Category: Blog

The Value Proposition of AI-Enabled B2B Content
ai-enabled b2b content

The Value Proposition of AI-Enabled B2B Content

Posted on | by Danni White

B2B marketing executives have changed their focus to AI over the past few years to increase the effectiveness of their campaigns and boost business growth.

The usual way of targeted marketing has been to determine signs of behavioral similarities, for example, subscribing to a newsletter related to a particular subject, and creating content according to the perceived preferences of an audience.

In the past 4 years, there has been a 270 percent growth in the number of brands making use of AI. It is also estimated that the global market of AI will get to $267 billion by 2027. These numbers are not surprising, especially when you consider all the advantages of using AI technology in B2B content marketing. 

By using AI-enabled tools, content creators will be able to automate a lot of cognitive duties. Also, they’ll be able to detect trends and predict what the future will hold, thereby ensuring the success of their marketing strategies.

How can AI and B2B Work Together?

Since increasing growth and sales have been the primary focus of every B2B marketer, they have to detect ways of leveraging data and technology and experimenting with new marketing strategies for winning the trust of their audience.

If the focus of the last decade of B2B marketing has been about things happening to marketers, then the next phase has to place more focus on the consumers – to place them at the center and heart of the revolution. Even though AI technology is the answer, we need to understand and implement the value proposition of AI-enabled B2B content due to the primacy of consumers in this technological era.

AI-Enabled Data Collection Strategies

One of the biggest challenges that B2B marketers encounter while building successful strategies is to gather, analyze, and interpret the data of customers. Doing this analysis with accuracy and precision is complex, expensive, and takes a lot of time – but it is also important. AI makes the process of data collection and analysis to be simpler by channeling information through algorithms to organize it in ways that are easily accessible and understood. 

By using machine learning applications, you can update data in real-time in a relevant manner. In other words, AI converts static data into dynamic data by adding behavioral, contextual, and real-time variables. Since AI has impacted how marketers gather and analyze data, it has changed how marketers can use the data to improve their marketing techniques. 

How will AI change the Future of Content Marketing?

1. Selecting an audience

AI can enable improved audience selection and segmentation by using tools to determine a target audience according to their previous behaviors. Marketers can provide tailored experiences that potential customers will find unique to their needs.

The amount of time and money you invest in digital ads in B2B content marketing is directly tied to importance. As you connect with audiences to take action, you’re getting the most value for your investment.

AI is revolutionizing this process by getting rid of the guesswork and targeting based on audience preferences. You can make use of this technology to show people the ads that they find most relevant – thereby reducing your expenses.

2. Content creation

The area of content creation is another way that AI will impact marketing in the future. AI-enabled B2B content is an important field of content intelligence, where AI tools provide data-driven ideas and feedback to content creators. By creating a continuous feedback loop, marketers will enhance their content creation efforts and get better results. 

Some ways in which AI will positively impact content creation include:

  • Efficiently optimizing posts on social media platforms
  • Create content that is data-driven and ranks better 
  • Personalizing emails and website copy that has a good ROI
  • Testing landing pages and determining the best-performing ones
  • Effective conducting keyword research

AI technology will make the process of content creation to be simpler and more effective by providing data-driven feedback and helping in content profiling, topic inspiration, and content accuracy. AI will also enable the creation of personalized content that will enhance your marketing efforts.

3. More efficient advertisement

AI technology enables marketers to determine what customers want, this means that it can easily discover trends and insights about an audience to enhance marketing efforts. 

Additionally, AI tools can change some segments of the advertising to personalize it according to the types of customers being attended to. It can do this by using different text, music, pictures, videos, and so on, to meet the preferences of an audience. AI programs will likely be able to create ads that are a lot more effective than those produced by humans.

Some of the use cases for AI in advertising are listed below:

  • Discover new ad channels that are not being used by other similar brands
  • Use AI tools on various ad platforms to optimize budget, targeting, and delivery
  • Make use of AI marketing platforms to buy media autonomously
  • Use machine learning algorithms to get the best layout, copywriting, targeting, and bids for bigger campaigns

4. Makes SEO simpler

Although it is impossible for AI to produce highly engaging content, it can competently enhance content for search engines. Most marketing strategies require a lot of time and effort. This includes creating backlinks, choosing keywords for SEO, analyzing trends in the market, and making planned acquisitions.

AI can assist marketers to improve these processes and make data simpler and more efficient, thereby, producing quality information without any disruptions. The complex and time-consuming SEO process will become more efficient and automated with time.

5. Personalization

It is estimated that about 57 percent of B2B customers depend on suppliers to meet their needs. Generic pitches will not be enough for B2B marketing and presenting information to your audience in a timely and suitable way isn’t too much to ask, particularly with the amount of data at your disposal. 

To get the most out of customer data, you must move away from the old marketing and sales processes and focus on a more personalized buying experience for B2B customers.

Conclusion

With the introduction of AI-enabled B2B content creation, most marketing executives have already started reaping its benefits. Even though most AI systems are presently hidden, their importance is obvious for all to see. AI will certainly reshape the B2B marketing sector for the better.

AI helps business owners to better understand their customers and be able to build smarter, more intuitive, and more efficient marketing processes. It is a great way to maximize existing opportunities and uncover newer, more impactful methods to increase revenue and drive long-term growth.

To learn more about how we integrate artificial intelligence and intent data, connect with the team at Byonic.AI.

Will AI Intelligently Automate the Jobs of Marketers in the Next Decade?
AI Automate the Jobs

Will AI Intelligently Automate the Jobs of Marketers in the Next Decade?

Posted on | by Danni White

Our world today is fast-paced and digital-oriented; this makes artificial intelligence (AI) to become one of the most effective social media and marketing tools. AI has helped businesses to attract, create, and nurture prospects.

Digital marketing is getting more popular with the introduction of innovative software applications and tools. Now, it’s difficult to lose the job of a digital marketer because new applications are being created daily. Technology has changed how businesses operate and AI automation is no exception to that rule.

If you’re worried if this new technology will replace the jobs of digital marketers in the future, that is difficult to predict. Even though artificial intelligence is likely here to stay, that doesn’t mean that workers are going to lose their jobs simply because of automation. While AI can outperform humans at routine tasks human employment is presently not threatened; rather, AI taking over some tasks will create new job opportunities in the future.

MEANING OF AI PROGRAMMING

AI uses computer systems and machines to simulate human intelligence processes. These processes mostly have to do with learning, reasoning, and self-correction.

When used by marketers, AI takes advantage of concepts such as machine learning and customer data to try and determine the next move of a customer and help in improving their journey.

Marketers can now profile their prospective customers better with the advancements in analytics solutions and the evolution of big data. Having these insights have helped marketing teams to augment the performance of their marketing campaigns and get a better ROI.

WHAT IS MARKETING AUTOMATION?

Marketing automation covers all forms of marketing campaigns on all marketing channels -including phone, direct mail, social, online, and mobile campaigns. Marketing automation builds something better by taking the capabilities of lead management systems, web analytics platforms, and customer relationship management software.

A MARKETING MANAGER’S ROLE HAS SHIFTED

In the world of AI automating marketing campaigns, marketing managers are still needed. A robot has just a 1.4 percent chance of taking the job of a marketing manager. Marketing automation is created to automate repetitive duties such as social media posts, email marketing, and ad campaigns; however, a human is still needed to define the automation. It is easy to automate repetitive tasks, but the decisions affecting those processes still need human input. Marketing managers are important for understanding the shift in the marketing space.

This shift needs to understand that that the duties of marketing managers have changed. As marketing managers spend less time managing various email lists, for instance, they are liberated from the unnecessary. Marketing automation helps marketing managers spend more time on creative processes, and less time on manual jobs. Emotional intelligence, perspective, and an understanding of the human psyche are important in strategizing and determining how to market to an audience.

DIGITAL MARKETERS CAN USE AI TO:

Quickly make business decisions by using available data

Understand their audiences better

Gather and then analyze bigger data

Personalize the preferences of customers

Do better research

Streamline content creation 

Just as it is with other technological changes in the past, AI will probably create a need for new skills rather than removing jobs. Marketing campaigns will still need people regardless of the changes we experience in the future. For instance, a human will have to be present to monitor conversations made with chatbots or to create conversation programs. It is also important to have people that can look at large amounts of data, understand the data, and communicate what it means to other team members so they can come up with a business strategy. 

BETTER PRODUCT AND CONTENT CREATION

The process of getting customer information to determine their future behaviors started about two decades ago. Over this time, big brands deployed collaborative filtering to drive recommendations to millions of customers. Today, a lot of tech companies have created their product offering and business models around the idea of targeting clients with highly important and customized products/services.

This is possible because of AI-based technology, demographics, and the profile information of customers. By regularly pairing and adapting to what a customer likes and dislikes, brands can now put forward new and tailored recommendations in real-time.

SMARTER AD CAMPAIGNS.

Advancements in big data and the growth of social media platforms have enabled marketers to create smarter and more effective ad campaigns. By Using AI-based techniques, marketing teams look at social media profiles and keyword search histories to ultimately create human-level outcomes.

MORE ADVANCED CUSTOMER SERVICE AND SOCIAL ENGAGEMENTS.

It is important to consider your customer service and social engagement before, during, and after making a sale. This will be important in getting loyal customers and getting more sales. Most customers reveal that they would leave a brand if it doesn’t provide them with top-notch customer service.

IMPROVED USE OF SEARCH ENGINES.

AI greatly influenced how we search for information on the internet, and the general quality of our search experience. The experiment by Google with AI in 2015 resulted in the creation of their machine learning-based algorithm, which assists process search results. Since that time, large e-commerce stores have followed suit by incorporating AI into their search engines to enable a smarter product search experience.

BEST WAY TO APPROACH INNOVATIVE TECHNOLOGY

People who are threatened by AI automation and have no interest in learning new skills may get left behind. A lot of digital marketers have taken advantage of this technology, enabling them to use AI automation in their businesses and remain competitive in the always-changing marketing landscape. As a digital marketer, you don’t have a lot to worry about because these jobs already need technological knowledge.

FINAL THOUGHTS

The future of AI will direct brands to a more adaptive model that helps them uncover higher value and do more satisfactory and creative roles. We need to adapt to new technology, skills, and education to fit the future of the workforce and effectively prepare for an intelligent future. 

With the predicted growth of AI across all departments and industries, the least a marketer can do is create time to implement AI solutions. By doing this, they’ll be ensuring that their marketing processes are geared towards continued success, both now and in the future. To learn more about how we integrate artificial intelligence in marketing and demand generation, connect with the team at Byonic.AI

How Much Does Machine Learning Cost Marketing Teams?
machine learning cost

How Much Does Machine Learning Cost Marketing Teams?

Posted on | by Danni White

Estimates show that by 2022, the cost of cognitive and artificial intelligence systems such as
machine-learning will amount to $78 billion.

This is a huge amount, which is why it’s crucial to know the costs and logic behind machine
learning consulting costs. This will help you to make informed and educated decisions regarding
machine learning consulting services.

Most business owners (from startups to large enterprises) wonder about the cost of artificial
intelligence. It is difficult estimating the cost of a machine learning project, without first having
an idea about the details.
To get started, it is best to understand the two types of machine learning projects, which are,
trivial and academic.

Trivial projects already have a solution out there – both the dataset and the model architecture
already exist. These projects are free to undertake, so, we’ll focus on the second type.

Academic projects require basic academic research – applying machine learning to a whole new
level or on entirely different data structures than other models.

How Much Machine Learning Costs Marketing Teams?

According to recent estimates, a machine learning project can cost an organization $51,750 to
$136,750. The type of data involved determines the high variance. This estimation is very
optimistic. For businesses that are based in the US and work with sensible data, the costs can
be on the higher levels, putting machine learning projects upwards of $108,500.

The cost of machine learning projects makes it less accessible for small businesses, startups,
individuals, and small teams that desire to tackle new issues or make their processes and
decision-making automated. The difficult part of this project is getting the required data.
Without data, it’s impossible to validate a machine learning solution, which could lead to a
deadlock.

(Also Read: 11 Ways Machine Learning Can Improve Marketing and Sales)

What Determines the Rates of Machine Learning?

Even though the consulting rates of deep machine learning may appear straightforward,
particularly when looking at average prices, a lot of factors could determine the cost of machine
learning consulting. It is important to understand these factors, whether you intend to hire a
consultant or work as one. Some factors that could determine the cost include:

Experience of the consultant

Experience impacts a product’s cost, no matter the field. Usually, data scientists that provide
machine-learning consulting services base their rates on their expertise and background.





Scope of the project
A project that is large and complicated will have a higher consulting rate. This is because
machine-learning or data scientist consultants are required to invest more time into setting up
and implementing the architecture of your solution.





Expected outcome
What you expect from the results is another factor that influences data consulting rates. In
large data, result expectations are what you expect when it comes to machine learning
projects.





What Machine Learning Consult Should Include





A machine learning consultant services should include:





• An initial consultation should be done before hiring
• Consulting or service proposal
• Hourly quote or flat rate
• Dataset provided by the client
• Expected results
• Specific requirements quality





Conclusion





The major benefit of machine learning is that it provides the ability to learn: the more data
system processes the more intelligent it will be. When you process larger data, machine
learning will recognize different patterns, and then build new analytical models. The secret of
generating higher amounts of quality data is to get a machine learning solution to market
quickly.





If you are a B2B marketer looking to increase sales, give ML/AI a chance to help you reduce
costs, increase revenue, and streamline all operations. Schedule a demo today with Byonic.AI
and see how marketers can run smarter, data-driven campaigns all in one place.

	


	





	
	

		Ethical Considerations for Artificial Intelligence	


	

		Article is about ethical artificial intelligence	

	

		
Ethical Considerations for Artificial Intelligence
		

			Posted on  |  by Danni White		

			

	

		

A revolutionary change has come into existence in our society due to the rapid growth of technology. Tech experts are trying to invent new robotics and machinery to perform specific tasks that humans cannot achieve accurately. Artificial Intelligence is one of the consequences of these attempts.





What is Artificial Intelligence?





John McCarthy was the first person who uses the term “Artificial Intelligence” in 1956. It is actually a replacement for human intelligence. It performs various functions with its complex computer algorithm system.





Machine learning and deep learning are the two main applications of artificial intelligence. Many other applications of artificial intelligence are also continuously performing several prognostications of different diseases in the medical field. Google translator, face identification in-camera, receive online orders, and approve credit card transactions are some other usages of AI.










Ethical Concerns





For using AI, you should know about its ethical concerns. Many instructions and admonishments are dickering with the problem of ethical artificial intelligence. The European Commission has brought out significant guidelines for ethical AI. The prime focus of its suggestions is principles of ethical artificial intelligence in the medical industry. Let us discover all principles one by one.





1.      Philanthropy and non-sensuality principle 





If you are using artificial technology, you need to be aware of its ethics. To preserve human dignity, you make sure that AI is beneficence for individual patients in the field of medicines.





The process of prognostication can defile the guidelines of ethical artificial intelligence because it gives false hope to patients. You can get rid of this puzzling situation by keeping information personalize. After the deep analysis of the report of a patient provided by the AI technology, describe a patient’s condition. It can be very valuable to escape from probabilistic and unethical AI methods.





Recurrent ANNs are using the new techniques of Artificial Intelligence. They are utilizing more probable information through their datasets. If a person is in the Intensive care unit and his condition is not stable, doctors use ethical AI to predict his state. It proves very beneficial for individual patients.





2.      Equality Principle





Unintentional injustice is a significant drawback of AI technology for individuals. The algorithm system of AI technology can lead the issues of inequalities and discrimination among different patients. The two main problems present below.





  1. There are many chances that AI algorithm yields fake results. It can mention the rare life probabilities for those patients related to discriminator biomarkers like the weight of the patient’s body. If you continue to detect, test and audit the AI results, you can quit this AI risk.





  1. Different cultures drive cultural biases. Humans train artificial intelligence model. So, there are many chances that they accidentally build an algorithm that affects specific community and environment.  





The problems mentioned earlier can solve if training of AI models excludes age, gender and environmental changes. For the establishment of patients’ health and dignity, you should train unbiased AI models. It will help to accurate prognostication of the course of any disease and supplies justice among patients. 





3.      Autonomy of patients as an AI principle





The respect of patients’ decision regarding their critical condition is necessary for the ethical use of AI technology. Sometimes, the decision of a patient and his caretakers creates a considerable risk of a patient’s life. This situation leads to the ethical examination of personal behavior.





If you predict patients’ health through AI technology, you need to know that it may be a false report. If you try to solve this falsifying state of a patient, it will not be possible through artificial technology.





According to the empirical research, the patients do not fully trust the physician’s probabilities because they estimated results through AI. So you should present possibilities of recovery in such a way that prove non-maleficence and beneficence for patients.





If you disagree with your patient decision, AI cannot be able to resolve your issue. You can find the solution to this issue by mutual agreement. You can discuss prognostications with your patient for getting approval of your point of view.





4.      Explicable principle





The principle of explicability is recently added to the list of ethical artificial intelligence in medical science. It is interlinked with all other AI principles.





Often, we face issue in the results of different patients’ cases. Actually, it happens due to the uncompleted input to AI models which are providing output.





Doctors handle the algorithm tools as “black boxes,” if they are not aware of the step of critical reflection. After analyzing the prognostication provided by the intensivists, a patient can appeal to justify the result. A patient can take this step if they feel trust issues regarding the probabilities of their report.





Some data science experts say avoiding AI technology if you want to build patients’ trust and get the authentic information. If you are still willing to use ethical artificial intelligence, test your AI models timely. It will help you to avoid inadvertently false prognostication.  





5.      Confidentiality 





Privacy is still an essential aspect for humans. When you are dealing with patients, you will definitely use their data in your AI algorithm system. So you need to care about the sensitive information of your patients.





Human rights are a crucial part of the constitution of any country. In Europe, General Data Protection Regulation (GDPR) adds new guidelines regarding data processing for ethical artificial intelligence technology. These instructions guide you to avoid the unethical use of artificial intelligence.





Final Thoughts





The sole purpose behind this article to inform you about ethical artificial intelligence. The abovementioned ethical considerations show that AI is an important technology that provides comfort in our lives through probabilities and has some drawbacks in the medical field too. 





If we further improve and refine this artificial intelligence system, there is no doubt that it will provide error-free prognostications about the disease of the patients. AI model can become the correct element in the intensive care unit of any hospital. But we should implement all these artificial intelligence principles with the legal ethics of medical science.

	


	





	
	

		AI-Focused Case Studies for Sales and Marketing Success	


	

		Article is about ai in sales and marketing	

	

		
AI-Focused Case Studies for Sales and Marketing Success
		

			Posted on  |  by Danni White		

			

	

				

						

						

					

			

						

				

							

Using artificial intelligence in sales and marketing is not about replacing people or reducing labor. AI incorporating is all about making sales and marketing strategies smarter. Forward-thinking sales representatives are using AI-powered systems to make sales tasks easier.



Consider the time you spend on preparing activities to close sales deals faster. Some of the sales tasks are quite important, such as pipeline management. However, they are tedious and take up much of your time. Hunting down the target audience and acquiring contact information is time-taking.



But AI tools for sales automate the operations and streamline tedious yet time-taking tasks.



Use cases for AI in sales



Here are few applications of deep learning and artificial intelligence in the sales department:



    

  1. 

    Pipeline management
    


    Building a sales pipeline is a challenging and tedious task. However, AI can speed up the process for you.
    

    2. 

  2. 

    Find converting leads
    


    AI tools identify the accounts and people in your database that are more likely to close. This allows you to prioritize the best sales opportunities.
    

    3. 

  3. 

    Hunts down the profitable market
    


    Deep learning, a sub-field of machine learning allows all the people who need your products or services. Moreover, the algorithms use unstructured data to provide accurate email addresses and phone numbers to sales reps.
    


     The predictive analysis process identifies all the people you should contact reach out to, but you are not.
    

    4. 

  4. 

    Amps up sales opportunities
    


    AI-powered predictive models in most tools help you to find Business-to-business audiences to hunt down ideal customers.
    


    This model does not only create a quality audience for you to target but also proves great for your products and services.
    

    5. 

  5. 

    Close sales
    


    Once you have built a high-quality, structured sales pipeline, AI helps you to close sales.
    

    6. 

  6. 

    Improved engagement with potential customers
    


    B2B marketers use AI to engage with prospects through chat and email. The software handles all the interactions and also initiates conversations with end-users.
    

    7. 

  7. 

    Predict high-quality lead conversions
    


    Machine learning platforms, such as Node.io predict the win rate and forecast possible lead conversions. Apart from offering product recommendations to top leads, it also forecasts your sales pipeline’s success.
    

    8. 

  8. 

    Score leads using predictive analysis
    


    AI utilizes the sales and marketing platform to score leads in your existing database. This way, you would know who is going to buy from you.
    

    9. 

  9. 

    Lead engagement
    


    AI-powered systems like Conversica ensure that leads that visit your websites do not fall through the cracks. The AI system is programmed to reply quickly, initiate immediate contact with leads and give tailored yet customized responses.
    

    10. 

  10. 

    Augments and automates all the sales operations
    


    AI systems are not just programmed to predict and prioritize. It automated and augmented the sales operations which free up a lot of time for sales reps.
    

    11. 

  11. 

     Automate data collection
    


    Salesforce uses an intelligent system to cover repetitive sales tasks. These operations include data collection for capturing leads, sending automatic notifications, and receiving insights about buying cycles.
    

    12. 

  12. 

    Replicate sales representatives
    


    Deep learning algorithms capture all the successful sales conversions of your representatives and replicate the phone calls, emails, etc.
    

    13. 






Use cases of AI in marketing



Some of the more common use cases of AI in marketing are as follows:



    

  • Content analysis to spot gaps and marketing opportunities
    • 

  • Content optimization by selecting high-performing keywords
    • 

  • Create buyer personas
    • 

  • Curate evergreen and data-driven content
    • 

  • Discover insights into the successful marketing campaigns and buying cycles
    • 




Conclusion



If you are a B2B marketer looking to increase sales, give AI a chance to help you reduce costs, increase revenue, and streamline all operations. Schedule a demo today with Byonic.AI and see how marketers can run smarter, data-driven campaigns all in one place.

						

				

					

		

					

		

				

			


	





	
	

		How AI and Intent Data Work Together	


	

		how ai and intent data works	

	

		
How AI and Intent Data Work Together
		

			Posted on  |  by Danni White		

			

	

		

AI is revolutionizing the world. It reduces costs through automation, and for some companies, it enhances customer experiences. AI can be used everywhere; especially, it helps in the B2B industry. AI and Intent data work together for the advancement in data collection to analyze true customers’ intent. In intent data, AI can be used for content processing and analysis.





Many corporations have used AI to automate processes, but those that use it mostly to replace workers would only see short-term improvements in productivity. Our research, involving 1,500 firms that found Humans and Machines working together, produces the most important performance improvements and a better understanding of customers’ preferences.





Despite advancements in data collection around shopping habits, companies still struggle to create better customer experiences. But Artificial Intelligence is rapidly taking over this smoothly and making it easy for firms to understand consumers’ intent.





Traditional analytics software, which is struggling to keep up with the fast-changing technology world, is still behind AI. Consumers today are constantly connected through numerous devices, making paths to purchase more like Gordian knots rather than straight, well-behaved lines.





Expanding the Usefulness of Intent Data





However, since intent data is still a relatively new tool, it comes with some challenges as its usage spreads rapidly across revenue operations. Companies want to normalize data from various sources and handle data handling across departments more effectively.





Here, we will discuss the AI (artificial intelligence) intent data on b2b marketing:





  • The current impact of intent data on B2B revenue operations





  • The growing number of business use cases for intent interoperability across strategic areas,





  • The challenges that many companies face when it comes to handling and incorporating intent data





  • Moreover, there are opportunities to improve productivity and increase the impact of purpose data.





  • More detailed buyer personas





  • More enticing deals that result in conversions





  • Email campaigns that are well-personalized





  • Landing pages that convert effectively










Marketing methods for artificial intelligence (AI)





It filters through the false positives and removes them, saving marketing teams’ time and effort. It reveals the false negatives – businesses that suit a marketer’s ideal consumer profile but did not appear on the intent data list.





Insight of Powered AI that Building a Smoother Path to Prospects





Getting intent data is like having a single piece of a 1,000-piece puzzle. The rest of the picture is provided by Marcom software that uses AI and machine learning. Although purpose data reveals one dimension of corporate activity, AI Marcom solutions can detect and categorize hundreds more. AI analyzes more than 40 billion data points to create more than 400 detailed corporate characteristics, such as the company’s products and services and the problems the company is trying to understand and solve for consumers.





The AI tool then creates “lookalike” profiles that fit sellers’ ideal customers in terms of scale, budget, and readiness to buy, as well as any other ABM-related metrics.





Multiplying the Number of Ideal Prospects with AI-Powered Insight





AI targeting tools show various businesses that might never appear on intent data lists, in addition to finding the best customer matches from the intent data. Many more companies that fit a seller’s ideal customer can be discovered using AI research. These businesses do not engage in internet searches that appear on purpose data lists, but their corporate profiles and recent actions make them potential “best customers” for sellers.





Intent Data Help Companies to Understand Consumers Preferences Better





Intent data is a great place to start looking for patterns that could suggest a company’s willingness to purchase a product or service. It’s only one of the signals that can help advertisers find and target their ideal customers.





By solving the two most significant problems with purpose data, AI-powered Marcom solutions help sellers extend their marketing efforts well beyond it.





As a result, they have a more complete, comprehensive, and reliable list of prospects that are closer to their ideal clients and information to help them communicate with them through tailored messaging.





To learn more about how we integrate artificial intelligence and intent data, connect with the team at Byonic.AI.

	


	





	
	

		How Deep Learning Informs B2B Sales and Marketing	


	

		The article explains how deep learning can boost b2b sales and marketing.	

	

		
How Deep Learning Informs B2B Sales and Marketing
		

			Posted on  |  by Danni White		

			

	

				

						

						

					

			

						

				

							

Using artificial intelligence and machine (deep) learning for marketing and sales purposes is not for the future anymore. Artificial intelligence in marketing is used to understand the customer base better.



While deep or machine learning provides customer insights that influence the decision-making process. In short, deep learning has eliminated the need for B2B companies to operate off complex and longer buying cycles.



Artificial intelligence and machine learning are used in sales and marketing for all kinds of operations- from warehouse logistics to improving the personalized shopping experience for customers.


It is a futuristic approach that works under many algorithms. But what is it and how can you incorporate it into your sales and marketing operations?



What is Deep Learning?



Deep learning is a field of machine learning concerned with various algorithms to set the functions and structure of the brain called neural networks. In other words, deep learning is an approach in which a machine imitates the human brain in processing and collecting valuable data. Moreover, it follows the patterns that help in effective decision-making.



Ways in which dee learning informs B2B sales and marketing



Here are few ways in which deep learning enhances the customer’s decision-making cycle:



    

  1. 

    Automating real-time customer’s buying journey
    


    It is a sub-field of machine learning that allows B2B marketers to access powerful customer insights from unstructured data available on the forms and websites, such as speech recognition, voice recognition, images, text, video analytics, text analysis, facial recognition, and much more.
    

    2. 

  2. 

    In short, it understands the needs, voices, and interests of the customer and uses insights to improve the buying cycle.
    


    Customers’ expectations and feedback are now gauged on a real-time basis, all thanks to AI and machine learning. Moreover, brands can fetch useful information and upgrade the quality of their services and products.
    


    Business organizations can articulate the right message to the key customers by using premium insights from machine learning.
    

    3. 

  3. 

    Using IoT products for analysis
    


    It can help thousands of brands to understand IoT products. For instance, home automation is creating profitable insights for business organizations all across the globe.
    


    The home automation systems capture data from different appliances and machines in different scenarios. The algorithms are set to monitor the behaviors in a cost-effective yet easy way.
    


    Analyzing the IoT products using deep learning can improve the interactions between the consumers and machine. The brands can upgrade their marketing chatbots’ interaction algorithms to cater to the needs of the customers.
    

    4. 

  4. 

    Improving customer experience through chatbots
    


    The presence of chatbots in marketing has revolutionized the whole industry. The chatbots use natural language processing, artificial intelligence, and data mining to interact with the end-users.
    


    Chatbots ensure timely and assistive conversations with users and personalize answers to give interactive replies to all the queries.
    


    Moreover, they can give humane suggestions and recommendations, based on the unstructured data. They can offer targeted products and increase the brand’s sales.
    

    5. 

  5. 

     Predictive data analysis process
    


    B2B marketers can use deep learning to run successful predictive data analysis. The deep learning algorithms play a significant role in developing crystal-clear ideas about customers’ needs, functional requirements, and preferences.
    

    6. 




Final Thoughts



Most tech-driven brands and business organizations, including Google are involved in deep learning projects. The significance of deep learning algorithms in B2B sales and marketing indicates that the industry has revolutionized and depends on AI for acquiring accurate customer preferences.

						

				

					

		

					

		

				

			


	





	
	

		Predictive Characteristics of AI in Marketing	


	

		Article is about Predictive of AI in Marketing	

	

		
Predictive Characteristics of AI in Marketing
		

			Posted on  |  by Danni White		

			

	

				

						

						

					

			

						

				

							

Some years ago, marketing was an arduous task for any company. The companies were using assumptions from sales forecasting to lead scoring. By utilizing these presuppositions, they were unable to get quality results.


Thanks to technology, it provides the solution to the substantial hurdles regarding marketing. Artificial Intelligence and its sub-applications like machine learning and deep learning have innovated the ways of marketing businesses to make huge revenue.


Artificial Intelligence in Marketing


AI technology works through the algorithm system on a computer system. Through data analysis, it gives macro-to-micro insights about customers. It tells that what has happened and what is going to happen in the future. These predictions prove very fruitful for any business.


It is estimated that sixty percent of companies were utilizing artificial technology in their CRM to get better results related to sales.


Predictive Features of AI in Marketing


No doubt, predictions about marketing strategies are one of the high-yielding features for any company. There are a lot of predictive characteristics of AI in marketing. Here we will explain the top ten predictive factors of AI for the marketing of a company.


    

  • 

    Forecasting Models for Customer Behavior
    

    Analyze preferences and predict customers’ behavior are the crucial needs of any company. Artificial Intelligence has made them easier with its three predictive models.
    

      

      


    • Propensity model: If you want to know authentic predictions about customer behavior, you should use this model. From buying to unsubscribe, it provides details about every action of your leads.
      • 

    

      

    • Cluster model: If you want customer segmentation, you need to utilize this model. It gives information about customers by considering demographic and interest-based factors.
      • 

    

      

    • Collaborative model: For recommendations of your services, products, and advertisements, collaborative filtering is the best. It judges the past behavior of leads and helps to qualify more leads scoring.
      • 

    

    • 

  • 

    Getting Quality Leads
    

    Without leads, you cannot run your business successfully in this digital world. Artificial Intelligence aids companies to get lead scoring and attract more customers.
    

    B2B companies are much indulged in increasing their leads to make more revenue. Whether you are an owner of a small company startup or growing a large enterprise company, lead qualification and scoring are necessary to achieve through AI predictive system.
    

    By providing the right content to the right customer at a suitable time, a business can alleviate its successful customers.
    

    • 

  • 

    Fetching Product/Services in the Market
    

    A marketing company can fetch the right product or services by utilizing Artificial Intelligence predictive systems. AI systems analyze the customers’ behavior whether they are researching products or want to make a purchase.
    

    By overarching supply chain management strategies and data visualization, you can get perfect outcomes. It also helps to know the right time to introduce new services or products to your potential customers.
    

    • 

  • 

    Content Suggestions
    

    If you don’t know which type of content you need to show your potential audience, you are at risk of losing customers. Content recommendation is the most critical factor for marketing. The reason behind the popularity of Google, YouTube, and Netflix is their content recommending characteristics.
    

    By integrating AI into your CRM, you can also predict what type of content your customers want and how you can engage them with your advertisement. Artificial Intelligence system provides you with standard and insightful data of your customers that help make your ad serving experience more benignant.
    

    • 

  • 

    Fabricating Marketing Strategies through AI
    

    Without marketing strategies, a company cannot market its brand. Now the time has gone when companies assemble their plans on assumption base data.
    

    These days, Artificial Intelligence accurately measures the leads’ data and assists companies in constructing powerful marketing strategies for their potential leads.
    

    AI optimizes data and tells that when people engage with your content. By identifying time, it gives assistance to run campaigns on time and captivate consumers for a company.
    

    • 

  • 

    Chatbot Technology
    

    A chatbot is a part of AI technology that can talk with your customers as your company representative. Many people fail to identify that whether they are talking with a chatbot or an actual human.
    

    Through chatbots, companies can provide 24/7 customer support which looks impossible if you hire a person for this task. It assists in resolving the queries of customers and helps to find the right product for them.
    

    • 

  • 

    Ad Personalization
    

    Personalization is indispensable if you wish to fascinate individuals towards the products or services of your company.
    

    The principal cause behind the success story of Facebook and Google is the personalization method. They do not show generalized content to their prospective audience but specific content that matches their interest.
    

    Artificial Intelligence technology enables you to personalize ads, emails, and any other kind of content. It scrutinizes a customer’s behavior and demographics and presents ads of those products or services they are looking for. Now, many companies are using AI to personalize ad supply for their potential audience.
    

    • 

  • 

    Set up the Best Price to Sell
    

    If you do not set up the best price for your products or services, you cannot get potential revenue. A company needs to consider three dynamic factors: prices of customers, cost of production, and demand.
    

    AI is the best way to review the above three factors for setting up a price. It automatically enhances or dwindles prices by analyzing marketing trends for specific products/services. It also optimizes the performance of a company based on price variables, consumer behaviors, and preferences.
    

    • 



Final Thoughts


Artificial Intelligence plays an essential role in the growth of any business. It counts the massive amount of data through its algorithm system and helps to run personalization ads.


In this article, we have discussed the top eight predictive characteristics of artificial intelligence in marketing. We hope you have gotten beneficial information about AI technology and can utilize it for your company.
						

				

					

		

					

		

				

			


	





	
	

		What is the Difference Between Artificial Intelligence vs. Machine Learning	


	

		Difference between machine learning and artificial intelligence	

	

		
What is the Difference Between Artificial Intelligence vs. Machine Learning
		

			Posted on  |  by Danni White		

			

	

				

						

						

					

			

						

				

							






In the twenty-first century, you’ve almost certainly heard the words “artificial intelligence” and “machine learning.” This is the technology era, and it is estimated that in 2021, 80% of emerging technologies will be AI-based. And, internationally, 37% of businesses are using AI in some way to boost their day-to-day operations.






Amazon, for example, reduced its delivery time by over 225% thanks to machine learning. So, if you’re not sure what these words mean and how to identify the difference between them, we will discuss their significant differences below.






Overview of Artificial Intelligence






Artificial intelligence, or AI, is the creation of a human-made machine mimicking human intelligence. The system has a computerized brain that can learn and solve problems in the same way that the human brain does. AI is achieved by first researching how the human brain thinks and how humans learn, determine, and work when trying to solve a problem and then using the outcomes to develop intelligent software and systems. When all of the ingredients are present, you can think of algorithms as a recipe that the computer must follow.






Artificial intelligence can be divided into three types:






    

  • Narrow AI
    • 

  • General AI
    • 

  • Super AI
    • 







Overview of Machine Learning






Machine learning is a computer science subfield which is also known as predictive analytics or predictive modeling. Its goal is to create new and leverage existing algorithms to learn from data to develop generalizable services that make accurate predictions or find trends, particularly with existing and new unseen data.






Machine learning, as previously mentioned, uses algorithms to automatically model and find patterns in data to predict a specific output or response. Statistics and mathematical optimization are extensively used in these algorithms.






The process of optimization entails determining the smallest or largest value (minima or maxima). The process is also known as a loss or cost function in the minimization case. Gradient descent is one of the most common optimization algorithms in machine learning, and the normal equation is another.






In a nutshell, machine learning uses learning algorithms and optimization techniques to learn a highly accurate predictive or classifier model automatically or discover unknown patterns in data.






Machine learning is also divided into four different categories:






    

  • Supervised
    • 

  • Unsupervised
    • 

  • Semi-supervised
    • 

  • Reinforcement
    • 







What is the Difference between Artificial Intelligence and Machine Learning?












Artificial Intelligence
Machine learning




Artificial intelligence (AI) is a technology that allows a computer to mimic human actions.
Machine learning is a subset of AI that allows a machine to learn from past data without programming explicitly.




Goal: The goal of AI is to make an intelligent computer system like humans to solve complex problems.
Goal: Machine learning aims to allow machines to learn from data and produce accurate results.




Scope: AI has an extensive scope.
Scope: Machine learning is constrained in scope.




Task: AI builds an autonomous system able to perform a variety of complex tasks.
Task: Machine learning aims to create machines that can only perform the tasks for which they have been trained.




Results: AI systems are concerned about maximizing the chances of success.
Results: Machine learning is mainly concerned with accuracy and patterns.




Wisdom: AI leads to intelligence or wisdom.
Knowledge: ML leads to knowledge.




Outcome:  Artificial intelligence can evaluate a variety of options before selecting the right one.
Outcome: Machine learning will choose the only answer it sees as the best, regardless of whether it is the best.














Final Thoughts








Artificial intelligence and machine learning are both science and mythical inventions. The idea that machines could think and perform tasks in the same way that humans do dates back thousands of years. Cognitive truths expressed in AI and ML systems are also not new. Artificial intelligence is the best and most logical next step in the evolution of computers and technology. Machine learning makes significant progress toward that goal when creating true AI. Moving in the right direction, having a clear vision, and having a distinct and unique purpose can help all of humanity.






To learn more about the variations in AI and ML and how both can be used as tools to advance B2B marketing initiatives, connect with the team at Byonic.AI.


						

				

					

		

					

		

				

			


	





	
	

		How Does Artificial Intelligence Work?	


	

		Article is about how ai works	

	

		
How Does Artificial Intelligence Work?
		

			Posted on  |  by Danni White		

			

	

				

						

						

					

			

						

				

							


Artificial intelligence uses computers and software to replicate human ways of problem-solving and decision-making. Anyone with only a basic understanding of AI technology would immediately associate it with robots. 






But if you ask an AI researcher how artificial intelligence works, they will tell you that it is a series of algorithms that can generate results without being explicitly instructed to do so. And they would be right. 






To summarize, the most common definition of artificial intelligence is: Humans have created an intelligent entity that is capable of intelligently performing tasks without being directly taught and capable of rational and human thinking, action, and decision-making






How is Artificial Intelligence Applied?






Artificial intelligence is commonly used to provide users with personalized suggestions based on past searches, purchases, and other online activities. In commerce, AI plays a critical role in product optimization, inventory planning, and logistics, among many other things.






Artificial Intelligence in education provides a valuable service to the world. In Artificial Intelligence, the Expert System is widely used. Spell correctors and spell checkers are the most common.


They serve as proofreaders, check for spelling and grammar errors, and offer all possible feedback to produce the best article possible.






In the automation industry, expert systems are used in 80% of the manufacturing process. Since the robot does not need a lunch break or a break hour, it saves labor costs, reduces errors, and produces maximum production in the shortest amount of time.


The man takes hours to perform a long and tedious job that the robot performs in seconds.






Human resources consider robotics with applied AI to be the most appealing and beneficial. Robots are programmed to perform repetitive tasks, resulting in increased productivity and efficient use of resources. Robots can diffuse, explore space, and be programmed to complete any job too risky for humans to perform. 






Collision sensors, cameras, and ultrasound sensors are used in advanced robotics research to enable robots to see, hear, and touch. The robots are used in space exploration and can adapt to various conditions and physical conditions.


Emotions interfere with human intellectual thinking, which is a problem for artificial thinkers. A robot is designed to think critically and make effective decisions in comparison to emotional handling.






Artificial intelligence has been implemented and rising successfully in areas such as communication, time management, education, cognition, health, safety measures, traffic control, purchasing, marketing, shopping, and planning in our daily lives.






The Following Are The Key Five Elements That Make Artificial Intelligence Successful:


    

  1. 

    

    Discover
    

    An intelligent system’s ability to explore data from available resources without human intervention is essential. The ETL algorithm then analyzes the extensive database and automatically determines the relationship between the material and the required problem solution. This not only solves a complicated problem but also identifies emergency phenomena.
    

    2. 

  2. 

    

    Predict
    

    This method uses classification, ranking, and regression to identify future events. Random forest, linear learners, and gradient boosting are the algorithms used here. When there is bias, estimation always goes wrong in some numerical values.
    

    3. 

  3. 

    

    Justify
    

    Human intervention is required for the application to produce a more identifiable and believable result. As a result, it must first comprehend and explain what is wrong and right and then provide humans with an appropriate response to the situation. Similarly, in the automation industry, it is essential to have a thorough understanding of the system to understand why it is being repaired and what needs to be done next.
    

    4. 

  4. 

    

    Act
    

    To discover, predict, and justify, intelligent applications should be active and live in the company.
    

    5. 

  5. 

    

    Learn
    

    The intelligent machine has a habit of studying and upgrading itself regularly to keep up with the world’s demands.
    

    6. 



Artificial Intelligence Encompasses A Wide Range of Subfields, Including The Following.




    

  • 

    Machine Learning | Learning from experience
    

    Machine learning helps to automate the creation of various analytical and statistical models. It finds hidden insights in data using neural networks, statistics, operations analysis, and physics without being specifically programmed where to look or what to infer.
    

    • 

  • 

    Neural Network | Making associations
    

    A neural network is a form of machine learning inspired by the human brain’s activities. Neural Network is an interconnected unit (like brain neurons) that process data by responding to external inputs and relaying data between them. The process to find connections and relevant meaning from undefined data requires multiple passes at the data.
    

    • 

  • 

    Deep Learning | Self-educating machines
    

    Deep learning uses massive neural networks with several layers of processing units to learn complex patterns in vast volumes of data, taking advantage of advancements in computing power and improved training techniques. Picture and speech recognition are two popular applications.
    

    • 

  • 

    Computer Vision | Understanding images
    

    To know what’s in a picture or video, computer vision uses pattern recognition and deep learning. When machines can process, evaluate, and comprehend images, they can capture and interpret images and videos in real-time.
    

    • 

  • 

    Natural Language Processing (NLP) | Understanding the language
    

    Computers’ ability to analyze, understand, and generate human language is known as natural language processing. Natural language interaction is the next level of NLP, allowing humans to communicate with computers using everyday language to perform tasks.
    

    • 

  • Cognitive Computing | Making inferences from context
    

    Though machine learning is based on the premise that machines should be able to learn and adapt through experience, AI is a broader term that refers to devices that can perform work “smartly.”
    

    • 



Conclusion


Machine learning, deep learning, and other methods are used in artificial intelligence to solve complex problems. Artificial intelligence systems are becoming more capable as their scale and complexity increase.


AI researchers are constantly developing software systems for various applications, including automatic learning, knowledge, speech recognition, and speech recognition. We looked at six branches of the Artificial Intelligence field based on how AI systems function. In the industries and organizations, the six areas have become a buzzword. Several companies are promoting it as a way to use it to serve their customers better.