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AI data analysis data capture firm election results and data capture Georgia runoff spending ReadWrite using AI for data capture

Data Capture Firm Uses AI to Dissect Georgia Runoff Spending

georgia runoff spending

With majority control of the Senate at stake, it’s no surprise that the recent Georgia runoff elections were the most expensive political race in American history. Wherever campaign dollars flow freely, as they did in Georgia, accountability questions proliferate. Where did that money come from, where did it go, and what influence (if any) did those spending choices have on results?

Election law requires that campaign expenditures be made public. Even so, it often takes weeks or even months for analysts to sift through a dense forest of information and build usable datasets. Important decisions have to wait; only after a dataset has been given the green light can anyone even begin to search for associated outcomes and trends.

Many have wondered whether there is a legitimate place for artificial intelligence in harvesting public data repositories. Can machine-generated data really be trusted? And even if it can be, can it replace human analysts?

When Data is Public but Nearly Useless

Each TV and radio station is required to carbon-copy invoices for political advertisements to the Federal Communications Commission, which makes them public record. This sounds great on paper, but the FCC mandate is to merely publish the invoice documents — and each election comes with tens of thousands of invoices.

The invoices are important because of the dates, callsigns, and amounts listed on them page by page. The data allows analysts to build spending maps and scrutinize campaign behaviors, but the data needs to be aggregated in a spreadsheet first. A spreadsheet analysis would ordinarily mean going through this massive amount of invoices by hand.

A Better Use of Data

Of course, it’s not just the FCC receiving invoices — all businesses receive invoices when they trade with each other. Any company of size faces the same problem as the data analysts who would like to use the FCC data.

One automation software company that solves the problem of translating business documents to data for enterprises decided to unleash its automatic data capture on the FCC invoices for the Georgia runoff elections. The vendor Rossum, in partnership with analysts from the e.ventures fund, just published the resulting data set.

AI-Powered Reporting Follows the Money

Ever since the 1976 film “All the President’s Men,â€� many of us have internalized the admonition to “follow the money.â€� The practice remains a tried-and-true method for discovering unseen relationships and shedding light on patterns of activity and motivations that we might otherwise miss.

Data capture technology cannot read the minds of campaign managers — something for which we can all be thankful. But the new breed of automation based on artificial intelligence might at least enhance our ability to better see the tracks they’ve left.

Accelerating Data Collection

In addition to eliminating the time-consuming tedium of keystrokes and accelerating data collection, policy analyst Jordan Shapiro found that Rossum’s processing of the Georgia spending data produced a greater degree of granularity. This granularity, in turn, enabled her to better grasp the thinking that lay behind decisions made by the various campaigns.

Following the Spend

“As a political analyst, Rossum’s data about 2020 Georgia runoff election spending gave me the opportunity to get inside the heads of the campaigns to see which areas they thought were more or less competitive based on how much each candidate spent in that region,� says Shapiro. “Particularly helpful for my work was the ability to compare county-level spending patterns with a shift in vote share between November and January.�

Data Suggests a Shift from Red to Blue

Southern states have historically been fertile territory for Republicans seeking election, but that appears to be changing.

An NPR report from January 2020 found more Black Americans are moving south, and as they relocate, they are contributing to a shake-up in election results. In 2000, Rockdale County (southeast of Atlanta), for example, was a predominantly white area with a Black population of approximately 18%. Today, that same area has a 55% Black occupancy.

Changing Demographics

Shapiro notes that Georgia’s changing demography, heavy campaign spending by Democrats, increased voter mobilization at the grassroots level, and a tumultuous national political stage all played a hand in what many saw as upset victories. Political turmoil and civic unrest on the national scene rocked Republicans and set the stage for Democratic wins. Both races were reasonably close, with Ossoff winning his race by about 55,000 votes and Warnock winning his with around 93,000 votes.

Regardless, Rossum’s analysis found something even more important than spending-outcome correlations: reasons to default to AI-driven analysis.

Why AI-Enhanced Reporting Will Be Big

The mandatory availability of campaign spending records affords skeptics and naysayers an opportunity to fact-check any reporting that emerges after an election. Reports that have been compiled using AI-powered scanning techniques can be fact-checked just as easily as traditional reports constructed by workers furiously pounding away on keyboards.

As AI-powered processes continue to improve, confidence in this newer methodology is certain to grow as well. As that happens, voters can expect to see more accurate, useful information in the run-up to Election Day.

AI-driven reporting benefits democracy in at least four ways:

1. Enhanced Transparency of Public Data

Much data is the subject of public record, but too often, it is not readily available for analysis and therefore carries only a fraction of its potential. The problem with campaign spending records is that key dates and amounts are scattered through scanned documents instead of being aggregated in a spreadsheet that can be readily analyzed for new insights. This problem is common for many registries, FoIA data releases, and internal government operations.

2. Rapid Data Analysis

The speed advantage of AI-enabled reporting systems over traditional methods of computation will make relevant data available much earlier than in past election cycles. Earlier delivery of results could, in turn, open up new options for campaign managers to consider as constituents respond favorably or negatively to various messages.

3. Added Depth and Surfacing of Less-Obvious Correlations

As noted above, Shapiro was able to take Rossum reporting on election data and cross-reference it with migration data. In doing so, she discovered a trend in specific Georgia counties shifting from presumed Republican strongholds to surprise Democratic wins.

4. More Informed Policymaking and Decisions

Tightening the link between election results and policymaking can serve as a check on any politician’s temptation to drift away from the will of the people they serve. A democratically elected official can only disregard his or her mandate for so long. Faster access to accurate information allows voters more time to assess a politician’s voting record.

What’s Coming in 2022

With another election cycle coming entirely too soon, keep an eye out for new applications of AI. Both parties will use the technology to unearth patterns, analyze results, and suggest new political strategies. Policymakers will check proposals against their constituents’ latest voting trends.

Will 2022’s political environment be any less fraught than the 2020s? Maybe not — but it will be more data-driven.

Image credit: edgar colomba; pexels

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2021 business strategies AI Connected Devices Data and Security data science Data scientists Soft skills

5 Vital Soft Skills Data Scientists Must Possess in 2021

data scientist

Technical skills are overrated, particularly in data science. Many data scientists quickly realize that much of their job challenges aren’t due to what they can or cannot do. Rather, the mentality with which they approach tasks matters a lot.

For instance, a data scientist who has mastered communication will present their insights better than their more (technically) skilled counterpart whose reports are jumbled. Likewise, extrapolating insights from raw data require a huge dose of creativity and critical thinking, both of which are not taught as technical skills but must instead be developed personally.

Other soft skills that are necessary for data scientists include business aptitude, problem-solving, and adaptability.

All of these are time-proof skills that transcend technological innovations. Success in 2021 and beyond as a data scientist will heavily rely on the development of these soft skills.

Critical Thinking

This author defines critical thinking as “the judicious and objective analysis, exploration and evaluation of an issue or a subject in order to form a viable and justifiable judgment.�

Critical thinking is often regarded as the most essential skill in data science.

It makes you well-informed, enhances your judgment, and makes you better equipped to make more effective decisions. As a data scientist, you must be capable of examining the available data from multiple perspectives. To develop critical thinking, do the following:

  • Question your assumptions: as a scientific field, your job is to apply empirical methods to analyzing data and extracting insights. However, the human mind remains subject to all kinds of biases and presuppositions. You must thoroughly interrogate them to hone your reason and avoid decision pitfalls.
  • Engage different perspectives: As social beings, we are drawn to people who act and think like us. But the lack of healthy dissent leads to poor decision-making. Thinking critically means consistently seeking out fresh perspectives. This doesn’t necessarily mean disagreement; it could be as simple as connecting with colleagues from another department in order to understand their outlook.

Communication

The purpose of data analysis is to make informed decisions. And your responsibility as a data scientist includes being able to present your findings in a clear manner to the non-data-scientists who have to make the decisions.

Your non-technical audience needs to know how you reached a specific conclusion, the justification for your methods, the implication of your findings, and why you consider one solution better than the other.

You can make your presentation more effective through storytelling. As Brent Dykes says in his book, Effective Data Storytelling,  “…narratives are more compelling than statistics if your goal is to make an impact on your audience.”

Visuals achieve the same effect; when used right, they help your audience see and understand patterns between scraps of data. Your insights don’t matter unless you can make others understand it and drive them to take the necessary actions.

Problem Solving

A data scientist is like a detective. Both workers investigate the available facts and data to address problems. In one case, the purpose is to solve crimes; on the other, the purpose is to deliver business value.

Data is what we make of it. And a data scientist needs to be resolute at, and equipped for, investigating issues to the root. Project managers love a data scientist who can identify creative solutions to problems.

For instance, discovering that your company’s customers behave in a certain way is different from why they behave so. And even then, the job is most likely not done. You must still use the available data to determine how to make the customers behave differently or to make the company adapt to the customers’ habits.

Data science is a continuous job of evaluating data and weighing options, determining why one approach to fulfilling a goal is better than the other. The consequences of your conclusions could be massive; so you need to get it right, at least based on the data available to you at the time.

Practice makes you a better problem-solver. There are websites that help you learn to tackle various data science challenges with real business impacts.

Business Aptitude

Analyzing data is one thing; contextualizing it to solve real business problems is another. Dr. N. R. Srinivasa Raghavan of Infosys is widely quoted thus: data science is more than just number crunching: it is the application of various skills to solve particular problems in an industry.

Without a good understanding of business processes and operations (such as supply chains, customer service, finance, human resources, logistics), it would be impossible to extrapolate actionable insights.

Data science is a field involving so much theory but has far-reaching practical implications. Therefore, a good data analyst is one that understands the business model and can quickly adapt to various business situations.

How does the business work? How does your company work? What do you know about your industry? How does your company make money? What product/service does your company deliver, and how does that work? What makes your company lose money? Who are your competitors?

These questions, and more, are important to understanding business operations. You can develop this by research. But you first need to possess a keenness for business and understand that data science is not just about Python, SQL and all the technical parts.

Adaptability

Adaptability has to do with how quickly you are able to adjust to new conditions, which may be positive or negative. In this information age, innovation grows at such a rapid pace that it is often difficult to keep up. We are living in a world of possibilities, and what’s new today can become outdated in a few months or years.

In fact, the tools you use for data analysis five years from now may be different from the ones you employ today.

Adaptability is also important for moments of crisis, a time when data scientists come under greater pressure to deliver. Consider the COVID-19 pandemic. The global spread of this virus has disrupted business operations everywhere and altered, perhaps permanently, the course of work and business.

When there is a setback, people seek answers; they want to know exactly what went wrong and how they can move forward.

Today, everyone relies on data. In this world of several unprecedented changes, you must be ready to adjust to the prevailing trends.

Conclusion

Soft skills deal with how you approach data. You may know all the technical bits of data analysis, but a wrong approach almost always leads to wrong results.

More importantly, the technical aspects may change. In five years or a decade, the currently popular data science tools may be entirely out of the limelight, edged by newer advanced tools.

But skills such as critical thinking and problem-solving will endure. Developing these skills early is a great way to secure your career in the future.

Image Credit: pixaby; pexels

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AI artificial intelligence Machine Learning natural language processing ReadWrite

How is Conversational AI Improving Customer Experience?

Conversational AI

The Conversational AI allows the program to be a part of human-like interactions. This set of technologies empower the applications to send automated replies. It is yet another example of the exponential rate of innovations happening in the artificial intelligence field.

As a result, businesses are investing in conversational AI technologies like Chatbots to serve customers round-the-clock. Although the benefits of using this advanced technology are innumerable, you need to answer certain questions while assessing a conversational AI solution.

Conversational AI is Still Evolving

We are still undergoing the phase of revolution wherein innovators are bridging the gap between the artificial and natural interactions among humans and computers. Constantly, developers are empowering Conversational AI technologies to decipher human actions and mimic human-like conversations.

According to research, the Conversational AI market size is expected to reach US Dollars 15.7 billion by 2024. This clearly depicts the interest of investors in this technology and gives a sign of a lucrative future scope for businesses.

The incorporation of context, relevance, and personalization after deciphering various languages and tones is the end goal of this set of technologies. Chatbots are integral components of these technologies. Consequently, they undergo continual enhancements.

Conversational AI is not the Same as Traditional Chatbots

What do you like more, scripted TV shows or reality shows? Traditional chatbots are the scripted ones and Conversational AI chatbots are the non-scripted ones. The former one works with scripted dialogues whereas the latter one works with the context.

When scripted traditional chatbots are created, developers feed the dialogues with proper keywords. The bots are able to respond with the most appropriate reply out of the many replies added to their memory.

When a user sends a particular text, the chatbot identifies the keywords and sends in the scripted replies. This adds tons of burden on the owner of the chatbots. Hence, they update the conversations to make them look realistic.

The traditional scripted chatbots are not able to converse in real-time with users by understanding the context of the whole conversation. As a result, this compromises the customer services of the businesses.

This particular loophole is looked after by the chatbots powered by conversational AI. They hold the capability to engage in any dialogue after grasping the context of the whole conversation. They do not follow a script because they have in-built conversational capabilities in the software. Let’s understand how they work in detail.

Work Process of the Conversational AI

Conversational AI works with a combination of technologies. With the integration of advanced technologies, Conversational AI performs the function of interacting like humans. Here are the steps involved in the work process of these technologies:

1. Accept the Inputs

The first step involved in the functioning of Conversational AI is to accept the inputs from users. These inputs can be in the form of text or speech. If the inputs are in the written form, text recognition technology is applied. On the other hand, if inputs are spoken phrases, then voice recognition technology is applied.

2. Comprehending

Text and voice recognition is done with AI technology natural language understanding (NLU). After the application reads the inputs, the user intent is understood before forming any kind of response. Usually, businesses can use conversational AI for comprehending responses in various languages. In a nutshell, this is one of the most difficult steps in the work process of a chatbot.

3. Creating Response

In this step, the Natural Language Generation (NLG) is used to create responses in a language that humans understand. After deciphering the intent of the human, dialog management is used to create responses. Finally, it converts the computer-generated responses into human-understandable language.

4. Delivering Response

Finally, the response created in the previous step is shared with the users in the expected form. Either the system delivers it as a text or conducts the production of human speech artificially. Are you able to recall the voice of Alexa or Google Assistant? They generate their responses by following this process only.

5. Learn from Experience

Conversational AI also has provisions for improving their responses for future interactions by learning from their experiences. By accepting suggestions, the application learns to deliver better responses in future conversations.

Technologies used in Conversational AI

The Conversational AI platforms use a set of technologies at the right times to complete the work process. All these technologies are empowered by Artificial intelligence. Let’s understand these technologies in brief.

1. Automatic Speech Recognition (ASR)

The application interprets the spoken phrases by deploying this technology. Adding to this, it converts the speech into texts for the app. Voice assistants like Alexa, Google Assistant, etc. use Automatic Speech recognition.

2. Advanced Dialog Management

This technology helps in forming the response to the conversational AI app. Dialog management arranges this response for the next technology. Further, converts it into something which humans can understand.

3. Natural Language Processing (NLP)

Conversational AI uses natural language processing along with its two subsets. The first one is Natural language Understanding which understands the meaning as well as the intent behind any text. It can decipher texts shared in multiple languages as per the programming.

Both chatbots, as well as voice assistants, use this technology. After ASR, voice apps apply NLU. The second one under the NLP technology head is Natural Language Generation. Conversational AI uses this in the last stage of the work process by Conversational AI.

It creates the responses by converting the computer-generated replies into a language that is understandable for humans. This technology deploys dialog management to conduct this task seamlessly.

4. Machine Learning (ML)

Machine learning is great at understanding a set of data. In conversational AI also, machine learning is used to understand the interactions that have happened over time. Also, ML identifies better responses to these interactions.

Therefore, it understands user behavior and guides the app to create better responses. Humans also join machine learning in this task and together make the Conversational AI app a better interactor for customers.

Benefits of Using Conversational AI for Better Customer Engagement

Businesses are struggling for quite a long time to improve their customer engagements. As a consequence, conversational AI tools like Chatbots have become an integral part of websites and apps. Hence, the developers are working hard to incorporate conversational AI in their solutions.

Conversational marketing has become a proven corporate strategy for millions of businesses operating across various domains including healthcare, tourism, education, etc. Let’s find out what exactly can Conversational AI do to empower customer engagement:

1. Never-ending Scalability

Contrary to human customer support executives, Conversational AI can provide solutions to as many customers as possible at one time. Therefore, you can scale up your operations to any limits. Moreover, it can provide human-like interactions around-the-clock without any interruptions.

2. Acts as a Supportive Wing

In an organization, teams work together towards achieving organizational goals. Conversational AI technologies work with human experts and take their burdens away. They do those tasks which are humanly not possible at the same consistency as that of Conversational AI. This leaves room for human experts to entertain customers only when required.

3. Reduces Cost

Investing in conversational AI solutions might seem an added expenditure to you. But in the long run, the functions it performs reduces your cost. You will not have to pay employees for all the shifts to satisfy customers with real-time conversations. These applications prove to be immensely cost-effective for businesses.

4. Offers Data Insights

As mentioned above, machine learning understands the past experiences and interactions to improve your Conversational AI potential for future interactions. This allows businesses to get an insight into the data.

Hence, you will be able to know your customers’ preferences, behavior, and requirements. Furthermore, you can utilize this data for various other purposes to improve your plans and strategies.

5. Improves Productivity

The primary reason for investing in conversational AI solutions should be the need to improve productivity. It enhances overall productivity with uninterrupted, credible, and prompt customer services.

24×7 support and human-like interactions decrease the risk of losing customers. Hence, conversational AI is capable of providing better customer engagement and ultimately a rise in customer retention rate.

Leverage Conversational AI in Omni-Channel Approach

Investing in conversational AI might seem lucrative after reading about its work process and benefits. Before taking the final call, make sure to identify the channels where you are going to leverage this technology.

When it comes to the customer experience journey, we need to take care of many gateways. With conversational AI solutions, you can provide live chats, social media interactions, messaging on various platforms like Whatsapp, SMS, etc., as well as emails.

Therefore, businesses are using the omnichannel approach. Under this approach, they use multiple engagement channels and offer a seamless and intuitive customer experience. It allows businesses to offer their customers a proactive engagement and prompt responses.

Conclusion

Across the world, businesses are deploying high-end artificial intelligence technologies. This, in turn, offers business solutions to enhance the engagement of customers. Therefore, we can these technologies to offer an improved experience to your users. Conversational AI holds the potential to strengthen customer and business relationships. All you need is to explore it efficiently!

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AI Culture Startups

What Is the Future of HR?

What Is the Future of HR?

Human resource (HR) departments have long been integral to organizational success, and they’re likely to remain that way for decades to come. But the nature of HR is likely to evolve with new technologies, research, and trends.

What does the future of HR look like?

Remodeling the Workforce

For starters, we may see HR leading the charge in remodeling the shape of the average workforce. Increasingly, employers and consumers alike are valuing diversity and inclusion; companies are working harder to ensure a mix of people from different backgrounds are included at all levels of the organization. In the future, this is going to become an even bigger priority.

But this is a minor change compared to the next generation of workforce management. We’re already starting to see a blend of human beings and machines in the workplace, and in the near future, this is going to become more prominent – even in businesses filled with mostly high-skilled, white-collar workers. How will you handle the transition from a human position to one handled by an AI algorithm? How will you ensure that humans and machines can collaborate and maximize productivity together? How will you optimize the balance between human beings and machines in the workplace? And how can you tell what an optimal balance is?

These will be the big-picture questions dictating HR development in the future.

Remote Work

Even before the COVID-19 pandemic, remote work was gaining popularity. Employees were getting a feel for the benefits of the arrangement, such as cutting down on commute time, improving flexibility, and even increasing productivity. At the same time, employers get to save money and see better results. After the pandemic forced businesses to rethink work and increase safety, these benefits became more apparent to a wider range of businesses.

Today, HR departments are evolving to treat remote work as the default – rather than a temporary or gimmicky new approach to conventional work. That trend is likely to continue into the future as remote work becomes even more widely accepted.

The Evolution of HR Software

Today’s HR departments and organizations rely on HR software like Rippling to handle things like payroll, benefits management, and employee device management. Using one platform, they can store, review, and gather information, send messages, and even generate reports to analyze data. It’s seemingly comprehensive.

But in the future, these platforms will likely become even more robust. We’ll see the addition of new streams of data, real-time analyses, and possibly the inclusion of machine learning and AI algorithms to increase productivity or improve results.

Culture and Unity

Part of HR’s job is to create and sustain the culture within an organization, and make the team feel unified. This is increasingly difficult in a world dominated by remote work, but it’s increasingly demanded by the workforce.

Accordingly, HR will need to find new channels for communication, teambuilding, and collecting employee feedback. Organizational culture management is going to evolve into new forms, and employees will have to develop a different set of expectations for how it’s facilitated. In line with this, HR leaders will have to remain agile, forging culture-based connections when they can while still preserving the structure of the business.

The Gig Economy: Here to Stay?

Technology is responsible for introducing the “gig economy.� Though freelancing and gig work concepts have existed for decades, apps like Uber, Fiverr, and Airbnb made it much easier for individuals to offer their services as freelancers. In turn, corporations have attempted to take advantage of this by relying more heavily on contractors and freelancers instead of making the investments in full-time employees. This is favorable as a cost-saving measure, but it also introduces more flexibility into the organization. And while workers miss some benefits, they also have more freedom to control their workloads and explore other opportunities.

However, it remains uncertain whether the gig economy is here to stay or whether it was something of a temporary detour. Either way, HR departments will have to adapt to keep in line with current trends.

The Employee Experience

We’re already seeing a wave of momentum favoring the development and maintenance of the “employee experience.� In other words, how does an employee feel about the business and engage with the business, from the moment they’re recruited to their ongoing career development? Positive employee experiences lead to higher morale, higher productivity, and higher employee retention. The subjective nature of the employee experience can also reveal a lot about how the organization operates.

In the future, employee experience will become an even higher priority – and become easier to measure and control. Better tools will make it easier for employees to provide feedback about their experiences throughout their careers, and better analytics platforms will make it easier to figure out which changes to make to improve the business.

Data-Driven Insights

Nearly all departments and all industries are increasingly relying on data to improve, and HR is no different. In the future, HR will become even more reliant on data to operate efficiently.

Today’s HR departments use a variety of data points to create images of job candidates, employees, and organizational efficiency, such as hours worked, employee retention, and metrics related to recruiting, training, and development. Data may become even more granular in the future, studying nuanced elements of employee behaviors from the moment they’re recruited.

Most of these data will be collected automatically, with the help of device tracking and robust HR software platforms – which leads to our next points.

AI and Automation

HR departments are also likely to incorporate more AI and automation. Automation is a no-brainer; if you can automate a task that ordinarily requires manual human effort, you’ll instantly reduce the hours your employees need to work. Not only does this save the organization money, it also frees up human employees to focus on more important things.

Artificial intelligence (AI) will also serve a bigger role in the future. With sufficiently advanced machine learning algorithms, HR leaders can quickly and efficiently crunch the numbers they’ve gathered and come to a final conclusion. And in the right context, a suitable AI could even handle previously human-exclusive tasks, such as handling employee conflicts or interviewing candidates.

Sustainability and Image

Today’s consumers care more about sustainability, and not just environmental sustainability. Human and social sustainability require businesses to engage in socially responsible hiring and employee management practices. Today, this includes hiring people from a diverse range of backgrounds, treating employees fairly, and compensating them well. In the future, these are going to become even bigger priorities for consumers, which means businesses will need to do more to make their operations transparent (and show off their sustainability efforts).

The very nature of human and social sustainability may also evolve in the near future. For example, if machines are gradually replacing human jobs in a certain industry, will it be considered socially sustainable or responsible to maintain at least some human jobs?

Cycles of Progression

Over time, the rate of change within HR departments is likely to increase; in other words, HR progression will be accelerating. As we’ve seen, technology tends to evolve exponentially. New technologies get incorporated into existing businesses that create even newer, better technologies. And once things like machine learning and big data analytics get thrown into the mix, it’s hard to stop that momentum.

This acceleration will also be fueled by competition. As HR departments begin pushing the limits of their productivity and effectiveness, other HR departments must follow suit to keep up. Nobody wants to be left in the dust with a years-old platform that’s now becoming obsolete in mainstream workforces.

Even with the onset of AI, automation, and a machine-heavy workforce, HR departments are going to remain important for productivity and sustainability for the foreseeable future. However, the role of an HR manager or HR director is going to change substantially in the coming years. No one can predict the future, but we can see many of these trends already developing in the present. The transformation is already unfolding.

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AI Customer Service Product Reviews

Strategizing for 2021 With Sentiment Analysis Using Product Review Data

strategizing 2021 analysis

2020 started with a lot of concern; individuals, businesses, and governments were all thrown into a state of confusion. COVID-19 ravaged the world and there was no known remedy.

2021, however, promises to be a year full of hope. Pfizer and its partner BioNTech have filed for emergency authorization in the US of their Covid-19 vaccine; the advanced trial showed the vaccine protects 94% of adults over 65.

With the view of a remedy at our reach, organizations will start strategizing for 2021. One thing we must learn to live with as a result of the pandemic is home working.

Most business will have to be conducted online as compared to before the pandemic. You will have to deal with the issue of more data that is going to be ferried from one spot to the other.

More than ever before, customer feedback will make a lot of difference in your products and services. You must consider the feelings and comments of your customers if you still want to be relevant and competitive in this “new� business landscape.

The business world is slowly getting used to big data; however, it is the source through which you get your data. One pertinent question you must be ready to answer is, do you have a strategy in place to enable you to gain useful insight into the data even when you have access to it?

Sentiment analysis using product review data

ResearchGate, in a study, revealed that more than 80% of Amazon product buyers trust online reviews in the same manner as word of mouth recommendations. There two channels through which you can get these online reviews: the first is review sites, while the second is social media.

While acquiring the data has been made easy, the data you get from these channels are, unfortunately, unstructured. To make any headway out of the data, you must put in several hours of human labor for structuring and analysis.

However, advancement in technology has made it relatively easy to deploy Natural Language Processing and machine learning into sentiment analysis using product review data. You can use several techniques and complex algorithms such as Linear Regression, Naive Bayes, and Support Vector Machines (SVM) are used to detect user sentiments such as sarcasm, context, and misapplied words.

When you use these techniques, the tool usually separates the reviews into positive, negative, or neutral tags. This will enable you to obtain the relevant insights within minutes.

The insights you have been able to obtain will indicate the needs of your customers and you can then use them for the following:

  1. Discover what your customers like and dislike about your product or service

Sentiment analysis using product review data will not only reveal the feelings of your customers towards your product; you will also understand what they think about your current approach. From this, you will know what improvements you have to implement.

You will have a clear insight into your customers’ mindset and how they interact with each other about your brand. The insights you gain from these will enable you to send content that resonates deeply with your target audience.

  1. Use your product reviews to know your status in the market.

Sentiments about your brand can shift radically and quickly, depending on what’s happening globally. For instance, the Cambridge Analytical Scandal was a big blow to Facebook; you can use sentiment analysis to appropriately monitor your brand’s status and focus on PR campaigns.

You will be able to shift and flex your efforts as quickly as the reviews.

  1. Develop actionable strategies to improve deficiencies

How do you package your product, for instance? Do you believe it has to be bigger or smaller? Can you afford to increase the price, taking into consideration a situation like the COVID-19 pandemic?

When you listen to your customers, you will know the step to take to boost engagement, raise satisfaction, and convert more customers to your brand.

  1. Boost customer conversion rate

While your effort must be geared at getting positive feedback, occasional negative feedback can also be useful. Since they are paying for your product or service, consider your customers as your most honest critics.

Their views are impactful and will help you to acquire new customers if you implement changes. Making adjustments based on insights from customer feedback will help you deliver better customer experiences, products, and services that will keep your customers coming back.

Once they are satisfied, they willingly spread the word to friends and family, bringing in new customers.

  1. Obtain real-time product insights anytime

Feedback through sentiment analysis using product review data is effortless and quick. It can provide you with real-time updates about how customers adjust to any recent change you may make.

  1. Improve service

The more you make positive changes to customer service, the more customers appreciate your gesture and become more loyal. To find out if these changes are necessary, you need to deploy aspect-based sentiment analysis. This will enable you to clinically dissect the problems that may or may not exist in your company.

Conclusion

It’s not just about having data; it’s about carrying out sentiment analysis using product review data. Sentiment analysis will give your brand the actual insight into the mindset of your customers.

Using the information in real-time enables your company to implement the necessary marketing strategies to become relevant and more competitive. You need to constantly watch and analyze the views of your customers because they can change their opinions quickly.

Customers can be erratic, but having a strategy in place that includes sentiment analysis in your digital marketing arsenal will go a long way to improve things.

Image Credit: shutterstock

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AI Alexa Voice Service amazon voice search Connected Devices Tech voice assistant voice assistant technology voice technology

Futurespective on Voice Technology from the Google Assistant Product Team

furture of voice by google

Technology isn’t silent anymore. It talks, and its voice shapes the way we live — working and virtual learning, shopping for cleaning supplies, playing daily music mixes, cooking new recipes, or exercising — all by just asking for it out loud. Since the pandemic hit, more and more brands realize the endless possibilities for interacting and engaging with users in a natural, contactless way.

Whether for working, learning, or playing, here’s why voice is the “natural,� touchless solution for next-level brand engagement.

Some of the world’s leading companies like American Express, Estée Lauder, Nike, Headspace, Campbells, Dunkin’, Snapchat, Tide, and Bank of America have started rethinking customer experience and brand strategy from the voice tech perspective and the opportunities it presents. Brands like these are finding that voice brings their relationships with customers to a new level. A touchless interface is a straightforward reason to adopt voice in the current pandemic. Still, another is how voice technology offers greater accessibility and inclusiveness to customers regardless of ability, race, age, gender, or geographic location.

The voice space has become a topic of heightened interest for thought leaders across industries, including Sofia Altuna, who heads Global Product Partnerships for Google Assistant and hosts VOICE Talks, a monthly live-stream series focused on the voice sector and the experts, technologists and innovations impacting voice technology. The coronavirus, she says, “has provided a new perspective of the importance of this technology.”

Additionally, in a recent VOICE Talks episode, she emphasized how inclusion and accessibility are being prioritized for ambient computing and noted that disabled rights and social justice are equally essential.

To learn more about the innovations in voice, the brand partnerships working to solve users’ needs, and the growing voice community (VOICE Talks has grown to nearly 50,000 users in four months), we recently had a conversation with Altuna, who is working (and exercising, cooking, learning and playing) and now filming VOICE Talks live from her apartment in New York. The interview is slightly edited for length and clarity.

What is so intriguing about voice technology for you?

I’ve always been very passionate about empowering people through technology, so one of the most intriguing things to me about this space is that voice is universal and easy for anyone to adopt. Voice is the most “natural” way to engage with technology and requires no user manual. All types of people of all ages are using Voice Assistant, defying the early adopter stereotype.

As host of VOICE Talks, what do you strive to bring to the monthly live streams?

Every month, we try to bring viewers insider content from the world’s leaders in voice technology. From industry trends to case studies to business tips to product demos and announcements — there is a lot we want to cover. We want the content to resonate with the viewers, so each episode also focuses on what questions or themes the viewers have submitted at #AskSofia. This is about reaching the community in a way that is meaningful and relevant to what they want to see, learn, and share with each other.

Tell us on a professional level why you are at the right place, at the right time, as host of Voice Talks and your work on the Global Product Partnerships?

Previous to working on the Google Assistant, I was already interested in the space and was involved with other projects at Google around Conversational AI. Since I joined the Assistant team three years ago, I’ve worked across multiple different product features globally and with many partners.

This has given me a broad understanding of the voice tech ecosystem, the possibilities and challenges across the platforms, and the opportunities for brands and users. Being at the intersection of product engineers and partners also provides a unique perspective to understand both the technical complexities and our partner brands’ vision, goals, and requirements. We work with partners to allow for powerful user experiences that help solve users’ needs.

How has your background prepared you for this role?

Having led the go-to-market strategy and execution for multiple Google Assistant initiatives globally with many different brands across multiple industries has provided me a broad view of the voice tech ecosystem and a good perspective. I’ve also participated in many conferences, client summits, and as a guest speaker at MBA classes. I’ve been passionate about raising my voice and sharing my perspective on this technology.

Typically events are always a great opportunity to learn about the ecosystem, exchange ideas, and listen to partner feedback. However, without these this year, VOICE Talks is a great platform to bring the voice community together and share learnings that can propel this technology into the future.

Fun fact: when I was 15, I also did a pilot for a Spanish TV show as a host. Maybe it was all practice to lead to this moment 🙂

Has the pandemic heightened your awareness of the importance of voice technology?

Definitely. Although we began our journey towards voice technology long before this current crisis, COVID-19 has provided a new perspective of the importance of this technology. First, as more people are at home, voice assistants can play a bigger role in work productivity, education, and family activities.

Secondly, people want to avoid touching shared devices (or any device), so I think Voice is poised to be part of the solution that helps shape our new normal and make our lives easier and safer. This is something that makes me excited about this space, of all the opportunity there is and the impact that we can have.

Why do brands want to include Voice in their strategy?

Today, brands are particularly excited to join the Voice ecosystem at the ground floor with the vision that it can grow into a large surface for their business.

There’s a clear new medium with Voice that users are getting more and more comfortable within their homes and on-the-go. As brands look to innovate and adapt to cutting edge technology, they partner with voice tech companies, like Google Assistant or Amazon, to learn what works for this new medium (hand in hand with us). The conversational design also seems deceptively simple, so brands incorporate voice technologies to create more seamless conversations with their customers and learn how these users engage with their brand via voice.

Google Assistant’s large footprint across devices (1B devices) also excites brands that are interested in making their content available across new surfaces.

Why should more consumer brands utilize voice technology?

Voice has taken a major leap forward, and it has emerged over the last couple of years as a new foundational interaction model in computing. As users start to have access to this technology everywhere, and this behavior becomes more normalized, if brands want to meet the users wherever they are, they’ll have to start incorporating voice technology into their strategy.

Voice technology also allows brands to engage key audience segments in personalized conversations through more natural and seamless interactions, which can ultimately drive retention and business growth.

Brands that are using voice technology as part of their strategy today are not just creating new experiences for their users but are beginning to learn and invest in the future of customer interactions (i.e., they are developing the technical know-how to navigate the new computing era — the first-mover advantage).

What are the one or two things that brands always ask you about building for a voice assistant?

The first question brands normally ask is: how should we think about what experience to build? Users are not just looking to access a brand’s website in audio form (at least not now). Voice is a much more “intent� base (i.e., use case base). Brands should spend time thinking about those moments where they can be truly assistive with voice and create re-engagement.

At first, it’s important to think about how to help users in sustained, often daily/weekly/monthly repeatable interactions. For example, it’s become common for food ordering apps to start their voice journey around use cases like “reordering,� as well as for banks to build an experience to quickly check your account balance or bills vs. purchasing a new credit card or opening an account.

Secondly, brands also ask questions about their personas. Voice can be the most natural and personal way to engage with brands – it has more to offer than a website or a device, so for the first time, brands really need to think about who they want to be and evolve their brand identity into a fully-developed personality. However, while this is important for a successful voice strategy, it can feel daunting and will likely require a lot of time since developing a voice that represents your brand is no small feat. For this reason, my advice for brands is to not let this deter them from starting to experiment now (without their own fully-developed personality), but rather to do both in parallel.

What do you want potential brand partners to understand by watching the next episode of VOICE Talks dedicated to predictions for voice technology that is coming on December 10?

Virtual assistants are increasingly becoming part of our daily life, but we are truly just at the beginning of this new era of voice and ambient computing.

This new era won’t just be something we launch, but something that we work towards — a new way of thinking about computing and about how we engage with technology. For this reason, VOICE Talks is not just about Google Assistant, it’s platform-independent, as it aims to teach viewers about the wider advances and opportunities in the space.

Given the novelty of this technology, when watching VOICE Talks, my hope is that brands can learn and be inspired by peers and users alike, from the top companies that are investing in this space and from the broader community.

The opportunity for Voice is huge. Through creating a platform that unites the community as VOICE Talks does, we can all learn from each other and propel this technology forward, creating extraordinary experiences that empower all users.

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Why are Enterprises Moving to Instant Messaging? Top Conversational AI Platforms for 2021

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Not so surprisingly, people today are more active on messaging platforms than on conventional mediums like on Email or call. According to the Gartner report 2021 Planning Guide for Customer Engagement, Enterprises and SMBs need to focus on the adaptation of cloud-based AI-driven technology to drive the effectiveness of self-service.

Today, more than 2.1 billion people use Facebook, Instagram, WhatsApp, or Messenger every day. WhatsApp recently announced that it had in excess of 2 Billion users — the majority of user reside in India.

Messaging platforms are popularly used to interact with acquaintances, particularly friends, family, or co-workers for informal and formal interactions.

If you’re in Indonesia, you’ll notice meeting updates and other annoying details of it are shared on WhatsApp. It is also customary for your cab driver to connect with you on WhatsApp. Business and ease, all mixed up on a messaging platform.

While a large number of media companies and marketers are still investing in legacy platforms like e-mail and ticketing systems to engage with employees and customers, here are some noteworthy points that will potentially change the game:

  1. Monthly Active Users (MAU) on messaging apps like Whatsapp, WeChat, etc are massive and this number is growing rapidly due to the ease of availability of data and devices.
  2. 66% of consumers want to interact with brands on messaging apps (The app of choice depends on geography). People believe this is a faster medium to get immediate resolutions – Twilio
  3. Initially, the messaging apps were focused on increasing their user base. More recently, we’ve noticed that new features such as payments and even a built-in NLP capability have been added so the apps can standalone.

Regardless of the device used, Android or Apple, people prefer to keep limited applications on their smartphones since it is cumbersome to keep switching from one channel to another. Take, for example, the Facebook Canva which is a landing page facility used by marketers inside the Facebook app. It disallows drop-offs that may be caused during the transition from Facebook to an external website for the sake of lead capture, thus reducing a step in the user acquisition journey.

Messaging apps are becoming vital for businesses to better connect with prospects, offer seamless support, and provide quick service.

Companies also use enterprise messaging apps like slack, hangouts, etc to better manage their employees. Employees can schedule meetings, apply for a holiday, request reimbursements, and more with the help of a virtual assistant.

Organizations have to modernize not just their customer engagement technology but also the way the team interacts, to not only keep up with the customer expectations but also to adapt to the “new normal� of distributed customer service teams. Modernizing both customer and agent capabilities is key for those organizations to reinvent themselves or rescale to new heights.

The benefits of automation reflect almost immediately and dramatically. It is estimated that by 2025, 10-15% of jobs in three sectors (manufacturing, transportation and storage, and wholesales and retail trade) will have a high potential for automation. There is a good deal of automation firms today working on groundbreaking technologies to build chatbots beneficial for the growth of enterprises. Particularly, we’ll take the following 4 popular players in the field and put down some key features they possess and lack.

#1. IBM Watson

Named after IBM’s first CEO and founder, Thomas J Watson to answer queries on the quiz show Jeopardy, Watson was created as a question answering (QA) computing system. It uses advanced natural language processing and machine learning technologies for fetching information, knowledge representation, and automated reasoning, to the field of open domain question answering. Watson has been one of the earliest to automate various business functions however it is missing some of the most crucial integrations today, for example, Microsoft teams, slack, and even WhatsApp. The support for multilingual languages and the capability of sentiment analysis to route to an agent when necessary aren’t provided either.

Humanizing the bots today is one of the important features that people say conversational AI lacks. With missing capabilities, enterprises that need to jump off the books will have to consider their options.

#2. Yellow Messenger

Yellow Messenger is a cognitive engagement cloud, offering various cognitive business functions like customer engagement, customer support, enterprise automation, and HR management. They have a range of channel integrations from Whatsapp for business, Google Assistant, Alexa, to Slack, PowerBI, and more.

With multi-lingual support, pre-built contextual response, prediction modules, self-learning systems, and many other sophisticated, proprietary tech, Yellow Messenger has successfully catered to clients across the globe. Founded in 2016, in Bangalore, India, Yellow Messenger is a horizontal platform that takes on unique use cases for businesses. Recently funded by Lightspeed venture capital firm Yellow Messenger aims to utilise the funds for developing better products and sourcing new talent.

Also, named the leading Conversational AI Platform in Gartner’s 2021 Planning guide for Customer Engagement.

In light of the COVID-19 pandemic, Yellow Messenger has also launched a chatbot in association with the National Health Authority to distribute the right information about the virus.

#3. Intercom

Founded in 2011 in San Francisco, CA, Intercom has come a long way in building customized bots for various companies focused on targeted answers. They helped House Call Pro grow from the time of its launch to 10,000+ customers today. Expensify, another client of theirs, found improved support and sales. Similarly, Baremetrics increased their billing by 30% with intercoms innovative products.

Conversational AI is transforming the way brands interact with consumers. Which process according to you, can be automated in your organization to save your expenses and maximize growth? Comment below.

#4. Dialogflow

Dialogflow by Google, initially called Api.ai and Speaktoit, was best known for its virtual assistant created for smartphones. While their voice assistants are supported across a bunch of devices ranging from wearables to phones, their language support is limited. It lacks self-learning capabilities. It cannot search the database for answers to queries for resolution. With their new chatbot release, Meena, we hope to see a wider spectrum of competence since it is open-sourced. Meena boasts to be the very first humanized AI.

2020 was a crucial year for AI. Automation will truly take off and conquer cubicle jobs in 2021. It will save a tremendous amount of revenue and time for organizations. How will you use automation to solve pressing business problems? innovatively?

Image Credit: tim samuel; pexels

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Artificial Intelligence Helps Nasa Design New Moon-Bound Space Suit

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Today, while most of us have a limited reach to what technology can do, it is essential to know that our scientists, AI development companies, and other tech companies have made remarkable progress in how technology has evolved. Artificial Intelligence that is human-like machines can do a lot more beyond our imagination. AI has made significant progress when it comes to the health care sector and has altered the businesses. We still have to explore more avenues when it comes to AI.

Artificial Intelligence does more than just improving well-being. It also does save a life. Many tech companies like Mobile App Development companies and AI Development companies are nowadays investing funds into AI to improve our medical system. AI is transforming our healthcare system, right from customized drug protocols to improvised diagnostic tools and robots to help in surgeries.

Not just it. AI has been helping NASA too. With the help of AI companies like Intel, Google, and IBM, NASA scientists are trying to solve space science problems using advanced computer algorithms. Machine learning, like AI, helps technology companies with faces in the pictures or speculate people’s interests. However, scientists believe that Artificial Intelligence has a deeper purpose that goes beyond our planet earth.

Recently, NASA revealed its next-generation spacesuit to be worn by astronauts on their next moon mission in 2024. The agency is planning to make the moon a new land for humans. It is the first time in the past 40 years that NASA has made such an upgrade to its spacesuit design – EMU Extravehicular Mobility Unit). The new spacesuit will make it easier to spend a vast amount of time kicking up moon dust.

How is this new Spacesuit helpful?

The new spacesuit gets designed in a manner that will allow them to twist and stretch at ease that was never possible before. They can effortlessly put on and take off the suit, exchange the parts for a better fit, and go a long time without making a fix.

However, the most significant upgrades weren’t in plain sight until they got unveiled last fall. The Astro knapsack transforms from a sizable chunk of fabric into an individual shuttle. The significance of the suit is the compact life-support system that keeps the uniform controlled and oxygenated, maintains the right temperature, and aids correspondence with the outside world. It takes an enormous task to stabilize all these activities; hence, NASA brought AI into the picture.

Difficulties and Resolutions:

Jesse Craftworks as a senior design engineer at Jacobs, a great engineering company in Dallas that was made to use by NASA to redo the xEMU life-support system. Dealing with this project requires a cautious exercise in careful control between contending needs. The life-support system not undoubtedly has to be safe. Still, it must also be adequately light to fit as far as possible for the lunar lander, and powerful enough to hold ours against the intense g-forces and vibrations it will encounter during a rocket launch.

Shoving more things into less space with decreased mass is the sort of intricate optimization issue that the plane engineers tackle most of the time. However, NASA wants their astronauts on the moon by 2024, and meeting that deadline implied that Craft and his partners couldn’t go weeks discussing the perfect shape of each widget. Instead, they’re coming up with a novel AI-fueled design software that can quickly come up with new segment structures.

The vice president of technology at PTC, Jesse Coors-Blankenship, says that the team believes AI is the tool that can do things quicker and better than a trained human can do. Engineers are also known for some of the technical stuff like structural simulation and optimization. However, with AI, they can do it quicker. This way to deal with engineering is called generative design. The primary thought is to nourish the software with a lot of prerequisites for a segment’s maximum size, the weight it has to shoulder, or the temperature it will get exhibited to and let the calculations figure out the rest.

PTC’s software joins a few distinct ways to deal with AI, like generative adversarial networks and genetic algorithms. A generative adversarial system is a game-like methodology where two AI calculations go head to head against one another in the competition to invent the most enhanced segment. The same technique gets used to generate pictures of people who are not even in existence. Genetic calculations, on the other hand, are comparable to natural selection. They create numerous designs, join them, and then select the best ones of the new generation and redo. Earlier also, NASA has used genetic calculations to create the most favorable and unusual antennas.

Craft says that the machine gets designed to deliver 100 or 1000 times more than humans could ever do. Also, it comes up with a resolution that is ideal optimization within our reach. It’s particularly handy given the final plan of the spacesuit life-support system is still in process. Even a tiny alternation to the prerequisites, later on, could bring on weeks of wasted work by experts.

Today, engineers are starting to utilize AI-drive design programming to refurbish everything from car chassis to high rises. The computations can seem quite alien-like. They’re cellular, streaming, and tendinous, with ample negative space. Craft says that they are using AI to stimulate design. They have predispositions for the proper angle, leveled surfaces, and round dimensions – thing’s that could get anticipated from human design. However, AI challenges your preferences and gives you a new perspective that you didn’t see earlier.

As of now, the segments that AI gets tasked with making are quite ordinary. A mechanical designer in NASA, Sean Miller, adds that they are still in the initial phase and don’t want to take a substantial risk that can engender disastrous failure. AI can diminish the mass on certain segments by up to 50% regarding space travel, every gram counts.

For the first time, when the scientists sent humans on the moon in 1972, AI was just a far-off dream. AI Development companies have offered the scientists solutions today, which has made it possible to discover a magnificent spacesuit. Even though we might not have the moon bases now, with some assistance from AI, it appears just a short time.

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The Best Way to Make a Customer Feedback Program Work for Your Brand

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How has your brand been handling your customer feedback program? Is it possible that you don’t have one in place? Today more than ever before, the customer is the king. Their powers to make or mar a business have increased tremendously. Here is the best way to make a customer feedback program for your brand.

Your customers have access to many social networks, and the competition has become more intense — now you are subjected to web-based ratings.

Due to the ease of accessing social media, a consumer can, with just ordinary clicks, post positive and negative feedback online, which can either make your brand soar or completely come crashing.

If you are unfortunate and don’t have an efficient customer feedback program that can be deployed to gather, analyze, and act upon the data, you are in the deep blue sea. On the other hand, if you have put in place a robust customer feedback program for your brand, you will avoid all manners of negative and detrimental reviews, with an assurance of a handsome return on investments (ROI).

Nemertes’ 2019-20 Intelligent Customer Engagement research study of 518 companies discovered that 66.7% of companies gather customer feedback. The feedback includes 43.6% use customer health scores to observe developments continuously.

The study also revealed that though about 50% make modifications frequently based on that feedback, 45.7% make adjustments periodically and believe they can do more than that.

The sad aspect of the discovery, however, is that 3.1% of those that gather customer feedback completely refuse to do anything with the information they have at their disposal.

After you have the gathered data — then what?

Once your data analyst has finished working on the gathered data using investments in survey and analytics tools, whatever changes that are recommended must be swiftly applied, which means that your brand’s C-suite must be aligned with the customer feedback program for prompt action.

Key players needed for the success of the program

Everybody who is part of the decision-making body in your organization must be involved in the program to guarantee expediency. However, there are some whose roles are very vital for successful customer feedback.

Some of these key players are as follows:

Chief customer officer (CUO)

In conjunction with the chief revenue officer, chief marketing officer, and maybe some others depending on the brand, should be solely responsible for the development of the business case. This should outrightly specify what relevant information they need from customers, how often this is needed, and which success metrics they will track.

The CUO is also responsible for analyzing the data, recommending change, and overseeing that customer service agents comply with new scripts or processes.

Chief information officer (CIO)

Your CIO should be responsible for selecting the tools you need for gathering and analyzing customer feedback. It’s also the CIO’s business to ensure that you have the best supporting network and server or cloud platforms.

The CIO must work with a team that oversees the training of AI data sets and data scientists, who develop the feedback programs and, in the end, give feedback to the CIO.

Chief marketing officer (CMO)

Your CMO is responsible for ensuring that you are getting positive ratings and curtailing negative feedback, using social media, guest posting, blog, and the web. Whenever you have any marketing program that is not aligned with your customer feedback, the marketing team ensures this is corrected.

Chief revenue officer

Your chief revenue officer oversees your sales strategy and SEO — and decides when to effect changes based on your customers’ feedback.

Mode of operation

For your customer feedback program to be effective, you need to follow the following steps:

  1. Collate your data

Your brand has its peculiarities; hence, your requirements must also be unique. It’s your business to determine the type of data you need and how to gather the data.

Metrics you can rely on to succeed in the collation include customer satisfaction (CSAT), net promoter score (NPS), transactional net promoter score (TNPS), customer effort score, post-call surveys, or custom surveys.

You may not have enough workforce to do the collation; you can easily outsource this service to a third party. The basic procedure is to send survey requests to customers to gather this information regularly.

It’s not just all about gathering data but ensuring that you get comments that are qualitative and useful at the end. You should be able to deduce from their comments the reasons for rating you either high or low.

  1. Analysis

After you have gathered the data, the next thing is to make sense out of it and this requires analysis. You determine the analytics tools that are relevant to your brand, it should not be an a-one-fixes all sort of affair.

According to Answerrocket, you can even deploy AI-enabled analytics tools to carry out the task where it is necessary. The essence is to determine when comments are negative, positive, and mixed.

  1. Recommendations

The data analysis tools you use should be able to make recommendations for a strategic but non-urgent change or raise an urgent red flag. The change could be due to complaints from a customer who has a point to iron out.

You can also implement changes based on recommendations from your data analysts, who have discovered some anomaly in the way things are handled based on the data. This anomaly could be from your marketing strategies, advertising campaigns, or branding.

Examples of recommendations you may come across include:

Act on the findings

It’s of utmost importance that you act on the data you’ve got and other recommendations. You can’t expect any meaningful impact from your analysis if this is not done.

It’s annoying to realize that some organizations will put in serious efforts to gather and analyze data to haphazardly implement the recommendations. In this era of AI and machine learning, you can deploy both humans and technology to act upon customer feedback.

The recommendations may be based on feedback from an individual, a group of customers with similar circumstances, or all your customers. The action you take will depend on the situation at hand and what the process dictates.

Degree of success

If you have been able to resolve the issues raised by your customers, you mustn’t stop there. There is the need to maintain a good CX, and that warrants that you keep on gathering more information from them regularly to ensure you are on the same page.

If the action you took worked perfectly, that might be a signal for you to embark on bigger projects since you are in the good books of your customers. If, on the other hand, your action did not have the desired impact, you need to innovate.

Whatever may be your outcome, don’t stop measuring success. And finally, revise.

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AI data and doctors Healthcare IoT social media and healthcare

AI is Neutral Technology: What May Be Harmful in Social Media Can Help Healthcare

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Netflix’s new “The Social Dilemmaâ€� documentary has been eye-opening for millions of viewers (see in: hundustantimes, dotcom), sparking conversation — and concern — about how the algorithms used by social media platforms manipulate human behavior.

Here is: “AI is Neutral Technology: What May be Harmful in Social Media Can Help Healthcare — By Dr. Darren Schulte, MD is Chief Executive Officer at Apixio.

By leveraging artificial intelligence that has become shockingly good at analyzing, predicting, and influencing user behavior. The film asserts that the resulting unintended consequences have created real-life dystopian implications: excessive screen time that causes real-world relationships to suffer, addictive behavior, alarming societal divisiveness, and even higher rates of depression, self-harm, and suicide.

These consequences as users look to social media for validation. Big tech corporations profit enormously by harvesting and analyzing their user data and manipulating their behavior to benefit advertisers.

While the film appears to give machine learning algorithms a bad rap, these algorithms aren’t inherently evil. It all depends upon what the algorithms are trained to do.

In fact, the use of AI algorithms in healthcare has tremendous potential to transform health care by improving individual patient outcomes and overall population health, enabling more personalized medicine, reducing waste and costs, and accelerating the discovery of new treatment and preventative measures.

The same type of algorithms showcased in the Social Dilemma can be trained to analyze data generated by patients, care providers, and devices (like wearables). 

The algorithms can even use surveillance of body functions (like lab tests and vital signs) to provide deeper and more accurate insight into individual health, health-related habits, and behaviors over time.

By combining that individual data with anonymous, aggregated population data, we can discover better treatments, refine clinical guidelines, and discover new therapies to improve overall population health.

Let’s consider these 11 ways AI can benefit health care applications.

  • Improve response to emergent diseases like COVID-19. One of the problems we’ve had with effectively treating COVID-19 patients is that there’s been a lot of experimentation and trial-and-error. However, even the data on the results of those therapies has been slow to propagate across the global medical community.

Hospitals and physicians only have data on the patients that they are treating themselves. With no cohesive system for sharing patient data. Providers in America, for example, have not been able to benefit quickly enough from the knowledge and experience of providers in Asia and Europe — where the virus spread first.

By leveraging AI to mine aggregated medical records from millions of individuals, we could see what treatments have been most effective for specific patient cohorts.

Even further, we could analyze the characteristics of those already infected to see which attributes make one more likely to develop the most severe symptoms. By identifying vulnerable populations faster, we can then take targeted steps to prevent infection and implement the most effective treatments.

As we have seen, the analysis and exchange of this data manually, takes far too long, contributing to the propagation and death toll. With AI, we can surface this knowledge much faster and potentially reduce the impact of the next novel disease.

  • Provide better patient surveillance. Identifying how – and how fast – COVID-19 spreads has also been a significant challenge. Scientists traditionally use a metric called R0 (pronounced “R naughtâ€�), a measure of the average number of people infected by one infectious individual.

Using R0 to predict COVID-19’s spread has been problematic for several reasons, including the fact that different groups use different models and data, and asymptomatic individuals can spread the disease without knowing that they are infected.

AI can help resolve this issue to improve patient surveillance by analyzing both medical records of patients who tested positive alongside contact tracing data that indicates the potential for infection. By combining this data and analyzing it at scale, medical authorities can use this insight to determine where to implement aggressive testing programs and more restrictive shelter-in-place measures to slow the spread of disease.

  • Improve the quality of care. Health care providers want to deliver the best quality of care to their patients. But one of the challenges they face is measuring quality and patient outcomes with empirical evidence. With patient data scattered across different sources like electronic health records (EHRs), lab results, imaging studies, it is difficult to aggregate and analyze.

By implementing systems that consolidate this data and allow providers to use AI to mine it for insights, physician practices and hospitals can identify trends among patients and implement quality improvement programs.

For example, if they see that individuals with certain characteristics fail to follow-up on important health concerns, providers can intervene with appointment reminders, transportation resources, provide telehealth options, or other interventions to keep patients engaged in their own care.

On the flip side, insurers are also concerned about care quality and ensuring patients get the best possible outcome at the lowest possible cost.

AI can help insurers track and measure patient outcomes as they move through the care system—from a primary care provider to a specialist to a hospital for surgery and into a rehab facility, for example—and identify providers or treatment protocols that may not be delivering optimal results. Insurers can then work with providers to implement new approaches to improve success rates and overall patient outcomes.

  • Identify and mitigate concerning trends. During a typical patient encounter, doctors only have access to the medical information for the patient in front of them. Consulting their patient history provides a limited view of factors that might indicate declining health. With data scattered across different systems, doctors do not always have all the data they need at hand.

AI can help surface broader indicators that a patient’s health may be declining over time.

By analyzing aggregate data across a large population, AI can show that patients with certain vital signs or trends in their data might be headed toward developing certain conditions, like diabetes or heart disease.

Physicians can use this information as a predictor of potential trouble and begin implementing preventative action. Some solutions can alert physicians to these insights as notifications within the Electronic Health Record (EHR) during the patient encounter. This allows physicians to take swift action to prevent disease progression.

  • Enable personalized medicine. The health care industry has been moving toward personalized medicine for years, aiming to transform the “one-size-fits-allâ€� approach to care into a customized plan for each individual. But this is practically impossible without access to aggregated data and insights that only AI can provide.

Consider the AI social media companies use to create and leverage personas to prompt engagement and drive advertising dollars. If we were to apply the same technique to build health care personas for each person, we could then provide this information to providers (with the patient’s permission).

Providers could then use tools like notifications, nudges, cues, or other communication (just like social media) to elicit positive behavior for better health.

For example, providers could target at-risk patients with prescription reminders, diet recommendations, or other resources relevant to their specific health situation.

  • Reduce diagnostic and treatment errors. Even the best providers can overlook important details and make mistakes, especially with the pressure they are under to squeeze more patients into a typical day.

Just as algorithms can help social platforms surface insights about their audience to woo advertisers, physicians can use algorithms to surface insights to diagnose and treat conditions accurately. For example, AI can highlight confounding conditions or risk factors for patients, allowing doctors to consider the individual’s entire health profile when making decisions.

AI can also aid in surfacing potential drug interactions that could put patients at risk. All of this can substantially lower the risk of errors that cause patients harm, not to mention reduce the risk of malpractice accusations.

The same way algorithms can identify Facebook users who might be interested in a new lawnmower and serve up an appropriate ad; they can help providers identify high-risk patients before they develop costly care needs. By culling through data to identify risk factors, AI allows providers to implement preventative and early intervention strategies.

For example, an algorithm might spot a specific obesity indicator that correlates with the risk for Type II diabetes or identify patients with high blood pressure that are at greater risk of heart attack, stroke, or kidney disease.

These insights can be delivered at the point of care, even during a patient encounter. If a patient displays a specific set of symptoms, as the data is entered into the EHR, the physician is alerted to the risk and can review trends in disease progression or confounding conditions to plot the best course of action.

  • Identify optimal treatment pathways through data-based referrals. Traditionally, when a patient needed to see a specialist, for surgery or physical therapy, for example, physicians typically referred to providers with whom they have existing relationships.

Unfortunately for patients, this does not always mean they get the best care for their unique situation. Does the provider have experience working with patients with co-morbidities? Do they specialize in complex surgeries or more typical procedures?

AI allows providers to refer to the best provider for each patient’s unique needs based on hard evidence of success and proven outcomes, rather than simply based on existing ties.

For example, if a patient with diabetes needs a knee replacement, AI can help primary care providers to identify orthopedic specialists and rehabilitation providers with proven, demonstrably better results in handling patients with this co-existing condition.

  • Reduce spending waste. About 30% of healthcare spending is considered “waste,â€� totaling up to $935 billion. Nearly $80 billion alone can be attributed to overtreatment or low-value care.

In other words, providers order more tests, services, and procedures that aren’t necessarily the best option—or even necessary at all—mostly in an effort to protect themselves against being accused of not doing enough and to meet insurer’s requirements (e.g., ordering x-rays before an MRI when an injury is clearly soft tissue related or sending patients for multiple repeat mammograms before conducting an ultrasound to evaluate a suspicious lump).

By mining data using algorithms, providers and insurers can focus on using the tests and procedures that demonstrate high value or necessary for specific instances. For example, is it necessary for patients on certain medications to get blood tests every 90 days? Do wellness visits add value to patients?

By looking at what is most effective across the larger population, AI can help point physicians in the right direction earlier, reducing unnecessary diagnostics and placing the patient on the path to better health more quickly.

AI thereby can reduce wasteful spending by identifying diagnostics that are most effective and economical, potentially saving patients and payers millions every year on ineffective tests and treatments.

  • Accelerate drug and treatment discovery. The current pathway to new drugs, vaccines, and treatments is long and arduous. On average, it takes at least ten years for new drugs to go from discovery to marketplace, with trials alone taking as long as seven years on average. For new vaccines, the average time to market is up to 12 years (which puts hope for a COVID-19 vaccine by year’s end into perspective).

One of the reasons the process is so slow is the lack of advanced data and analytics capabilities in the process.

The use of AI to analyze patient and drug performance data could substantially accelerate the time to market for new drugs and vaccines, which could save lives.

Just as the lack of data analytics meant doctors struggled to devise effective COVID-19 protocols, the inability to rapidly analyze trial data and evaluate new use cases for existing drugs prevents patients from getting the treatment they need.

Algorithms can accelerate this analysis and get much-needed medicines into the hands of patients faster.

All this time can add up to a significant cost and take away from time spent in direct, face-to-face time with patients.

AI can help reduce this burden and lower operational costs by automating manual processes like prior authorizations, reducing retrospective chart reviews by surfacing the right data to the right people earlier. The right data, quickly obtainable, will help physicians make better, faster decisions.

These efficiencies enabled by AI, on the administrative side, ultimately lower the cost of health care services for both patients and payers and frees up more resources to improve direct patient care.

The negative use of social media comes when the data influences human behavior bringing negative consequences.

For the most part, technology is neutral. But in the wrong hands with the wrong motives or objectives, the use of algorithms can raise serious ethical questions.

The same algorithms that cause us to feel more anxious, isolated, or depressed when leveraged by social media can also be used to help us heal, stay healthy, and achieve optimal well-being.

The questions are all about the algorithm’s objective and training, testing, and user feedback data that are used by the algorithm.  The reality is that managing both individual and public health in the 21st century requires access to data and insights.

Without data-driven insights, we are just guessing what will work in healthcare and what doesn’t.

Leveraging algorithms to analyze health care data empowers physicians to devise a truly personalized care plan for each individual. The physician can improve the quality of care overall and lower health care costs by tapping into collective insight and knowledge gleaned from millions of patient records.

Image Credit: karolina grabowska; pexels

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