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Improve Retail Business With Machine Learning


Technology has transformed how customers and brands communicate with each other. Shoppers were once dependent on face-to-face, in-store interactions to make purchases and receive support. Now, shoppers do their research before entering a store (81 percent of shoppers conduct online research before buying) and hardly rely on salespersons to help them make decisions. Retailers, however, have understood that by embracing technology, they can extend their storefronts to their customers’ fingertips.

Shoppers can make purchases from within social media apps and compare prices without leaving a store. While these technologies have propelled the retail industry further into the digital age, the technology that is still evolving will have the largest impact on the future of the customer service and retail industries.

Embracing Big Data

More retailers are tracking customer shopping habits through data sources such as social media, purchase history, consumer demand, and market trends. By relying on big data technology to gain a deep understanding of shoppers and their buying trends, retailers can maximize customers’ spending and encourage customer loyalty.

According to research by Accenture report, 70 percent said that big data is necessary to maintain competitiveness, and 82 percent agreed that big data is changing how they interact with and relate to customers.

Matching Products with People

Machine learning technology boosts the reach of big data analytics and can help create an exceptional shopping experience. Innovative retailers can tap into the power of machine learning algorithms to do things like determine available products from outside vendors or recommend the quantity, price, shelf placement, and marketing channel that would reach the right customer in a particular area.

Further, the capability to automate everything through advanced analytics and machine learning soon will mean that basic customer service will be performed by bots that can predict our needs and provide service in the fastest, most immediate way possible: by offering us items we didn’t know we needed. As retailers gain more insight into their customers and products, machine learning will be able to match buyers and sellers based on buyers’ needs and product availability.

Digital Assistants

Shopping is becoming increasingly programmatic. In the future, services like digital assistants (Siri, Cortana, etc.,) will learn more about us and offer us relevant and personalized product offers. Say, for example, you use a particular brand of perfume. Your digital assistant will learn your shopping and usage habits and offer you the best deal on the product at the right time. It might even place the order for you.

Improving the backend

Machine learning and advanced analytics will not only change how we shop and provide customer service, but also simplify how retailers perform basic operations. Data science and machine learning give us the ability to automate so much of the heavy lifting required to find insight within a pile of data. With these tools, retailers can find useable and useful data to change the shopping experience for consumers.

Technology enables us to create an index of every product in the world, enabling retailers to offer customers the best prices, keep products adequately stocked, and track competitors’ minimum-advertised-price violations. A central database of the world’s product information enables retailers to offer the best shopping experience for buyers.

An innovative-technology approach to customer service and commerce will combine data about our behaviors and choices with data about products and product attributes to create the best shopping experience. This approach takes the guesswork out of purchasing and makes the shopping experience more cherishable for everyone.

Top 5 Big Data Trends In 2020

Big Data Trends

When the world big data rapidly expanded a decade ago, there were no signs that they would slow down. It is primarily aggregated across the internet, such as social networking, web search requests, text, and media files. IoT devices and sensors produce another gigantic share of data. These are the main reasons for the global big data market growth of 49 billion dollars.

Spark will Widespread

Apache Spark is a platform for data processing that can easily perform tasks on very large data sets and also spread the functions of data processing over many devices, either on its own or in combination with other distributed computing resources. These two qualities are important to the worlds of big data and machine learning that require vast data stores to sharpen the masses of computer power. Spark removes some of the programming burdens from developers with an easy-to-use API which sums up many of the grunt tasks of distributed computing and big data processing.

Apache Spark has been one of the main computing frameworks that spread throughout the world. Spark offers native binding for Java, Scala, Python, and R languages, and supports SQLs, data sharing, machine learning, and graphic processing. The Spark software can be used in several ways.

The convergence of IoT, Cloud, and Big Data

In order to facilitate interaction between machines and humans (M2H) and machines (M2 M), the Internet of Things is an opportunity for simplifying operations in many areas. Until now it has been greatly improved. In most cases, sensor-generated data is transmitted for analysis to the Big Data System and final reports are made. This is also the main interconnecting point of the two technologies.

For the next ten years, IoT is expecting a future of $19 trillion in the web industry, which will give room for more IoT and Big Data research and development.

Cloud computing plays a significant part in the storage and management of the data by generating an immense amount of data. It is not only about big data growth but also the development of platforms such as Hadoop for data analytics. As a consequence, it provides new cloud computing opportunities. Therefore, service providers like AWS, Google, and Microsoft have cost-effectively their own Big Data Solutions for businesses of all sizes.

Mixed Reality will improve Data Visualization

AR and VR have gained a lot of traction among customers in the past few years. With the launch of Pokémon Go, Augmented Reality had garnered around 100 million users within just a few weeks of launch. Though AR or VR might not be very useful for large corporations, the concept of Mixed Reality might very well be. Mixed reality combines the virtual world with our real-world and devices like Microsoft Hololens are already gaining traction. Mixed Reality will offer huge opportunities for organizations to better perform tasks and also to better understand the big data.

Deep Learning

Deep learning is an advanced form of machine learning which is based on neural networking. Deep learning help recognize specific items of interest from massive volumes of unstructured data. It is mostly useful for learning from huge volumes of structured and unstructured data. Thus businesses and organizations should pay more attention to deep learning algorithms to deal with the heavy influx of big data.

Data Virtualization

Data virtualization will see strong momentum this year. Data virtualization has the ability to unlock the hidden concepts and conclusions from a large set of data. It also allows enterprises and organizations to retrieve and manipulate data on the go.

To address big data problems, the management and use of computer and data-intensive systems require huge amounts of highly distributed datagrams. Virtualization offers the additional flexibility needed to realize large data platforms. Although virtualization is theoretically no prerequisite for big data analysis, in a virtualized environment software frameworks are more effective.


As mentioned earlier, this year will be an exciting year for big data, and analytics systems will become the top priority for organizations. These systems are expected to perform well operationally, and fulfill promises of business value to the organization.

Customer Cognizance Through Omnichannel

Decor Cover

In the last decade, marketers have progressed slowly but steadily. It was not uncommon for marketers, 10 years ago, to focus primarily on their domain and use SEO and SEM for traffic management. Marketers cannot condone such a gap in their perception of the customer in today’s omnichannel environment. 


Recently, marketers have also begun to use other channels such as mobile displays, social media, and programmatic displays. Initially, these channels have been managed independently to produce “multi-channel marketing,” but later, they integrated their channels to provide a “cross-channel” brand message. 

For companies that are focused on attracting and retaining consumers with more choice and higher expectations, omnichannel marketing has become a priority.  

Like other trades in the modern era, omnichannel is an area of tremendous potential, frequently debated, but seldom well accomplished. This practice includes addressing obstacles and uncertainties that hinder other businesses from making their true vision happen. 

There is no doubt that data is growing at an exceptional rate and, especially, the increasing number of consumer data fundamentally alters the way brands work in the region.  

Given the impending future without cookies, improvement in personalized targeting increased with consented customer information such as Consumer Data Platforms and Consumer Panels. The growth in 1st-party data provides marketers with an excellent view of the customers in their online world. Companies are also increasingly equipped to furnish marketers with offline customer data, such as Point-of-Sale (POS) systems, geofencing, and beacons. 

Impacts of Isolated Online and Offline Data 

The inability of a brand to integrate its data online & offline adversely affects the ability of a company to intelligently trigger the omnichannel, holistic strategic considerations, and exploit media usage nuances. Their value at a scale is all at the ultimate cost of the marketer’s desired return on media expenditure due to a lack of understanding and a lack of attribution between online & offline world. Silo-based media and two-dimensional digital media consumption are still high across the Asian Pacific both for online and selected offline outlets. 

Digital’s share of ad spend across the region will expand from just over 50% in 2019 to 59% by 2023, with online video contributing 20%, and although less spent on conventional offline media platforms in general, these traditional channels still account for a substantial proportion of promotional budgets. Out-of-Home, is still going strong in the South East Asia region, having grown +19% in 2019. In planning for these campaigns, brands are of course using the rich data for both their online and offline consumers to inform their segmentation and targeting. The issue is that both data sets live in isolation from one another, and media planning teams are unable to use online data to influence offline media planning and measurement, and vice versa. The lack of a bridge results in media planning that is divorced from the complex reality of how consumers behave across channels.  

Consumers aren’t two-dimensional, unlike our existing online and offline data usage. They do not live in online and offline siloes, and their purchases are no longer predictable.  

Most marketers are familiar with the all-round concepts of web-building (where consumers collect information about the goods online and then buy them offline) and showrooming (where consumers are visiting the stores physically to look and feel the product before making their purchase online), But even those concepts are oversimplified representations of how dynamic the purchase journey has become. Consumers do not flow constantly through a funnel but a maze of actions and connections with the brand in the path to purchase as they move through online and offline touchpoints. In fact, understanding this maze and getting a single view of the consumer when they seamlessly move online and offline is critical. 

Bridging the Gap Between Online and Offline Data 

Digital networks have been leading the way since they attempted to link or bridge the gap between offline and online platforms. Why is it required? Why cannot customers get a different experience for each channel? It is because, with the advent of digital platforms, consumer perceptions have changed drastically. 


Easier to communicate with brands using digital technologies led them to expect smoother and seamless offline/physical brand interactions. In short, they wanted an all-round experience that would blur the distance between offline networks.

Cross-channel marketing is all about delivering marketing strategies to consumers through different platforms both offline and online. 

The marketers must merge networks with each other in order to bridge the gap between the offline-online, to transfer data, and to process it from one channel to the other. Different technologies like APIs, RFID, etc. may be used. 

Vanity URLs often act as a cross-channel marketing tool that reduces offline distance. Such URLs are shortened web addresses that are easy for customers to recall. Using these URLs on offline assets like a flyer, print ad, or banner, consumers can be motivated to search the URLs on phones, tablets, or computers. 

Marketers will sell the goods online and have their customers picked up from the brick and mortar shop to create a seamless shopping experience.  

And salespeople can also allow products bought online to return to a physical shop. Items bought from physical stores can also be returned digitally by requesting a pick-up of the items. The cashback can also be credited directly to the customer’s bank account. 

There was one of America’s famous retailers who experimented in bridging the gap between online and offline data—Macy’s. The company noticed that consumers frequently review items on their website before visiting a physical store. The company thus wanted to give consumers exposure in the store in order to see if their favorite items were available in the nearest shop. Macy also offered various shipping options, such as home delivery, click-and-collect, etc., which really resonated with consumers and helped increase the company’s revenues. 

Marketers may use location-based targeting to drive consumers to purchase products while they are on the go. 

It can be achieved by providing a smartphone push notification when they enter or exit a geofenced area. They can also be monitored by beacons when entering the shop. Marketers use beacon technology to give customers a personalized experience while they shop. Therefore, the offline-online link is enhanced and at each stage of the customer journey, there is an interaction. 

For instance, a person enters the mall and triggers the geofence. An app pushes notification pops-up on his device about some exciting offer going on in his favorite store. The customers are encouraged to visit the store even though they do not intend to go to it in the first instance in real-time. 


When consumers expect enriching experiences across all touchpoints, irrespective of online and offline platforms, marketers will strive to achieve that. A clear transition should be made from an offline to an online platform from the consumer viewpoint, and vice versa. To step up the ladder, marketers must follow omnichannel marketing strategies that primarily bridge the gap from offline online to provide consumers with a clear and seamless experience across all available channels. 

Will Automation Eliminate Manual Testing?


We live in an era where software development has been revolutionized by AI (Artificial Intelligence) & ML (Machine Learning). It is expected that testing will be taken over by automation with its new developments and advancements, but that is not the case. Software manual testing has been around for many decades since initial software development, and the industry has taken multiple shifts. However, its scope remains the same. In this article let us explore the impact automation testing has over manual testing.

Why is manual testing still relevant?

New Projects: Projects in pilot phases which begin as a concept and take shape during early sprints require testing to be done manually. Using automation testing during the initial phases of a project would be expensive as it undergoes continuous changes. Using manual testing in these cases would be cost-efficient and easy to accommodate changes.

End 2 End Testing: Automation testing can be used to test single systems or integration levels in detail. Whereas, End 2 End testing involves multiple systems and require manual testing. Automation testing that runs an End 2 End test scenario has many challenges, especially systems have different tech stacks. Design changes involving systems in End 2 End testing impacts maintenance cost.

Maintenance Cost: For small projects or components, automation testing costs higher than manual testing. Performing quick manual testing would suffice for smaller projects/ components that undergo frequent changes rather than updating test scripts after rerunning those tests manually.

UX Testing: Maintenance costs are proportional to UX changes. Each time UI/UX change, test cases break and raise a false fail. When changes are encountered in a script, there are rework/ maintenance to achieve the test pass. This impacts the next UI changes again. So, for an application with frequent UI/UX changes, automation testing is costlier than manual testing.

Visual Testing: While there are few automation tools available in the market, AI & ML are incorporated with visual testing to achieve 100% test result. But the number of hours required to train AI to understand minute changes in UI would be expensive than performing manual tests. Sometimes, human eyes can find a little misaligned text boxes, which could be challenging for an automation tool. Such automation tools with AI & ML are expensive compared to manual testing.

User Acceptance Testing: There is no way user acceptance testing could be automated. Beta users/client team must experience the end product by simulating user experience using manual testing.

How automation testing can be leveraged?

Let us discuss the areas where automation has to be implemented to support manual testing efforts.

Regression: When a part of the product is regression and the product or UI changes, tests have to be automated using open source software. Using automation testing can therefore save manual testing time.

Integration Testing: API level automation can be quickly created like manual testing. Tools like Postman enables to create tests that can be automated using runner feature. When manual testing is performed, requests are stored as collection. This stored collection can be run any time as a test suite to rerun the test scenarios.

Smoke Test on CI/CD: Automating test scripts for smaller projects are expensive. However, using smoke test scenario would reduce the cost. Smoke tests undergo changes to get added to CI/CD pipeline for project code deployment capturing blocker/showstopper issues during code deploy to QA/Stg environment, before the code is released to production.


Manual and automation testing complement each other. Manual testing is as important as automation testing and there can be no project that is purely manual. There will always be an area where automation can be leveraged with open source tools which are no-cost and low maintenance. No project can completely use automation testing as client expectation keeps changing; manual testing is the way to handle frequent changes and ad-hoc testing requests. It is up to the project management to decide how and where manual and automation testing have to be implemented to provide a customer satisfied product delivery.

Emerging Healthcare Trends Post Covid-19


COVID-19 reveals how fragile many world’s health systems and services are, forcing countries to make difficult choices to best meet people’s needs. Though patient care systems have progressed long before coronavirus disruption, online health consultations, involvement of healthcare providers, and remote monitoring of patients are becoming increasingly accepted.

New approaches to delivering healthcare services

Improvement in healthcare can be achieved irrespective of geographies. New healthcare approaches are paving way with modern infrastructure, service requirements and analytics.

1. Enhanced pharmacy experience: Pharmacies are expanding their touchpoints to provide medicines and other healthcare services. Grocery stores are also being used to sell drugs along with other products to retain customers and give them single time purchase experiences. Many leading brands are exploring options to set up healthcare retailing and experimenting delivery of medicines using drones.

2. Increase in contactless experience: Coronavirus apprehension is more prevalent in clinics and hospitals. Technology-enabled workflows are used to improve patients’ registration before visiting a clinic. Facial recognition programs are used to remove the need of touch, requiring registration stands in hospital lobbies. Routine tests are also getting virtual, and many diagnostic procedures via remote-controlled devices are increasing. With the rise of bandwidth, modern mobile apps, and desire to isolate themselves from society throughout the pandemic, both clinicians and consumers are preferring virtual calls. Virtual video care is been initiated in many locations around the world to reduce long-distance travel for patients.

3. Big Data in healthcare: Big data uses the collection of all data points on COVID-19 from around the world. These data are used by mathematical models to identify geographic locations, establish mortality prediction models, estimate testing and test delivery requirements, and guide policymakers, healthcare providers, and other key players in decision making. It is essential to note that while big data gives us the perspective that we wouldn’t have otherwise, all variables required to make a specific decision are not automatically considered.

4. AI and data analytics: Health industry’s defensive line against COVID-19 was primarily helped by AI and data analytics. Machine Learning and data analysis have a significant role in understanding the spread of the disease and the efficacy of the different responses to the infection. Studies have utilized these methods for monitoring the capacity of hospitals to classify high-risk patients, and many agree that AI could be used to plan for similar situations in the future.


A seamless product design is to gain an intuitive understanding of the trajectory of patients in the modern post-pandemic period and to identify high-impact touchpoints for digital interaction. Every patient population varies in their digital communication preferences, whether by socioeconomic status or other demographic factors. Each health system must develop a digital environment suitable for its patients, while continuing to address the needs of caregivers who provide and maintain the environment

How Healthcare Technology is Transforming Patient Care?


Be it any field, today’s innovative technology is literally revolutionizing every industry, and healthcare is no exception. Digital innovation in healthcare is here to stay. Like most industries, healthcare is evolving with new technology.

Technology will transform different aspects of health care including diagnostics, treatments, and delivery of care in the future. Though technology has been the major force behind innovation in every industry, healthcare has been less affected by the rapid growth of technological innovation. However, this is changing.

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Can a Monolithic team handle Microservices?



As monolithic structures become too big to manage, many companies are drawn to break them down into microservices. It’s a worthwhile trip, but it’s not simple. To do this well, we need to start out with a simple service and then build services based on vertical capabilities that are essential for the company and are subject to frequent changes. Such services should at first be broad and ideally not depend on the remaining monolith. We should ensure that every migration step represents an improvement on the architecture as a whole.

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