#KNexus

[kin-nit]

Nexus: "WHY ARE THERE TWO OF YOU?!"

Jade: "HOW AM I SUPPOSED TO KNOW!??"

I gave cap cut my soul and it can forever keep it until further notice sdsd

Nexus and Jade are my property I mean beloved children you heard children--

[nexonmarketinsights]

Confocal Microscopy Multi-Laser Engines Market Market Overview: Exploring Industry Expansion by 2032

New Research Report on “Confocal Microscopy Multi-Laser Engines Market Market” provide insightful data on the main market segments, dynamics, growth potentials and future prospects of industry. The study covers complete analysis on changing market trends for industry. The report shows the year-on-year growth of each segment and touches upon the different factors that are likely to impact the growth of each market segment. Each segment has analyzed completely on the basis of its production, consumption as well as revenue. And also offers Confocal Microscopy Multi-Laser Engines Market market size and share of each separate segment in the industry.

Get a Sample Copy of the Report at - https://www.proficientmarketinsights.com/enquiry/request-sample/1252

The global Visual E-commerce Platform Market size was USD 515.02 million in 2024 and the market is projected to touch USD 1083.17 million by 2031, exhibiting a CAGR of 10.5% during the forecast period.

Top Key Players in the Confocal Microscopy Multi-Laser Engines Market Market:

Curalate (U.S.)

Inveon (U.S.)

Knexus (U.S.)

Olapic (U.S.)

Photoslurp (U.S.)

Pixlee (U.S.)

Stackla (Canada)

Request Sample for Covid-19 Impact Analysis - https://www.proficientmarketinsights.com/enquiry/request-covid19/1252

The Confocal Microscopy Multi-Laser Engines Market market research report presents a comprehensive assessment of the market and contains thoughtful insights, facts, historical data, and statistically supported and industry-validated market data. It also contains projections using a suitable set of assumptions and methodologies. The research report provides analysis and information according to market segments such as geographies, application, and industry.

Market split by Type, can be divided into:

3D Technology

360 Degree Imaging Technology

Virtual Reality Technology

Market split by Application, can be divided into:

E-commerce

Brand Marketing

Report presents a detailed picture of the market by the way of study, synthesis, and summation of data from multiple sources by an analysis of key parameters. Our Confocal Microscopy Multi-Laser Engines Market market covers the following areas:

Confocal Microscopy Multi-Laser Engines Market market sizing

Confocal Microscopy Multi-Laser Engines Market market forecast

Confocal Microscopy Multi-Laser Engines Market market industry analysis

Inquire or Share Your Questions If Any Before the Purchasing This Report -https://www.proficientmarketinsights.com/enquiry/pre-order-enquiry/1252

What Global Confocal Microscopy Multi-Laser Engines Market Market Report Offers?

Provides strategic profiling of key players in the Confocal Microscopy Multi-Laser Engines Market market.

Drawing a competitive landscape for the world Confocal Microscopy Multi-Laser Engines Market industry.

Describes insights about factors affecting the Confocal Microscopy Multi-Laser Engines Market market growth.

Analyze the Confocal Microscopy Multi-Laser Engines Market industry share based on various factors- price analysis, supply chain analysis etc.

Extensive analysis of the industry structure along with Confocal Microscopy Multi-Laser Engines Market market forecast 2020-2024.

Granular Analysis with respect to the current Confocal Microscopy Multi-Laser Engines Market industry size and future perspective.

Regions Covered in Confocal Microscopy Multi-Laser Engines Market Market Report:

North America (United States, Canada and Mexico)

Europe (Germany, UK, France, Italy, Russia and Turkey etc.)

Asia-Pacific (China, Japan, Korea, India, Australia, Indonesia, Thailand, Philippines, Malaysia and Vietnam)

South America (Brazil etc.)

Middle East and Africa (Egypt and GCC Countries)

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[experts-consult-blog]

What expert research says about Machine Learning

Machine learning is a field of study which creates an ability in the computers to learn without being precisely programmed. It is a branch of artificial intelligence which enables the computer’s capability to learn without being detailed programmed and enables them to perform the task intelligently. A complex data process is carried out machine learning by learning from data, instead of following of getting into pre-programmed rules.

Determining and properly understanding the structure and patterns hidden in the data is the main purpose and aim of machine learning. It is largely based on the ability of the computers to go deeper extract the available data even in the absence of a theory of the data structure.

Experts Insights says that machine learning became very famous in the 90s. Machine learning term was coined by Arthur Samuel in 1959 at IBM, who was an American pioneer in the field of computer gaming and artificial intelligence. In 1989, the commercialization of machine learning on personal computers was done. In 2002, Torch, a software library of machine learning was released.

Types of Machine learning-

Various types of machine learning are

Ø Supervised learning

Ø Unsupervised learning

Ø Reinforcement learning

End Users of Machine Learning

End-use industries of machine learning are

Ø BFSI

Ø Healthcare

Ø Government

Ø Automotive

Ø Education

Ø Telecom

Ø Retail and E-commerce

Ø Others

Machine learning mainly focused on the advancement of computer programs which can be switched when they are exposed to new data. It has multiple uses in this era which includes face detection, image classification, speech recognition, antivirus, genetic, signal diagnosing and among others.

In the BFSI industry, machine learning is used in multiple ways. It helps in increasing the sales & marketing, customer centricity and digitalization and among others. In this sector, machine learning also helps in fraud prevention, risk management, loan underwriting, algorithmic trading and among others.

In the healthcare sector, the application of machine learning is increasing day by day which helps to identify and diagnose the diseases and ailments which are hard to diagnose. It is also used in the early drug discovery process, medical imaging, personalized medicine, smart health records and among others.

Machine learning is also helpful for the government to deliver better, cost-effective and customer-friendly services.

Industry experts of the automotive industry believe that machine learning can help them to achieve marketing goals. It is precisely connected with product innovations, such as self-driving cars, parking, and lane-change assists.

Machine learning in the education sector helps the institutions in adopting cloud technology which has helped in reducing various operational costs. Machine learning is promising fraud detection essays, individual grade analysis and among others.

Insights from experts say that machine learning is being used in the telecom industry to enhance their customer service. Machine learning in telecom industry plays a vital role in network performance data, social media data, fraud mitigation, identifying and improving server application and amongst others.

Experts from Retail and E-commerce industry have analyzed that this industry has grown and improved a lot with the deployment of machine learning as it allows the e-commerce business to create a personalized customer experience. It even helps the retailers in reducing customer service issues before they issue.

E-commerce search results are improved every time a customer shops on the website based on their personal preferences and history with the implementation of machine learning in e-commerce and retail industry.

Other end users of machine learning include manufacturing industry, robotics, transportation, oil and gas and among others.

Machine learning is widely being adopted by the industry experts for making informed decisions for achieving the objectives and goals of their businesses and eases their customer service operations and provides customer-centric services.

The global machine learning market was valued at the US $ 1.29 billion in 2016 and is anticipated to reach at a value of US $ 39.98 billion by 2025.

Major factors driving the growth of machine learning market are technological advancements and mushrooming of data generation.

Unavailability of skilled machine learning professionals is the major factor restraining the growth of this market.

The adoption of machine learning by the increasing demand for intelligent business processes and rising adoption of modern business applications and tools is foreseen to create lucrative opportunities for the growth of machine learning market.

Various challenges faced by the industry experts for the adoption of machine learning are the inaccessible data, its inflexible business model and the affordability of organizations as it requires tremendous revenue charges for a company for the implementation of machine learning.

Major players functioning in the machine learning market includes

1. Alesco Data

2. Ant Works

3. HireIQ Solutions

4. Knexus Research Corporation

5. Pienso

6. Anaconda

7. Aspen Technology

8. Kim Technologies

9. Microsoft Corporation

10. Intel Corporation

11. Google Inc.

12. HP

13. SAP SE

14. IBM

15. Amazon

Future Insights

Expertsconsult believes machine learning will eliminate 50% of the supply chain predictions error, reduce transportation cost by 10% and cut administrative expenses by 40% in the future. Machine learning will also minimize waste and drive unequaled efficiency by eliminating bottlenecks, streamlining inventory management, optimizing production and logistics. According to expert’s surveys, it is predicted that if machine learning is coupled with big data and healthcare app development can generate a value of $100billion per year in healthcare and machine learning is also proceeding for preventive healthcare in this new era. According to the analysis by industry experts, it is believed that machine learning has the potential to create an additional value of $2.6T by 2020 in sales and marketing and a value of up to $2 T in manufacturing and supply chain planning.

Conclusion

The primary reason for the adoption of machine learning platforms is to improve customer experience and it is being adopted by 82 % of marketing leaders to improve every aspect of their personalization strategies.

[marktayl00r]

Top Strategies to foster Customer Engagement for Telcos

In a highly competitive telecom industry, customer engagement is a key factor in building and maintaining a competitive edge. Telecom customers no longer make decisions exclusively on price points and features. They expect reliable experience from their service providers. Moreover, cannibalization of voice and messaging business by entrants like Facebook messenger and WhatsApp has compelled the telcos to look for innovative ways to protect their customer base. 

Building a modern customer-centric and engaging experience is a daunting task for the Telcos. It requires serious thinking and strategizing. Here are the top industry-proven strategies for telcos to foster customer engagement. 

Digitizing Customer Service 

Moving from traditional customer support and service to digital customer service can be seen as a key driver for telco business transformation. Switching to e-care reduces the contact center operating expenses by 25-30%. Online customer support can also lead to improved CX as 76% of telecom customers claim to be fully satisfied with their digital-only journeys versus a 57% satisfaction rate for traditional channels. (Source: McKinsey & Infopulse)

To enhance customer engagement, moLotus - an innovative mobile video customer interaction platform - has revolutionized the way Telcos can serve their customers. The ‘Rate-it’ capability of moLotus guarantees real-time customer ratings and feedback for different service issues to the Telcos enabling them to analyse and improve upon. moLotus automation has significantly reduced the call centre headcounts contributing to lower overheads giving a competitive edge. 

Customer service chatbots also offer an exciting opportunity for telcos. They can slash their contact center costs and improve CX at the same time.  

Making Communication More Personalized

Customer churn is a major issue plaguing telcos. For every customer loss, Telecoms operators end up paying twice: once by losing the future revenue of the customer, and then in the investment to acquire a new customer. (Source: Knexus & Gemius Global)

Dynamic telco marketers have found a solution to this customer churn. They have started using customer data to segment and target for personalized recommendations, offers and messages to increase conversion rates, sales and customer loyalty. These 1 on1 real-time personalization efforts differ from the traditional methods. Personalization has gone beyond offers and messages to include content, interactions, needs and behaviours of individual customers rather than only segments. Telcos should not invest their time and resources mastering the old mass personalization techniques. In fact, they should embrace disruptive marketing tools like moLotus to send highly individualized, automated and interactive content to their customers for better engagement.

Sales Process Automation and Transformation

Telcos should empower their sales teams with instant access to the right sales automation tools and information for better sales closure and conversion and upscaling average revenue per conversion. 

A CRM tool with high integration features could be handy in this regard. An internal CRM dashboard should include all the process information and sales procedures that the telco sales team would follow when working with a prospect. It can also include sales forms that will allow the team to log data on the go and upload it to the central CRM. This way the sales agents won’t waste time on double-handling the data and better engagement of their customers.

Taking Advantage of Big Customer Data

Telecom companies are in an interesting position. On one hand, they are already possessing huge amounts of customer data in their systems. On the other hand, most telecom players are still struggling to analyze and transform that data into meaningful insights. Application of predictive analytics in the telecom industry will lead to massive payoffs, especially in terms of customer engagement and profitability. 

Telcos can transform the vast array of structured and unstructured data into enriched customer profiles via advanced analytics tools. moLotus with its rich advanced analytics features can be a game-changer. Telcos should explore moLotus for unlocking and monetizing the unmonetized customer data.

Exchange Rewards with Actions

The subscribers are more than happy to reciprocate when the telcos provide them with something they like. Telco marketers should reward loyal customers with special offers, discounts, or coupons and in return request them to leave reviews or create user-generated content. Telcos can use moLotus special capabilities for creating reward campaigns like mRedeem. moLotus’ mContest runs hassle-free, cheap and fast contests for subscribers with superior response and engagement.

The Bottom Line

Next-generation digital marketing strategies stated above offer real opportunity for the Telcos & Brands to engage their ever-demanding customers in a challenging landscape. A customer experience reformation would certainly yield Telco business transformation. Telcos that advance their digital customer engagement will experience the biggest upside.

[agilemediaguy-blog]

Hey Knexus thanks for the follow! Please let me know if you want to hear about any specific digital marketing topic.

Hey Knexus thanks for the follow! Please let me know if you want to hear about any specific digital marketing topic.

— AgileMediaGuy (@agilemediaguy) November 19, 2018

via Twitter https://twitter.com/agilemediaguy

[customersupportservices]

Knexus Customer Services vs Opinator 2018 Comparison https://comparisons.financesonline.com/knexus-customer-services-vs-opinator?utm_source=contentstudio&utm_medium=referral OutsourceCustomerSupport LiveChatOutsourcing

[outsourcecustomerservice]

ThoughtBuzz vs Knexus Customer Services 2018 Comparison https://comparisons.financesonline.com/thoughtbuzz-vs-knexus-customer-services?utm_source=contentstudio&utm_medium=referral LiveChatOutsourcing CustomerSupport

[livechatoutsourcing]

Knexus Customer Services vs iCRM 2018 Comparison https://comparisons.financesonline.com/knexus-customer-services-vs-icrm?utm_source=contentstudio&utm_medium=referral ChatOutsource CustomerSupport

[cristian-randieri]

Tweeted

Knexus Thanks for following us ! https://t.co/N4BVre07pB

— Cristian Randieri (@C_Randieri) August 8, 2017

[iwillreadthesesomeday]

via Instapaper: Unread

[rmsharma-blog]

The most asked question among marketers is which is better option Omnichannel or Multichannel. are they same or is there any difference between them. Read the full article explaining Omnichannel vs Multichannel with insights and example.