In today’s digital marketing world, things are changing fast. And AI in marketing is a big part of that.

Artificial intelligence (AI) is rapidly transforming the marketing landscape, making it increasingly important for marketers to integrate AI into their work.

How marketers can leverage AI

AI can provide marketers with a number of benefits, including:

• Increased efficiency and productivity: AI can automate many time-consuming tasks, such as data analysis, content creation, and ad targeting. This frees up marketers to focus on more strategic tasks, such as creative development and campaign planning.
• Improved personalization: AI can be used to collect and analyze data about individual customers, such as their purchase history, browsing habits, and social media interactions. This data can then be used to create personalized marketing campaigns that are more likely to resonate with each customer.
• Better decision-making: AI can be used to analyze large amounts of data and identify patterns and trends that would be difficult or impossible to spot manually. This information can then be used to make better marketing decisions, such as which channels to target and what content to create.
• Enhanced customer experience: AI can be used to provide customers with more personalized and relevant experiences, such as product recommendations and chatbots that can answer customer questions.
• Increased ROI: AI can help marketers improve their ROI by optimizing their campaigns and targeting their ads more effectively.

For example, Netflix uses AI to personalize its recommendations for each user. By analyzing a user’s viewing history, AI can determine which movies and TV shows the user is most likely to enjoy. This personalized approach has helped Netflix increase user engagement and retention.

Another example is Amazon, which uses AI to power its product recommendations and search engines. AI helps Amazon understand user search queries and recommend the most relevant products. This has helped Amazon to increase sales and improve customer satisfaction.

Overall, AI is a powerful tool that can be used to improve marketing effectiveness and efficiency. As AI technology continues to develop, we can expect to see even more innovative and transformative applications in the field of marketing

Credits: Unsplash

Read about: The rise of AI driven technology in gaming industry

The advantages of Generative AI in marketing 

Artificial intelligence has emerged as a game-changer in crafting marketing strategies that resonate with target audiences. Advanced algorithms are capable of analyzing vast datasets, identifying trends, consumer behaviors, and market dynamics.  

Read about AI-powered marketing in detail

Incorporating generative AI into your marketing and creative strategies can be transformative for your business. Here are several ways to leverage this technology:

1. Personalized Content Creation:
   • How: Generative AI can analyze customer data to create personalized content, such as emails, social media posts, or even articles that resonate with specific segments of your audience.
   • Benefits: It can significantly increase engagement rates and conversion by delivering content that is tailored to the interests and behaviors of your customers.
2. Automated Ad Copy Generation:
   • How: Use AI tools to generate multiple versions of ad copy, test them in real-time, and automatically optimize based on performance.
   • Benefits: This leads to higher efficiency in ad campaigns and can improve return on investment (ROI) by finding the most effective messaging quickly.
3. Enhanced Visual Content:
   • How: AI can help design visual content such as graphics, videos, and even virtual reality experiences that are both innovative and aligned with your brand image.
   • Benefits: It can create visually appealing materials at scale, saving time and resources while maintaining high quality and consistency.
4. Dynamic Product Recommendations:
   • How: Implement AI to analyze customer data and browsing habits to provide real-time, dynamic product recommendations on your website or app.
   • Benefits: Personalized recommendations can increase average order value and improve customer satisfaction by making shopping experiences more relevant.
5. Customer Insights and Trend Analysis:
   • How: Employ AI to sift through vast amounts of data to identify trends, preferences, and patterns in customer behavior.
   • Benefits: These insights can inform your product development and marketing strategies, ensuring they are data-driven and customer-focused.
6. Optimized Media Spend:
   • How: AI algorithms can be used to allocate advertising budgets across channels and timeframes most likely to reach your target audience efficiently.
   • Benefits: You’ll be able to maximize your media spend and reduce waste by targeting users more likely to convert.
7. SEO and Content Strategy:
   • How: Generative AI can help in generating SEO-friendly content topics, meta descriptions, and even help in keyword research.
   • Benefits: It improves search engine rankings, drives organic traffic, and aligns content production with what your audience is searching for online.
8. Interactive Chatbots:
   • How: Develop sophisticated AI-powered chatbots for customer service that can handle inquiries, complaints, and even guide users through a purchase.
   • Benefits: It enhances customer experience by providing instant support and can also drive sales through proactive engagement.
9. Social Media Monitoring:
   • How: Use AI to monitor brand mentions and sentiment across social media platforms to gain insights into public perception.
   • Benefits: Allows for quick response to customer feedback and adjustment of strategies to maintain a positive brand image.
10. Voice and Visual Search:
    • How: Prepare for the increasing use of voice and visual search by optimizing content for these technologies.
    • Benefits: Ensures your products and services are discoverable through emerging search methods, potentially giving you an edge over competitors.

By integrating generative AI into your marketing and creative strategies, you can expect to see improvements in customer engagement, operational efficiency, and ultimately, a stronger bottom line for your business. It’s essential to keep a close eye on the performance and to ensure that the AI aligns with your brand values and the needs of your customers.

Artificial intelligence (AI) is rapidly transforming the digital marketing landscape, bringing about advancements that were previously unimaginable. AI is enabling marketers to personalize customer experiences, automate repetitive tasks, and gain deeper insights into customer behavior. Some of the key advancements that AI has brought into marketing and digital marketing world include:

• Personalized customer experiences: AI can be used to collect and analyze data on individual customers, such as their browsing history, purchase patterns, and social media interactions. This data can then be used to create personalized marketing campaigns that are more likely to resonate with each customer.
• Automated repetitive tasks: AI can be used to automate many of the time-consuming tasks involved in marketing, such as data entry, email marketing, and social media management. This frees up marketers to focus on more strategic tasks, such as creative development and campaign planning.
• Deeper insights into customer behavior: AI can be used to analyze large amounts of data to identify patterns and trends in customer behavior. This information can be used to develop more effective marketing campaigns and improve customer satisfaction.

Overall, AI is having a profound impact on the marketing and digital marketing worlds. As AI technology continues to develop, we can expect to see even more innovative and transformative applications in the years to come.

How AI is helping leading companies market products

Here are some examples of companies around the world that are using AI in different areas of marketing:

• Coca-Cola: Coca-Cola has developed an AI-powered creative platform called Create Real Magic. This platform allows fans to interact with the brand on an ultra-personal level by creating their own AI-powered creative artwork to potentially feature in official Coca-Cola advertising campaigns.

• Nike: Nike uses AI to create personalized marketing campaigns based on individual customer data. The company also uses AI to optimize its advertising spend and improve its customer service.

• Sephora: Sephora uses AI to power its chatbot, Sephbot. The bot can answer customer questions about products, suggest new products to customers, and even make product recommendations based on a customer’s previous purchases.

• Nutella: Nutella uses AI to create personalized packaging for its products. The company uses AI to generate images that are based on a customer’s social media profile

Future trends in digital marketing using AI

Artificial intelligence (AI) is already having a major impact on digital marketing, and this is only going to increase in the coming years. Here are some of the key trends that we can expect to see:

• Hyper-personalization: AI will be used to create hyper-personalized marketing campaigns that are tailored to the individual needs and preferences of each customer. This will be made possible by analyzing large amounts of data about customer behavior, such as their purchase history, browsing habits, and social media interactions.

• Automated decision-making: AI will be used to automate many of the time-consuming tasks involved in digital marketing, such as keyword research, ad placement, and campaign optimization. This will free up marketers to focus on more strategic tasks, such as creative development and campaign planning.

• Augmented creativity: AI will be used to augment the creativity of human marketers. For example, AI can be used to generate new ideas for content, create personalized product recommendations, and develop innovative marketing campaigns.

• Voice search optimization: As more people use voice assistants such as Siri, Alexa, and Google Assistant, marketers will need to optimize their content for voice search. AI can help with this by identifying the keywords and phrases that people are using in voice search and optimizing content accordingly.

• Real-time marketing: AI will be used to enable real-time marketing, which means that marketers will be able to respond to customer behavior in real time. For example, AI can be used to send personalized messages to customers who abandon their shopping carts or to offer discounts to customers who are about to make a purchase.

These are just a few of the ways that AI is going to transform digital marketing in the coming years. As AI technology continues to develop, we can expect to see even more innovative and transformative applications.

Unlocking the Power of LLM Use-Cases: AI applications now excel at summarizing articles, weaving narratives, and sparking conversations, all thanks to advanced large language models.

A large language model, abbreviated as LLM, represents a deep learning algorithm with the capability to identify, condense, translate, forecast, and generate text as well as various other types of content. These abilities are harnessed by drawing upon extensive knowledge extracted from massive datasets.

Large language models, which are a prominent category of transformer models, have proven to be exceptionally versatile. They extend beyond simply instructing artificial intelligence systems in human languages and find application in diverse domains like deciphering protein structures, composing software code, and many other multifaceted tasks.

Furthermore, apart from enhancing natural language processing applications such as translation, chatbots, and AI-powered assistants, large language models are also being employed in healthcare, software development, and numerous other fields for various practical purposes.

LLM use cases

Applications of large language models

Language serves as a conduit for various forms of communication. In the vicinity of computers, code becomes the language. Large language models can be effectively deployed in these linguistic domains or scenarios requiring diverse communication.

These models significantly expand the purview of AI across industries and businesses, poised to usher in a new era of innovation, ingenuity, and efficiency. They possess the potential to generate intricate solutions to some of the world’s most intricate challenges.

For instance, an AI system leveraging large language models can acquire knowledge from a database of molecular and protein structures. It can then employ this knowledge to propose viable chemical compounds, facilitating groundbreaking discoveries in vaccine and treatment development.

LLM Use-Cases: 10 industries revolutionized by large language models

Large language models are also instrumental in creating innovative search engines, educational chatbots, and composition tools for music, poetry, narratives, marketing materials, and beyond. Without wasting time, let delve into top 10 LLM use-cases:

1. Marketing and Advertising

• Personalized marketing: LLMs can be used to generate personalized marketing content, such as email campaigns and social media posts. This can help businesses to reach their target customers more effectively and efficiently. For example, an LLM could be used to generate a personalized email campaign for customers who have recently abandoned their shopping carts. The email campaign could include information about the products that the customer was interested in, as well as special offers and discounts.

• Chatbots: LLMs can be used to create chatbots that can interact with customers in a natural way. This can help businesses to provide customer service 24/7 without having to hire additional staff. For example, an LLM could be used to create a chatbot that can answer customer questions about products, services, and shipping.

• Content creation: LLMs can be used to create marketing content, such as blog posts, articles, and social media posts. This content can be used to attract attention, engage customers, and promote products and services. For example, an LLM could be used to generate a blog post about a new product launch or to create a social media campaign that encourages customers to share their experiences with the product.

• Targeting ads: LLMs can be used to target ads to specific audiences. This can help businesses to reach their target customers more effectively and efficiently. For example, an LLM could be used to target ads to customers who have shown interest in similar products or services.

• Measuring the effectiveness of marketing campaigns: LLMs can be used to measure the effectiveness of marketing campaigns by analyzing customer data and social media activity. This information can be used to improve future marketing campaigns.

• Generating creative text formats: LLMs can be used to generate different creative text formats, such as poems, code, scripts, musical pieces, email, letters, etc. This can be used to create engaging and personalized marketing content.

Here are some other use cases for large language models in marketing and advertising:

• Content creation: LLMs can be used to create marketing content, such as blog posts, articles, and social media posts. This content can be used to attract attention, engage customers, and promote products and services.
• Measuring the effectiveness of marketing campaigns: LLMs can be used to measure the effectiveness of marketing campaigns by analyzing customer data and social media activity. This information can be used to improve future marketing campaigns.
• Targeting ads: LLMs can be used to target ads to specific audiences. This can help businesses to reach their target customers more effectively and efficiently.

2. Retail and eCommerce

A large language model can be used to analyze customer data, such as past purchases, browsing history, and social media activity, to identify patterns and trends. This information can then be used to generate personalized recommendations for products and services. For example, an LLM could be used to recommend products to customers based on their interests, needs, and budget.

Here are some other use cases for large language models in retail and eCommerce:

• Answering customer inquiries: LLMs can be used to answer customer questions about products, services, and shipping. This can help to free up human customer service representatives to handle more complex issues.
• Assisting with purchases: LLMs can be used to guide customers through the purchase process, such as by helping them to select products, add items to their cart, and checkout.
• Fraud detection: LLMs can be used to identify fraudulent activity, such as credit card fraud or identity theft. This can help to protect businesses from financial losses.

3. Education

Large language models can be used to create personalized learning experiences for students. This can help students to learn at their own pace and focus on the topics that they are struggling with. For example, an LLM could be used to create a personalized learning plan for a student who is struggling with math. The plan could include specific exercises and activities that are tailored to the student’s needs.

Answering student questions

Large language models can be used to answer student questions in a natural way. This can help students to learn more effectively and efficiently. For example, an LLM could be used to answer a student’s question about the history of the United States. The LLM could provide a comprehensive and informative answer, even if the question is open-ended or challenging.

Generating practice problems and quizzes

Large language models can be used to generate practice problems and quizzes for students. This can help students to review the material that they have learned and prepare for exams. For example, an LLM could be used to generate a set of practice problems for a student who is taking a math test. The problems would be tailored to the student’s level of understanding and would help the student to identify any areas where they need more practice.

Here are some other use cases for large language models in education:

• Grading student work: LLMs can be used to grade student work, such as essays and tests. This can help teachers to save time and focus on other aspects of teaching.
• Creating virtual learning environments: LLMs can be used to create virtual learning environments that can be accessed by students from anywhere. This can help students to learn at their own pace and from anywhere in the world.
• Translating textbooks and other educational materials: LLMs can be used to translate textbooks and other educational materials into different languages. This can help students to access educational materials in their native language.

4. Healthcare

Large language models (LLMs) are being used in healthcare to improve the diagnosis, treatment, and prevention of diseases. Here are some of the ways that LLMs are being used in healthcare:

• Medical diagnosis: LLMs can be used to analyze medical records and images to help diagnose diseases. For example, an LLM could be used to identify patterns in medical images that are indicative of a particular disease.
• Patient monitoring: LLMs can be used to monitor patients’ vital signs and other health data to identify potential problems early on. For example, an LLM could be used to track a patient’s heart rate and blood pressure to identify signs of a heart attack.
• Drug discovery: LLMs can be used to analyze scientific research to identify new drug targets and to predict the effectiveness of new drugs. For example, an LLM could be used to analyze the molecular structure of a disease-causing protein to identify potential drug targets.
• Personalized medicine: LLMs can be used to personalize treatment plans for patients by taking into account their individual medical history, genetic makeup, and lifestyle factors. For example, an LLM could be used to recommend a specific drug to a patient based on their individual risk factors for a particular disease.
• Virtual reality training: LLMs can be used to create virtual reality training environments for healthcare professionals. This can help them to learn new skills and to practice procedures without putting patients at risk.

5. Finance

Large language models (LLMs) are being used in finance to improve the efficiency, accuracy, and transparency of financial markets. Here are some of the ways that LLMs are being used in finance:

• Financial analysis: LLMs can be used to analyze financial reports, news articles, and other financial data to help financial analysts make informed decisions. For example, an LLM could be used to identify patterns in financial data that could indicate a change in the market.
• Risk assessment: LLMs can be used to assess the risk of lending money to borrowers or investing in a particular company. For example, an LLM could be used to analyze a borrower’s credit history and financial statements to assess their risk of defaulting on a loan.
• Trading: LLMs can be used to analyze market data to help make improved trading decisions. For example, an LLM could be used to identify trends in market prices and to predict future price movements.
• Fraud detection: LLMs can be used to detect fraudulent activity, such as money laundering or insider trading. For example, an LLM could be used to identify patterns in financial transactions that are indicative of fraud.
• Compliance: LLMs can be used to help financial institutions comply with regulations. For example, an LLM could be used to identify potential violations of anti-money laundering regulations.

6. Law

Technology has greatly transformed the legal field, streamlining tasks like research and document drafting that once consumed lawyers’ time.

• Legal research: LLMs can be used to search and analyze legal documents, such as case law, statutes, and regulations. This can help lawyers to find relevant information more quickly and easily. For example, an LLM could be used to search for all cases that have been decided on a particular legal issue.
• Document drafting: LLMs can be used to draft legal documents, such as contracts, wills, and trusts. This can help lawyers to produce more accurate and consistent documents. For example, an LLM could be used to generate a contract that is tailored to the specific needs of the parties involved.
• Legal analysis: LLMs can be used to analyze legal arguments and to identify potential weaknesses. This can help lawyers to improve their legal strategies. For example, an LLM could be used to analyze a precedent case and to identify the key legal issues that are relevant to the case at hand.
• Litigation support: LLMs can be used to support litigation by providing information, analysis, and insights. For example, an LLM could be used to identify potential witnesses, to track down relevant evidence, or to prepare for cross-examination.
• Compliance: LLMs can be used to help organizations comply with regulations by identifying potential violations and providing recommendations for remediation. For example, an LLM could be used to identify potential violations of anti-money laundering regulations.

Read more –> LLM for Lawyers, enrich your precedents with the use of AI

7. Media

The media and entertainment industry embraces a data-driven shift towards consumer-centric experiences, with LLMs poised to revolutionize personalization, monetization, and content creation.

• Personalized recommendations: LLMs can be used to generate personalized recommendations for content, such as movies, TV shows, and news articles. This can be done by analyzing user preferences, consumption patterns, and social media signals.
• Intelligent content creation and curation: LLMs can be used to generate engaging headlines, write compelling copy, and even provide real-time feedback on content quality. This can help media organizations to streamline content production processes and improve overall content quality.
• Enhanced engagement and monetization: LLMs can be used to create interactive experiences, such as interactive storytelling and virtual reality. This can help media organizations to engage users in new and innovative ways.
• Targeted advertising and content monetization: LLMs can be used to generate insights that inform precise ad targeting and content recommendations. This can help media organizations to maximize ad revenue.

Bigwigs with LLM – Netflix uses LLMs to generate personalized recommendations for its users. The New York Times uses LLMs to write headlines and summaries of its articles. The BBC uses LLMs to create interactive stories that users can participate in. Spotify uses LLMs to recommend music to its users.

8. Military

• Synthetic training data: LLMs can be used to generate synthetic training data for military applications. This can be used to train machine learning models to identify objects and patterns in images and videos. For example, LLMs can be used to generate synthetic images of tanks, ships, and aircraft.
• Natural language processing: LLMs can be used to process natural language text, such as reports, transcripts, and social media posts. This can be used to extract information, identify patterns, and generate insights. For example, LLMs can be used to extract information from a report on a military operation.
• Machine translation: LLMs can be used to translate text from one language to another. This can be used to communicate with allies and partners, or to translate documents and media. For example, LLMs can be used to translate a military briefing from English to Arabic.
• Chatbots: LLMs can be used to create chatbots that can interact with humans in natural language. This can be used to provide customer service, answer questions, or conduct research. For example, LLMs can be used to create a chatbot that can answer questions about military doctrine.
• Cybersecurity: LLMs can be used to detect and analyze cyberattacks. This can be used to identify patterns of malicious activity, or to generate reports on cyberattacks. For example, LLMs can be used to analyze a network traffic log to identify a potential cyberattack.

9. HR

• Recruitment: LLMs can be used to automate the recruitment process, from sourcing candidates to screening resumes. This can help HR teams to save time and money and to find the best candidates for the job.
• Employee onboarding: LLMs can be used to create personalized onboarding experiences for new employees. This can help new employees to get up to speed quickly and feel more welcome.
• Performance management: LLMs can be used to provide feedback to employees and to track their performance. This can help managers to identify areas where employees need improvement and to provide them with the support they need to succeed.
• Training and development: LLMs can be used to create personalized training and development programs for employees. This can help employees to develop the skills they need to succeed in their roles.
• Employee engagement: LLMs can be used to survey employees and to get feedback on their work experience. This can help HR teams to identify areas where they can improve the employee experience.

Here is a specific example of how LLMs are being used in HR today: The HR company, Mercer, is using LLMs to automate the recruitment process. This is done by using LLMs to screen resumes and to identify the best candidates for the job. This has helped Mercer to save time and money and to find the best candidates for their clients.

10. Fashion

How LLMs are being used in fashion today? The fashion brand, Zara, is using LLMs to generate personalized fashion recommendations for its users. This is done by analyzing user data, such as past purchases, social media activity, and search history. This has helped Zara to improve the accuracy and relevance of its recommendations and to increase customer satisfaction.

• Personalized fashion recommendations: LLMs can be used to generate personalized fashion recommendations for users based on their style preferences, body type, and budget. This can be done by analyzing user data, such as past purchases, social media activity, and search history.
• Trend forecasting: LLMs can be used to forecast fashion trends by analyzing social media data, news articles, and other sources of information. This can help fashion brands to stay ahead of the curve and create products that are in demand.
• Design automation: LLMs can be used to automate the design process for fashion products. This can be done by generating sketches, patterns, and prototypes. This can help fashion brands to save time and money, and to create products that are more innovative and appealing.
• Virtual try-on: LLMs can be used to create virtual try-on experiences for fashion products. This can help users to see how a product would look on them before they buy it. This can help to reduce the number of returns and improve the customer experience.
• Customer service: LLMs can be used to provide customer service for fashion brands. This can be done by answering questions about products, processing returns, and resolving complaints. This can help to improve the customer experience and reduce the workload on customer service representatives.

Wrapping up

In conclusion, large language models (LLMs) are shaping a transformative landscape across various sectors, from marketing and healthcare to education and finance. With their capabilities in personalization, automation, and insight generation, LLMs are poised to redefine the way we work and interact in the digital age. As we continue to explore their vast potential, we anticipate breakthroughs, innovation, and efficiency gains that will drive us toward a brighter future.

You may have heard the buzzword data-driven marketing. In this blog we will discuss what is required to really be data-driven in marketing initiatives that help us achieve a better return on marketing investment?  

We will talk about:

• What data-driven marketing is
• How to create and implement marketing strategies based on data
• The challenges marketers face in a data-driven environment, and
• Tools to help deliver a higher return on investment (ROI). 

Today’s marketing is a lot different from decades-old gut-driven marketing. Now we have the most reliable sources of information available in real-time. With tools like HubSpot, Salesforce, Zoho, and many others you can track every interaction of your lead from the initial stages of the buyer’s journey to post-purchase.  

This helps devise long-term strategies that are based on actual data rather than gut feelings. With the information available you can optimize current campaigns to achieve the best ROI. 

However, to achieve the desired results you need to first understand what’s important in terms of your customer interaction and what metrics matter the most, so you don’t get lost in an enormous amount of data. 

Become skilled in data-driven marketing with Data Science Dojo’s certificate programs. 

What is data-driven marketing and why is it important? 

Data-driven marketing is an approach where marketers build strategies based on the analysis of big data. This includes using tools to drive insights from raw data to turn it into actionable insights based on customer interaction. Marketers identify trends and base their long-term strategy on insights driven by data analysis. 

So, why should we invest in a data-driven marketing approach? Besides the obvious answer of clarity and efficiency of marketing processes, a data-driven approach helps identify a target audience, creates a seamless experience for customers, helps choose the best communication channels, and creates personalization. 

• Help define the target audience 

One of the most important aspects of any marketing campaign is to have a laser-sharp definition of the target audience. With the help of insights from different marketing touchpoints collected and analyzed, we can define the target group for our products/services who are most likely to benefit. 

Once we have a clear definition of the target audience and created personas everything else falls in place to create a synergized marketing campaign to reach the right audience at the right time with the right communication. 

• Create a seamless customer experience 

Successful marketing is not only about lavish advertising campaigns and promotion but to create and delivering superior value to customers. This in turn helps build the brand, create an army of loyalists, and drive positive word of mouth. 

Data-driven marketing helps analyze trends in the market and understand user interaction with marketing touchpoints to create workflows and processes. This helps improve service delivery and exceed customer expectations. Data-driven organizations are able to deliver a smooth experience over the customer lifecycle minimizing hurdles and delivering great value. 

For example, at Data Science Dojo, customer success is the most important driver of overall organizational health. We have created automated processes that are personalized to each individual customer and lead. This helps the marketing team nurture potential leads into high-value customers and delivers a seamless experience to help build a community of brand advocates. So far, we have trained more than 10,000+ professionals from around the globe and built a community of data enthusiasts. 

• Optimizing communication channels 

With the help of data coming in from different marketing touchpoints, marketers can identify the best possible communication channel for each product category, target audience, and customer segment.  

Marketers no longer need to rely on one size fits all solutions but instead create personalized communication based on user behavior, demographics, psychographics, and other factors being captured through CRM and ERP systems.  

For example, Starbucks, one of the leading coffee brands in the world used a data-driven approach to create an end-to-end marketing experience for their customers by using a mobile application as their primary communication channel. Marketing identified an opportunity to grow the customer base by offering a loyalty reward program based on user interaction with the brand touchpoints. According to Risenews case study today Starbucks is running the most successful loyalty program with over 24 million active users.  

• Creating personalization 

Customers are skeptical about generic marketing messaging that pushes them to buy. A recent study by Marketo shows that consumers are fed up with repeated generic messages being blasted at them. 63% of the respondents said they are highly annoyed while 78% said they will only engage with the brand offers if it relates to their previous interaction. 

Personalization is not just an add-on to good messaging, but it has become a necessity to survive in a cluttered communication environment where users are receiving thousands of brand messages from multiple platforms.  

With data analysis of user queries, interaction, and common questions, and defining the entire sales process marketers can create personalized communication based on user segment and need.  

The sales team plays a vital role in delivering personalized messaging but with a data-driven approach, we can minimize redundant tasks and focus on delivering personalized messaging based on user interaction. 

How a data-driven approach helps improve ROI 

Now that we have a clear understanding of how significant data is to any marketing effort, we can talk about its impact on overall business goals and specific measurable marketing objectives. 

Research suggests benefits of a data-driven marketing approach are huge. We get greater customer loyalty, improved lead generation, and increased satisfaction. 

According to ZoomInfo about 78% of organizations that follow data-driven approach verifies increase in lead conversion and customer acquisition. 

Another study by Forbes reveals that 66% of marketing leaders believe that data lead to increased customer acquisition. 

To improve your return on marketing investment it is important to give the right attribution to each marketing activity. This helps us identify the best drivers of growth and invest more time and money in that particular marketing activity or channel. 

Learn more about marketing attribution in this short tutorial 

Challenges to a data-driven approach 

According to Campaign Monitor, 81% of marketing professionals consider the implementation of data-driven strategies extremely complicated. 

So, what are some of the challenges to achieving a data-driven marketing overhaul? 

1. Gathering data: many data-driven marketers are overwhelmed by the idea of collecting data without any automation. In most cases, the abundance of data makes it difficult to narrow it down to the most useful data for analysis.  

Solution: There are multiple CRM and ERP systems available at very competitive costs that deliver precise information on your customer that can be used to create a better user experience. 

2. Pulling data: Manually pulling and updating data regularly is a laborious task  

Solution: Creating a marketing dashboard that helps keep track of real-time data. Less time should be spent collecting data and more time analyzing and making decisions. There are multiple tools available to connect and visualize your data. Platforms like Hotjar, Adverity, and Improvado help collect, organize, and seamlessly visualize data so you as a marketer can focus on planning and making data-driven decisions. 

 3. Data silos: Challenge of data silos created at each departmental, functional, or team level which is not accessible to the entire team makes the marketing job difficult. A recent survey shows only 8% of the companies have a centralized data repository.  

Solution: To overcome the challenge of data silos there needs to be an organization-wide effort to modernize and embrace change. This is going to include setting up common standards, changing culture, and embracing new marketing analytics platforms. 

A marketing strategy based on data 

Building a data-driven strategy or just strategy itself is a vast topic with much research being conducted on the best way to do so. The only thing you need to keep in mind is your current business environment, this includes internal, external, and current organizational requirements.  

Here is a quick walkthrough of steps involved in a data-driven strategy 

Step 1: Strategy 

The first step is to identify long-term strategy, this means figuring out your long-term goals, specific and measurable objectives, and detailing down to tactics.  

Once you have a clear understanding of your overall business strategy as well as a marketing strategy you can focus on data that is relevant to your goals. 

Step 2: Identify key areas 

Data is scattered across organizations coming from all directions and multiple customer touchpoints. It is important to identify key areas of interest that align with your overall business strategy and objectives. Once we have key focus areas, we can continue investing more in building capabilities in that area. 

Step 3: Data targeting  

After identifying focus areas, it is time to target datasets that will answer all the burning questions related to your business and marketing objectives.  

This means identifying already available information and channels and figuring out the most valuable information. At this point of your data-driven strategy, the goal is to streamline data collection and presentation methods so that marketing can only focus on key areas of business value and not waste time on non-essential data reports.  

Step 4: Collecting and analyzing data 

In this step, you need to identify key stakeholders in data collection and analysis. There may be teams or individuals at each data collection and distribution point based on the size of the organization. The idea is to keep the process of collection and dissemination of data seamless, integrated, and in real-time.  

This may require an organization to implement integrated ERP systems or CRM systems to connect data coming in from various sources based on our identified key focus areas and show relevant information to each team. 

Step 5: Turning insights into action 

The final step of a data-driven strategy is to turn insights gained from data analysis into actionable items. ROI will depend on how useful your insights are and how successfully they were implemented to achieve marketing objectives. At this step, you need to have a clear understanding and a game plan for the implementation phase, actions that will improve business and create value for customers. 

Learn how to visualize data to tell a story 

Become a truly data-driven marketer 

Becoming data-driven is a continuous process, if you think you are data-driven now, technology and competitive environment will change in the next 6 months making your current data-driven strategy obsolete. As a marketer you need to constantly improve and update so does your marketing strategy. With this guide, you can get started with becoming more data-driven and less gut-driven to make sound marketing decisions based on real-time data. This will not only help achieve measurable marketing objectives but improve return on marketing investment and improve overall business value. 

References 

https://www.forbes.com/forbesinsights/data_driven_and_digitally_savvy/ 

https://www.marketo.com/articles/personalization-definition/ 

https://www.campaignmonitor.com/resources/infographics/the-eye-opening-truth-about-data-driven-marketing/ 

https://www.mckinsey.com/capabilities/growth-marketing-and-sales/our-insights/the-big-reset-data-driven-marketing-in-the-next-normal 

https://datasciencedojo.com/blog/data-science-toolkit/ 

Marketing analytics tells you about the most profitable marketing activities of your business. The more effectively you target the right people with the right approach, the greater value you generate for your business.

However, it is not always clear which of your marketing activities are effective at bringing value to your business.  This is where marketing analytics comes in. Running an Amazon seller competitor analysis is crucial to your success in the marketplace. Using a framework to monitor your competitors’ efforts is a great way to ensure you can beat them at their own game.

It guides you to use the data to evaluate your marketing campaign. It helps you identify which of your activities are effective in engaging with your audience, improving user experience, and driving conversions. 

Grow your business with Data Science Dojo 

Data driven marketing is imperative in optimizing your campaigns to generate a net positive value from all your marketing activities in real-time. Without analyzing your marketing data and customer journey, you cannot identify what you are doing right and what you are doing wrong when engaging with potential customers. The 6 features listed below can give you the start you need to get into analyzing and optimizing your marketing strategy using marketing analytics 

 Learn about marketing analytics tools in this blog

1. Impressions 

In digital marketing, impressions are the number of times any piece of your content has been shown on a person’s screen. It can be an ad, a social media post, video etc. However, it is important to remember that impressions do not mean views, a view is an engagement, anytime somebody sees your video that is a view, but an impression would also include anytime they see your video in the recommended videos on YouTube or in their newsfeed on Facebook. The impression will be counted regardless of whether they watch your video or not. 

Learn more about impressions in this video

It is also important to distinguish between impressions and reach. Reach is the number of unique viewers, so for example if the same person views your ad three times, you will have three impressions but a reach of one.  

Impressions and reach are important in understanding how effective your content was at gaining traction. However, these metrics alone are not enough to gauge how effective your digital marketing efforts have been, neither impressions nor reach tell you how many people engaged with your content. So, tracking impressions is important, but it does not specify whether you are reaching the right audience.  

2. Engagement rate 

In social media marketing, engagement rate is an important metric. Engagement is when a user comments, likes, clicks, or otherwise interacts with any of your content. Engagement rate is a metric that measures the amount of engagement of your marketing campaign relative to each of the following: 

• Reach 
• Post 
• Impressions  
• Days
• Views 

Engagement rate by reach is the percentage of people who chose to interact with the content after seeing it. It is calculated by the following formula. Reach is a more accurate measurement than follower count, because not all of your brands followers may see the content while those who do not follow your brand may still be exposed to your content. 

Engagement rate by post is the rate at which followers engage with the content. This metric shows how engaged your followers are with your content. However, this metric does not account for organic reach and as your follower count goes up your engagement by post goes down. 

Engagement rate by Impressions is the rate of engagement relative to the number of impressions. If you are running paid ads for your brand, engagement rate by impressions can be used to gauge your ads effectiveness.  

Average Daily engagement rate tells you how much your followers are engaging with your content daily. This is suitable for specific use cases for instance, when you want to know how much your followers are commenting on your posts or other content. 

Engagement rate by views gives the percentage of people who chose to engage with your video after watching them. This metric however does not use unique views so it may double or triple count views from a single user. 

Learn more about engagement rate in this video

3. Sessions 

Sessions are another especially important metric in marketing campaigns that help you analyze engagement on your website. A session is a set of activities by a user within a certain period. For example, a user spent 10 minutes on your website, loading pages, interacting with your content and completed an interaction. All these activities will be recorded in the same 10-minute session.  

In Google Analytics, you can use sessions to check how much time a user spent on your website (session length), how many times they returned to your website (number of sessions), and what interactions users had with your website. Tracking sessions can help you determine how effective your campaigns were in directing traffic towards your website. 

If you have an E-commerce website another very helpful tool on Google Analytics is behavioral analytics. With behavioral analytics you see what key actions are driving purchases on your website. The sessions report can be accessed under conversions tab on Google Analytics. This report can help you understand user behaviors such as abandon carts. This allows you to target these users with targeted ads or offering incentives to complete their purchase. 

Learn more about sessions in this video

4. Conversion rate 

Once you have engaged your audience the next step in the customers’ journey is conversion. A conversion is when you make the customer or user complete a specific action. This desired action can be anything from a form submission, purchasing a product or subscribing to a service. The conversion rate is the percentage of visitors who completed the desired action.

So, if you have a form on your website and you want to find out what the conversion rate is. You would simply divide the number of form submissions by the number of visitors on that form’s page (Total conversions/total interactions). 

Conversion rate is a very important metric that helps you assess the quality of your leads. While you may generate a large number of leads or visitors, if you cannot get them to perform the desired action you may be targeting the wrong audience. Conversion rate can also help you gauge how effective your conversion strategy is, if you aren’t converting visitors, it might indicate that your campaign needs optimization. 

5. Attribution  

Attribution is a sophisticated model that helps you measure which channels are generating the most sales opportunities or conversions. It helps you assign credit to specific touchpoints on the customers journey and understand which touchpoints are driving conversions the most. But how do you know which touchpoint to attribute to a specific conversion?  Well, that depends on which attribution models you are using. There are four common attribution models. 

First touch attribution models assign all the credit to the first touchpoint that drove the prospect to your website. It focuses on the top of the marketing efforts funnel and tells you what is attracting people to your brand 

Last touch attribution models assign credit to the last touchpoint. It focuses on the last touchpoint the visitor interacted with before they converted. 

Linear attribution model assigns an equal weight to all the touchpoints in the buyer’s journey. 

Time decay attributions is based on how close the touchpoint is to the conversion, where a weighted percentage is assigned to the most recent touchpoints. This can be used when the buying cycle is relatively short. 

What model you use is based on what product or subscription you are selling and what is the length of your buyer cycle. While attribution is very important in identifying the effectiveness of your channels, to get the complete picture you need to look at how each touchpoint drives conversion. 

 Learn more about attribution in this video

6. Customer lifetime value 

Businesses prefer retaining customers over acquiring new ones, and one of the main reasons is that attracting new customers has a cost. The customer acquisition cost is the total cost that you incur as a business acquiring a customer. The customer acquisition cost is calculated by dividing the marketing and sales cost by the number of new customers. 

Learn more about CLV in this video

So, as a business, you must weigh the value of each customer with the associated acquisition cost. This is where the customer lifetime value or CLV comes in. The Customer lifetime value is the total value of your customer to your business during the period of your relationship.

The CLV helps you forecast your revenue as well, the larger the average CLV you have the better your forecasted revenue will be. CLV is calculated by dividing the annual revenue generated from customers by the average retention period (in years).  If your CAC is higher than your CLV, then you are on average losing money on every customer you make.

This presents a huge problem. Metrics like CAC and CLV are very important for driving revenue. They help you identify high-value customers and identify low value customers so you can understand how to serve these customers better. They help you make more informed decisions regarding your marketing effort and build a healthy customer base. 

 Integrate marketing analytics into your business 

Marketing analytics is a vast field. There is no one method that suits the needs of all businesses. Using data to analyze and drive your marketing and sales effort is a continuous effort that you will find yourself constantly improving upon. Furthermore, finding the right metrics to track that have a genuine impact on your business activities is a difficult task.

So, this list is by no means exhaustive, however the features listed here can give you the start you need to analyze and understand what actions are important in driving engagement, conversions and eventually value for your business.  

Online videos account for 80% of all consumer internet traffic. Here are some video marketing platforms that make data analysis easy.

The importance of video marketing cannot be overstated. By 2019, video traffic will account for 80% of all consumer internet traffic, and one-third of all online activity is spent watching video content. If your company is looking to expand its presence online, video is the way to go.

We get it, you’re not a data scientist, you’re just a marketing manager, coordinator, or intern looking to increase the company’s online presence. Here’s the thing, you don’t need to be a data scientist to receive valuable information from your videos. There is a plethora of platforms out there with readymade tools that data-driven will make you a data driven video professional. But which ones should you use? That’s why we’re here! In this post, you’ll find what we, the Data Science Dojo Video Team, picked as our top video marketing platforms.

YouTube

YouTube needs no introduction. Nearly one out of every two internet users are on YouTube, and the site is the second largest search engine on the web. Because of YouTube’s rapidly growing popularity, it shouldn’t come as a surprise this would be the first platform on our list.

Photo by

Christian Wiediger on Unsplash

At its core, YouTube is a video hosting platform that uses your content as the business model, much like Facebook. It’s the reason all the tools are free to use, otherwise, it wouldn’t have caught on as quickly.

Features:

• Create & customize your own channel
• Integrations with Google Analytics & tools
• Subscribers, Likes, Sharing buttons
• Free Live streaming
• Powerful recommendation engine
• Subtitle generation
• Scheduling public videos
• Advertising using uploaded videos

YouTube is almost the complete package when it comes to video marketing. But remember, YouTube’s business is keeping users on the site, so the tools they give you are designed to do just that. This can bring complications if the goal is to send users to your site. Which brings us to the pros and the cons to using YouTube as a platform.

Pros:

• Massive userbase
• No upload limits
• Monetization options
• Google search seo
• Powerful analytical dashboards
• Can upload almost anything

Cons:

• Very competitive
• Strict content ID system
• Restrictions on linking interactive media
• Very limited embedding options
• No security on videos
• Lacking support / help articles
• Ads on the videos
• Other channels are advertised with the recommendation engine

Don’t underestimate how difficult it will be to get a foothold on your target audience. Since YouTube’s so massive, there is a ton of competition to have the top video in the search results. Therefore, it’s important to know how your videos rank in the system. Applying plugins can help you understand and analyze your video rankings. We use VidIQ and TubeBuddy to help us with our rankings.

• Tubebuddy: Allows users to quickly manage their YouTube channels and improve SEO, views, and subscriber growth. While it has some SEO features, it focuses more on Bulk updating and ease-of-use.
• VidIQ: More focused on SEO, it has a more robust tagging system and a nice cleanly laid out interface. If SEO is what you want, look no further.

There’s no harm in using both as the “free” features are more than enough to get you going.  We do suggest paying the extra money for Tubebuddy as the features it adds are time saving especially on larger channels.

Vimeo pro

Vimeo is the closest competitor to YouTube with over 700 million active users. However, Vimeo caters to a completely different audience. Vimeo is considered the “professionals” choice when it comes to video hosting platforms. This is largely to do with the better compression and lack of ads on the site. A lot of Indy filmmaker content is found there, and they push that genre much higher than, say, a tutorial.

Regardless, It is another great video sharing platform that offers some unique features over its competitors.

• No advertisements
• A cleaner and more customizable layout
• Wider range of embedding options
• More interaction elements
• Greater security for your content
• Higher quality encoding
• Branding customization
• Hide videos from Vimeo and keep them public

The cons of using Vimeo:

• Lower viewer numbers
• No advertising options
• Must pay for advanced features
• Upload limits
• Lacks automatic subtitle options
• Lacks bulk updating
• Limited integrations and 3^rd party support
• Live streaming is expensive
• No scheduling options

Unlike YouTube’s features, Vimeo’s are not entirely free. Vimeo currently offers five membership plans, which vary in features and upload limits. With the free plan, you can see very basic analytics like:

• The number of times your video was loaded
• The number of plays, finishes, likes and comments
• Your most popular videos

For a marketer, that is not good enough, so upgrading to a Pro plan allows you to see engagement and duration graphs. This makes the analytics much more like YouTube’s.

Depending on the content you produce, Vimeo is a great tool, but for gaining visibility, it may not be the right tool since YouTube has twice the users. It is also worth noting that YouTube is a Google product and Vimeo is not. This means Google will place YouTube videos much higher on search results than Vimeo, but sometimes Google will display the same video from both platforms.

We use Vimeo more as an embedding tool than anything else. We aren’t trying to necessarily reach the audience on Vimeo, but the seamless embedding is an important tool for our website. It acheives the same goal at a cheaper price than other sites like:

• Wistia
• Vidyard
• Brightcove

It’s not outlandish to use both platforms, but because Vimeo requires a paid plan to get the deeper analytics, YouTube may still be a better choice.

Facebook

Photo by William Iven on Unsplash

Facebook is the most popular social media site on the planet. With over 2 billion active users, it is not slowing down. When you think of video platforms, Facebook is not one of them, but it has recently been making steps in changing this fact. It is defiantly behind when it comes to the likes of Vimeo and YouTube especially in the metrics department. However, it has been busy adding new features that makes the platform hard to ignore.

Facebook features:

• A New creator studio for video management and performance monitoring
• Returning viewers vs new metrics
• Metrics specific to Facebook news feed
• Ad performance monitoring
• Retire and backdate video posts
• Facebook Live Streaming
• Automatic Captioning

In recent years, Facebook has been pushing users to start uploading videos to their platform. With this push, they have been treating posts with YouTube and Vimeo links as thumbnails instead of imbedding the content. This explains why Facebook has been adding video tools so quickly. But as it is, Facebook is best used as a supplemental platform and not the main driver of your content.

Facebook pros:

• 2 Billion users
• Free Live Streaming
• Free video tools
• Has a multitude of apps that work with it
• No Ads in videos

Facebook cons:

• Closed platform (can’t embed posts)
• No Google Search Results
• Interface has a slight learning curve
• Lacks depth in metrics
• No interactive content

If you’re looking to market to Facebook users, it’s better to use Facebook’s native video hosting and not just because of the imbedding issues. Since Facebook videos don’t show up on web search results, Facebook is considered a closed garden in terms of video hosting. But that’s why Facebook should be supplemental to YouTube or Vimeo as it is a social media site first, video hosting second.

Other up and coming video platform options are:

• Instagram
• Periscope
• Linkedin

None of these tools are perfect, each one has their pros and cons, audiences, and metrics. But using them together and understanding your data can provide you with the knowledge you need to continue putting out quality content. Take the time to understand the tools as well as the data they provide and see how you can use them to your advantage.

In the next blog, we will explore video metrics and how to manage them using online tools!

A list of top machine learning algorithms for marketers that can help to understand trends in user behavior, which further assist with SEO and marketing-based decisions on big data.

The way to advertise and manage your SEO is changing. The tools of the trade for marketers, product managers, and SMBS are ever-evolving. This next wave of MarTech has been ramping up and might put some of us out of business.

We should keep an eye on the cutting-edge machine learning algorithms in marketing and SEO and neural network (AI) technologies being used to make our market assessments more accurate, campaigns more successful, and our customers ultimately more satisfied. However, don’t get too lost in how the algorithms work. Just remember their purpose:

“Is the end-user getting the result they want based on how they’ve communicated their search query?”

Understanding how machine learning algorithms work is critical to maximizing ROI. Here are the top 9 machine learning algorithms that work to influence keyword ranking, ad design, content construction, and campaign direction:

1. Support Vector Machines (SVM)

Classification is the process that facilitates segmentation. Simply put, SVMs are predictive algorithms used to classify customer data by feature, leading to segmentation. Features include anything from age and gender to purchase history and channels used.

SVM works by taking a set of features, plotting them in ‘n’ space  ̶ ̶  ‘n’ being the number of features  ̶, and trying to find a clear line of separation in the data. This creates classifications.

For example, Mailchimp is a popular customer relationship management (CMR) tool that uses its own proprietary algorithm to predict user behavior. This allows them to forecast which segments are likely to have high Customer Lifetime Values (LTV) and Costs Per Acquisition (CPA).

2. Information retrieval

Keywords, keywords, keywords…Sometimes the simplest solutions are the most powerful ones. A lot of machine learning algorithms designed to assess the market can be difficult to comprehend.

Information Retrieval algorithms — like the one that powers Google’s “Relevance Score” metric — use keywords to determine the accuracy of user queries. These types of machine learning algorithms are elegant, powerful, and to the point. This is part of the reason why SEO software such as SE Ranking uses a version of it called Elasticsearch to provide marketers with a list of keywords built using input from the user. The RL algorithm’s basic process follows a 4-step process:

• Get the user query

• Break up the keywords

• Pull a preliminary list of relevant documents

• Apply a Relevance Score and rank each document

In step 4, The Relevance Score algorithm takes the sum of specific criteria:

• Keyword Frequency (number of times the keyword appears in the document)

• Inverse Document Frequency (if the keyword appears too often, it actually demotes the ranking)

• Coordination (how many keywords from the original query appear in the document)

The machine learning algorithm then attaches a score that gets used to rank all of the documents retrieved in the preliminary pull.

3. K-Nearest neighbors algorithm

The K-Nearest Neighbors (K-NN) machine learning algorithm is one of the most basic of its kind. Also known as a “lazy learner algorithm,” K-NN classifies new data based on how similar it is to existing data points. Here’s how it works:

Say you have an image of some kind of fruit that resembles either a pear or an apple, and you want to know which of the two categories it belongs to. A KNN model will compare the features of the new fruit image to the datasets for pear images and the datasets of apple images and based on which category the new fruit’s features are most similar to, the model will sort the image into the respective category.

In a nutshell, that’s how the KNN algorithm works. It’s best used in instances where data need to be classified based on preset categories and defining characteristics.

For example, KNN machine learning algorithms come in handy for recommendation systems such as the one you might find on an online video streaming platform, where suggestions are made based on what similar users are watching.

If you want to learn further how to implement a K-NN algorithm in Python, sign up for a training program to get you started with Python.

4. Learning to Rank (LTR)

The Learning to Rank class of machine learning algorithms is used to solve keyword search relevancy problems. Users expect their search results to populate a page and be ranked in order of relevancy. Companies like Wayfair and Slack use LTRs as part of their search query solutions.

The LTR can be separated into three methods — Pointwise, Pairwise, and Listwise.

Pointwise assesses the relevance score of one document against the keywords. Pairwise compares each document against the keywords and includes another document into the calculation for a more accurate score.

It’s like getting an ‘A’ on a test, but then you notice that the kid sitting next to you got one more correct question than you, and suddenly your ‘A’  isn’t so impressive. Listwise uses a more complicated machine learning algorithm based on probabilities to rank based on search result relevance.

5. Decision trees

Decision trees are machine learning algorithms that are used for predictive modeling. For a marketing analogy, as a user moves through a sales funnel, they’re likely to apply a few criteria:

• Behavior-based triggers – the user clicks or opens a link or field;

• Trait-based values – demographic, location, and affiliation information about the user;

• Numerical Thresholds – having now spent X dollars, the user is more likely to spend ‘X+’ in the future.

The simplicity of decision trees makes them valuable for:

• Classifications and regressions — plotting binary and floating values in the same model (ex. Gender vs. annual income);

• Handling many parameters at once — each ‘node’ in a tree can represent a single parameter without the entire model being overwhelmed;

• Visual and interpretive diagnostics — it’s easy to see patterns and relationships between values.

Word of caution: the more nodes you add to a decision tree, the less interpretive it becomes. You eventually start losing sight of the forest for the trees.

6. K-means clustering algorithms

K-means clustering algorithms are a part of unsupervised learning partitioning methods. In Layman’s terms, this means it’s a type of machine learning algorithm that can be used to break down unlabeled data into meaningful categories.

So, for example, if you owned a supermarket and wanted to divide your entire set of customers into smaller segments, you could use K-means clustering to identify different customer groups. This would then allow you to create specific marketing campaigns and promotions targeted to each of your customer segments, which would translate into more efficient use of your marketing budget.

What makes K-means clustering unique is that it allows you to predefine how many categories or “clusters” you’d like the machine learning algorithm to produce from the data.

7. Convolutional neural networks

Convolutional Neural Networks, or CNN for short, are machine learning algorithms used to help computers look at images the way humans do.

Whereas a human can readily identify an apple when shown an image of an apple, computers merely see another set of numbers and identify an object based on the pattern of numbers that make up the object.

CNN works by training a computer to recognize those number patterns of an object by feeding it millions of images of the same object. With each new image, the computer improves its ability to spot the object.

Now that almost anyone can pull out their phone and take a picture wherever they are, it’s easy to imagine how powerful CNN can be for any kind of application that involves picking out objects from images.

For example, companies like Google leverage CNN machine learning algorithms for facial recognition, where a face can be matched with a name by observing the unique features of each face in an image. Similarly, the CNN machine learning algorithm is being tested for use in document and handwriting analysis, as CNN can rapidly scan and compare an individual’s writing with results from big data.

8. Naïve Bayes

The Naive Bayes (NB) machine learning algorithm is built on Bayes’ famous theorem that determines the probability of two outcomes — the probability of A, given B. What makes this algorithm so ‘Naive’ is that it is based on the assumption that the predictor variables are independent.

For marketers, this can be retooled to determine the possibility of a successful lead magnet, campaign, advertisement, segmentation, or keyword, given that you know the relevant features like height, age, purchase history, or big data concerning your customer base.

If you want to get into math, Great Learning gives a wonderful introduction here. Suffice it to say, that the NB machine learning algorithm answers two questions,

• “Is this the type of person to perform X behavior?”

• “Is this the type of content to achieve X outcome?”

NB is mighty when dealing with large amounts of text-based behavior data like customer chatter online.

Feeding customer dialogue through an NB machine learning algorithm helps predict product and service reviews, measure social media & influencer marketing sentiment for trends, and predict direct marketing response rates.

9. Principal component analysis

Classification leads to evolved segmentations. Principal Component Analysis is used to find strong or weak correlations between two components by plotting them on a graph and finding a trend line.

But what happens when the target market comes with 30+ features? This is where the process of PCA in combination with a machine learning algorithm becomes incredibly powerful for analyzing multivariate big data sets.

Instead of having two groups that correlate, you start to get clusters correlating with one another, where the distance between clusters now suggests strong or weak relationships.

For marketers, the component axes are no longer single features you choose but are determined by the PCA machine learning algorithm.

All of this helps to answer the question: “Which features are strongly correlated and can therefore be used for better segmentation targeting?”

Begin your journey to understand machine learning algorithms