How is Data Analytics Used in Content Marketing?

Pranita Sanas | October 25, 2021

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In the disruptive world of digital marketing, oftentimes marketers are faced with questions like
What are the characteristics of your content strategy that is driving leads, likes, comment and shares?
Is your content engaging the right set of audiences?

Are you sure your existing piece of content will increase customer engagement?

The answers to these questions will reveal the performance level of your content. The data thus revealed, helps you in planning your future strategies and get aid you in analysing the gap in your existing strategies. Lack of data insight is the biggest reason why your content strategy fails to engage the right set of audiences. Every successful piece of content starts with well-researched and widespread data.

Data analytics has dramatically influenced the way the business functions restricting the marketing strategies and the way the daily activities are carried out; hence making data analytics an effective tool for companies to stay ahead of the competition in their respective industries.

To make a focused content marketing strategy and to reach your target audience, you need data analytics to understand your target audience.

Businesses adopt big data to analyze the quantity and quality of the target audience, distinguish their interest and preferences, and understand the type of content they admire or seek. One can draw such information through platforms like as Facebook, LinkedIn, Instagram, Twitter, Quora, etc. Thus, a successful content marketing strategy is designed through a survey of targeted customers and valuable insights to remodel the business performance.

According to a survey report, 73% of digital marketers use their website analytics to monitor their website content performance. Based on the data thus gathered they can understand the gaps in their content strategy which will help them in creating future strategies.

Everything About Content Marketing Strategy

Content Creation and Content marketing strategies enable the marketers to grow as a publisher by setting up audiences and drawing their attention. An expertly-crafted piece of content can address any business market leaders and develop a brand through acquaints and long-term customers.  But for your content marketing effort to work you need a solid content marketing strategy in place.

The first step in creating a content marketing strategy is content analytics. Content analytics is a widely accepted technique used to count qualitative information about your content. The technique saves a lot of time and effort by sorting data and comparing information about different content assets to summarize it into useful information. The range of content assets used varies from a simple word text, image post on social media, ebook, journals, article, infographic, to your website content. Here, the main objective of content analysis is to deliver qualitative content backed by valued data gathered through the analysis. Hence content analysis forms the first step in creating effective content marketing strategies.

Content marketing strategy works as a blueprint to assemble audiences by publishing, sustaining, and expanding pursuant and informative content that informs, entertains, and inspires end-users. It helps in converting strangers into followers and followers into clienteles.

Unlike the conventional mode of marketing, which is less productive than the data-backed content marketing strategy, they can anticipate and transform leads into potential customers.

Challenges of Content Creation for Marketing

Gone are the days when marketers invested only on creating quality content and marketing strategies. This is because, back then your audience had simple expectations. Hence coming up with marketing strategies was easy and simple. However in this tech-savy world your audience expect more than just quality content. They want brands to impress them with quality, relevancy, and a bit of other real-time factors. Hence to create strategies for modern-day customers you need the help of data analytics.

“With data analytics identified as the number one tech initiative driving 2021 investments, CIOs are facing the challenge of unlocking data insights and incorporating AI/analytics working with a largely remote workforce and while still maintaining a human connection. In this environment, a few emerging product marketing trends can be identified to help stay ahead of the game, not just in 2021 but beyond”

During an interview with Media7, Richard Stevenson, Chief Executive Officer at Red Box.

  1. Inability to produce high-quality content
  2. Failing to generate content consistently
  3. Prioritising expertly-crafted content to become thought leaders
  4. Difficulty in understanding different buyer personas
  5. Creating content in appropriate formats
  6. Measuring content reach and count
  7. Creating content to increase organic traffic, generate leads, and build trust
  8. Aligning your content marketing strategy with the buyer journey
  9. Building a content strategy that ensures one piece of content leads to the next
  10. Failing to promote the content on the right social media platform

The Importance of Data in Content Marketing

The value of data, in particular for digital content marketing, is truly immense.
Data can help identify your target audience and the trending topics, channel-specific platforms of potential customers, and the influencers relevant to those groups. By implementing data analytics as part of your process you can meet two significant agendas:

  1. Provide insights to create informed content
  2. And to evaluate post-distribution performance with metrics.  

Speaking about data-backed content creation, Thomas Zawacki, Chief Digital Officer, Strategy & Innovation at Data Axle, explains,

“Consider the four C’s of marketing strategy: customer, channel, creative, and conversion. Then, using data analysis and AI/ML predictive modeling, weigh each element differently depending on your KPIs. The ideal media mix for each company and each program is going to be different, so you need to be open to all of them and let data/intelligence lead.”

Data-Driven Techniques to use in Content Marketing Strategy that can Boost Your Rank.

There are many different techniques, and it is impossible to know what works and what doesn’t.

Following are the top data-driven techniques to use in your content marketing strategy to boost your ranking.

  1. Set content goal – awareness, engagement, promotion, or conversion
  2. Post content more often, keep your audience engaged and updated.
  3. Try to interlink your content and build a connecting bridge for your next piece of content.
  4. Come up with attractive headlines, add your primary keyword to it for better readability.
  5. Supplement your content development with social media postings for better content reach.
  6. Repurpose your content in the form of videos, graphics, GIFs, infographics, posts, SlideShare, etc.
  7. Analyze and list down the topics that aren’t covered on your; this will help you with your next piece of content.
  8. Mention links to the sources and gives backlinks to increase on-page engagement.
  9. Make your content as informative as possible. Your content piece should come out as an article by a thought leader/influencer.
  10. Video content is one of the most trending forms of content creation recently.
  11. Use tools such as Grammarly and Hemingway to improve the content quality and ensure that the content is error-free.
  12. Create content with the brand, promote your brand voice, audience persona, and brand tone so that your reader understands and relates to your brand.

How to Use Data Analytics to Create More Effective Content?

Creating high quality content with metrics such as uniqueness, relevancy, and usefulness for readers, high-valued, visually appealing, and stats is essential in creating compelling content. We all understand the need to start using the data more smartly. However, it can be hard to know where to begin. Businesses need to update their content marketing strategies by collecting new data to retrain algorithms, keep pace with the changing requirements, expectations and anticipate customer behavior.

According to The Global State of Enterprise Analytics report by BI company MicroStrategy, 56% of participants said data analytics help with “faster, more effective decision-making” at their companies. Other benefits of data analytics are:

  • 64% said it improves efficiency and productivity
  • 51% said its better for financial performance
  • 46% said it helps in identifying and creating new product and service revenue
  • 46% said it improves customer acquisition and retention
  • 44% said it improves customer experiences
  • 43% said it has a competitive advantage

Hence you need to use analytics to,
  • Formulate a hypothesis about your content performance
  • Determine a question to support it
  • Create a report to answer the question with report/data
  • Take action based on your analysis

Here’s an example of a content marketing strategy using data analytics;

During an interview with Media7, Tara Ryan, Chief Marketing Officer at Incorta, explained how Data Analytics platform-Incorta, simplifies and processes data to provide meaningful information at speed to help marketers make informed decisions.

“At the digital pace of business today, that can mean millions of dollars for business leaders in finance, supply chain or even marketing. Speed is a key differentiator for Incorta in that rarely has anyone been able to query billions of rows of data in seconds for a line of business owners. In the world of Big Data, AI, Machine Learning and Predictive Analytics, this is important. In other words, someone in charge of the supply chain for a major food brand can reduce millions in wasted orders if they understand in real-time what customers are ordering across the globe on any given day or hour. In times like and especially with Covid, the amount of data an organization can automate, organize, ingest and analyze against can mean millions in savings and efficiencies. The direct link between data, analytics and business impact is something I get excited about.”

Final thoughts

Data analytics has influenced the way the business functions. Its influence has limited the marketing strategies and spread how the daily activities of a business are led. This makes data analytics an effective tool for businesses to anticipate competition in the industries. Big data is a widely used strategy to apply content marketing by giving access to data that allows marketers to improve their content reach their target audience.

Frequently asked questions

How data analytics encourages content creation?

Data analytics is a widely used strategy to create well-researched content by giving access to data that allows marketers to improve their content reach their target audience.

How to practice data analytics?

Data analytics can be practiced in the following way;
  • Define your Objective.
  • Understand Your Data Source.
  • Prepare Your Data.
  • Analyse Data.
  • Report on Results.

What are data analytics used for?

Data analytics helps individuals and businesses to develop data-driven content. Data analysts analyse raw data for insights and trends.



We are a network of experts in Business Intelligence and Business Analytics collaborates on projects defining and implementing leading-edge tools for data analysis. Our goal is to turn data into valuable information for business, displaying visual and accessible way for anyone. We are not a consultancy to use: network work minimizing the costs of each project and we do not rely on specific tools, but on the needs of each client. We are present in various sectors: Hospitality, Automotive, Aviation, Fashion Retail Management Human Resources…


What are the Benefits of Data Modeling for Businesses?

Article | January 21, 2022

Businesses that are data-driven are well-known for their success, as data is widely considered to be a company's most valuable asset. Understanding data, its relationships, and the law requires the use of data modelling techniques. Sadly, people who are not familiar with data modelling best practises see them as a pointless documentation exercise. In the eyes of others, it is a hindrance to agile development and a waste of money. A data model is more than just documentation because it can be implemented in a physical database. Therefore, data modelling is not a bottleneck in the development of an application. Due to these benefits, it has been proven to improve application quality and reduce overall execution risks. Data modeling reduces the budget of programming by up to 75%. Data modeling typically consumes less than 10% of a project budget. Data Modelling- Today’s Scenario Data models methodologies for data modelling have existed since the dawn of time. At the very least, it's been around since the dawn of the digital age. In order for computers to deal with the bits and bytes of data, they need structure. Structured and semi-structured data are now part of the mix, but that doesn't mean we've reached a higher level of sophistication than those who came before us in the field of computing. As a result, the data model lives on and continues to serve as the foundation for the development of advanced business applications. Today's business applications, data integration, master data management, data warehousing, big data analytics, data Lakes, and machine learning require a data modeling methodology. Therefore, data modeling is the foundation of virtually all of our high-value, mission-critical business solutions, from e-Commerce and Point-of-Sale to financial, product, and customer management, to business intelligence and IoT. 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Finally, its visual aspect makes it easier for business users and subject matter experts to communicate and collaborate. Let us look into some of the core benefits of data modeling for businesses. Enhanced Performance Following Data modeling techniques and best practices prevents the schema from endless searching and give results faster, resulting in a more efficient database. The data model's concepts must be concise to ensure the best performance. It's also crucial to accurately convert the model into the database. Higher Quality Data Data modeling techniques can make your data precise, trustworthy, and easy to analyze. Inaccurate data and corruption are even worse than application errors. Data can be adequately understood, queried, and reported on as a good data model defines the metadata. Developers can foresee what can lead to large-scale data corruption before it happens because of the visual depiction of requirements and business rules. Reduced Cost Effective data modeling techniques detect flaws and inconsistencies early in the process, making them significantly more accessible and less expensive to fix. As a result, data models allow you to design apps at a reduced cost. Data modeling often takes less than 5%-10% of a project's budget, and it can help lower the 65-75 percent of a project's budget that is usually allocated to programming. Better Documentation By documenting fundamental concepts and language, data model methodologies lay the groundwork for long-term maintenance. The documentation will also aid in the management of staff turnover. As an added bonus, many application providers now provide a data model upon request. For those in the information technology field, it's common knowledge that models are a powerful tool for explaining complex ideas in a simple and straightforward manner. Managed Risk An application database that contains numerous related tables is more complex and thus more prone to failure during development. On the other hand, data model techniques quantify software complexity and provide insight into the development effort and risk associated with a project. Therefore, the model's size and the degree of inter-table connectivity should be considered. Summing up Any business can benefit greatly from data modelling methods and techniques. To the untrained eye, data modelling may appear to be distinct from the type of data analytics that actually add value to a company. In order to make data storage in a database easier and have a positive impact on data analytics, data modelling is an essential first step. Frequently Asked Questions What is data modeling? In software engineering, data modelling refers to the use of specific formal techniques to develop a data model for an information system. 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7 Apps to Learn and Practice Data Science Effectively

Article | January 19, 2022

Rapid digitization is generating an enormous amount of data every second. The field of data science employs highly unstructured data to draw meaningful insights that can help businesses make smart decisions. It is one of the ever-evolving technologies that is reshaping the landscape of the digital world. To be a data scientist, one must have the skills, methods, and knowledge of top-notch algorithms that process and execute data efficiently. This article highlights seven apps that can help you grow in your career as a data scientist. 1. SmartWindows SmartWindows is a productivity app for Windows 10 and Windows 11 users. It is a handy desktop application for data scientists when it comes to getting a 360-degree view of multiple desktop files. A data scientist works on multiple data files at a time. Owing to this, excessive switching between apps becomes very inefficient and time-consuming. SmartWindows eliminates that overhead by allowing users to arrange multiple desktop apps and data files on-screen and save the screen arrangement in the SmartWindows profile. Once saved, users can restore the profile at any time, whereby all files will be restored at the exact same position and same windowsize on the desktop screen will be a single click. SmartWindows allows users to create unlimited profiles and up to six displays. It auto-restores the screen arrangements on one or many screens. You can work on multiple work zones and save and restore them at any time. SmartWindows supports multiple browsers such as Google Chrome, Firefox, Microsoft Edge, etc., and restores dozens of tabs all with one click. It also restores Smart Layouts on Windows 11 without the hassle of creating new layouts before getting started on work. 2. QPython QPython is a script engine that runs Python programs on Android. 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Elevate consists of more than 35 games that enhance self-confidence, productivity, math, writing, and learning skills. 5. Math Workout It is an educational application to test the analytical skills of users, keeping their minds healthy. It involves multiple exercises and tests to increase the processing power of the mind through mathematics. Mental arithmetic helps enhance data science skills. The Math Workout has some cognitive games and mental exercises in more than ten languages that interpret the cognitive psychology of users and train their brains accordingly. The app would make you perform calculations at your fingertips, allowing you to process tough calculations in a matter of seconds. 6. Programming Hub The Programming Hub is a platform developed for developers. It consists of more than 5000 computer programs and 20+ courses for programmers to practice and learn programming languages. Whether you are a beginner or an expert, the Programming Hub offers a wide range of programming courses to help you with problem-solving skills with multiple programs and software frameworks. The app specializes in providing programs for artificial intelligence, R programming, C, C++, C#, Java, and more. 7. NeuroNation As the name suggests, NeuroNation is a brain training app that is designed to improve the brain activity of users through various exercises. It enhances the processing power of the brain and the logical thinking capability of users. It has a set of 60 different exercises, games, and activities. It is a website and an app for both Android and iOS. It has extraordinary features to increase scientific basics, personalization, fun, and motivation among the learners.

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How Data Science Industry is changing - A view from 2022

Article | January 12, 2022

The industry of Data Science has been popular since a decade or so. The aim and workflow of the field has undergone a lot of changes since then. From basic reporting and analytics to predictive and cognitive analytics, data science revolutionized the concept of “Computers that can think”. As of today, Data Science and its subfields are one of the greatest in demand and has a great competition in the industry. Apart from improving businesses, Analytics has proved its capability in various sectors and applications. This has changed the overall structure of the field of Data Science and the opportunities available. The large amount of data and its wide scope comes with plenty of various opportunities and developments. AI, AI everywhere AI started as a boom few years back wherein it saw great potential, but now it’s everywhere. From Research Labs to Education, Healthcare and even in personal devices, AI has taken up various forms, solving many problems and improving various products and services. Even now, experts states that the full potential of AI is not completely utilised and is expected to be used in a few years’ time. The wide use of Artificial Intelligence has motivated many start-ups to focus on the use of AI to build solutions and products. Industries at all scales have taken a move to include AI in their services / products to increase efficiency through intelligent behaviour. The “Mimicabilty” of human brain functionality comes with great potential – Namely Automation and Optimization of various tasks. Data Centric is the trend With a large amount of data generated daily on servers, there comes a need of shifting from model-centric to data-centric. Let us have a glimpse into what each approach means. 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The newer models are much more advanced that AI seems to be – Natural Language Processing algorithms can now carry out tasks on speaking, predict a sentence and even tell what sentiment it has. Computer Vision Algorithms are now assisting doctors to detect defects and diseases through X-rays and MRI images. This all points to the immense capability in the field of AI. In future it is predicted that setting up an AI system will be equivalent to setting up the mind of a human being – A situation called singularity. More opportunities coming up As seen previously, there are a lot of opportunities coming up in Data Science field. With its vast number of applications in every sector, there are a lot of openings coming up. Start-ups and developed industries are now shifting to AI solutions because of its dynamic nature and intelligent decision-making capability. The number of jobs in Data Science is expected to rise in the coming years. Especially Post COVID, organizations experienced a surge in various technologies, out of which Data Science is one of the major fields. Data Science and Artificial Intelligence is one of the most demanded fields for research to happen. Aspiring researchers have a good demand in the AI and ML field. Tech giants have marked their presence in improving AI algorithms, making the systems more efficient and “Intelligent”. Artificial General Intelligence is one of the applications of AI which is focused on varied problem solving rather than a restricted problem domain and is expected to bring a significant change in the AI focused problem solving.

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Effective Ways to Prevent Data Breaches

Article | November 26, 2021

Data breach prevention is going to be the need of the hour as cybercrime continues to grow. Cybercrime is a growing threat to businesses of all sizes. Due to this unprecedented time many companies shifted to work-from-home model. Statics show data breaches are on a rise and can have devastating long-term financial set-back or reputational repercussions to your organization. As a result, businesses must ensure that their data is secure to avoid substantial loss or theft. As data breaches happens in different ways, there is no such thing as a one-size-fits-all remedy. Security needs a multifaceted approach to be effective. In this article we’ll find out different ways prevent data breaches. Impact of a data breach on businesses A data breach can destroy a business, especially for small and medium-sized businesses (SMB). Data is a valuable asset for any business especially, the data related to customers and payments. Cybercriminals find this data valuable. Lack of planning and security creates vulnerabilities for criminals to exploit. It is estimated that 60% of small and medium-sized enterprises will close within six months after the attack. Larger businesses or agencies, on the other hand, will survive. Nevertheless, they too will suffer the consequences. A data breach can impact businesses in the following ways; Financial Businesses must compensate for both immediate and hidden fines (fines, public relations, legal fees, and punitive regulatory measures) for a data breach. In addition, business needs to compensate customers, refund any stolen funds, and bear a share value loss. A smart organization will use this opportunity to develop data security and disaster recovery strategies, which entails financial investment. Fines and fees – The PCI Security Standards Council may impose fines or penalties for a data breach. Both regulatory organisations and card network brands will have different fines. Forensic investigations – Major consequences of a data breach include, the business that was attacked will be accountable to perform a forensic investigation to determine the causes of the data breach. These investigations are costly and often yield valuable evidence and insights to prevent future data breaches. Future security costs – Victims of a data breach may have to bear costs of credit monitoring for customers whose data was compromised. This may also include the costs of identity theft repair, card replacement, and additional compliance requirements from the PCI. Reputation Having a good reputation is the most prized asset for any organization. As a business, one must constantly put effort into building and maintaining brand integrity. A single compromising episode like a data breach can trash the best of reputations. According to a PwC report, 85% of customers won't shop at a business if they have concerns about their security policies. Customers value their privacy, and a data breach will be perceived as a lack of regard for their data and privacy. Furthermore, 46% of businesses reported that security breaches harmed their reputation and brand value. Intellectual Property The product blueprints, business strategies, and engineered solutions are some of your most valuable assets for any organization. Your trade secret gives you an added advantage over your competitors. Hence it needs to be protected as some may not hesitate to use breached intellectual property. Other significant consequences of a data breach include; A data breach can pit the CEO against the CISO Poisoned search results on your corporate brand Loss of sales after a data breach Unexpected expenses Less attractive to new employees, especially in tech positions Legal penalties after a data breach Understanding the aftermath of a data breach is an important step to safeguarding your business. The next step is to create an action plan is to protect what you've worked so hard on. How does a Data breach happen? Data breaches sometimes can be traced back to planned attacks. But, on the other hand, it can result from a simple oversight by individuals or flaws in the infrastructure. Accidental Insider For instance, an employee uses a co-worker's computer and reads files without proper approval or permission. However, the access is unintentional/accidental, and no personal information is revealed. The data was breached, however, because it was read by an unauthorised person. Malicious Insider This person deliberately accesses/shares data with the intent of causing harm to an individual or company. The malicious insider may have genuine authorization to use the data, but the intent is to use the info in nefarious ways. Lost or Stolen Devices Any laptop or external hard drive with important information on it that is not encrypted or unlocked goes missing. Malicious Outside Criminals These are hackers who attack several vectors to collect information from a network or an individual. Global cost of data breach According to the Ponemon Institute's Cost of a Data Breach Report, global data breaches cost $3.86 million on average in 2020. The amount in 2020 was somewhat lesser compared to 2019 when it hit $3.92M. The same report found that the average cost of a data breach in 2020 totaled $8.64M. Ways to prevent a data breach Conduct employee security awareness training Control access to data sensibly Update software regularly. Require secure passwords and authentication Simulate phishing attacks Evaluate accounts Limit access to your most valuable data. Review your user account lifecycle processes Insist on complex and unique passwords Protect against authentication bypass Store sensitive personal information securely and protect it during transmission Consider implementing a secure SSO solution Secure all endpoints Segment your network and monitor who's trying to get in and out Manage Vendors - Third-party vendors must comply. Conclusion Protecting against data breaches may appear to be a time-consuming procedure. You will be in a better position if you take an encrusted step to secure your data using various methods, policies, and procedures to ease security threats. FAQ’s How does a data breach impact an organization? Depending upon the company and data type, the consequences may include destruction or corruption of databases, leaking of confidential information, the theft of intellectual property, and regulatory requirements to inform and possibly compensate those affected. What is the most common data breach? Hacking attacks are the most common cause of a data breach. However, it is often a weak or lost password that is the vulnerability that the opportunist hacker is exploiting. { "@context": "", "@type": "FAQPage", "mainEntity": [{ "@type": "Question", "name": "How does a data breach impact an organization?", "acceptedAnswer": { "@type": "Answer", "text": "Depending upon the company & data type, the consequences may include destruction or corruption of databases, leaking of confidential info, the theft of intellectual property, and regulatory requirements to inform and possibly compensate those affected." } },{ "@type": "Question", "name": "What is the most common data breach?", "acceptedAnswer": { "@type": "Answer", "text": "Hacking attacks are the most common cause of a data breach. However, it is often a weak or lost password that is the vulnerability that the opportunist hacker is exploiting." } }] }

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We are a network of experts in Business Intelligence and Business Analytics collaborates on projects defining and implementing leading-edge tools for data analysis. Our goal is to turn data into valuable information for business, displaying visual and accessible way for anyone. We are not a consultancy to use: network work minimizing the costs of each project and we do not rely on specific tools, but on the needs of each client. We are present in various sectors: Hospitality, Automotive, Aviation, Fashion Retail Management Human Resources…