Sundeep Reddy Mallu pursued Electrical Engineering at the undergraduate level and began his journey by selling seeds to the farmers in his native place. Grasping the basics from his early years, he postgraduate in management and business administration with Marketing and IT as his majors. He gives credit to this stellar journey that made him adept with Data Science skills and being able to deliver business values to Fortune 100 clients globally.

Sundeep is currently serving as SVP Analytics at Gramener to advise executives on aligning their data science strategy with business vision. He is also responsible for building teams to apply analytics and data visualisation, while also helping businesses adopt a data-driven culture. His keen interest in the application of Data Science and Machine Learning in ESG makes him believe that the action of humanity in the next decade would be critical.

“It’s no longer within 1.5ºC of temperature rise. It’s the case of how far are we overshooting it. This means ESG, as an overall ecosystem is gaining traction. This is evident in the number of companies who are making public commitments to carbon-neutral initiatives. This means that the climate effects are slowly starting to show up in the company top lines and bottom lines.”, he indicates.

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Hilton asked Sundeep about the drivers that helped him apply his skills to agriculture in India. He fondly replies that though India is a country with its own share of challenges, he has been able to provide leverage by introducing simple yet effective technological concepts to farming.

While citing the exemplary work he has done in the country, Sundeep adds, “The kind of solutions that we could bring into the Indian context was way different from the ones in the US, particularly due to the huge difference in the percentage of population dependent on farming as their primary source of income. Therefore, while the AI and ML applications have been in practice for a very long time in the US, India is still experimenting with tailor-made solutions. With startups budding up in the country, there seems to be a whole lot of optimal progress being made here”. 

On this note, Hilton was interested to know what prompted Sundeep to join Gramener. In the words of Sundeep, “The vision of Gramener is about the public impact. While the organisation is a commercial entity, the underlying goal that the founder’s support is the impact it makes on society. The intent is to take the help of data to narrate inside stories to create action so that impossibilities can come to life. Gramener focuses on custom solution-building in pharmaceutical and life sciences, logistics, and ESG.”

He further adds, “Given that our ESG solutions span not just across climate or agritech alone, it’s the fusion of these multiple problems that interlap with each other. And we, being at a position, where we can understand the science part of it the climate standpoint, understanding the solution point of it from a technology standpoint, we are fortunate that we are able to play a small role in this big picture of assisting in the ESG space to our clients.”

On being asked by Hilton about how data is managed, Sundeep tells him that to reduce the noise and create relevant data, technology helps them connect to various data sources. They are also able to aggregate the data, and standardise the data cleaning access and data quality checks, thus reducing the manual effort.

Sundeep told Hilton that three basic ways to maintain data ethics at Gramener involve the collection of only the required data, maintaining standards even while procuring the data from the third party, and aligning the policies while complying with industry regulations.

Explaining the challenges while providing solutions that meet ESG goals, Sundeep says, “The popular culture, folklore, is for companies like big companies consuming every data that they can collate and use it. They have the means and resources to spend on these. When it comes to the ESG space, you already have a limited monetary means that you have. That is a space where we invest a lot of time to optimize that stretch the dollar to the longest possible impact. And getting the data right is a big step in how much you invest. Because each of these solutions has to be maintained in a longer haul. It’s n0t that you built that and it’s done. These are the systems that have to survive and fight any of the windows. Unlike other engagements, ESG engagements don’t give you results in a quarter, they have their long tail where they play and out and they generate value, which means every system that you build has to withstand the test of the time. And that’s where creating a long pull- the total cost of ownership of a solution goes a long way. And hence, starting with data is key”. 

Concluding the interview that promises to enlighten the audiences with its deeper impact and awareness about the challenges in creating a sphere of intersection between technological advancements (AI, Data Sciences, and ML) with ESG, Hilton asks Sundeep to give his views on the future that lies here. “The hunger for data is constant. What we will see increasingly happen in my view is that the access to data would become uniform. The differentiation would not be a lot more different. How data gets put to use for digital making is what will go into a drastic change”, he shares.

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Hernaldo Turrillo

Hernaldo Turrillo is a writer and author specialised in innovation, AI, DLT, SMEs, trading, investing and new trends in technology and business. He has been working for ztudium group since 2017. He is the editor of openbusinesscouncil.org, tradersdna.com, hedgethink.com, and writes regularly for intelligenthq.com, socialmediacouncil.eu. Hernaldo was born in Spain and finally settled in London, United Kingdom, after a few years of personal growth. Hernaldo finished his Journalism bachelor degree in the University of Seville, Spain, and began working as reporter in the newspaper, Europa Sur, writing about Politics and Society. He also worked as community manager and marketing advisor in Los Barrios, Spain. Innovation, technology, politics and economy are his main interests, with special focus on new trends and ethical projects. He enjoys finding himself getting lost in words, explaining what he understands from the world and helping others. Besides a journalist, he is also a thinker and proactive in digital transformation strategies. Knowledge and ideas have no limits.

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Why this book matters:

• Clearly demonstrates the growing impact of machine learning and data analytics and how this is transforming customer insights
• Combines an academic overview of machine learning with industry information providing a unique bridge between the two
• Includes practical case studies from well-known companies from a variety of industries such as marketing, IT, banking, and telecommunications
• Reveals the opportunities and challenges of the transformation process for businesses in general and for customer insights in particular
• Gives support for those who consider the machine age of customer insight as a big opportunity and convinces those who are still skeptical by means of practical use cases
• Short, concise chapters written by outstanding thought leaders from renowned universities in Europe & the US and from innovative firms in different industries
• An essential read for professionals, researchers, and students alike

We are now entering a new machine age. Emerging machine learning technologies are reaching the mainstream, transforming the way business value is created, and the race to harness the benefits for better insight-based decision making is on.

Across industries, players are affected by the pace of progress of machine learning tools, developing technologies and the abundance of data, but businesses must master these new capabilities to generate the insights that will enable them to thrive in this new age.

New book, The Machine Age of Customer Insight offers a short pit stop in the race for better, faster customer insights from machine learning tools. It explains the transformation, its growing impact, and the necessary capabilities for organisations to invest in. It provides a comprehensive, practical overview which addresses both academics and practitioners providing a unique bridge between the two.

The Machine Age of Customer Insight illustrates the opportunities and challenges of the transformation process for businesses in general, and specifically for customer insights which has been accelerated by the pandemic, drawing upon case studies of how well-known companies have gained a competitive advantage by transforming customer insights into business value. It also discusses the success factors in using machine learning tools including using data privacy for competitive advantage and data collection and the experience economy.

Written by leading thought leaders from renowned universities in Europe and the United States as well as from innovative firms, this book is divided in three sections which explore key areas:

. How the field of customer insights is being transformed

. Analytical tools which can generate customer insights

. How the management of customer insights can lead to success

Each section includes short, concise chapters written by the different thought leaders on topics of their expertise. It is an essential read for business leaders, students, researchers, and professionals alike.

The Machine Age of Customer Insight by Martin Einhorn, Michael Löffler, Emanuel de Bellis, Andreas Herrmann, and Pia Burghartz, published by Emerald, available on Amazon.

EXCLUSIVE ARTICLES AND INTERVIEWS AVAILABLE:

The authors of The Machine Age of Customer Insight are highly respected thought leaders in their field, and are available for expert comment, guest articles and interviews on a range of customer insight and AI topics including:

. 5 ways AI is transforming customer insights

. How voice and facial coding can be used in market research

. Leveraging customer insights with 5G

. Machine learning tools: a brief overview

. A step-by-step guide for data scraping

. How data privacy will be a drive for competitive advantage

. What is the experience economy and should your company care?

. How to turn business data into business value

. How the pandemic has accelerated AI customer insights

About the authors:

Dr. Martin Einhorn is Director of Customer Evaluation and Analytics at Porsche AG and lecturer at Sigmund Freud University Vienna.

Prof. Dr. Michael Löffler is Vice President for Sales Planning and Strategy at Porsche AG, leading several departments responsible for sales and marketing strategy, worldwide training, organisational development, and innovation.

Prof. Dr. Emanuel de Bellis is Assistant Professor of Marketing at the University of Lausanne. His research explores how consumers perceive and use autonomous products and other AI-based technologies.

Prof. Dr. Andreas Herrmann is Director for Marketing and Research Methods at the Institute for Customer Insight at the University of St. Gallen and Visiting Professor at the London School of Political Science.

Pia Burghartz is a PhD candidate and research associate at the Institute for Customer Insight at the University of St. Gallen. Her research deals with consumers’ acceptance of autonomous driving and shared mobility.

Hernaldo Turrillo

Hernaldo Turrillo is a writer and author specialised in innovation, AI, DLT, SMEs, trading, investing and new trends in technology and business. He has been working for ztudium group since 2017. He is the editor of openbusinesscouncil.org, tradersdna.com, hedgethink.com, and writes regularly for intelligenthq.com, socialmediacouncil.eu. Hernaldo was born in Spain and finally settled in London, United Kingdom, after a few years of personal growth. Hernaldo finished his Journalism bachelor degree in the University of Seville, Spain, and began working as reporter in the newspaper, Europa Sur, writing about Politics and Society. He also worked as community manager and marketing advisor in Los Barrios, Spain. Innovation, technology, politics and economy are his main interests, with special focus on new trends and ethical projects. He enjoys finding himself getting lost in words, explaining what he understands from the world and helping others. Besides a journalist, he is also a thinker and proactive in digital transformation strategies. Knowledge and ideas have no limits.

The post Welcome to the age of better, faster, more valuable customer insights appeared first on OpenBusinessCouncil Directory.

Artificial Intelligence is a super hot topic at the moment. But most people don’t know how to fully grasp it. You’re probably interacting with AI more than you realize. It decides if your incoming email is spam, if you’re qualified for a loan, and whether or not that person on a dating website is a good match for you. Even down to the music suggestions you are listening to on Spotify and Siri directing you to the nearest restaurant. And all those things are not actually magic and in the ‘future is now’ cliché kind of way you are on the receiving end of this technology every single day. So, the future isn’t now. It’s actually yesterday. 

Why are AI and Machine Learning So Important? 

Training an algorithm to predict future outcomes, using a principal component model to learn about your clients’ personalities, analyzing text to extract sentiments automatically on your product and grouping 650,000 lines of data to cluster clients into different segments with machine learning – all sounded like rocket science to millions of entrepreneurs some 15 years ago. Now lots of ecommerce business owners do it as easily as they use Excel or Illustrator. So, if you want your business to succeed right here, right now, make sure to harness the potential of AI and machine learning. 

• If you’re still hesitant, follow the link to learn more about machine learning services and discover new opportunities for your business.

In five years or so, ‘proficient in machine learning’ will be a must-have on any manager’s CV. But for now it is still a rare skill, which you would be well-advised to develop to take your startup or existing business to the next level. Being knowledgeable about AI and machine learning will give you a strong competitive advantage over your rivals and help you employ the latest techniques wherewith you’ll be able to boost your entrepreneurial venture even more. 

You have probably heard about that, but the digital skills gap is growing exponentially, and you’re going to be required to learn an ever-growing number of new fundamental skills if you want to stay relevant and on top of your game. Master the fundamentals and you can master the rest! 

Business Benefits

AI and machine learning specifically are fundamentally going to change your role not only as a manager or marketer, but also business owner or CEO. This will allow you to be much faster and make use of much bigger data sets, including that of your competitors. You’ll be able to make predictions, correlations, and understand which of your efforts give you the highest ROI. AI will free you from all the repetitive monkey work and allow you to hone in on important aspects like strategizing or team dynamics. 

So, what are the most important questions that AI can answer for you right now? Well, the array is truly endless. Among of the most popular ones are:

• If someone discovers your product or service today, how likely is that person to actually sign up and purchase it? 
• Which of your visitors are most likely to buy your goods or avail themselves of your services? 
• How do your customers feel about your products and services provided?
• How much will your customer spend over their lifetime? 
• Can you discover the psychographics (the interests, habits, opinions, attitudes) of your customers using machine learning? 

The list can be continued, of course. But the most important thing about it is that the answer is ‘Yes’ to all of the aforementioned questions. There is so much more you can apply AI and ML to. And there are lots of real-life examples illustrating the applicability and effectiveness of techniques in question. Thus, we all know that Donald Trump won the presidential elections. But did you know that it was AI that helped him target different personality types with different messages? We’ll bet you had no idea. Amazon also uses AI to create its recommendation engine and show you your next ‘holy-moly-I-need-this-product’ recommendation. Another big company, ASOS, knows exactly how much you’re going to spend with them over the course of your customer lifetime thanks to ubiquitous AI technologies. OTTO, in their turn, utilizes the AI algorithm designed for the CERN laboratory to cut down on stock and predict what people will buy in the next month. And that’s with almost 90% accuracy.  

Now that you know so much about the business benefits of AI and machine learning, you’ll definitely want to give it a shot! It’s the surest way to future-proof your business and ensure your venture will stay relevant and competitive in the market.

Hernaldo Turrillo

Hernaldo Turrillo is a writer and author specialised in innovation, AI, DLT, SMEs, trading, investing and new trends in technology and business. He has been working for ztudium group since 2017. He is the editor of openbusinesscouncil.org, tradersdna.com, hedgethink.com, and writes regularly for intelligenthq.com, socialmediacouncil.eu. Hernaldo was born in Spain and finally settled in London, United Kingdom, after a few years of personal growth. Hernaldo finished his Journalism bachelor degree in the University of Seville, Spain, and began working as reporter in the newspaper, Europa Sur, writing about Politics and Society. He also worked as community manager and marketing advisor in Los Barrios, Spain. Innovation, technology, politics and economy are his main interests, with special focus on new trends and ethical projects. He enjoys finding himself getting lost in words, explaining what he understands from the world and helping others. Besides a journalist, he is also a thinker and proactive in digital transformation strategies. Knowledge and ideas have no limits.

The post AI and ML as an Inexhaustible Source of Business Opportunities appeared first on OpenBusinessCouncil Directory.

Organisations that are working with artificial intelligence (AI) or machine learning (ML) have, on average, four AI/ML projects in place, according to a recent survey by Gartner, Inc. Of all respondents, 59% said that they have AI deployed today.

The Gartner “AI and ML Development Strategies” study was conducted via an online survey in December 2018 with 106 Gartner Research Circle Members – a Gartner-managed panel composed of IT and IT/business professionals. Participants were required to be knowledgeable about the business and technology aspects of ML or AI either currently deployed or in planning at their organisations.

“We see a substantial acceleration in AI adoption this year,” said Jim Hare, research vice president at Gartner. “The rising number of AI projects means that organisations may need to reorganise internally to make sure that AI projects are properly staffed and funded. It is a best practice to establish an AI Centre of Excellence to distribute skills, obtain funding, set priorities and share best practices in the best possible way.”

Today, the average number of AI projects in place is four, but respondents expect to add six more projects in the next 12 months, and another 15 within the next three years. This means that in 2022, those organisations expect to have an average of 35 AI or ML projects in place.

Customer Experience (CX) and Task Automation Are Key Motivators

Forty percent of organisations named CX as their top motivator to use AI technology. While technologies such as chat bots or virtual personal assistants can be used to serve external clients, most organisations (56%) today use AI internally to support decision making and give recommendations to employees. “It is less about replacing human workers and more about augmenting and enabling them to make better decisions faster,” Mr Hare said.

Automating tasks is the second most important project type — named by 20% of respondents as their top motivator. Examples of automation include tasks such as invoicing and contract validation in finance or automated screening and robotic interviews in HR.

The top challenges to adopting AI for respondents were a lack of skills (56%), understanding AI use cases (42%), and concerns with data scope or quality (34%). “Finding the right staff skills is a major concern whenever advanced technologies are involved,” said Mr Hare. “Skill gaps can be addressed using service providers, partnering with universities, and establishing training programs for existing employees. However, establishing a solid data management foundation is not something that you can improvise. Reliable data quality is critical for delivering accurate insights, building trust and reducing bias. Data readiness must be a top concern for all AI projects.”

Measuring the Success of AI Projects

The survey showed that many organisations use efficiency as a target success measurement when they seek to measure a project’s merit. “Using efficiency targets as a way of showing value is more prevalent in organisations who say they are conservative or mainstream in their adoption profiles. Companies who say they’re aggressive in adoption strategies were much more likely instead to say they were seeking improvements in customer engagement,” said Whit Andrews, distinguished research vice president at Gartner.

Hernaldo Turrillo

Hernaldo Turrillo is a writer and author specialised in innovation, AI, DLT, SMEs, trading, investing and new trends in technology and business. He has been working for ztudium group since 2017. He is the editor of openbusinesscouncil.org, tradersdna.com, hedgethink.com, and writes regularly for intelligenthq.com, socialmediacouncil.eu. Hernaldo was born in Spain and finally settled in London, United Kingdom, after a few years of personal growth. Hernaldo finished his Journalism bachelor degree in the University of Seville, Spain, and began working as reporter in the newspaper, Europa Sur, writing about Politics and Society. He also worked as community manager and marketing advisor in Los Barrios, Spain. Innovation, technology, politics and economy are his main interests, with special focus on new trends and ethical projects. He enjoys finding himself getting lost in words, explaining what he understands from the world and helping others. Besides a journalist, he is also a thinker and proactive in digital transformation strategies. Knowledge and ideas have no limits.

The post Revealed: Leading Organisations To Double The Number Of AI And ML Projects In Place appeared first on OpenBusinessCouncil Directory.

Machine learning reveals controversial search ranking factors in credit cards sector

SEO and machine learning are critical for businesses and when it comes to alternative investments and credit cards are critical. More and more business decisions are coming from the technology that highlights product and visibility online. This is what happens in most of the fintech and any business. The bellow study highlight the importance of the controversial search rankings from special Google for the Credit Card Industry.

Summary: Artios, a London-based SEO agency that uses machine learning to determine sector specific ranking factors, analysed the top 100 search results in Google.com for 100 search phrases picked at random for the Credit Card industry. Their data modelling team analysed more than 170 potential ranking factors.

Key findings:

• The volume of inbound NoFollow links are by far strongest predictor of search visibility, potentially debunking the myth that NoFollow links don’t matter. Starting from a low base, a single NoFollow link can improve search rank by 8 places . At higher positions, each time you double the amount of NoFollow links, search rank increases by 2 positions.

• DoFollow links may not be the golden bullet we thought they were. Of the 170 ranking factors analysed, follow links were 38th in order of importance. The below are all more influential on search performance than DoFollow links.

  • Volume of NoFollow links

  • Server response time

  • Topical relevance. Conveying to search engines a strong sense of what the site is *about* (not, repeat not, keyword density)

• Emotion matters and Google.com likes scary copy. Using language that invokes fear increases rankings. We see higher Google.com rankings when fear is moderate. The effect is relatively small (only around half a position on average) but seems statistically significant.

• Linking out is more important than previously envisioned. Especially for the largest websites. 100,000 outbound links equate to a one-position ranking increase.

• You’re only as good as your first paragraph. Readability really matters. The study findings suggest that if paragraph 1 falls short on readability, it can’t be mitigated with great copy further down the page.

• Google lowers its expectations of your site depending on your current position, and rewards accordingly. Google treats sites that are already high ranking differently to those that rank low. Search performance is a different type of game in the lower tier.

NoFollow Links

The biggest predictor of rank by far is by far the number of NoFollow backlinks. For each doubling in the number of NoFollow backlinks we see the average Google Rank increase by 2 positions. This is quite interesting because it shows an exponential effect.

This is big news because the SEO community has argued about whether NoFollow backlinks count or not for years. So we now have statistical evidence to prove that NoFollow backlinks do predict improvements in rankings.

But it could be more correlation than causation; sites that possess significant brand awareness attract NoFollow links and sites that invest in content that satisfies the user query also happen to engage in advertorial campaigns.

NoFollow links predict search rank up to point. There’s a theoretical ceiling on how much of an impact they have

The table and plots show us that it becomes increasingly hard to increase Google Rank as you increase the number of NoFollow Backlinks.

• Going from having no NoFollow links to one Nofollow link increases the average Google Rank by around 8 places.

• However, going from 100 NoFollow Backlinks to 1000 only increases Google Rank by around the same amount.

• The sites with the most NoFollow links tended to belong to big brands with well known websites.

  • Interestingly, the URL with the highest number of backlinks only ranks 97 for it’s one keyword.

 DoFollow Links

DoFollow backlinks by contrast, are the 38th most important Google ranking factor.

Andreas Voniatis, lead data scientist at Artios believes this is good news: “As an industry we’ve long been fixated with earning quality DoFollow links. Most SEO practitioners have felt a pang of disappointment after securing a great link, only to discover the rel=NoFollow tag next to their domain.

“This part of the study suggests these worries are unfounded. Variety of link type (followed and not), referring domain and link quality – essentially an extremely natural link profile – are the key to improved ranking.”

Outbound links

For large sites, linking out matters

In fact for every additional link, the effect on rankings is -0.00001471. This equates to approximately 100,000 links to move a ranking by 1 position. Of course you’d need a lot of content to justify amount of links.

Andreas Voniatis: “This finding is mostly relevant to content heavy sites sitting at the top of their niche, comparison sites and resource sites for example. To justify this volume of outbound links, you’d need tens of thousands, if not hundreds of thousands, of pages on your site. However, smaller sites can emulate this practice by sticking to a rule of including one outbound link per page, minimum.

“I’d personally recommend including outbound links wherever there is a claim or assertion that needs sourcing or wherever there’s an opportunity to enlighten the user further. The fear of leaking traffic should be lower now we know Google rewards the practice of linking out.”

Semantics

Emotion matters, as does ‘sense’

Using natural language processing (relying on the NLP libraries used provided by the Python programming language), we found that sites with a high probability of evoking fear in their readers have a high probability of ranking well. In our study, each word would be turned into a vector and have certain probabilities assigned to it in terms of the emotions and the sentiment they convey.

It’s also extremely important to give Google.com a strong sense of what the site is about. This isn’t achieved through keyword volume.

The wider industry claim is that search engines are less dependent on words. Our analysis bears this out. We found that pages with a high computed likelihood of ‘being about’ credit cards scored higher rankings on average than web pages that didn’t.

This isn’t the same as keyword density (percentage of copy words containing keywords) as that is just a keyword count. Concepts look at the relationship between words within the copy and a score is computed to see if the body of text is about a topic. This was also apparent in the title tags.

Andreas Voniatis: “This finding was perhaps the most interesting. As an industry we’ve known that semantics play a large part in search performance, but this is potentially the first piece of evidence to suggest Google uses emotion of language to judge a site’s usefulness.

“Google appears to be applying some degree of regulation by proxy on credit card sites. Industry regulators encourage businesses in the financial services sector to emphasise the risks of their products over the benefits. This typically results in a higher degree of fear emotion in the copy.”

Google’s lowered expectations

“SEO is a lot like elite sport. – It’s a different game entirely when you’re in the lower leagues.“ – Andreas Voniatis

Google treats high ranking (top 20 positions) differently to the bottom 80. The rules are the same, but the game is different. Similar to elite sport, small margins make a big difference at the top, but in the lower tiers, effort and hard work get you a long way.

• When analysing the top 20 sites we found the following factors increase rankings:

  • Fresher content (Buzzsumo Published Date)

• Higher First Paragraph readability (measured by FKRE) – increasing your readability from -200 to 200 will increase your rank on average by 1 position

• Wider link authority variety

• Overall Sentiment Ranking

• When analysing the bottom 80 sites we found the following factors increase rankings:

  • Site speed

  • content heaviness

Andreas Voniatis summarised: “The tactics used by top flight brands will be very different to start up sites with less inbound linked content by comparison. According to the stats, those starting afresh will need to focus heavily on building authority.””

What doesn’t matter?

Word count – The analysis indicates that there’s no optimum word count for a web page. The number of words in the body copy simply isn’t a ranking factor. Naturally sites with thin or no copy may present related user experience issues and invoke a high bounce rate, but as far as the credit card sector goes, the sheer number of words doesn’t have an impact.

Social media – Our study found no evidence that social media metrics such as Likes, Shares, Retweets have an impact on Google.com rank.

Social media metrics had a zero R-squared value with rankings (zero means zero chance of explaining the relationship). This doesn’t mean that social media has literally no impact on search, but our analysis couldn’t find any statistical evidence.

Other findings

The model shows that everybody starts from a baseline ranking of 86 and the ranking goes higher or lower depending on the ranking factors.

So for example, for every increase in AHREFs rank of the ranking domain, the higher the ranking by 0.59 ranking positions

For every increase in domain authority, we get an improvement in rankings of 0.13

creditcards.com had the largest number of high rankings averaging #6 considering their large coverage of 70 keywords, followed by nerdwallet.com

Mypremiercreditcard.com had the highest average ranking of 3.6 although they only represented 14% of the dataset.

HTML content size, those with less content on average ranked higher by 2 clear positions.

About Artios:

Artios is an Artificial Intelligence (AI) driven SEO agency based in London. Its core services are content machine learning driven strategies for SEO.. Artios uses proprietary AI technology to help deliver predictive and quantified marketing.

About the study:

We used a machine learning model to predict the Google Ranking for for a given variation of a keyword, using all the other variables associated with each keyword

The predictive modelling process used supervised classification and regression as the Google Ranking prediction problem is a regression problem. Our models are resistant to over-predicting, so it will continue to predict well on future data.

Our algorithm looks at the data and selects the variable that best predicts the outcome. Then, it splits the data into two sections, depending on their value of that ranking factor. Each data sample in a ranking factor split further by looking at other ranking factors.

After more than 50 replications of the model, training and testing on different subsets of the data, the average absolute error was 17.71. This means that, on average, the model predicts the Google Ranking wrong by about 18 on data unseen by the model.

Hernaldo Turrillo

Hernaldo Turrillo is a writer and author specialised in innovation, AI, DLT, SMEs, trading, investing and new trends in technology and business. He has been working for ztudium group since 2017. He is the editor of openbusinesscouncil.org, tradersdna.com, hedgethink.com, and writes regularly for intelligenthq.com, socialmediacouncil.eu. Hernaldo was born in Spain and finally settled in London, United Kingdom, after a few years of personal growth. Hernaldo finished his Journalism bachelor degree in the University of Seville, Spain, and began working as reporter in the newspaper, Europa Sur, writing about Politics and Society. He also worked as community manager and marketing advisor in Los Barrios, Spain. Innovation, technology, politics and economy are his main interests, with special focus on new trends and ethical projects. He enjoys finding himself getting lost in words, explaining what he understands from the world and helping others. Besides a journalist, he is also a thinker and proactive in digital transformation strategies. Knowledge and ideas have no limits.

The post Machine Learning Reveals Controversial Google Search Ranking Factors in Credit Cards Sector appeared first on OpenBusinessCouncil Directory.