Artificial Intelligence

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Prescriptive Analytics

Prescribing action required to deal with predicted future events using data from a variety of sources. Often associated with simulations in various business scenarios. Typical examples are market strategy planning, clinical data analysis for insights into early-stage drug development, decision support system etc.

Predictive Analytics

Prescriptive Analytics, which use optimization and simulation algorithms to advice on possible outcomes and answer: “What should we do “. Prescriptive analytics is the third and final phase of business analytics, which also includes descriptive and predictive analytics Prescriptive analytics allows users to “prescribe” a number of different possible actions to and guide them towards a solution. Prescriptive analytics attempt to quantify the effect of future decisions in order to advise on possible outcomes before the decisions are actually made. At their best, prescriptive analytics predicts not only what will happen, but also why it will happen providing recommendations regarding actions that will take advantage of the predictions.

These analytics go beyond descriptive and predictive analytics by recommending one or more possible courses of action. Essentially they predict multiple futures and allow companies to assess a number of possible outcomes based upon their actions. Prescriptive analytics use a combination of techniques and tools such as business rules, algorithms, machine learning and computational modelling procedures. These techniques are applied against input from many different data sets including historical and transactional data, real-time data feeds, and big data.

Examples of Prescriptive Analytics are in manufacturing unit can be Optimize production, Scheduling and inventory in the supply chain to make sure that are delivering the right products at the right time and optimizing the customer experience.

Prescriptive Analytics

Predictive Analytics

Predicting the likely future outcome of events often leveraging structured and unstructured data from a variety of sources. Typical examples include customer lifetime value analysis, revenue forecasting based on outcomes, and prediction of adverse event occurrence.

Predictive analytics services involve extracting information from existing sources of data, and determining patterns, and predicting future trends and results. It uses different techniques to make such predictions, like artificial intelligence, statistical modelling, machine learning, etc .

AQ4’s predictive models use variable selection algorithms and best-practice, cross-validation methods to break through the noise in big data, pinpoint important variables, and deliver accurate predictions of market outcomes based on validation data.

Machine Learning

Machine learning (ML) is a subset of artificial intelligence in the field of computer science that often uses statistical techniques to give computers the ability to "learn" with data, without being explicitly programmed. This allows business to transform the data into insights and intelligence using machine learning algorithms. ML has variety of applications to identify market needs, minimize risk or optimize resources that enables business to quickly identify patterns, gain insights, and make decisions faster.

Behaviour Analytics

Digital consumer behaviour is well captured today that helps profile and divide into Customer Attributes. Additionally, Customer Attributes are enriched with additional offline data based on a customer identifier, with demographics data, loyalty-program data, and any other customer-based data that may affect your business or your customers’ behaviours on your digital experiences. While business analytics has a more broad focus on the who, what, where and when of business intelligence, behavioural analytics narrows that scope, allowing one to take seemingly unrelated data points in order to extrapolate, predict and determine errors and future trends. It takes a more holistic and human view of data, connecting individual data points to tell us not only what is happening, but also how and why it is happening.

AQ4’s machine learning services helps organizations to develop custom solutions that process high volumes of data and run custom made algorithms to learn how to perform a task by themselves.

Some of the uses case of machine learning services are :

  • Categorize images (such as MRI studies, photos, or satellite imagery)
  • Look for keywords in massive numbers of text documents or emails
  • Flag potentially fraudulent transactions
  • Personalize product recommendations based on customer behavior
  • Enable software to accurately respond to voice commands
  • Predict weather patterns or other climate conditions
  • Translate languages in text or audio

Sentiment Analytics

Sentiment analysis provides a way to understand the attitudes and opinions expressed in texts. It is text mining to analyse the emotional content of text programmatically. When human readers approach a text, we use our understanding of the emotional intent of words to infer whether a section of text is positive or negative, or perhaps characterized by some other more nuanced emotion. The sentiment analysis tools consider the text as a combination of its individual words and the sentiment content of the whole text as the sum of the sentiment content of the individual words. Sentiment analysis scores are compared across certain segments, companies can easily identify common pain points, areas for improvement in the delivery of customer support, and overall satisfaction between product lines or services.

AQ4’s Sentiment Analysis services provide customized social media sentiment analysis tools which can be used to measure your reach and influence on all the major social media channels.

With our Sentiment Analysis services , find out what your customers, employees, and partners are saying about you by analyzing social media channels, customer service data and emails with our sentiment analysis services. We transform raw, cold machine data into rich human intelligence to detect hidden signals, adopt different points of view, separate fact from fiction, and see how your reputation is actually shaping up

Social media sentiment analysis enables organizations to get insights regarding their market reputation by using data science. Instead of relying on cold calling, sending out feedback forms or conducting surveys by employing a huge field staff, companies can analyze what consumers think about their brand by using text mining tools.

This means that if your brand is getting a lot of criticism online, Sentiment Analysis tools will help you identify that in real time so that you can take appropriate actions and solve the issue before it becomes a major crisis.

With help of Natural Language Processing (NLP), we make sense of juicy customer reviews, emotional comments on social media or troublesome tweets. Python script is used which is the better choice when it comes to text analysis due to its built-in natural language processing models.

Behavioural Analytics

Digital consumer behaviour is well captured today that helps profile and divide into Customer Attributes. Additionally, Customer Attributes are enriched with additional offline data based on a customer identifier, with demographics data, loyalty-program data, and any other customer-based data that may affect your business or your customers’ behaviours on your digital experiences. While business analytics has a more broad focus on the who, what, where and when of business intelligence, behavioural analytics narrows that scope, allowing one to take seemingly unrelated data points in order to extrapolate, predict and determine errors and future trends. It takes a more holistic and human view of data, connecting individual data points to tell us not only what is happening, but also how and why it is happening.

AQ4’s Behavioral analytics services provides insights into the relationships, trends and patterns that exist within the micro-geographies around us – such as retail stores, hospitals and sporting arenas.

Retail Behavior Analytics gives actionable information, allowing you to optimize your venue based on how people interact with your physical space and signage, and answers questions like:

  • How did a customer move between my merchandise zones?
  • What is the foot traffic past my point-of-sale?
  • Did visitors find this display a barrier to moving through my premises?

Behavioral Analytics surfaces unusual and highly risky behavior often invisible to other security solutions.

BioMetric Behavior Analtyics

Text mining is the process of exploring and analyzing large amounts of unstructured text data aided by software that can identify concepts, patterns, topics, keywords and other attributes in the data.

Text analytics is an application enabled by the use of text mining techniques to sort through data sets.

Typical text mining tasks include text categorization, text clustering, concept/entity extraction, production of granular taxonomies, sentiment analysis, document summarization, and entity relation modeling.

Text analytics helps organizations find potentially valuable business insights in corporate documents, customer emails, call center logs, verbal survey comments, social media posts, medical records and other sources of text-based data.

Text analytics use case

Text analytics are into AI chatbots and virtual agents that companies deploy to provide automated responses to customers as part of their marketing, sales and customer service operations.

Text mining uses include screening job candidates based on the wording in their resumes, blocking spam emails, classifying website content, flagging insurance claims that may be fraudulent, analyzing descriptions of medical symptoms to aid in diagnoses, and examining corporate documents as part of electronic discovery processes.

Text mining can also help predict customer churn, enabling companies to take action to head off potential defections to business rivals as part of their marketing and customer relationship management programs. Fraud detection, risk management, online advertising and web content management are other functions that can benefit from the use of text mining tools.

In healthcare, looking at the patients symptoms they report, the Text Analytics may be able to help diagnose illnesses and disease.

Text Mining

Product Analytics

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Natural language processing (NLP) is the ability of a computer program to understand human language as it is spoken. NLP is a component of artificial intelligence (AI).Natural Language Processing tools and techniques help process, analyze, and understand unstructured “big data” in order to operate effectively and proactively.

NLP are based on deep learning, a type of AI that examines and uses patterns in data to improve a program's understanding. Deep learning models require massive amounts of labelled data to train on and identify relevant correlations, and assembling this kind of big data set is one of the main hurdles to NLP currently.

NLP can be used to interpret free text and make it analyzable. Using sentiment analysis, data scientists can assess comments on social media to see how their business's brand is performing

AQ4’s Natural Language Processing services cover a range of needs, from data acquisition and processing to analytics, entity extraction, fact extraction, and question answering systems .

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