Sectors – Big Data Made Simple https://bigdata-madesimple.com One source. Many perspectives. Fri, 15 Nov 2019 12:16:01 +0000 en-US hourly 1 https://wordpress.org/?v=4.9.7 https://bigdata-madesimple.com/wp-content/uploads/2018/10/bdms-favicon.jpg Sectors – Big Data Made Simple https://bigdata-madesimple.com 32 32 Cultivate relationships with users on your website: 4 simple tactics https://bigdata-madesimple.com/cultivate-relationships-users-website-4-simple-tactics/ https://bigdata-madesimple.com/cultivate-relationships-users-website-4-simple-tactics/#respond Fri, 15 Nov 2019 12:16:01 +0000 https://bigdata-madesimple.com/?p=35865 Relationships have become a significant factor for success in any venture and knowing how to cultivate it is what separates the truly successful from those who are not. Cultivating a relationship with website users Before the emergence of the internet, which led to the creation of websites, individuals related with themselves, and brands physically. Developing … Continue reading Cultivate relationships with users on your website: 4 simple tactics

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Relationships have become a significant factor for success in any venture and knowing how to cultivate it is what separates the truly successful from those who are not.

Cultivating a relationship with website users

Before the emergence of the internet, which led to the creation of websites, individuals related with themselves, and brands physically. Developing fruitful relationships was not difficult because of the physical presence and the ability to connect on an emotional level.

All that changed when the internet became the next big thing, and individuals and brands started building websites to connect with more people beyond their sphere. It took away the physical element of the relationships, with things going virtual.

The challenge became how to cultivate relationships with users on websites to create a bond that translates to trust, loyalty, and in the case of brands, sales.

4 simple tactics to cultivate relationships

The answer is not as complicated as it may appear, and these four tactics will help you easily cultivate a relationship with users on your website.

#1 Find out what your users want

The biggest mistake any human being can make is assuming to know what’s in the mind of another person. Even if you figure it out, it can change in the next moment, or say, a month. The fact remains that human wants are constantly changing depending on several economic, environmental, and social factors.

Hence, it would be wrong to assume that you have accurate knowledge of what your users want. To this end, always make an effort to find out what they want and deliver it to them. A sure way to achieve this is by carrying out surveys. These surveys can be done monthly, bi-monthly, or quarterly, depending on what suits you and your users.

The surveys should be targeted towards a particular area, and the simplest way to carry out a study is by preparing a questionnaire. The latter should not be too lengthy; it should be direct and provide more than boxes to tick true or false, or select options. It should give room for your users to air their views. Doing this shows that you are genuinely interested in what they have to say, and valuing their opinions is one of the best ways to cultivate relationships with users on your website.

Note that if your website is an e-commerce site, or offers other types of services, you shouldn’t try to get your users to patronize you while carrying out a survey. Stick to what it’s meant for, and as a plus, provide them with valuable information while at it.

#2 Give your users credible information and updated content

With access to an endless stream of data on the internet, it’s easy to plagiarize content without giving credit to the authors. Flip that, and you’ll see it’s equally simple for your users to discover if the information you are giving to them on your website is credible.

To cultivate relationships with users on your website, give, and make available to them, credible information. If an article on your website blog has statistics, ensure they are from a trustworthy authority. Don’t create situations that will leave your users wondering if they can trust information gotten from your site.

Second-guessing you will lead to a lack of trust, and without trust, there can be no loyalty, and without the two, you can’t hope to have a relationship of any kind with your users.

Don’t stop at providing credible information, take a step further to ensure that the content you put on your website is updated. If the business of your website is in the tech field, update it regularly with the latest inventions in technology. If your site is the last to give updates, your users will abandon it and go to another that is more proactive.

A frequently updated site gives value to the users and shows you are interested in cultivating a relationship with them. Also, your static pages, that is your “Services,” and “About” pages should be renewed and kept up to date. Don’t mislead your users into thinking you still offer a defunct service; it won’t bode well for your relationship.

#3 Offer social proof and prioritize security

One disadvantage of the internet is that it’s the most accessible means for people to get scammed and fall into the hands of fraudsters. With so many bogus websites with poor security, users are wary of giving out information that will lead them into the hands of fraudsters.

Thus, you must show users that your website is trustworthy and reliable. Without it, you won’t be able to cultivate a relationship with users on your website. To prevent this from happening, offer social proof, and prioritize security.

Testimonials, reviews, and profiles of those your website are excellent examples of social proof. Note that some bogus sites have social proof, so figure out how to best convince your users of your genuineness.

In prioritizing security, keep your software updated and inform your users about those updates. Always search for malware, and fix any if found, immediately. The assurance of a safe and secure environment helps to build a relationship with users. One that thrives on trust.

#4 Make the website easy to navigate

These four tactics and everything discussed above will be irrelevant if your website is not easy to navigate. Your site shouldn’t be understandable only to you. Also, if your homepage does not scream at first glance, “ease-of-use,” then you can be sure no one will venture beyond it.

To cultivate relationships with users on your website, you have to ensure they can easily and quickly find what they’re looking for. It sends the message that you had them in mind when you designed the website. And you value the time they are spending on your site.

Make every button self-explanatory. Use straight forward menus and navigation. And ensure what lies beyond the click of a button is what the user read on the list. If you have these, then you’re ready to cultivate a relationship with users on your website.

Start cultivating relationships

As mentioned earlier, the relationship with your customer is the bedrock of most successful ventures. Both for a website and business. What will keep you long-term is how well you relate with your users. So, prioritize their needs over yours. Offer them value for their time, and always take an honest and straightforward approach.

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5 ways big data disrupted research for mutation identification and causes https://bigdata-madesimple.com/5-ways-big-data-disrupted-research-mutation-identification-causes/ https://bigdata-madesimple.com/5-ways-big-data-disrupted-research-mutation-identification-causes/#respond Wed, 06 Nov 2019 11:21:13 +0000 https://bigdata-madesimple.com/?p=35812 The rise of big data has become a significant turning point in biomedical research. Twenty years ago, it cost $100 million to sequence a single human genome. With the advent of high throughput sequencing technologies, sequencing the human genome has become faster and much more cost-effective — average costs today are closer to $1,000 — … Continue reading 5 ways big data disrupted research for mutation identification and causes

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The rise of big data has become a significant turning point in biomedical research. Twenty years ago, it cost $100 million to sequence a single human genome. With the advent of high throughput sequencing technologies, sequencing the human genome has become faster and much more cost-effective — average costs today are closer to $1,000 — and has opened up new doors for targeted diagnostics and therapies.

The mass of big data flowing in from all sources — from whole genome and whole exome sequencing and analysis to single-cell RNA sequencing, to RNA seq analysis — has changed the way academic researchers and biotechnology companies approach the study of cancer and rare genetic diseases, for example. Coupled with advances in artificial intelligence and machine learning, it has made the analysis of highly diverse biomedical datasets possible.

Big data and AI in the identification of driver mutations

AI has not only sped up the processing and analysis of biomedical data, but it also makes the analysis of highly complex sets of data possible on a level that could not be achieved with manual processing. One of the biggest challenges of integrating big data analytics into genomic analytics is the elimination of unnecessary noise.

For example, next-generation sequencing (NGS) technologies like whole-genome sequencing (WGS) and whole-exome sequencing (WES) often generate an extensive list of thousands of variants. DNA-seq and RNA seq analysis generally reveal that a majority of these variants are benign, but any rare mutation needs to be treated as potentially pathogenic.

Available academic tools can winnow out benign variants on the basis of minor allele frequency, segregation, text-mining, genotype quality, dbSNP data, and predicted pathogenicity. However, none of these tools can define the causative mutations of a patient’s phenotype by the method of elimination. The identification of driver mutations always demands additional investigation, including the use of external databases, and the determination of common rare variants among patients with similar diseases.

Big data in characterizing and categorizing rare and deadly diseases

Big data has influenced the way researchers categorize cancers. There was a time when “lung cancer” or “kidney cancer” were perfectly acceptable diagnoses. Today, scientists and oncologists understand that lung cancer or kidney cancer can refer to several different diseases, each of which arises from distinct mutations.

Identifying mutations in tumor cells is no longer difficult. Running a complete DNA or RNA seq analysis on the isolated nuclear matter is not challenging either. However, telling apart the disease-causing mutations from the non-driver mutations found in the tumor cells can be a challenge.

Comparative analysis using the biomedical dataset on de novo mutations, SNPs at the disease site, and CNV data promise new techniques for the determination of driver mutations for the different types of cancers. The distinction of one kind of cancer from another based on their driving mutation or differences in molecular mechanisms is opening new windows for personalized treatment, precision pharmacology, and targeted therapy.

Cancer classification and increased survival rates

In the last couple of years, the big data approach to cancer research has changed how doctors describe non-small-cell lung carcinoma (NSCLC). It is now categorized by the predominant mutation found in NSCLC cells and not by the organ or tissue affected by the disease.

This approach of using DNA and RNA seq analysis to categorize cancer according to its driver mutations rather than the organs or tissues it affects has enhanced the chances of survival and improved the prognosis for hundreds suffering from cancers caused by rare genetic mutations. Using treatments that target specific mutations in a single gene can help reduce the chances of treatment failure and other side-effects people associate with chemotherapy.

Big data analytics and discovery of other driver mutation for genetic diseases

An information-rich approach is not only helping in the diagnosis and treatment of cancer, but it is also helping the scientific community unravel the mystery of autism genetics. In a large study on autism, biomedical data from over 600 families were studied using RNA seq analysis. Participants were children diagnosed with autism with unaffected parents and siblings.

The study showed that there are hundreds of genes at play, but six significant candidates became the focus of several research groups working on the genetics of autism. In 2014, another similar study resulted in the discovery of 27 genes with rare de novo mutations in those diagnosed with autism.

The year 2016 saw a breakthrough in the study of autism genetics when collaborative research combined data on de novo mutations with data on inherited mutations and CNV data. The Autism Genome Project played a significant role in the subsequent discovery of the 65 genes now linked with autism, and the 6 CNVs now considered the driving mutations. The study further went on to confidently identify 28 “autism genes” that will undoubtedly make the diagnosis of anyone with the charted mutations easier and faster in the near future.

What does big data analytics hold for the future of diagnostics and treatment of genetic diseases?

Whether it is autism genetics or cancer genetics, scientists are finally acquiring the sequencing tools, analytics algorithms, expansive datasets, and robust models necessary for searching beyond the exome. To date, most studies have focused on SNPs and mutations that occur within the exome, leaving around 98% of the genome unexplored. With big data analytics and AI, the scientific community is seeing new, powerful tools at its disposal that can aid in the identification, study, and targeting of disease-causing mutations.

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Driving innovation in global health: 2019 trends https://bigdata-madesimple.com/driving-innovation-global-health-2019-trends/ https://bigdata-madesimple.com/driving-innovation-global-health-2019-trends/#respond Mon, 04 Nov 2019 12:41:41 +0000 https://bigdata-madesimple.com/?p=35803 If you’ve watched the recently-launched Netflix original series about Bill Gates, you are well aware that huge advances were registered when talking about global health. When living in a developed or developing nation, it’s hard to imagine people still have to risk every time they drink water or go to the toilet. However, when talking … Continue reading Driving innovation in global health: 2019 trends

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If you’ve watched the recently-launched Netflix original series about Bill Gates, you are well aware that huge advances were registered when talking about global health. When living in a developed or developing nation, it’s hard to imagine people still have to risk every time they drink water or go to the toilet. However, when talking about global health, you’re forced to look at the big picture. The truth is, even though there’s a lot of ideas concerning driving innovation in this sector, there’s still a lot more to be done. So, no matter if you’re a Gym Expert or a leading scientist, we can all pitch in and make the world a better place.

Registered progress

No matter how you put it, there’s no way to deny the progress being made in the past. If we look back to the 19th century, every country had its struggles with poverty and sickness. However, the agricultural and industrial revolutions gave the perfect conditions to improve the situation considerably. Just some rough data shows that in the last 15 years, maternal mortality was cut in half while the same can be said about child mortality. However, besides various statistics, the real victories are the ones that see illnesses being eradicated forever. That’s the case of smallpox that claimed over 300 million victims in the 20th century, and it was officially declared as eradicated in 1980.

Going back to the series we mentioned in the introduction, various charities and institutions are driving health inventory even further. And the next big challenge is tackling polio. Back in 1988, there were 350,000 cases of polio worldwide. However, thanks to a sustained effort from the World Health Organization, Bill & Melinda Gates Foundation, and all the stakeholders involved in the process, polio was very close to also being eradicated. And that’s the case for many issues that developing countries are facing when it comes to health, medicine, and hygiene.

So, as there’s no doubt that innovation played a significant role in all the progress registered so far, what are the exact factors that helped us achieve that? And how can we use them to improve things even more as we keep discovering new technology and better ways to handle old problems. Everyone is involved in making sure progress keeps being registered.

Digital revolution: driving medicine into a new age

Information and the way it helped connect everything from databases to smartphones and the Internet has played a major role in enhancing global health. Access to all kinds of studies and instantly sharing data between patients and medics or between specialists made it a lot easier to tackle bigger problems. Remote treatment is possible now for a long list of illnesses, and that makes the entire system more efficient. Of course, you need to have an internet connection to rip all the benefits of the information revolution. No matter if we are talking about the benefits of self-driving cars or a doctor in Nigeria being able to get a second opinion from a US colleague via high-speed connections, it’s apparent that data changed everything.

Artificial intelligence

You can’t possibly talk about innovation without mentioning Artificial Intelligence at some point. Cutting-edge projects help diagnose diseases faster and with more accuracy than a human doctor. Technology and driving make an impact in all fields of medical care. AI is capable of providing a new revolution with all that.

Thanks to its unlimited learning capabilities, AI-based systems might be able to predict diseases. And help prevent most illnesses. Even reducing the time to find answers to questions like how long does it take to charge a car battery while driving, leaving that in the AI’s hands, is a step forward. Humans can dedicate their time to more pressing aspects. We are forced to make a distinction. Between top nations like the US, Germany, Switzerland, or Canada regarding AI health systems, and the underdeveloped countries. However, implementing the model in leading nations will be another breakthrough. Making it available at large scale.

Consumerism

Even though most people, from the ones doing driving jobs to scientists, consider consumerism as a bad thing, it has positive aspects that should not be neglected. Since more and more people get access to medical information, the expectations rise. And that’s pushing providers and companies involved in the process to deliver less expensive and higher-quality services. So, the next time you think consumerism is bad, just think about the role it plays in driving innovation in health services.

Conclusion

So, whether we’re talking about the vetting process for a Safeway driving lesson or global-scale operations in fighting epidemics, these are some of the main factors playing a role in global health. Of course, the discussion can be taken further for each country in part.

However, looking at this century driving, innovation could be considered still at the beginning. It’s up to the most influential nations and how much funds do they invest in research to really make a huge deal when talking about global health.

What are your thoughts on how we’re currently handling global health programs? Do you think that more influential people like Bill Gates have the power to change something but are not interested? Share your ideas with us, and let’s see if there’s anything we’ve missed.

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Incorporating artificial intelligence in email marketing https://bigdata-madesimple.com/incorporating-artificial-intelligence-in-email-marketing/ https://bigdata-madesimple.com/incorporating-artificial-intelligence-in-email-marketing/#comments Tue, 29 Oct 2019 09:55:02 +0000 https://bigdata-madesimple.com/?p=35778 While email marketing is a great way of accelerating sales and generating revenues, it is artificial intelligence which is taking email marketing to new heights. In fact, it wouldn’t be wrong to say that artificial intelligence is the future of email marketing. Artificial intelligence, with its ability to utilize a given data through computer and … Continue reading Incorporating artificial intelligence in email marketing

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While email marketing is a great way of accelerating sales and generating revenues, it is artificial intelligence which is taking email marketing to new heights. In fact, it wouldn’t be wrong to say that artificial intelligence is the future of email marketing. Artificial intelligence, with its ability to utilize a given data through computer and carry out a particular function through trial and error is revolutionizing the way email marketing strategies worked. Here’s how AI achieves this in three steps.

As easy as 1, 2, 3

Artificial intelligence has all the features that can change the field of email marketing for better. There are three things to keep in mind.

  • Detect: AI comes with the feature of detecting which elements of a given set of data are the most predictive ones while deciphering which ones to pay attention upon and which to ignore.
  • Deliberate: Further, AI also has the ability to analyze the most predictive attributes against each other amidst the collected data while making a recommendation or answering a question.
  • Develop: Machine learning, which is a subfield of AI helps it in programming and reprogramming itself while maturing with each iteration. It can even modify and analyze data based on extracted information and the results of experimentation.

In addition, with artificial intelligence revenues increasing from around 9.5 billion U.S. dollars in 2018 to an expected 118.6 billion by 2025, it is quite clear that AI is going to have a massive growth which certainly would lead to the unprecedented development of email marketing too. Here’s how it can benefit email marketing in the long run:

  1. AI can offer a detailed analysis of various email marketing campaigns.
  2. It can even enhance the daily results and performance of email marketing.
  3. AI can assist businesses in identifying new customers while retaining existing ones.
  4. It can provide better insights into the business growth.

In short, incorporating AI in email marketing strategies can lead to higher conversion rates, better personalization, smart segmentation, boost customer gratification and thereby witness a considerable increase in customer retention.

Now, let’s take a broader look at how marketers can incorporate and leverage AI to make the most out of their email marketing campaign.

Optimizing subject lines

Subject lines are one of the most important aspects of any email strategy. While a good email subject line will attract customers’ attention and encourage them to click through and read it, a bad subject line will conveniently be ignored or even worse, marked as spam. Therefore, acing your subject line is critical for the success of your email marketing strategy. Earlier, a good subject line required a lot of experiments and analysis but, with the emergence of AI, it has become a simple task. AI with the help of algorithms generates subject lines that can attract higher click through rates. It analyzes and takes into account the results of each and every marketing campaign and ultimately optimizes and improves your subject line over time. Phrasee is one such tool that uses artificial intelligence to create more effective subject lines.

Sending hyper-personalized emails

With 74% of marketers believing that targeted personalization increases their overall customer engagement rates, it becomes all the more imperative to incorporate hyper-personalization in emails. This is where AI can come in handy. With the help of predictive analytics, AI can get hold of complex algorithms, customer data and machine learning to predict the future behavior of customers on the basis of previous interactions and data trends. With these data-driven insights, you can create customized emails that are personalized for each of your individual customers. In other words, AI is a great way of resonating with your target audience at a personal level. In fact, the best way to collect valuable data is to ask the customers for their preferences when they sign up for your email or receive the welcome email. Here’s how BESPOKE does it.

Working towards smart segmentation and targeting

As different subscribers have different interests and preferences, it is critical for email marketers to understand that categorizing them separately and sending them different emails is imperative. This is where segmentation can do the needful. Segmenting your subscriber list based on criteria like demographics, geographical location, purchase history, position in sales funnel can help you target your customers better. Further, with the emergence of AI and machine learning, it has become easier for email marketers to get access to precise details of customers and their specific behavioral signals and therefore segment them into clear, new  and distinct groups. In short, segmentation if done properly can take your email campaign to next level. Take a look at how RescueTime makes good use of segmentation in its marketing email by studying the behavior of the customer and sending a personalized analysis report to each of its customers.

Optimizing the email sending time

While too many frequent promotional emails might annoy your customer base, too little of it can also bring you at risk of being taken over by competitors. However, AI eliminates all the guesswork involved and optimizes the email sending time by analyzing the subscriber’s activity history. In addition, AI can also zero out the personal habits, time zones and downtime of your customer to precisely schedule the right time of sending the emails. For instance, here’s a cart abandonment email with an incentive of free delivery from Jack Wills which has a great chance of success if sent on a calm and peaceful Sunday morning rather than sending on a hectic Monday morning.

Going for product recommendations

According to a 2013 report by Mckinsey, 35% of customers on Amazon and 75% of customers on Netflix purchase and watch anything specific respectively based on product recommendations. This fact has not gone obsolete even today. Therefore, sending product recommendation emails based on your customer’s browsing and purchasing history cannot only help you in building customer loyalty but also enhance the click-to-convert ratio of your emails. AI with its ability to analyze the customer’s online activity and purchasing pattern can help you send relevant product recommendation emails in real-time while boosting engagement and increasing the click-through rates as well as sales. Here’s an email by Netflix in which they send a recommendation email based on the shows the user has watched in the past.

Taking a deep insight into the customer lifecycle

An effective email marketing campaign requires marketers to get a deep insight into the customer lifecycle and send them contextual emails throughout. AI and predictive analytics assist marketers in generating and analyzing insights about the customers by extracting data based on their behavior and interests. With such robust data, it is very easy to create an email campaign that informs and engages customers in a better way while maximizing the ROI and revenues. This cart abandonment email from Sunglass Hut is an ideal example of leveraging customer data and engaging them.

Wrap up

Artificial intelligence sure is a worthwhile investment for any email marketer. Incorporating AI-powered email marketing in your business wouldn’t only improve your ROI but also lead to optimum utilization of resources. In conclusion, AI and machine learning have become an integral part of any email marketing strategy. Therefore, if you wish to be successful in the long run of business, it is high time you go the AI way. Make your email marketing campaign stand out amidst the bottleneck competition.

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Quantifying cyber risks: what does a CFO say? https://bigdata-madesimple.com/quantifying-cyber-risks-what-cfo-say/ https://bigdata-madesimple.com/quantifying-cyber-risks-what-cfo-say/#respond Fri, 25 Oct 2019 12:10:14 +0000 https://bigdata-madesimple.com/?p=35771 Organizations spend millions of dollars on network security but still become a victim of breaching. Now several organizations get breached via an application code vulnerability and face cyber risks. The latest innovative attack methods and technologies to deal with these vulnerabilities show up all the time. Therefore, to maximize the efforts at assessing security risks, … Continue reading Quantifying cyber risks: what does a CFO say?

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Organizations spend millions of dollars on network security but still become a victim of breaching. Now several organizations get breached via an application code vulnerability and face cyber risks.

The latest innovative attack methods and technologies to deal with these vulnerabilities show up all the time. Therefore, to maximize the efforts at assessing security risks, resources should be allocated such that the most effective tools and strategies are used to protect the vital information assets.

To understand the risks and possible costs of a data breach is crucial. How would a company react if their confidential information was spread to the wrong target audience? How much would it cost the business? However, many people think that this thing won’t happen to them or assume that someone mistakenly receives sensitive data will be honest and delete it later.

Things aren’t the same as many people assume. The increasing incidents of cybersecurity is an alarming situation and contradict the fact that cybercriminals might not use their data. In this entire situation, the CFOs’ role is crucial.

In this post, we’ll discuss the role of CFOs along with their perspectives in reducing cyber risks. Let’s read on and find more about it.

Growing incidents of cybersecurity breaches:

In 2016, Protiviti conducted Finance Priorities Survey of 650 US CFOs and founded cybersecurity as a significant emerging issue, along with earnings and margins performance. Similarly, when CFOs analyzed top concerns among CFOs in 2016, it also found the risk of data breaches and information security as a significant cause of worry.

Research from various professional service company Accenture and Ponemon Institute, which conducts independent research on data protection as well as emerging information technologies, unfolds many hidden truths.

The institute research reveals that Australian organizations experienced an 18% increase in the number of security breaches cases during the year 2018.

The Australian businesses bumped up annual security spending by 26% to US$6.9 billion. However, an average ransomware attack where the hacker has a company’s data until a ransom is being paid typically costs us$89,000 to recover from.

Moreover, the cost of data breaches leads to $US2.1 trillion globally in the present year.  According to Juniper Research, it is four times the estimated cost of breaches in 2015

It is essential for CFOs and finance leaders to play a significant role in cybersecurity as the cyber risks are increasing by each passing day.

Role of CFOs

Cyber breaches cost money. However, a recent report reveals that CFOs are way down the executive pecking orders when it comes to setting the directions for cyber strategy.

The Cyber and the CFO report, from the Association of Chartered Certified Accountants and Chartered Accountants Australia and New Zealand, tells that only 8% of the CFOs are involved in cybersecurity strategy, despite cybersecurity being a significant business risk.

The CEO tops the list at 285 for setting the strategies for cybersecurity, then chief information security officer 18%, IT manager 13%, and chief information officer 11%.

The report is based on a survey study of more than 1500 members globally in the previous year. It also found that 10% of the respondents don’t know who was responsible for cybersecurity in their organizations. What was more shocking that most were even not sure if their business ever suffered from a data breach or not.

CFOs need to understand that their organization is under significant threat at all times so, they must stay informed. It doesn’t mean that CFOs should become tech experts. However, they should need to show their leadership skills towards cybersecurity.

Steps for safety and protection and reducing cyber risks

A CFO works for a security company and is responsible for their company’s security budget. CFO plays a crucial role in developing measures to prevent data breaches. These measures or practices ensure the security of financial information and assess the risk of the economic impact of a violation on all data. Apart from the steps, everyone should be educated about internet safety. The internet safety guide is essential in today’s era. Everyone should know about the significant internet concerns along with strategies to overcome it.

The following three steps seem to be useful, to begin with protecting sensitive data and guide security terms while finalizing the correct security investments.

The steps are discussed as follows:

1. Define the organization’s risk tolerance:

Start determining the company’s risk tolerance. It is an exercise that involves leaders up to the broad level. It varies significantly according to your need that either you are a risk-averse or a risk-taker.

By developing an understanding and knowledge of tolerance levels to protect the company’s assets practically takes us beyond the culture of fear, also, into the one which encourages and empowers participants to make strategic decisions.

2. Take a record of sensitive data and evaluate solutions based on security requirements:

A constant complicated issue that the finance team and its members face is giving current data-sharing practices in protecting sensitive data such as financial statements and customer information.

It is essential to take a record of sensitive data within the organization and understand various kinds of data risks organization come across. By doing so, you can plan and prioritize protections accordingly.

Depending on what you want to protect, pick up solutions that are in alignment with those specific security requirements. You must have basic protections like endpoint security, firewalls, and network and perimeter security. Make sure to employ a data-centric approach, where the data itself is protected by encryption and real-time access controls.

3. Organizational risk assessment:

Quantifying cyber risks consist of two factors, the probability of an event happening and the potential cost if it does.

To evaluate probability, it is vital to have a partnership with the IT organization to know where the data resides, what is the current security posture is, and how data is being accessed. To understand where and how you are vulnerable is essential, especially when the answer is beyond your risk tolerance level.

Take notes of your organization’s security policies and how consistently those policies are being implemented and managed. Let’s suppose if you’re a SOC2 compliant, your risk will be reduced by the identified controls within the circumscribed bounds of your system.

Additionally, take into account those practices and policies for data that leaves your repositories, like information that is shared with banks, customers, investors, outside vendors, and other constituents. It is vital to recognize what data goes outside the organization and assess what protection methods are used.

For assessing cost, consider and understand the nature of the information being held and also the potential financial impacts on your organization for contractual penalties, litigation, privacy regulation penalties, and reputational damage.

Conclusion

Cybercrime is becoming a serious financial issue. CFOs and their team members are responsible for the integrity of an organization’s data. Now, it’s time for CFOs to play a leading role in their organization’s cybersecurity. Have a plan in place which is led by a potential CFO in advance to reduce the cyber risk along with its legal impacts.

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FinTech 2019: 5 uses cases of machine learning in finance https://bigdata-madesimple.com/fintech-usecases-machine-learning-finance/ https://bigdata-madesimple.com/fintech-usecases-machine-learning-finance/#comments Tue, 15 Oct 2019 12:40:47 +0000 https://bigdata-madesimple.com/?p=35729 We all know about machine learning when it comes to Japanese droids or Rhoomba intelligent vacuum cleaners, but how is machine learning being used in finance and fintech? As you will discover, the use of machine learning is both prolific and amazing. We will soon look back and wonder how we lived without machine learning. … Continue reading FinTech 2019: 5 uses cases of machine learning in finance

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We all know about machine learning when it comes to Japanese droids or Rhoomba intelligent vacuum cleaners, but how is machine learning being used in finance and fintech? As you will discover, the use of machine learning is both prolific and amazing. We will soon look back and wonder how we lived without machine learning.

#1 Fraud protection and fraud prevention

“Machine learning will automate jobs that most people thought could only be done by people.”  – Dave Waters

The brilliant way that machine learning has been implemented to help protect against fraud is amazing when you consider the sheer weight of staff/human time required to do the same job. Machine learning can pick up on several factors all at once and put them together to figure out which threat is where, and then guard against that threat with something as simple as an access denial or the removal of vital information.

Malware is more advanced

This is especially important these days when malware doesn’t simply load onto computers but is instead split up and cast around the Internet, where victims pick up pieces like they were part of the malware puzzle. New threats are identified by the thousand every month, and hacking attempts in the US alone are blocked at a rate of millions per day. Machine learning is so sophisticated that it can block requests before they even reach US networks.

Governments use machine learning

“Machine Learning: A computer is able to learn from experience without being specifically programmed.”- SupplyChainToday.com

The US government receives at least one million cracking or hacking attempts per day from China alone, and they would be successful if it were not for machine learning perpetually countering their attempts and learning from it. Successful hacking and cracking reached its peak in 2011, and has slowly gone down each year thanks to advanced machine learning (the 2014 Fappening doesn’t count as it was not a case of hacking, it was phishing).

#2 Automated financing

Algorithmic trading is already quite common, albeit open to manipulation as we have seen with newer cryptocurrency markets. Automated financing is also seeing a surge within finance and fintech circles. What is happening is impressive to say the least.

The problem is human error

We have all seen the great things peer-to-peer lenders are doing without the help of banks, but they are all powered by humans. Each element, even basic online applications, are administered, managed and overseen by humans. This leaves the system open to attack, open to human error, and makes the system very slow.

Some companies are using machine learning

There are companies like Leads Market that have integrated automated financing to such a level that the machine learning systems can anticipate a lender and a publisher’s needs with the submission of just a small amount of information. Everything from the quality control process to precision lead generation is streamlined thanks to innovative machine learning. This, along with continuous follow ups with clients, is how the company is able to operate so efficiently without having to rely on having a massive staff team.

#3 Banking security

Oddly enough, it is easier to research details into how the US and UK governments use machine learning to guard against threats than it is to find a bank that will spill the beans. Though we all expect that they use machine learning for their security, there is also a notable increase in the amount of machine learning involved with the money transfer network. Banks are exploiting big data that they are pulling from their use of money-transfer networks and are using machine learning to identify possible fraud threats.

How banks detect fraud

It is not just about which country the money is going to, or which company is having fraud reports issued about it. They are even able to use demographics, though it is not clear how since they will not release information about the personal data they are using.

Wire transfers are changing

Credit cards have been using machine learning to monitor fraud possibilities for years, but banks are focusing more on wire transfer, which are typical avenues for fraud because they send money quickly and are very difficult to reverse. The next time you send a wire transfer and suddenly the transfer is frozen for two working days, you may have machine learning to blame.

#4 Automated marketing

We all know how machine learning has affected automated marketing when it comes to email marketing and chatbot marketing. They have made it so people can think they are having a conversation with a real person. Reality is that they are really talking to a very well-programmed machine. Yet, it is only recently that machine learning has entered the world of free website promotional methods such as use in social media, on forums, blog comments and so forth.

According to Larry Page –

“Artificial intelligence would be the ultimate version of Google. The ultimate search engine that would understand everything on the web. It would understand exactly what you wanted, and it would give you the right thing. We’re nowhere near doing that now. However, we can get incrementally closer to that, and that is basically what we work on.”

More than just a spam bot

Wait a minute, you cry. Spambots have existed for years. Automated social media is almost a bedrock of many online marker’s free promotional methods. Although this is true, automated systems (like Buffer, for example) still require epic amounts of user input. Simply because social media, forums, Internet servers, government groups, and spam catching software have become very good at spotting auto-generated content.

Countering safeguards set up by social media

High-end machine learning means a program can identify the safeguards that social media providers set out. And counter them with the publication of thought-out material that is unique to each profile, or group, or even to each comment section. This level of machine learning is not cheap. But it can essentially automate the promotion of a website on places like social media. While avoiding having social media accounts banned or marked as spammers.

#5 Insurance claims and fraud

Put in simple terms, insurance companies are entering the details of a claim into the computer. The computer then runs a comparison against all other claims and their outcomes. It then gives a reading on what the claim outcome is likely to be. This isn’t very impressive at first. However, an unexpected benefit is that it is able to identify factors that indicate that fraud may be occurring.

Probability becomes a warning system

Obvious factors are taken into account by the software. Including how long the claimant has had an account with the company. And if the claimant has a lot of debt and the details of the incident itself. It even takes smaller things into account like the account holder’s age, gender, policy changes and so forth. By taking this information into account, it can compare said factors quickly. It will then generate a percentage warning that suggests the claim may be part of some type of fraud.

Final Thoughts: know the limitations of machine learning

Many foolish people claim that machine learning is the key to predicting the future with absolute certainty. They also believe that bitcoin will rise, and they can learn how to manipulate the stock market. It is just not possible. The philosopher Nassim Nicholas Taleb wrote the book “Black Swan” in which not only did he confirm the chaos theory, but he proved by their very nature that any prediction is wrong because the past cannot be used to predict the future.

Nassim goes on to show how people use the information to predict the future.  He also explains how their confirmation biases make them think they are correct. Even if machine learning could calculate every single variable, it wouldn’t matter. Simply because of what Nassim proved and because of the Chaos theory. Plus, the more you rely on such methods, the harder you fall when they fail.

In short, machine learning has a lot of uses. We’re going to see its adoption across far more sectors than we imagine. However, it has limitations as well. This means some of the most exciting machine learning prospects (those involved in prediction) are ironically the most useless.

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Top online courses to kick-start your career as a big data engineer https://bigdata-madesimple.com/top-online-courses-career-big-data-engineer/ https://bigdata-madesimple.com/top-online-courses-career-big-data-engineer/#respond Mon, 14 Oct 2019 13:10:48 +0000 https://bigdata-madesimple.com/?p=35718 Modern society is rapidly turning data-centric. Moreover, digitalization has already gained much dominance in all aspects of professionalism. In such a condition, you can expect to get an outstanding professional career in being a big data engineer. Leading MNCs in the country are always eager to hire promising professionals who can work as big data … Continue reading Top online courses to kick-start your career as a big data engineer

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Modern society is rapidly turning data-centric. Moreover, digitalization has already gained much dominance in all aspects of professionalism. In such a condition, you can expect to get an outstanding professional career in being a big data engineer.

Leading MNCs in the country are always eager to hire promising professionals who can work as big data engineers. If you are currently on a break and desiring to kick-start your professional career, big data engineering is the pick for you. As an engineer of big data, you will need to deal with massive chunks of data. The things that you can excel in while being a big data engineer are handling, sorting and analysis of the data.

Nowadays, a suitable online big data course is always available for you. In spite of being an online course, you can easily gain a decent knowledge of the subject. Here, you can know about everything related to the online courses that can turn you into an excellent big data engineer. Mostly, online providers give a PG certificate once you complete the course successfully.

Why should you take online courses for big data engineering?

If you are thinking to start your career in the IT sector anytime soon, it is best for you to opt for an online course in big data engineering. Also, professionals already working in the major IT firms can start on with the course. On successful completion, they can expect a decent appraisal in their workplace for the enhanced skills concerning data management.

The Java developers can also opt for the courses of big data engineering. It can help them to update themselves as the Hadoop developers. Prominent similarities between Java and Hadoop actually makes it easier for Java developers to become big data engineers.

Online courses are flexible

The main convenience of taking an online course is concerning to its flexibility. If you are a professional, it will be very tough for you to maintain the classes regularly. If you have a flexible structure of the classes, you can get access to it anytime you want. The only thing you would need is a decent internet connection.

You can easily complete your professional assignments for the day and complete gaining the necessary knowledge regarding big data engineering. Furthermore, you can get the opportunity to access the classes from anywhere. The online course is not like a traditional course where you need to attend the four-walled classrooms routinely.

Provides practical experiences

The online big data engineering courses can deliver you ample opportunities to understand the course practically. There are different workshops and projects that you can get from the website of the course providers. You can even work on different projects and participate in the workshops online. This can improve your practical knowledge of big data engineering. This also improves the chances for you to get jobs in the industrial sector.

An all-rounded curriculum

One of the major advantages of online courses is the curriculum they offer. With the help of the free tools available from the internet and the theoretical knowledge from the international libraries, the students will surely make a big impact on the hiring managers. But it doesn’t stop here. To make sure that today’s students become tomorrow’s professional, online courses offer a number of projects and examples that make sure that the students have indeed become skilled professionals.

Job placements are available

One of the most important aspects of an online training program is job assistance. Once the students have graduated and have become trained professionals, these programs offer a number of companies to hire the skilled professionals of tomorrow, thanks to the plethora of tie-ups from the number of companies that are in need of skilled professionals. These companies include well known transnational companies such as Facebook, HCL, HP and several other well-known names. It does not stop there. These companies also make sure that the students have a very impressive individual CV through the individual resume assistance, and they are also given mock interviews to prepare for the interview process by the hiring managers.

Alumnus status is a plus

Once you graduate with these courses, you get alumnus status from a number of prestigious institutions. With the alumnus that has already graduated from the institution, you can get an understanding of the skills that you are applying. It also includes a better understanding of the market and a number of other insights from the already experienced professionals working in the field.

Enough time to complete a module

Even if you are into an online course, the providers make sure that all the modules are covered properly. UpGrad ensures to provide you more than 400 hours in the total PG course on big data engineering.

Doubt clearance

This is one of the leading conveniences that you can get from the online courses of big data engineering. In case of any doubt, you can easily communicate with the instructors. They can guide you to perfectly clear all your doubts.

So, these are some of the leading benefits and coverages you can get from the online courses of big data engineering. Try to check the payment structure of the provider website before you start with the course. If you are not a salaried professional, you must consider the budget to be comfortable while researching about the programs.

In the present time, MNCs that work for data analytics and management have a good amount of vacancies for professionals who know about big data management software and their operations. Therefore, you can expect to get jobs easily in the market. Moreover, you can expect your salary to be ample as companies want people who have updated knowledge about programming. Always consider all the aforementioned aspects while opting for an online course from any provider.

 

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Banking and the cloud: should banks adopt cloud computing solutions? https://bigdata-madesimple.com/banking-cloud-computing-solutions/ https://bigdata-madesimple.com/banking-cloud-computing-solutions/#respond Fri, 11 Oct 2019 10:42:50 +0000 https://bigdata-madesimple.com/?p=35714 Ever since the inception of cloud-computing solutions, several sectors, including banks and Fintech, are slowly migrating to it. With numerous benefits, the cloud is turning out to be the ultimate storage option for your data. Even for personal storage, platforms, such as Google Drive or Dropbox are leading the race. Migrating from traditional data centers … Continue reading Banking and the cloud: should banks adopt cloud computing solutions?

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Ever since the inception of cloud-computing solutions, several sectors, including banks and Fintech, are slowly migrating to it. With numerous benefits, the cloud is turning out to be the ultimate storage option for your data. Even for personal storage, platforms, such as Google Drive or Dropbox are leading the race.

Migrating from traditional data centers to the cloud might sound daunting but the endless benefits makes it an option to consider.

What are cloud computing technologies?

In layman’s term, cloud is a form of storage which allows users to store data on the internet.

Cloud computing is the delivery of computing services over the internet; services like servers, software, networking, and analytics.

The three major services classified under the cloud environment are SaaS, IaaS and PaaS

  • SaaS (Software as a service): Delivering software applications using the internet on a subscription basis
  • IaaS (Infrastructure as a service): Using IT infrastructure, data storage, operating systems with IP connectivity
  • PaaS (Platform as a service): On-demand environment for developing, delivering, and managing applications.

The evolution of cloud computing has enabled several industries to create a platform that is customer-centric with great security and high efficiency.

Banking and FinTech

The banking and finance sectors have seen phenomenal growth over recent years. This is due to the growth in FinTech as well as technologies such as machine learning, artificial intelligence and cloud computing. With development of cognitive computing abilities, banks are changing the way they interact with customers.

AI and cloud computing also help banks eliminate redundant tasks to a more innovative style of work. Cloud computing services have also evolved into a one-stop solution for any problems associated with data storage. Other services include storing, managing, and accessing information. By teaming up with FinTech companies that utilize secure cloud-based technology, banks can also find unique solutions for better user experiences, personalization, and automation. AI-led personalization engine, maya.ai, is one such platform.

The top cloud providers are Microsoft Azure, Amazon Web Services, Google Virtual Cloud and IBM Bluemix.

Benefits of using cloud in banking

As banks are slowly making a move to the cloud, it’s important to understand the different benefits.

Improved data security

Data security breaches have severe consequences for both users and vendors. Data leaks can cost millions of dollars in revenue. Since most banks still rely on on-premise systems, beach in data security is on the rise.

Cloud technology has paved the way to a successful alternative that prevents harm to data. With state of the art equipment and up to date software, the cloud provides a great platform to safeguard data. Management of financial data would require banks to select cloud providers that meet criteria like:

  • Certifications and standards
  • State of the art technology
  • Reliability and performance
  • Migration support
  • Service dependencies
  • Partnerships

Reduced infrastructure costs

A big problem with on-premise systems is the complex adaptability to organizational changes. Changes to the IT infrastructure, hiring, and workload management, require adequate time to execute.

With banks constantly expanding, a cloud system can adapt to these needs quickly. By migrating, changes in the IT infrastructure becomes manageable. Organizations comfortably scale up their needs immediately.

High efficiency

The efficiency of a financial institution increases multi-fold when in a cloud environment . By hosting data on cloud,  banks can enjoy benefits like:

  • Disaster recovery
  • Quality control
  • Loss prevention
  • Flexibility and mobility
  • Sustainability

Any challenges that arise can be resolved quickly. Organizations can focus on decreasing fixed and variable expenses too. This helps with the financial health and the overall infrastructure.

Payment gateways that are not on cloud suffer from slow and complicated payment processes. Slow transactions happen due to different technologies used between the buyers and sellers. By migrating the whole system to cloud, both buyers and sellers are brought together on a shared application. This will improve transaction speeds and makes tracking data easier.

Hosting made easy

For secure transactions and a smooth customer experience, banking portals will need 100% uptime. While on-premise systems have worked well, it will need regular maintenance to prevent any disruption. Even small issues can have adverse effects on the way bank functions. Migrating to a cloud will guarantee 99.99% uptime. The servers will be available through maintenance and the maximum uptime factor help with quick payments and fund transfers.

Software applications

Applications such as ERP and CRM (Customer relationship management) are mostly hosted on the cloud. As these applications are a part of the SaaS model, vendors have complete control over them. This will help in improving the support system.

What stands between cloud and banks

While cloud seems like the way to go, there are a few limitations that might prevent financial institutions with the switch. Due to security concerns, many banks are hesitant about migrating. The security of financial information is of utmost importance, so it’s important for banks to use encrypted cloud service to manage risks. The use of shared services or a public cloud is not recommended.

The future is in the clouds

Migrating to the cloud can be taxing but through transition the process becomes easy. As cloud is slowly becoming a high priority for banks, this move will provide an edge in optimizing operating and capital expenses. The benefits are apparent, and the cost savings can be significant. Cloud computing will elevate how banks function and make the process smoother for both clients and users.

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Guide to GDPR compliance for small businesses: 2020 [Infographic] https://bigdata-madesimple.com/guide-gdpr-compliance-small-businesses-2020-infographic/ https://bigdata-madesimple.com/guide-gdpr-compliance-small-businesses-2020-infographic/#respond Wed, 09 Oct 2019 13:50:18 +0000 https://bigdata-madesimple.com/?p=35702 The General Data Protection Regulation (GDPR) came into force on 25 May 2018. The regulation was created in order to protect the procedure of processing EU citizens’ personal information. All organizations must ensure that they are compliant with GDPR if they process European Union citizens’ personal data. The impact of GDPR From the first day … Continue reading Guide to GDPR compliance for small businesses: 2020 [Infographic]

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The General Data Protection Regulation (GDPR) came into force on 25 May 2018. The regulation was created in order to protect the procedure of processing EU citizens’ personal information. All organizations must ensure that they are compliant with GDPR if they process European Union citizens’ personal data.

The impact of GDPR

From the first day that the regulation came into effect, it had a notable influence on multiple widely known companies. Mostly because many businesses simply were not prepared well enough for the legislation that covers so many elements within the business. The most common areas where companies failed to be fully compliant with GDPR were the lack of consent, and not having an awareness of the type of personal data they process. Additionally, a low-level of security over consumer’s personal data was another area that made numerous firms struggle the most when this legislation was implemented.

When it comes to the biggest fines to date for not being compliant with this regulation, Google, Marriott, and British Airways suffered the most. The three companies received the largest fines of €50 million, €109 million, and €202 million respectively. Basically, any organization that processes EU citizens’ personal data and is not GDPR compliant has a risk of getting punished and can receive fines that can get up to €20 million or 4% of the company’s yearly turnover, whichever is higher.

How are small businesses coping?

Nonetheless, large businesses are not the only ones who run a risk of getting fined in case of not being compliant with the regulation; SMEs can also get penalized for non-compliance.

In fact, the data says that the majority of small businesses are struggling with GDPR-compliance to this date, even though the legislation has been in effect for more than a year.

The main reason for such a tendency might be the fact that most SMEs have a limited budget, which makes it harder to follow all the latest changes in the law while implementing them according to the guidelines. Moreover, in order to be fully compliant with GDPR, it also requires an ongoing effort from the company, meaning continuous resources must be dedicated to compliance.

As a whole, this regulation is not an easy duty for any company, mainly because it covers so many elements of the business.

As an example, organizations that have their own website must ensure that their web design is GDPR compliant. Data audits have to be done, to be sure about the type of personal data that is being gathered online. Besides that, all consent forms have to be clear and concise.

Additionally, firms have to make sure that the level of data security is high enough, in order to minimize the chances of data breaches. Furthermore, employees have to be knowledgeable about this regulation, so that they are ready to report the data breach incidents to their supervisory authority when it’s needed. What is more, the business’s payments systems also have to be secure, whether the payments between the firm and consumer are performed online or with the help of card machines.

What can small business owners do to comply?

Including everything that was mentioned before, there are many more elements of GDPR that every business owner has to go through in order to ensure his organization’s compliance with GDPR.

In addition, the infographic below, created by Market Inspector, teaches you all the most important aspects of this legislation that you should know. It also provides a 10-step guide teaching you how to make any small business complaint in 2020.

Infographic About the GDPR for Small Business

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The biggest impact of machine learning for digital marketing professionals https://bigdata-madesimple.com/biggest-impact-machine-learning-digital-marketing/ https://bigdata-madesimple.com/biggest-impact-machine-learning-digital-marketing/#respond Tue, 08 Oct 2019 12:32:22 +0000 https://bigdata-madesimple.com/?p=35696 The digital marketing profession is undergoing a tremendous shift. Machine learning is altering the dynamic between advertisers and customers in countless ways. A study by Gartner found that 30% of companies will use AI in their sales by the end of next year. A stunning 97% of marketing managers believe that machine learning is the … Continue reading The biggest impact of machine learning for digital marketing professionals

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The digital marketing profession is undergoing a tremendous shift. Machine learning is altering the dynamic between advertisers and customers in countless ways. A study by Gartner found that 30% of companies will use AI in their sales by the end of next year.

A stunning 97% of marketing managers believe that machine learning is the future of their profession. Social media marketing, PPC advertising, native ads, and media buying are just some of the disciplines that are evolving with machine learning.

A number of industry influencers have discussed the ways machine learning is changing the profession. Here are some factors keep in mind.

Segmenting customer groups more effectively

When I first entered the digital marketing profession, I believed that the success of my campaigns began and ended with my ads. I was confident that my ROI would be stellar, as long as I used great graphics and copy. This is a common mistake that new marketers make.

Over time, all marketers will discover the importance of customer targeting. All customers respond differently to any set of online ads. Targeting arguably has a larger impact on the ROI of a marketing campaign than the ads and landing pages.

One of the biggest challenges that any marketer faces is trying to fine-tune their targeting. This is complicated because the customer segments that you initially thought would convert well might not be the most profitable. As this Amazon PPC case study showed, the difference in performance between various keywords can be incredible.

Mariya Yao, the CTO of the firm Metamaven, wrote an article on Forbes describing the benefits of machine learning for customer segmentation.

Machine learning is helping solve this challenge. New machine learning algorithms are able to identify the customer groups that convert the best. This data can be used to identify the best keywords for an SEM campaign, as well as the customer demographics that correlate with the highest conversion rate.

Smart automation and talented marketers will both be vital

In a series of interviews by QuanticMind, several experts all said the same thing: the future of marketing lies in the intersection between smart people and smart automation. Joe Martin of Adobe, Serena Ehrlich of BusinessWire and Travis Wright of Digital Sense all echoed the same view.

Many people fear that automation is going to make almost every career obsolete. These experts argue this is far from the case.

Machine learning is offering new insights and solutions. However, it is not a substitute for the creative problem-solving skills that human decision-makers bring to the table. Machine learning is giving them more leverage to handle complex problems. AI is also helping them automate more mundane tasks, which helps them conserve their cognitive resources to focus on more important challenges.

User-generated content is improving the ROI of machine learning

The CEO of Upwork stated that machine learning and user-generated content are a match made in heaven. His company has started using AI to analyze lots of user-generated content. This helps them optimize their email marketing and SEM campaigns.

Improving user personalization

Personalization used to be a handy feature to stand out from the competition. Today, it has become essential in many industries. Customers expect personalized content when interacting with their favorite brands online.

Machine learning is essential for creating personalized content. Here are some ways brands are starting to use machine learning for personalization:

  • They are using tools like to monitor customer feedback on various platforms under the company’s own control. This content helps them determine the mood and personal perspective of the customers. Machine learning algorithms can create content to change the perspective of customers with negative sentiments about the brand. They can also use more aggressive marketing copy for customers that have highly favorable perspectives.
  • Machine learning technology can look at various customer segments to see how they respond to different content. They can create custom content for users depending on their location, age, and income thresholds.
  • Content can be personalized based on events users have previously taken.

Personalization is one of the most powerful ways to get more value from content marketing. Machine learning is the foundation of it.

Machine learning is the future of digital marketing

The digital marketing profession is constantly evolving into new technology. Machine learning is one of the biggest breakthroughs in the industry. Savvy marketers will invest more heavily in machine learning in the years to come.

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