The behaviour of the new and unknown disease is too complicated and unpredictable for data science to handle

People today aspire to use Big Data in elections, sports, healthcare, business, national planning, and where not. Michael Lewis’ 2003 book Moneyball, depicted how the manager of Oakland Athletics built up a successful baseball team by using data and computer analytics to recruit new players. ‘Moneyball’ culture soon began to dominate every bit of our life. And Silicon Valley entered into it with a new set of professionals — data scientists — who, according to Harvard Business Review, is the most attractive job of the 21st century.

People today expect data science to devise profit-making business strategy, come up with winnable election tactics, or generate world cup-triumphing playing mechanisms. Often, data scientists also aspire to do so by using the heart-beat of data. But, can they succeed? Big Data Analytics maybe like ‘churning the ocean’ in search of ‘nectar’ hidden deep in it, as depicted in the great epic Mahabharata. That’s a gigantic project, for sure. One needs a lot of efforts and expertise to obtain the nectar, but there’s every chance to get deceived by other substances — including deadly poison — obtained in the process of churning.

The ongoing pandemic, however, provided a golden opportunity for data science to exhibit its strength. It was its litmus test as well. As early as April 2020, a Harvard Business Review article perceived: “In many ways, this is our most meaningful Big Data and analytics challenge so far. With will and innovation, we could rapidly forecast the spread of the virus not only at a population level but also, and necessarily, at a hyper-local, neighbourhood level.”

Misleading predictions

As Covid-19 yielded loads of freely available data, various data scientists came up with lots of predictions and strategies — that of the eventual number of infected, eventual number of deaths, duration of lockdown needed to control the pandemic, etc.

In fact, forecasting the trajectory of the disease over time became almost a fashionable exercise to many. No wonder, in many cases, these were even contradictory in nature, and eventually most of these predictions proved to be utterly wrong, misleading and useless.

Predicting the future course of events by using the techniques of data science reminds one of the Tom Cruise starrer 2002 Spielberg movie Minority Report, where the PreCrime police force of Washington DC in 2054 even predicts future murders using data mining and predictive analyses!

In practice, data science often use statistical models and techniques, which are based on various underlying assumptions. Often, the real data doesn’t satisfy the assumptions of these models.

For example, for analysing the data of the pandemic, models such as SIR, SEIR or some of their variants were widely used. But, the dynamics of a new and unknown disease maybe far more complicated and unpredictable, and it’s most likely that they would fail to satisfy the assumptions of those classical models or their tweaks. Thus, serious error is bound to occur, which would get compounded with loads of data. Then, running routine software packages for analysing big data is never adequate, and is often incorrect.

With the ever-expanding horizon of ‘Internet of Things’, data is growing exponentially. The size of the digital universe was predicted to double every two years beyond 2020. The ongoing pandemic might have induced a higher rate of increase!

However, unless some event like Cambridge Analytica breaks, we can’t usually understand that our every footstep is added to the ocean of data. The world has become data-addicted. But, with so much data, the needle is bound to come in an increasingly larger haystack.

In 2008, Google launched the web service ‘Google Flu Trends’ project, with an objective to make accurate predictions about outbreaks of flu by aggregating Google Search queries. The project, however, failed — people often search for disease symptoms that are similar to flu, but are not actually flu. And when the much-hyped ‘Google Flu Trends’ project turned to a disastrous failure, people came to understand that big data might not be the holy grail.

Also, current computational equipment are certainly inadequate to handle millions of variables and billions of data points. The number of pairs of variables showing significant ‘spurious’ or ‘nonsense’ correlation would increase in the order of the ‘square of the number of variables’, which are almost impossible to identify.

Thus, churning the ocean of big data may yield both nectar and poison. Separating them out is a daunting task. Statistics is still in its infancy in this context, and is not equipped yet to handle these kinds of problems. Let’s be honest to admit that.

Overall, data science, being reliant on ‘statistics’ for its models and analyses, may not be ready yet for complex predictions such as the complicated yet verifiable trajectory of Covid-19. For the time being, data science’s best bet maybe to get engaged into open-ended unverifiable problems.

The writer is Professor of Statistics, Indian Statistical Institute, Kolkata

The post Data science can’t predict Covid trajectory, yet appeared first on Artificial Intelligence.

In the earliest days of the pandemic, machine learning showed exceptional promise for COVID-19 diagnosis. Reliably, early machine learning models outperformed doctors in recognizing the telltale COVID-induced pneumonia on CT scans from hospitalized patients. However, more conventional testing methods quickly lapped machine learning-based methods, detecting the onset of COVID well before hospitalization and with greater accuracy. Now, a year later, a team of researchers led by the University of Cambridge has concluded a review of COVID diagnosis ML models, finding that even in 2021, none of the proposed models are suitable for clinical use.

The researchers whittled down 2,212 studies, eventually focusing on 62 studies – most of which were not peer-reviewed – published between January 1st and October 3rd of 2020, all of which presented machine learning models for diagnosing or predicting COVID-19 infection based on X-rays and/or CT scans. These 62 studies collectively described more than 300 such models – and the researchers found all of them substantially lacking.

“The international machine learning community went to enormous efforts to tackle the COVID-19 pandemic using machine learning,” said James Rudd, one of the senior authors of the review and a member of Cambridge’s Department of Medicine. “These early studies show promise, but they suffer from a high prevalence of deficiencies in methodology and reporting, with none of the literature we reviewed reaching the threshold of robustness and reproducibility essential to support use in clinical practice.”

The issues were wide-ranging: some studies suffered from poor data quality, while others were not reproducible and yet more exhibited biases in their design. By way of example, the authors pointed out that some of the datasets used to train some of the machine learning models included scans from children. “Since children are far less likely to get COVID-19 than adults, all the machine learning model could usefully do was to tell the difference between children and adults, since including images from children made the model highly biased,” explained Michael Roberts, a member of Cambridge’s Department of Applied Mathematics and Theoretical Physics. 

Other datasets were too small, some were poorly labeled. Some models used the same data for training and testing. And, overwhelmingly, the designers of the models failed to meaningfully incorporate input from radiologists and clinicians who might have insight into the real-world implications of the data and diagnoses at hand. “Whether you’re using machine learning to predict the weather or how a disease might progress,” Roberts said, “it’s so important to make sure that different specialists are working together and speaking the same language.”

Better late than never, though, and to that end, the reviewers have some recommendations for machine learning model developers working on COVID diagnosis: know the data you’re working with, especially when it comes to public datasets; work with diverse, large datasets; and, crucially, include better documentation to allow for reproducibility.

The post Machine Learning for COVID Diagnosis Falls Short appeared first on Artificial Intelligence.

The coronavirus pandemic has transformed the lives of thousands of employed professionals all across the globe, including the ones in the data science industry. This crisis brought upon a new normal of working from home and pushed analytics to the forefront. Analytics professionals had to alter the way they work to keep up with the times. 

Data analytics and data science professionals who have been working in the industry for a while would understand how the COVID transformation will affect the field. However, a junior-level professional who is just starting out his/her career would experience a different scenario than what they expect. The pandemic might be tormenting for new graduates or amateur data scientists because more companies have no interest in recruiting fresh analytics professionals. That is why it is more important than ever to build your resume with a certification course. Here is how the career in data science will be different post COVID:

1. Increased competition

With virtual hiring and remote working in place, the norms of recruitment have changed. Companies are no longer required to hire talents from their area. This will create more competition, especially for amateur data scientists and freshers who have just begun their careers. Graduates will now be competing with not only the professionals from their own city or country, but also with those living thousands of miles away. However, there is an upside to this. This has also increased opportunities for data science professionals who can now apply for a job outside their country and gain a better salary. 

In order to stay relevant amidst this crisis, it is important for young data science professionals to gain appropriate skill sets and upskill themselves continuously. There are several online edtech companies that offer basic as well as advanced data science and AI courses. By enrolling yourself in one of these programs, you can help yourself sustain during these trying times. The landscape is evolving and businesses are relying heavily on advanced technologies. In order to stay ahead of the curve, it is important for data science professionals to continue learning.

2. Isolated learning process

Upskilling has always been an important aspect of the career of a data scientist. However, now that the pandemic has disrupted how businesses work, many want to hire professionals who have advanced skill sets. So, upskilling has become more important than ever to make advancements in the field. According to a LinkedIn report, 64% of professionals like data scientists have increased their focus on learning during the lockdown. 

However, since the companies have mandated working from homes for their employees, the learning and upskilling process has become isolated. Before COVID, companies offered training programs and in-person workshops for young data scientist professionals so that they could learn the skills needed for the business and get accustomed to the new workplace. The lockdown has omitted the process entirely and professionals have to rely on online programs to learn these skills. Moreover, these data scientists will be working from home for a long time that restricts their communication with their teammates, thus impeding their learning process.

On the other hand, online programs have become the only source for the new data scientists to enhance their skills and gain the knowledge they need to work in the field. It is important that you select the right online course that provides you with practical experience, interaction with the teacher, and other scientists. With the right program, you will be able to take on any challenges you might be facing in the workplace.

3. Increased efforts for collaboration

Data science is one of those fields that require immense collaboration between the team members to solve a problem. Through this effective collaboration, the data scientists are able to help the company enhance its business operations, create better products, and make informed decisions. A data scientist cannot work in isolation and collaboration is what will determine the success of the project.

This collaboration is especially important for new data scientists who have just joined the company. But, thanks to the pandemic, data scientists and analytics professionals are working by collaborating online. But these online collaborations are filled with challenges that require in-person training to understand the problems and business better. These challenges reduce the efficiency and productivity of the data scientists and create a large communication gap between the team members and the supervisors. 

In the field of data science, asking the right questions is important to solve business problems. With online collaboration, amateur professionals will face issues asking the right question at the same time leading to ineffective collaboration and hammering their work. That is why data scientists have to make a lot of effort to communicate in times of online collaboration.

4. Increased contract-based hiring

Another transformation occurring in the workplace post COVID will be contract-based hiring. This is applicable to almost every profession in the world including analytics professionals and data scientists. Once the pandemic cedes, companies will employ cost-cutting measures and hire freelancers, contract-based employees, and gig workers. This will allow them to keep the employer’s tenure for a limited time and avail data science capabilities for a specific project. 

Even though recruiting freelancers or contract-based hiring is beneficial for the businesses post COVID, it will bring added challenges of increased competition. Even when you want to be hired full-time, you might only be recruited as a contract-based worker because contract-based workers have cost edition benefits and flexibility in the organization. Companies realize that employees don’t have to be in-office or on the payroll for certain functions. 

Post-COVID, with the perspective of cost-cutting companies, will be looking for people who are generalists, instead of specialists. And that is why young data scientists need to have more than domain-specific knowledge. They have to resell themselves to have an understanding of the overall field of data science. And the best way to do that is through a data science course in Bangalore that you can take from the comfort and safety of your home. 

The post Career in Data Science post COVID appeared first on Artificial Intelligence.

We are living through one of the most challenging and devastating health crises in living memory. This year has brought untold loss of life and livelihoods, the true worldwide repercussions of which are still to be seen. The COVID-19 pandemic has also altered the global business landscape, accelerating the pace and volume of data created through increased remote working and digital transacting, and fast shifting the economic realities for all business leaders. Against this backdrop, technology – artificial intelligence (AI), in particular – now has an even bigger role to play in helping organizations and countries to adapt, keep us safe and improve how we live and work.

But the thirst and drive to innovate with these new technologies at speed must be balanced with the need to carefully build consumer trust in those same innovations. Bringing consumers on this journey will be key. A new report from Longitude explores this precise balance between engendering trust in technology and fostering AI innovation. This is a topic that I am very passionate about and one that I spoke on at Davos this year, so I want to explore some of the report’s findings in more detail here.

Transparency is everything

Until recently, there has been too much focus on what AI can do and not how it does it. Today’s organizations must be able to demonstrate that their systems and algorithms are responsible, fair, ethical and explainable. In a word, that their AI is trustworthy.

High-profile cases of misuse of AI by global technology firms have dented consumer trust in AI. The subsequent fallout has also raised greater global awareness of the broader issues around the use of data and our personal information.

The result? Trust in technology can no longer be assumed – it must be earned. In this sense, organizations must think of their technology as ‘guilty until proven innocent’. The onus is on them to proactively demonstrate the responsible use of their technology to the world and to be prepared to explain and justify decisions made by those systems when required. Here we have the right to meaningful information about the logic, significance and envisaged consequences of automated decisions or what is also called ‘the right to explanation’, as laid out in the EU’s General Data Protection Regulation (GDPR). Businesses must consider how they apply these technologies – only using personal information when it is needed and with the user’s consent. By building these principles into AI as it is developed, businesses can ensure that it is ethical and transparent from the outset.

We are already seeing the impact of this transition to ethical AI. A recent Capgemini study found that 62% of consumers placed more trust in a company whose AI was understood to be ethical, while 61% were more likely to refer that company to friends and family, and 59% showed more loyalty to that company. Those who openly communicate in this way about how their technology works are more likely to be trusted by consumers to use AI to its full potential.

AI is our best problem solver

The COVID-19 pandemic has accelerated digitalization at a rate we could have never imagined. This has created a volatile environment with a plethora of challenges to overcome and opportunities to exploit. In the payments industry, we have had the challenge of protecting consumers and businesses against an explosion in cyberattacks and fraud – for example, our NuData technology, which verifies users based on their inherent behaviour, has seen attacks become more sophisticated, with one in every three attacks now emulating human behaviour. Account creation attacks, where bad actors create fake accounts for subsequent fraudulent use, have increased by 500% during the pandemic, compared to the same period in 2019 – one global retailer experienced a 679% increase in suspicious account creations alone. Overall, global fraud rates have hit a near-20-year high, according to the latest PwC figures, with 47% of companies reported to have experienced fraud over the past two years.

It would have been impossible to maintain our defences without the implementation of AI on our network. It is, and will continue to be, a vital part of adapting to and securing this new world. As AI becomes more powerful and pervasive, we must put systems in place to ensure that it is developed and deployed ethically.

Consumer driven, consumer focused

Consumers create a huge amount of data. By 2025, we will be creating an estimated 463 exabytes every day. And that is only going to increase – the oft quoted formula is that 90% of the data that has ever been created was created in the last two years. AI-driven systems have been invented to help turn some of this information into recognizable benefits for the people who create that data – making our lives work for us.

But AI is a technical and complicated tool. The trust that is needed for it to be most effective will come when consumers see and feel its real-world benefits in action. In this sense, trust can be a key differentiator – a competitive advantage for businesses. Only those who are trusted to operate AI will be able to maximise the benefits of its value-added services in years to come. Not only can AI deliver safety for consumers online or revolutionise their shopping experiences; it is also revolutionising farming as well as giving the environment a new lease of life. For those that get it right, the possibilities are endless.

The big picture

At times of such uncertainty, it can be difficult to look too far ahead. But now is the time for business leaders to take a step back and look at the bigger picture. The landscape has changed, and that change is permanent. Our digital futures have been brought forward and society will continue to demand higher levels of transparency in the way that AI is used to solve new challenges.

Responsible development of, and engendering trust in, technology will be crucial to business success in the ‘next normal’ – but more importantly, to building a world that is more prosperous and more equal for all.

The post Covid made companies AI friendly, but consumers are yet to trust it appeared first on Artificial Intelligence.

Dublin, June 15, 2020 (GLOBE NEWSWIRE) — The Global Data Science Platform Market study with 100+ market data Tables, Pie Chat, Graphs & Figures is now released by Data Bridge Market Research. This Data Science Platform market report serves to be an ideal solution for better understanding of the market and high business growth. It has become the requisite of this rapidly changing market place to take up such marker report that makes aware about the market conditions around. This Data Science Platform market report comprises of an array of factors that have an influence on the market and industry which are industry insight and critical success factors (CSFs), market segmentation and value chain analysis, industry dynamics, drivers, restraints, key opportunities, technology and application outlook, country-level and regional analysis, competitive landscape, company market share analysis and key company profiles

Due to the pandemic, we have included a special section on the Impact of COVID 19 on the Global Data Science Platform Market which would mention How the Covid-19 is affecting the Industry, Market Trends and Potential Opportunities in the COVID-19 Landscape, Covid-19 Impact on Key Regions and Proposal for the Global Data Science Platform Market Players to Combat Covid-19 Impact.

Key Market Features in Global (United States, European Union and China) Data Science Platform Market:

The report highlights Data Science Platform market features, including revenue, weighted average regional price, capacity utilization rate, production rate, gross margins, consumption, import & export, supply & demand, cost bench-marking, market share, CAGR, and gross margin.

Analytical Market Highlights & Approach

The Global (United States, European Union and China) Data Science Platform Market report provides the rigorously studied and evaluated data of the top industry players and their scope in the market by means of several analytical tools. The analytical tools such as Porters five forces analysis, feasibility study, SWOT analysis, and ROI analysis have been practiced reviewing the growth of the key players operating in the market.

List of Best Players profiled in Data Science Platform Market Report;

Google, Inc., Domino Data Lab, IBM Corporation, Datarobot, Inc., Microsoft Corporation, Wolfram, Continuum Analytics, Inc., Dataiku, Bridgei2i Analytics, Feature Labs, Datarpm, Rexer Analytics, Civis Analytics, Sense, Inc., Alteryx, Inc., Rapidminer, Inc., IBM, Snowflake, MeritDirect, Cazena, CBIG Consulting, Loggly, Clairvoyant, Arcadia, Experfy, Datatorrent, Jethro, Tableau, VMware, New Relic, Alation, Tera Data, SAP, Alpine Data Labs, SiSense, Thoughtworks, MuSigma, Cogito, Datameer among others

Key Benefits:

The report provides a qualitative and quantitative analysis of the current Data Science Platform market trends, forecasts, and market size to determine the prevailing opportunities.
Porter’s Five Forces analysis highlights the potency of buyers and suppliers to enable stakeholders to make strategic business decisions and determine the level of competition in the industry.
Top impacting factors & major investment pockets are highlighted in the research.
The major countries in each region are analyzed and their revenue contribution is mentioned.
The market report also provides an understanding of the current position of the market players active in the Data Science Platform industry.

Our analysts monitoring the situation across the globe explains that the market will generate remunerative prospects for producers post COVID-19 crisis. The report aims to provide an additional illustration of the latest scenario, economic slowdown, and COVID-19 impact on the overall industry.)

Key poles of the TOC:

Chapter 1 Global Data Science Platform Market Business Overview
Chapter 2 Major Breakdown by Type
Chapter 3 Major Application Wise Breakdown (Revenue & Volume)
Chapter 4 Manufacture Market Breakdown
Chapter 5 Sales & Estimates Market Study
Chapter 6 Key Manufacturers Production and Sales Market Comparison Breakdown
…………………..
Chapter 8 Manufacturers, Deals and Closings Market Evaluation & Aggressiveness
Chapter 9 Key Companies Breakdown by Overall Market Size & Revenue by Type
…………………….
Chapter 11 Business / Industry Chain (Value & Supply Chain Analysis)
Chapter 12 Conclusions & Appendix

What Businesses Can Hope to Get in Business Intelligence on Data Science Platform Market?

The study insights on the Data Science Platform market growth dynamics and opportunities highlights various key aspects, in which crucial ones are:

Which are the technology and strategic areas that emerging, new entrants, and established players should focus on keep growing in the industry-wide disruptions that COVID-19 has caused?
Which new avenues bear incredible potential during the ongoing COVID-19 lockdown restrictions?
Which policies by governments can give the top stakeholders support their efforts of consolidation?
What new business models are gathering pace among companies to remain agile in post-COVID-era?
Which segments will see a surge in popularity in near future, and what calibrations players need to make to utilize the trend for an elongated period?

About Data Bridge Market Research:

An absolute way to forecast what future holds is to comprehend the trend today!

Data Bridge set forth itself as an unconventional and neoteric Market research and consulting firm with unparalleled level of resilience and integrated approaches. We are determined to unearth the best market opportunities and foster efficient information for your business to thrive in the market. Data Bridge endeavors to provide appropriate solutions to the complex business challenges and initiates an effortless decision-making process. Data bridge is an aftermath of sheer wisdom and experience which was formulated and framed in the year 2015 in Pune.

The post DATA SCIENCE PLATFORM MARKET COMPETITIVE ANALYSIS, MARKET ENTRY STRATEGY, PRICING TRENDS, SUSTAINABILITY TRENDS AND INNOVATION TRENDS appeared first on Artificial Intelligence.

New Jersey, United States,- The Data Discovery Market has grown rapidly and contributes significantly to the global economy in terms of sales, growth rate, market share, and size. The Data Discovery Market Report is a comprehensive research document that provides readers with valuable information to help understand the fundamentals of the Data Discovery report. The report provides details on business strategies, market requirements, players dominating the market, and a futuristic perspective of the market.

The report will be updated with the latest economic scenario and market size in relation to the ongoing COVID-19 pandemic. The report covers the growth prospects as well as current and futuristic sales estimates in a post-COVID scenario. The report also covers changing market trends and dynamics due to the pandemic and provides an in-depth analysis of the impact of the crisis on the overall market.

The report covers extensive analysis of the key market players in the market, along with their business overview, expansion plans, and strategies. The key players studied in the report include:

Qlik Technologies
Spotfire
Tableau Software
Inc
Datawatch Corporation
Datameer
Inc
Tibco Software Inc
Sap Se
Cloudera
Inc
Birst
Inc
Clearstory Data
Platfora
Oracle Corporation
Microstrategy
The report provides a comprehensive analysis in an organized manner in the form of tables, graphs, charts, figures, and diagrams. The organized data paves the way for thorough examination and research of the current and future outlook of the market.

The examination of the Data Discovery industry provides an in-depth analysis of the key market drivers, opportunities, challenges, and their impact on the working of the market. The technological advancements and product developments, driving the demands of the market are also covered in the report.

The report provides comprehensive data on the Data Discovery market and its trends to assist the reader in formulating decisions to accelerate the business. The report provides a complete overview of the economic scenario of the market, along with benefits and limitations.

Data Discovery market report contains industrial chain analysis and value chain analysis to provide a comprehensive view of the Data Discovery market. The study is composed of market analysis along with a detailed analysis of the application segments, product types, market size, growth rate, and current and emerging trends in the industry.

The report further studies the segmentation of the market based on product types offered in the market and their end-use/applications.

In market segmentation by types of Data Discovery, the report covers-

Software
Service
Other
In market segmentation by applications of the Data Discovery, the report covers the following uses-

Smes
Large Organization

Geographically, the market is spread across several key geographical regions, and the report covers the regional analysis as well as the production, consumption, revenue, and market share in those regions for the forecast period of 2020-2027. The regions include North America, Latin America, Europe, Asia-Pacific, and the Middle East and Africa.

Radical Coverage of the Data Discovery Market:

Insightful information regarding the Data Discovery market
Identification of growth in various segments and sub-segments of the Data Discovery market
Strategic recommendations for investment opportunities
The report covers significant statistics related to the industry along with products, applications, price analysis, demand & supply, and production and consumptions rate
Emerging trends and current market segment analysis to help investors formulate new business strategies
Accelerates the decision-making process through the availability of the drivers and limitations

Key Questions Addressed in the Report:

Which segments are expected to show significant growth over the forecast period?
What is the forecast estimation of Data Discovery market growth?
What are the factors that are likely to restrain the growth of the market?
What are the key driving factors of industry growth?
Which region is expected to dominate in the forecast period?
Which markets are significantly positive for developing businesses?
What is the expected growth rate of the industry throughout the forecast period?
Which market segments are expected to boost the growth of the industry?
Who are the dominating players of the Data Discovery industry?
What are the strategic business plans undertaken by the key players of the industry?

Thank you for reading our report. The report is available for customization based on chapters or regions. Please get in touch with us to know more about customization options, and our team will ensure you get the report tailored according to your requirements.

Market Research Intellect provides syndicated and customized research reports to clients from various industries and organizations with the aim of delivering functional expertise. We provide reports for all industries including Energy, Technology, Manufacturing and Construction, Chemicals and Materials, Food and Beverage, and more. These reports deliver an in-depth study of the market with industry analysis, the market value for regions and countries, and trends that are pertinent to the industry.

The post Data Discovery Market Size 2020-2027 appeared first on Artificial Intelligence.

The Covid-19 outbreak has been one of the worst tragedies to befall mankind. Its impact is felt by almost all industries and businesses around the world. The prolonged lockdowns, due to the debilitating effects of the pandemic, have led to a huge decline in demand for goods and services, which in turn, is causing revenues to nosedive.

This unprecedented health crisis is further creating a negative impact on the already low consumer sentiments within the global retail sector. India is the second largest employer providing direct or indirect employment for about 50 million people with a monthly business volume of about $75 billion.

While the debate continues on whether Covid-19 is a black swan or the new normal, businesses have to orient their models to suit the ‘new normal’ ways of the customer. That would mean bringing in a few dimensions that could help project the big picture better and working on them to mitigate the risk, increase market share and thereby mobilising the wheels of economy.

Today, due to the social distancing norms, customers who would otherwise prefer visiting a physical retail outlet may have to embrace e-commerce platforms for fulfilling their needs. Given the above reality, the businesses that understand their customers better and engage with them in meaningful ways, can retain them. Catering to the customers’ needs through alternate channels is the only way to mitigate losses caused due to the pandemic. Data science, or data collection, integration, governance, and analysis can be leveraged to navigate successfully through this crisis.

Seven key areas where Data science can helps retailers are:

Identifying shopping pattern

Data analytics helps in understanding the customers better through their buying patterns. For instance, it captures all the interactions the customers have with the brand; what kind of products they view, in what price range,what time of the day, week, or year they log in to the online store, what kind of products they add to their cart, their click through rates, and so on.

Cross-selling and upselling

Affinity analysis models such as market basket analysis, one of the key techniques used by large retailers, is a popular machine learning technique involving a set of statistical affinity calculations. It is used to study combination of products that most frequently occur together in orders and uncover the association between these various products that customers buy. These relationships can be used for cross-selling and can be further explored by other data science tools to curate product promotions, upselling techniques and better recommendations for the customers. Large retailers, such as Amazon and Netflix, use this extensively.

Data-backed price management

Today, customers have a wide range of choices for products at the lowest rates. They quickly navigate through different sites, compare the prices and choose good deals. Big brands like Amazon, use data analytics to process large volumes of data, to process competitor’s prices, product sales, customers actions and geographical preferences for developing dynamic pricing algorithms. Amazon’s software is built to manage consumers’ perception of price. It can identify the goods that loom large in consumers’ perceptions and keep their prices carefully in line with competitors’ prices, if not lower. The prices of all the other products are allowed to drift upward.

Customer classification and segmentation

Predictive analytics classification models such as Discriminant analysis and Logistic regression can classify customers based on certain variables into loyal customers and brand switchers. This information can be used to design various marketing strategies to reward the loyalists and have more meaningful engagement with the brand switchers to minimise the customer churn. Machine learning algorithms such as K-means help market segmentation and predict how likely a customer segment X will respond to a 10 per cent discount on a certain product or how a particular consumer responds to a certain combo offer.

Brand perception and positioning

For any business to stay in the competition, it becomes imperative to understand how the customers perceive their brand. Machine learning algorithms and multidimensional scaling helps answer questions such as what goes on in the customers’ mind when they think of their brand, where do the customers place their brands or products in comparison to their competitors, and mainly who is perceived to be their competitors. They also provide robust spatial maps to understand each dimension better enabling the businesses reach out to customers with more precision and strategise their new product launches and product positioning more effectively.

Supply chain

When customers place orders on an online platform, they expect information on order tracking services while the goods are still in transit. Even well-known brands in e-commerce often face difficulty in meeting these expectations, leaving the customers dissatisfied. This happens because the supply chain is dependent on third parties for services such as warehousing and transportation. Under this scenario, using supply chain analytics, that integrates multiple pieces of information from multiple parties on multiple products, helps in catering to the customers’ expectations and fulfilling their needs on product tracking.

Customer satisfaction

While all the above-mentioned areas contribute to customer satisfaction in one way or the other, the overall experience of a customer with the brand or product or service is what determines the satisfaction level. Complaint handling mechanism is the most crucial factor for enhancing customer satisfaction. Customer grievances can now be addressed on Facebook and Twitter, and social media , where the customer reaches out to the brand with his/her complaint and feels acknowledged to receive a response directly on their Facebook page or Twitter handle. Social media analytics models and Sentiment analysis can be used to understand how happy or unhappy customers are and weigh their expectations from the product.

Conclusion

Now, e-retailing is probably all set to create the biggest revolution in the retail industry as a response to the challenges thrown by the pandemic. Retailers should leverage the digital retail channels, by spending less on physical infrastructure and investing more in the data space. They must not only try to retain, but also broaden their customer base from tier 1 cities and reach out to more customers in tier 2 and tier 3 cities, whose buying potential is perhaps not tapped to the extent it should be. Taking clues from the above mentioned data science tools, businesses must take advantage of such times to sail through this tricky situation and make a positive, long-term impact on the customers’ minds.

The post Leveraging data science to revamp e-retailing during Covid and beyond appeared first on Artificial Intelligence.