Chatbots could drive $112 billion in retail sales by 2023

… and AI, chatbots will take away revenue from other marketing channels, a recent study examining retail brands by Juniper Networks claims. In fact …

As technology improves with natural language processing and AI, chatbots will take away revenue from other marketing channels, a recent study examining retail brands by Juniper Networks claims. In fact, retail sales from chatbots will nearly double annually, reaching $112 billion by 2023, the study says. And retailers will see an increase in savings, thanks to the automation of customer sales and support processes.

“Retailers can expect to cut costs by $439 billion a year in 2023, up from $7 billion this year, as AI-powered chatbots get more sophisticated at responding to customers,” Juniper said.

The study also suggests that as retail brands shift to chatbot technology, more consumers will feel comfortable interacting with chatbots to resolve customer service issues and make direct purchases. A consumer-facing survey by marketing firm Uberall indicates that 20% of consumers surveyed are “very interested” in chatbot experiences from brands. 80% indicated that they have had “generally positive” interactions with chatbots.

Why we should care

As martech advances, consumers will also adapt their online behaviors and their expectations for digital interactions will change. According to the report, chatbots are positioned for rapid growth in the retail industry; we need to better understand how that will impact our digital marketing efforts and attribution methodologies going forward.

For digital marketers who haven’t yet experimented with chatbots, it is time to consider the best solution for your brand. Digital marketing is experience-driven; creating positive interactions with your customers with chatbots will be crucial to customer retention and driving sales.


About The Author

Jennifer Videtta serves as Third Door Media’s Senior Editor, covering topics from email marketing and analytics to CRM and project management. With over a decade of organizational digital marketing experience, she has overseen digital marketing operations for NHL franchises and held roles at tech companies including Salesforce, advising enterprise marketers on maximizing their martech capabilities. Jennifer formerly organized the Inbound Marketing Summit and holds a certificate in Digital Marketing Analytics from MIT Sloan School of Management.

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Hadoop Big Data Analytics Market Summary, Demand, Size, Growth, Worldwide Analysis And …

Hadoop Big Data Analytics Market Forecast 2019-2025 report include Porter’s Five Forces Analysis (potential entrants, suppliers, substitutes, buyers, …

Hadoop Big Data Analytics Market

Hadoop Big Data Analytics MarketForecast 2019-2025 report include Porter’s Five Forces Analysis (potential entrants, suppliers, substitutes, buyers, and industry competitors) which provides crucial information for knowing the Hadoop Big Data Analytics industry. This report includes Overview, Classification, Industry Value, Price, Cost and Gross Profit. It also offers in-intensity insight of the Hadoop Big Data Analytics industry masking all vital parameters along with, Drivers, Market Trends, Market Dynamics, Opportunities, Competitive Landscape, Price and Gross Margin, Hadoop Big Data Analytics market Share via Region, New Challenge Feasibility Evaluation, Analysis and Guidelines on New mission Investment.

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Global Big Data Analytics In Healthcare Market Share Revenue To Surge To USD 68.03 Billion By …

The Latest Report Available at Zion Market Research, “Big Data Analytics in Healthcare Market: by Component (Software [Electronic Health Record …

The Latest Report Available at Zion Market Research, “Big Data Analytics in Healthcare Market: by Component (Software [Electronic Health Record Software, Practice Management, and Workforce Management], Hardware [Data Storage, Routers, Firewalls, Virtual Private Networks, E-Mail Servers, and Others], and Services), By Deployment Type (On-Demand and On-Premise), By Analytics Type (Descriptive, Predictive, and Prescriptive), and By Application (Financial Analytics [Claim Processing, Revenue Cycle Management, Risk Adjustment & Assessment, and Others], Clinical Data Analytics [Quality Control, Population Health Management, Clinical Decision Support, Reporting and Compliance, and Precision Health], and Others): Global Industry Perspective, Comprehensive Analysis and Forecast, 2017 – 2024” provides pin-point analysis for changing competitive dynamics and a forward looking perspective on different factors driving or restraining industry growth. The Big Data Analytics in Healthcare Market is the most blooming and promising sector of the industry. This overall Big Data Analytics in Healthcare Market has been ascending at a higher rate with the enhancement of inventive strategies and a raising customer tendency. The wide-coming to Big Data Analytics in Healthcare Market is a wide field for players offering gigantic entryways for advancement. The overall Big Data Analytics in Healthcare Market is the establishment of the all-inclusive enhancement perspectives and prospects, as the headway of a specific thought requires diverse mechanically supported considerations, theories, and techniques.

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Some of the Major Big Data Analytics in Healthcare Market Players Are:

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The Big Data Analytics in Healthcare Market is widely partitioned reliant on the predictable updates in the enhancement of parameters, for example, quality, trustworthiness, end customer solicitations, applications, and others. The Big Data Analytics in Healthcare Market report contains general successful parameters, confinements, and besides has in detail illumination of the noteworthy data close by the present and future examples that may concern the advancement. The comprehensive Big Data Analytics in Healthcare Market report elucidates within and outside representation of current advancements, parameters, and establishments. The worldwide Big Data Analytics in Healthcare Market also gives knowledge related to the monetary circumstances that would be useful for businesses and startups.

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Promising Regions & Countries Mentioned In The Big Data Analytics in Healthcare Market Report:

  • North America ( United States)
  • Europe ( Germany, France, UK)
  • Asia-Pacific ( China, Japan, India)
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Key Topics Covered:

1. Introduction

2. Executive Summarya

3. Big Data Analytics in Healthcare Market Market Dynamics

4. Global Big Data Analytics in Healthcare Market Competitive Landscape

5. Global Big Data Analytics in Healthcare Market Therapy Type Segment Analysis

6. Global Big Data Analytics in Healthcare Market Therapeutic Area Segment Analysis

7. Global Big Data Analytics in Healthcare Market End-User Segment Analysis

8. Global Big Data Analytics in Healthcare Market Regional Segment Analysis

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Key Highlights of the Big Data Analytics in Healthcare Market Report:

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Also, Research Report Examines:

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  • By Product Type, Applications & Growth Factors
  • Industry Status and Outlook for Major Applications / End Users / Usage Area

Thanks for reading this article; you can also get individual chapter wise section or region wise report version like North America, Europe or Asia.

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What is the IIoT?

Conversations about the IIoT often stray into discussions about Big Data, machine learning, and predictive analytics – all of which can aid the proper …

First things first – the term IIoT refers to the industrial internet of things, a network of smart sensors and analysis tools which enhance ongoing production processes by leveraging data. You may also have heard the term Industry 4.0, which is pretty much the same thing: the end of the era of so-called dumb machines, and the rise of smart, interconnected industrial equipment.

Conversations about the IIoT often stray into discussions about Big Data, machine learning, and predictive analytics – all of which can aid the proper use of the data gathered through machine-to-machine communication.

Back to the beginnings of IIoT

In their latest information technology report, Gartner have revealed that 53 per cent of oil and gas companies have failed to implement IIoT technology, and are not planning to do so. This is a massive issue for the oil and gas industry, with billions of dollars worth of revenue stuck in uncommunicative plants. With the majority of companies yet to jump on the bandwagon, the real changes in IIoT will occur in two place: with those companies at the forefront of the Fourth Industrial Revolution, and with those yet to be persuaded to commit to it.

Reading all this as a decision-maker in a company without a robust IIoT strategy may feel like being presented with an insurmountable obstacle – but beginning to work with IIoT is a simple transition. Many solution providers have entry-level packages available, and often, this is no more complicated than simply implementing a system of sensors and processors to allow equipment to communicate. The data provided by this system can then be stored, analysed, and accessed on an interface that is also connected to the network of sensors.

The IIoT tug-of-war

The evolving nature of IIoT’s leading edge is both a blessing and a curse for those oil and gas companies looking to implement new strategies. On the one hand, the ever-changing operating models mean that older and simpler technologies are becoming more comprehensive and affordable. On the other, many models are becoming obsolete at a faster and faster rate. Perhaps this constant drive to succeed is one of the reasons that so many companies, all of whom are aware of the IIoT, are simply refusing to start their journeys.

Without IIoT in place, decision-making units within oil and gas companies are relying on smaller data sets, which can often involve a significant amount of guesswork. Even within companies with huge volumes of data, the inability to properly leverage that information can significant stymie attempts to improve business processes.

Building up IIoT connections

We’ve already discussed the implementation of sensors as the first step in an IIoT strategy – but once you’ve connected all your assets – from those out in the field to those in your main facility – what happens next?

With remote and automatic monitoring now possible, the next step is to establish a cloud environment that is sufficient to manage the data and provide some insights that would otherwise be difficult to arrive at. Predictive analytics within your enterprise should now be functioning holistically, with most of your equipment connected to your cloud or IT system.

However, as soon as materials, products, or fuel leave your facility – your IIoT strategy becomes obsolete. Connecting the entire supply chain should be the next priority. Predictive analytics can then apply to everything from the schedule according to which replacement parts are ordered, to when fuel transports should depart. The ultimate goal should be complete connectivity, including offshore plants, vehicles, logistics, and intercontinental data transfer.

The Cloud: an IIoT bastion

With data at the centre of all decision-making processes within any type of company, data should be centrally-accessible from all points along the chain. Once the glut of industrial data can be managed to the extent that IIoT applications can make recommendations on the back of it, the benefits to the operation should become swiftly apparent. Process implementation will take place in a fraction of the time, analytics engines will be able to work with multiple often seemingly-incomparable data types, and storing vast amounts of data for auditing and regulatory purposes far simpler.

According to Gartner’s report, some businesses are only harnessing 30 per cent of the value inherent to their collected and stored data – due primarily to not being able to asses their business processes supported by data. An easier way to describe this problem is that of data lakes becoming stagnant data swamps: bigger isn’t necessarily better – it depends on how you use it.

IIoT and the bottom line

We’ve already seen the extent to which IIoT can unlock revenue trapped in badly-utilised data. According to McKinsey, the total value that could be released in the next five years if all O&G make efforts to employ IIoT is somewhere between four and 11 trillion US dollars. So in what other ways can IIoT help release this?

Oft-overlooked, HS&E can cost companies a vast amount of money if improperly managed – not counting the enormous human price of serious breaches of health and safety. Oil and gas is frequently referred to as the most dangerous industry in which to work, and the remote nature of so many sites only exacerbates the risk to life, and the cost of safeguarding employees and assets. One major benefit of the IIoT is that predictive maintenance functions can completely negate costly, high-risk inspection journeys – and help plan the safest and most timely points during the year that travel should be attempted. With hundreds of incidents around extraction sites every year, minimising the human element as much as possible protects workers, those responsible for them, and the company coffers.

Transportation is another key area for IIoT improvements, as tracking the progress and location of vehicles can not only help with predictive analytics such as departures and fuel consumption, but can also help combat fuel adulteration and theft – as it becomes impossible for product to go off-grid. Real-time updates supported by multiple connections such as GPS and mobile, can also minimise downtime during failures and ensure maintenance and repair occurs as soon as possible.

IIoT classifications

As an all-encompassing, company-wide initiative, an IIoT strategy is often split into separate departments or areas. There are, of course, no consistent breakdowns, but a common distinction that we at Oil & Gas IQ use is as follows:

Intelligent maintenance

Intelligence maintenance is the element of IIoT that prioritises the management systems used to reduce unexpected downtime, and minimise breakdown frequency. This usually applies to the pre-existing management systems and assets.

Automation

This can apply both to buildings and machines, from auto-heating and lighting, to increasing precision during production. New solutions have been able to monitor temperature and humidity in both workspaces and pipelines, as well as transportation and wells: there really is no part of the business that cannot benefit from automation.

Machine-machine communication

This subset deals with the communication between assets without human intervention. With the rise of mobile devices and the mechanisms now in place for data transmission across series of networks, technologies such as telemetry, robotics, and low-code are now far more prevalent.

Logistics

Rather than simply relying on human discretion, or inferences drawn from incomplete data sets – IIoT can fully transform the logistics process – from orders of components and lubricants to predicting the point of failure and replacement of heavy machinery.

So, why do we need to invest in the IIoT?

Of all industries, oil and gas is one of the most asset-heavy and data-rich. It is high-risk, employs tens of thousands of people within individual companies, and has some of the longest supply chains in the world. Data is constantly being pulled and collected in hugely-varying packets from myriad sources, making it extremely difficult to standardise and analyse. IIoT strategies can provide data virtualisation to homogenise data sets, and can store vast amounts of information on the Cloud.

Many of the greatest risks to both life and profits in oil and gas are time sensitive, but without the IIoT, it is impossible for solutions to be effectively implemented. The adoption of IIoT applications will improve business efficiency, safety, capex and opex, and reduce the negative effects of having assets spread over a wide area. It’s an inevitable transformation – let’s just hope none of us get left behind.

If you enjoyed this guide, then you may be interested in our upcoming digital event IIoT in Oil & Gas: Online 2019.

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Big Data Analytics in Banking Market Increasing Demand by Production, Consumption and …

The Big Data Analytics in Banking Market report provides a holistic evaluation of the market for the forecast period (2017–2026). A detail analysis of …

Global Big Data Analytics in Banking Market Overview

The Big Data Analytics in Banking Market report provides a holistic evaluation of the market for the forecast period (2017–2026). A detail analysis of the growth determinants, prevailing trends, emerging opportunities, and longstanding restraints has been offered, along with an in-depth intelligence on key market dynamics. The study also provides a scrutiny of the Big Data Analytics in Banking market’s supply chain, raw material demand/supply, top producers & consumers, regional demand patterns, consumption patterns, and pricing strategies.

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Global Big Data Analytics in Banking Market Top Key Players

IBM, Oracle, SAP SE, Microsoft, HP, Amazon AWS , Google, Hitachi Data Systems, Tableau ,New Relic , Alation, Teradata and VMware

Global Big Data Analytics in Banking Market Research Methodology

The research methodology is a combination of primary research, secondary research, and expert panel reviews. Secondary research includes sources such as press releases, company annual reports and research papers related to the industry. Other sources include industry magazines, trade journals, government websites and associations were can also be reviewed for gathering precise data on opportunities for business expansions in Big Data Analytics in Banking Market.

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Global Big Data Analytics in Banking Market Scope of the Report

This report provides an all-inclusive environment of the analysis for the Big Data Analytics in Banking. The market estimates provided in the report are the result of in-depth secondary research, primary interviews, and in-house expert reviews. These market estimates have been considered by studying the impact of various social, political and economic factors along with the current market dynamics affecting the Big Data Analytics in Banking growth.

Along with the market overview, which comprises of the market dynamics, the chapter includes a Porter’s Five Forces analysis which explains the five forces; namely buyers bargaining power, suppliers bargaining power, threat of new entrants, threat of substitutes, and degree of competition in the Big Data Analytics in Banking. It explains the various participants, including software & platform vendors, system integrators, intermediaries, and end-users within the ecosystem of the market. The report also focuses on the competitive landscape of the Big Data Analytics in Banking.

Global Big Data Analytics in Banking Market Competitive Landscape

The market analysis entails a section solely dedicated for major players in the Big Data Analytics in Banking Market wherein our analysts provide an insight to the financial statements of all the major players, along with its key developments, product benchmarking and SWOT analysis. The company profile section also includes a business overview and financial information. The companies that are provided in this section can be customized according to the client’s requirements.

Global Big Data Analytics in Banking Market Geographic Scope

  • North America

    – U.S.

    – Canada

    – Mexico
  • Europe

    – Germany

    – UK

    – France

    – Rest of Europe
  • Asia Pacific

    – China

    – Japan

    – India

    – Rest of Asia Pacific
  • Latin America

    – Brazil
  • Rest of the World

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