network is completely linked. Machine translation and speech recognition NLP applications fall under this type of ANN.

2. Convolutional Neural Network (CNN)

A CNN neural network offers one or many convolutional (looped or coiled) hidden layers. It combines several MLPs to transmit information from input to the output. Moreover, convolutional neural networks can offer exceptional results without the need for semantic or syntactic structures such as words or sentences based on human language. Moreover, it has a wider scope of image-based operations.

3. Recursive neural network (RNN)

A recursive neural network is a repetitive way of application of weight inputs (synapses) over a framework to create an output based on scalar predictions or predictions based on varying input structures. It uses this transmission operation by crossing over a particular framework in topological order. Simply speaking, the nodes in this layer are connected using a weight matrix (traversing across the complete network) and a non-linear function such as the hyperbolic function ‘tanh.’

4. Recurrent Neural Network (RNN):

Recurrent neural networks provide an output based on a directed cycle. It means that the output is based on the current synapses as well as the previous neuron’s synapses. This means that the recorded output from the previous information will also affect the current information. This arbitrary concept makes it ideal for speech and text analysis.

5. Long short-term memory (LSTM):

It is a form of RNN that models a long-range form of temporal layers accurately. It neglects the use of activation functions so it does not modify stored data values. This neural network is utilized with multiple units in the form of “blocks,” which regulate information based on logistic function.

With an increase in AI technology, the use of artificial neural networks with NLPs will open up new possibilities for computer science. Thus, it will eventually give birth to a new age where computers will be able to understand humans better.

Blockchain Technology in Life Sciences

In the medical sector, new and innovative therapies keep improving life sciences. However, the same innovation challenges the supply chain. Life science is in a desperate need for authenticated and secure drugs that can become available whenever required.

Why is blockchain a solution?

Blockchain technology has gained an immense level of growth in terms of investment. Experts project that this growth can reach up to a $3 billion market level as we all reach 2025.

Blockchain technology is applicable in almost every step of the supply chain of life science. And at each of those steps, this technology offers a unique benefit.

1. Provenance

Verifying product’s origin point becomes immutable with this technology. Tamper-proof blocks allow the use of digital markers. This way, all the chances of counterfeit product get diminished. Plus, life science supply chain meets every regulatory requirement.

2. Record management

The industry faces a need for extensive documentation. Highly complex records are created and managed, which increases the costs and administrative activities. All these processes can become automatic with this technology. Businesses need smart contracts that include regulations and logic of processing. Hence, all business data can get verified without wasting any time or money.

3. Sensitive data security

Access control is also possible with this technology. Networks act according to incorporated rules and restrict access to critical medical information. Hence, authorities know who accesses certain information and when. This can become a great advantage for healthcare consumers as well, who want to keep their health records confidential.

4. Managing internal process

There is a huge list of internal processes that life science companies have to manage. Tracking products, transactions, and factory operations are a few major internal processes. All in all, companies have to concentrate on their products in different steps such as raw material collection, packaging as well as labeling.

Choosing this technology can integrate each and every process spread across systems. Companies can skip multiple reconciliations and track everything with the help of a single ledger. This ledger will be available to every authoritative body. So, no need to hassle.

5. Multi-party collaboration

Collaboration is the most valuable property of this technology. Hence, clinical trial officers, trial sponsors, and multiple regulators can access and share data at the same time. A secure and shareable network brings transparency to the supply chain. Hence, every considerable party receives trustworthy collaborative network to rely on.

With speed and trust in life science, this technology offers the ability to transform the industry for good. Complications can go away and sensitive data can become more secure.

Happier patients

Soon blockchain is going to enable companies to present more relevant data for patients. Accurate information, continuous product availability, and other features can improve patients’ satisfaction. However, it all comes down to the manageability of health care and drug companies.

Life science sector requires a technology boost to attain much-needed goals in terms of security, speed, and quality. Hopefully, companies will understand this requirement and move forward in this direction as soon as possible. Only time will tell!

Angular JS Technology – The tech that rules the Internet

Often described as the HTML for applications and software, AngularJS is one of the go-to tools for frontend programming nerds. An extension of Java, this frontend framework does have several features that set it apart from contemporary tools. One of the most noticeable features of AngularJS is its application on the internet. It can be argued that the use of AngularJS has made web browsers and their various extensions more interesting. Let us have a look at what makes this particular tool such a top draw.

What is AngularJS?

When talking about AngularJS there are two words that quickly follow – ‘user interface’. UI is one of the most desired aspects of a website that draws in viewers and influences their choice to make use of its various features. AngularJS is primarily used to develop applications for the internet. Since it was first launched in 2009, most applications and websites have made use of AngularJS to a great extent. AngularJS has built-in directives which are applied to incorporate new syntax into browsers.

Features 

While in its essence AngularJS may seem like any other framework with several similarities, but what truly sets it apart is its comprehensive set of features that cover pretty much any requirement you might have as a web application developer. So let us have a look at some of the most prominent features that have made AngularJS so popular.

1) The AngularJS Community: Given its decade-long existence and the number of users it has worldwide, AngularJS has a well-developed community which serves as a repository of information which includes tutorials, tips, guides etc. The wealth of experience within the community is also something that makes this framework desirable.

2) Open Source Coding: AngularJS makes use of open-source codes which allow a great deal of flexibility for developers. The freedom to customize as the developer wishes is something that will eventually lead to better quality of a product. The level of security that this framework offers is another desirable aspect of its open source nature.

3) Documentation: AngularJS offers comprehensive documentation with all the desirable features one might possibly need to get working.

4) Two-Way Data Binding: Possibly the most desired aspect of angularJS. This type of binding facilitates the cohesion between the DOM and the model that you are using. The simplicity of this binding process is truly an appealing factor.

5) Templates: The templates that Angular JS has to offer are in traditional HTML. AngularJS acts as a buffer between the DOM and browser as it feeds the template into the browser.

6) The architecture: While AngularJS does not fully utilize the model-view-controller (MVC) architecture, it does apply the basic principles. The way in which it functions, it would be more appropriate to say that it uses the Model-View-ViewModel (MVVM). This model offers the perfect environment to begin crafting your application.

What’s new with AngularJS

Being an Open source framework, Angular users enjoy constant updates for the main framework as well as all extensions. The latest version of AngularJS, version 5.1 was released in december 2016 along with the updated version 1.6 of Angular CLI. While the framework update does not have many new features, many of the bugs in the previous versions have been fixed in this version. The regular freezes and crashes have been minimized. One of the stability fixes made is in the Service Worker package which was previously experiencing several crashes on loading. Another update added in 5.1 is the i18n update which now allows you to set local ids and customize them.

How Relevant will AngularJS be for your Future

Given its general nature and purpose, AngularJS faces severe competition in the market. There are many alternatives that are in use today for the same purpose. However, the same can be said of Java on which all these tools run.

Angular’s appeal at this point is at its highest. Developers prefer the use of Angular because of its directives and the wide range of possibilities they entail. While Python related tools do pose a threat to AngularJS, its relatively brief legacy, for now, remains unchallenged and the testaments of its potential are more than serve as a deterrent to switching completely to alternatives.