Tag: artificial intelligence

Unleash AI to Improve Your B2B Email Marketing
Article is about How to Improve B2B Email Marketing with AI

Unleash AI to Improve Your B2B Email Marketing

Posted on | by Danni White
There are countless strategies that marketers implemented in their campaigns in order to see more conversions. With that said, the most popular and useful method continues to be the email with 91% of marketers saying that email marketing is essential in their efforts. This means that the focus of any campaign should be to improve email marketing.

  1. Use AI to optimize your subject lines

When a person writes a subject line, there is always an element of bias, not to mention a lack of resources to generate the best outcome. Even expert copywriters will write subject lines with many limitations that can severely hinder the results of the email campaign. This can often lead to content that is not going to be hitting the inbox and is destined to make its way to the spam folder. When this happens, your chances of engagement are completely ruined and your campaign ends up being a very frustrating experience. Using Artificial Intelligence for this purpose is going to help generate subject lines based on pattern matching. AI will make use of algorithms that are going to help generate optimal subject lines depending on your niche and product. 




    
 	
  1. 

    Upgrade email personalization
    

    2. 


One of the biggest pitfalls of email marketing is the lack of personalization involved. When people open emails that look like templates, they are very likely to lose interest. With that said, it can be hard to personalize bulk emails due to how long this task would take and how many resources would be required.

You can forget about this time-consuming task with the use of Artificial Intelligence. The way that AI handles predictive analytics with the use of customer data and complex algorithms is enough to generate optimal product recommendations, headlines, and many other aspects of your email messages.

    
 	
  1. 

    Optimize the timing of your emails
    

    2. 


The exact time of the day when your emails are most likely to be seen by your potential customers is very difficult to determine. Marketers would usually have to resort to experimenting and split testing in order to determine this factor. This would usually mean spending weeks and even months just trying to find the best timeframes to send their content.

When you implement AI into your efforts, you will be able to determine the click volume, the open volume, the best time to send emails, the best days to send emails, and all of this will be customized depending on your target industry.

It is important to note that over 90% of marketing emails that receive any clicks are sent on weekdays. This means that the weekend are less likely to be a good time to engage most audiences. It is also notable that the highest engagement was found right in the middle of the week. This means between Tuesday and Thursday.



Final thoughts

AI has become a major component of most successful digital marketing strategies. It saves time and it helps your entire team maintain a more productive flow of work. We gave you 3 of the most essential and useful ways to use AI for B2B email marketing, but the advantages it brings are vast and the ways in which AI can be implemented are growing in numbers as this technology continues to see constant upgrades.

It is essential to find quality assistance to help implement new strategies that strengthen your campaigns. Start using AI to boost your email conversions by scheduling a call with the sales team at Byonic.AI today.
						

				

					

		

					

		

				

			


	





	
	

		How AI Can Accelerate Your B2B Buyer Journey	


	

		ai buyers journey	

	

		
How AI Can Accelerate Your B2B Buyer Journey
		

			Posted on  |  by Danni White		

			

	

		


Machine learning and artificial intelligence have allowed us to anticipate the behavior of the consumers by analyzing the data stored through sales and interaction on social media networks. Artificial intelligence and machine learning do not only increase sales and reduce man work but also improve the b2b buyer journey.


Artificial intelligence bots can optimize the communication with the leads and prospects to ensure that the answers to all the queries of the consumers are relevant and on time.


Fast interaction with the customer matters


Did you know that more than 53% of the customers never open the website or decide to abandon it if your website takes more than three seconds to load? Quick response to the customer’s query is essential in enhancing the experience. Nowadays, the market is quite saturated. Therefore, the customers don’t wait for you to respond. Instead, they find your company’s alternative in the meantime.


The worst part is most of the competitor companies have your leads’ information. They can call your potential customers and take away your sales.


You are likely to miss customers and sales opportunities unless you respond quickly and in real-time. But how do you grab sales opportunities actively for 24 hours in a day? Simple; AI is the solution to your problem.


Let’s say you work for a clothing brand and someone requests for the store timings in his/her neighborhood. Now, the artificial intelligence assistant on your website or social media messaging app could provide basic information about the nearest store timings in a friendly and conversational manner. Moreover, a Chabot could even email the live location or map to the customer.


The AI assistant on your website will not only upgrade your CRM as per the new information on leads and prospects but will also nurture the leads by engaging in a conversational manner.


Once the customer is satisfied with the initial conversation, a connection could be built and personalized follow-ups from your customer representatives could lead the customer to the sales funnel.


However, the parameters for lead engagement are quite different for every company. An AI assistant can easily identify potential leads, answer queries, respond to quick requests and even handle objections. When the assistant has handled the initial communication with the customers, you can hand the follow-up to human representatives.




Artificial intelligence supporting your marketing objectives


AI truly nurtures and supports your marketing objectives by handling customers, engaging prospects, and carrying out conversations 24/7.


AI Chat and email bots can seek specific information on customers, set up an appointment for your clients, and even carry out a smooth marketing journey.


Apart from conversing, AI assistants can also perform reporting and analysis on the marketing strategy. With the help of AI, you’d be able to optimize and run a successful marketing campaign.


AI in marketing is important


Time is of the essence in an organization. However, artificial intelligence does not only optimize your buyer’s journey but also saves a lot of the time that you spend in optimizing sales and marketing strategies. More than 72% of the businesses in the survey agree that AI has played a significant role in reducing work. This has allowed the teams to concentrate more on methods and non-mundane tasks as bots carry out all the repetitive and basic tasks.


How does AI influence the B2B buyer’s journey?


AI can transform your buyer’s journey in the following ways:


Create audience awareness


Artificial intelligence has the ability to target the customers who might want your products and services. This allows you to create buyer personas based on potential customers.


Create interest in your products


Your automation system delivers great information and insights on your products and services. Bots might show research to introduce your product in the market. Moreover, you could also use them to encourage people to sign up for your webinars and newsletters.


Audience considers your products


The consideration stage is where you send personalized messages to the potential customers who are already engaging with your brand.


Evaluate your products


Evaluation is important as customers are keen on comparing prices, weighing pros, cons and finding alternatives. In this stage, it is important to show why they should choose your product over other products in the market.


Interact and promote your products


Once your customers have completed the funnel stage, you could bombard them with satisfactory messages.


Artificial intelligence improves customer experience


Artificial intelligence allows us to focus on high-quality leads by exceeding customers’ expectations. More than 75% of the marketers lose sales because their leads never convert. However, 61% of the businesses agree that artificial intelligence is now one of the most important aspects of their marketing and sales strategy.


Final Thoughts


Artificial intelligence has a long way to go because it has just started. But you can make the most out of it by automating your marketing campaigns and B2B buyer’s journey.


To learn more about artificial intelligence can help move the buyer’s journey of your B2B customers, connect with the team at Byonic.AI.

	


	





	
	

		5 Effective Lead Nurturing Tactics Using AI-Based Tools	


	

		lead nurturing tactics	

	

		
5 Effective Lead Nurturing Tactics Using AI-Based Tools
		

			Posted on  |  by Danni White		

			

	

		






In recent years, AI has had a revolutionary effect on the marketing world. Harvard Business Review research says AI can increase lead generation by 50 percent and reduce costs by 60 percent. This means it can also increase revenue for businesses.





In addition to that, businesses can analyze and interpret the vast amounts of social media data that exists on the internet today. AI-based tools can provide companies with eye-opening insights about consumers, which leads to considerable improvements in lead generation. Businesses today have access to powerful social analytics and CRM tools that utilize customer behavior to maximize ROI.





What is Lead Nurturing?





In lead nurturing, information and resources are provided to your potential customers at every stage of the buyer’s journey to engage, support, and build relationships.





While some leads generated by your marketing team are ready to buy, many will have some questions and concerns about your product or service, and therefore lead nurturing is necessary to help them move from the awareness stage to the decision stage. 





Artificial Intelligence for Lead Scoring





Lead scoring helps marketing and sales teams avoid wasting time on inactive leads. An individual lead’s importance is ranked according to what action they took in the sales funnel. This is known as lead scoring.





Some companies score leads based on points earned from different actions. If a demo of a product is available, for instance, then sign up for the newsletter may be more beneficial. A lead’s priority is determined by the points it accumulates. Traditional scoring models are not used by everyone. 





The predictive lead scoring approach uses aggregated data to predict a lead’s value in advance through an algorithm-based model.










Effective Lead Nurturing Tactics





1. Content targeting can be leveraged





Lead nurturing isn’t the same for all companies. Enhanced results can be achieved by nurturing leads with targeted content as the research shows. 





Understand your buyer personas by working to identify them. Using this information, you can then craft an assortment of content sites based on the characteristics of each persona, such as their interests, goals, and objectives.





2. Paid Ad campaigns that are laser targeted





You can use AI-based solutions to break down your social media data into vital and reliable target lists. This grouped list of prospects can prove vital for your paid ad campaigns. 





Various social media platforms such as Twitter, Facebook, and Instagram make it possible for businesses to upload customer lists they intend to target precisely. By uploading the lists created using AI-based means, you can greatly reduce your CPA and increase your lead generation rates.





Besides, you can use social platforms to generate lookalike audiences according to your data. This audience is made up of consumers that have similar qualities like gender, age, demographics, profession, and interests with your current customers. This is again a great way of locating target customers that’ll be particularly interested in your brand.





3. Send personalized emails





One highly effective method of nurturing leads is email marketing- and how personal those emails are have proven to produce better results. According to research by Accenture, 41 percent of clients switched businesses because of a lack of personalization.





You can personalize your emails in several ways to improve your lead nurturing technique. It is possible to send triggered emails when a web visitor does something like downloading content, clicking a link, visiting a particular page on your website, or spending more time on your site. 





4. Separating prospects





Consumers of different ages, professions, demographics, and cultures regularly consume social media content. Therefore, it can be challenging knowing and narrowing down the potential list of people that’ll be the best fit for your business.





Businesses can create highly targeted and hyper-personalized ads, content, and emails to improve lead generation by using their segmented lists.





5. Higher brand awareness





Social media data is broad and spread across different platforms. It is difficult to manually organize data, analyze it, and design campaigns on it to increase lead generation.





Tools that are based on artificial intelligence collect data, information, and metrics of social media engagement in one portal. So, anytime a user engages or makes contact with your business, the data will be kept in your analytics tools.





Conclusion





Since a lot of buyers will not buy your product or service from the first point interaction, you need lead nurturing to facilitate a more personalized approach that increases your chances of conversion. The goal is to create and nurture a relationship with every prospect through honest interest and progressive trust. 





To learn more about demand generation marketers are using AI tools for effective lead nurturing results, connect with the team at Byonic.AI.

	


	





	
	

		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.

	


	





	
	

		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.

	


	







	
	

		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.


						

				

					

		

					

		

				

			


	





	
	

		15 Benefits of Artificial Intelligence in Marketing	


	

		Benefits of AI in Marketing	

	

		
15 Benefits of Artificial Intelligence in Marketing
		

			Posted on  |  by Danni White		

			

	

		

Artificial intelligence is a technology that assists in accomplishing specific tasks based on a computer-based algorithm system. It proves to be at least a partial substitute for human intelligence in almost every field. Marketing is one of those fields which utilizes this type of technology. AI enhances the functionality within companies to meet their marketing goals. From demand forecasting to multichannel reach, AI can be used in multiple ways among marketers and demand generation specialists.





Here, we will discuss 15 benefits of the use of artificial intelligence in marketing.





1. Uncover new opportunities





According to the Narrative Survey, 61% of business executives are using AI in marketing. It reveals new opportunities and predicts the latest market trends with the help of an AI database system in 2016.





Data-driven insight through AI is a helping hand for marketers to innovate their services and products. Without any human assistance, AI executes various more beneficial tasks than those who do not apply artificial intelligence to their businesses.





2. Demand predictions





The report (Sizing the prize) of PwC shows that artificial intelligence or deep learning proves very fruitful for decision-makers to analyze customers’ behavior.





Sentiment analysis uses to explore customers’ likings for a specific product or a service. The foundation of it lies in machine learning which is an application of AI. It tells about the customer preferences and predicts the demands of the upcoming orders from different customers.





3. Meliorating revenue





According to the combined survey of EverString and Heinz, almost 50% of business-to-business (B2B) marketers use some form of AI in marketing. There is a significant role of AI in B2B marketing. B2B marketers believe that it boosts your revenue with its complex algorithm system.





4. Insightful 360 customer view





From marketing to pricing, every company’s activity impacts the increasing or decreasing sales of its company.





So, it is crucial to find the views of customers to get more leads or revenue. For this purpose, companies integrate Artificial Intelligence into their CRM that presents a single view after analyzing and concluding a heavy amount of data.





5. Discover maturity level among your customer bases





By exploring the data of each customer, you can find out the maturity level of any customer. Social networking sites, tools, and apps play an indispensable role to gain insights from customers.





AI in marketing collects data of campaigns, database, and social media reports and prepare a conclusion. Companies analyze this final data and drive sales.





(Also Read: What is AI Marketing?)





6. Convert leads into sales





A company requires not only leads but also sales to get revenue. Without sales, leads are useless for organizations. It is a fact that there is an enormous difference between leads and sales of any company. With the help of machine learning, companies find and target those leads which have more chances of converting into sales.





7. Never miss a sales opportunity





If you want to grow your business, it is super necessary to be proactive and never miss a single chance of sales opportunities.





The needs of customers, the right time to sell, and competitors’ analysis are three main factors to get more sales opportunities. You can increase sales and make better strategies for the future with the optimized data provided by the Artificial Intelligence system.





8. Avert loss of revenue 





Revenue loss is a disastrous hurdle for any company. With AI, you can cope loss of revenue of your company.





You can analyze why customers are leaving your products or services and why they are going to other brands. You can purpose mindful strategies to provide excellent services to your customers with data-driven insights of Artificial Intelligence.





9. Automated lead scoring





Leads score are very crucial for a company. AI automates the process of lead scoring and accurately certifies more leads.





Marketers get their projects done without any strenuous efforts and involvement of humans. All lead scores qualify by machine learning.





10. Data segmentation





It is a process which concise heavy amount with AI. It is one of the proven benefits of AI in marketing. You cannot get these benefits if you use human intelligence.





AI provides deep insights or datasets. You can quickly achieve various marketing goals with the help of AI datasets.





11. Personalization





It is always important to know about your previous customers’ behavior to make them regular customers of your company.





AI draws data analysis based on customers’ sentiments and feedback against your product or service. You get not only lead generation but also the engagement of your customers for your marketing company.





12. Customer behavior





It is no doubt that you can use different sources to know the visibility of customers. Email, Mobile phones, websites, and social media platforms are gaining popularity to find new potential customers for your marketing company.





AI provides support to count the action of your customers too. With a deep learning approach, you can find out the interests of your customers.





13. Personalized user experience





AI is a handful of tools to personalize the experience of users. Many companies use artificial intelligence to get the personalization of users.





For example, Amazon suggests to their customers for purchasing various products by analyzing their previous data or browsing history. It is actually an example of the use of AI in marketing. It would be best to integrate AI into your system to suggest products or services to their customers.





14. Multidimensional reach





There are different sources like messenger, SMS, Facebook, and Email to reach your diverse customers. There is enormous data on these sources. So, it is hard to find valuable data of your potential clients.





AI assists in getting authentic information about your customers. It analyzes the data of each customer for marketers that is a bit difficult for a human.





15. Near perfect timing estimates





If you do not act on time, you cannot get valuable results. With Artificial technology, you can estimate when you should take action and when a response could be expected from your customers.





After judging all marketing plans and datasets, timing is another crucial element to get success for your marketing business.





Final Thoughts





No doubt, Artificial Intelligence draws a remarkable change in the marketing field. The sole purpose of this article is to give information about the benefits of AI in marketing. We hope you get a piece of valuable knowledge about AI after reading it.





To learn more about the benefits of AI in B2B marketing and how you can start seeing results, contact Byonic.AI today for a demo. Schedule a call today.

	


	





	
	

		Benefits & Uses of AI in Digital Marketing	


	

		Article explains use and benefits of AI in digital marketing	

	

		
Benefits & Uses of AI in Digital Marketing
		

			Posted on  |  by Danni White		

			

	

		

Artificial intelligence (AI) is a field of study that focuses on developing intelligent machines that can think and respond like humans. It has a lot of potential for digital marketing in the future. Organizations are increasingly turning to AI for cutting-edge applications that provide multiple overarching benefits.





As a business owner or marketer, it is time to identify your business’s problems and how accurate insights can solve these issues.





In this article, we have gathered AI data in digital marketing into one place that will help you get precise insight.





1. Machine Learning and Continued Learning





Machine learning techniques assist digital marketers in discovering valuable information from massive amounts of big data.





Machine Learning tools in digital marketing help identify market trends, forecast demand, and provide personalized services to customers. As a result, AI marketing’s Machine Learning tools and algorithms enable marketers to perform revenue-generating operations.





2. Smart Advertising on Social Media





Many social media platforms have built-in advertising systems that brands can use to enhance their marketing operations results. You no longer have to struggle to develop compelling captions and images for your ads when AI tools can do this work for you.





AI can also analyze your ads’ performance and make recommendations to fuel your campaigns’ effectiveness and drive better results. Due to AI, companies will no longer have to waste their marketing budget on ads that don’t work. You can target prospects and customers with the right message at the right time and get them to convert quickly.





3. Bot and AI Chats on Your Website





Businesses are interested in AI and adopting digital transformation to modernize customer communication and improve internal processes. Artificial Intelligence (AI) is playing a significant role in 2021 as it is being adopted by many businesses and enterprises. 





There are many benefits of using AI chatbots on your websites as they are always available. According to the research, over 50% of customers expect a business to be available 24/7. They are designed to reply instantly and consistently. Chatbots are not limited to shopping; they can also be used for repetitive tasks such as arranging meetings and researching a topic.





4. It Reduces Marketing Costs with the Best ROI





Artificial intelligence can fully or partially automate time-consuming and labor-intensive tasks that are causing your team to become overwhelmed. As a result, your marketing operations will require less workforce, and you’ll be able to utilize your ad budget properly. 





Thanks to the data-driven predictions and decisions you make, your campaigns are bound to generate impressive results that contribute to revenue growth.





5. Increased Personalization for Customers





Artificial intelligence (AI) developed marketing strategies, and approaches have evolved drastically over a few years.





In this fast-moving world, marketers need to work fast to keep up – but on the plus side, they have a lot of opportunities opened up for them to be creative. Personalization used to be an excellent option for marketing, but today, it must create a positive customer experience. 





Businesses can make a positive, personalized journey for their customers by micro-segmenting them, providing them with highly relevant content, leveraging omnichannel data, and leveraging AI. 





Personalization improves the customer experience, resulting in higher customer satisfaction, increased revenue, and long-term loyalty.





6: Refined Content Delivery on Social Media





Using artificial intelligence to track your Audience’s social media interests and preferences will give you a clear understanding of how to appeal to them by creating content they’ll want to engage with and share. 





Artificial intelligence allows businesses to gain a better understanding of their target audience and their preferences. They’ll be better equipped to create content that entices their interest and compels them to take the desired action.





7: AI Can Decide Content to Create and Where to Show It





You can use AI to help you decide what content to create and when, how, and where to publish and distribute it. The whole process can be done with a single click.





By outsourcing these repetitive tasks to marketing software, you can increase your productivity and focus your efforts on strategic marketing planning, face-to-face customer interactions, and other areas where humans excel over computers. 





AI marketing automation platforms allow you to segment your customer base and target different groups with the most appropriate marketing strategy. 





You can improve customer experience and strengthen customer relationships by segmenting customers based on their location, the pages they visit, or their buying preferences.





8: Automated Marketing Process





Much like automation, artificial intelligence applications make it possible for machines to complete fundamental human tasks. Visual perception, speech recognition, decision-making, and adaptability are common skills that AI is currently imitating. 





Marketers can create many campaigns and journeys without worrying about which one to send to each customer next because of AI. Marketing automation can be improved by using AI models to quickly identify all available campaigns for each customer and determine the following best action for them.





9: Processing Big Data





Big data is a broad concept that encompasses data collection from a variety of sources. It is a valuable tool for running digital marketing campaigns. Big data enables digital marketers to segment large data sets and send personalized content to customers conveniently via a chosen communication channel.





10. Understanding and Predicting Customer Behavior





When AI and consumer behavior are closely related and used together, they provide valuable insights. To survive in the marketing world, AI is being used to analyze customer online buying behavior. 





Customers are compelled to check out the brand’s offers due to new AI features behavioral marketing such as customized marketing messages.





The use of AI appears to be speeding up the growth of behavioral marketing. Chatbots, personalized feeds, and machine learning tools that track behavior have turned marketing on its head, with traditional methods no longer bringing the needed results. Artificial intelligence is something on which marketers can rely, and it benefits both brands and customers.





11: Better Business Intelligence





Users can derive meaningful insights from raw data with the help of business intelligence. To help organizations make more data-driven decisions, business intelligence (BI) incorporates business analytics, data mining, data visualization, data tools and infrastructure, and best practices.





The value of traditional business intelligence is undeniable. This practice’s ability to be nimble and scale as new norms are adopted is restricted in an environment where increasing competition, speed, and the need for accuracy are required.





More importantly, our blind spots may be hindered by allowing what has worked to continue to be the go-to solution. Because of this, we tend to miss critical insights that would have been apparent under an AI framework.





12: Better User Experiences





People prefer to do business with companies that offer excellent customer service. Customer satisfaction and retention, on the other hand, begin with getting to know your customers.





You will be able to understand your audience’s preferences and get closer to them by incorporating artificial intelligence into social media.





This gives companies an edge to create content, target ads, and make product or service changes to enhance their users’ experiences.





You can identify problem areas and fix them right away and respond to issues and complaints promptly to provide the best user experience.





Conclusion





Artificial intelligence (AI) is becoming more pervasive, a basic understanding of the technology is essential for long-term business success. Whatever role you hold in your business, understanding AI may help you solve problems in new and innovative ways, saving time and money. It may also assist you in developing and designing future products and services with a low or no budget.





Want to start implementing some of these best practices for artificial intelligence into your enterprise? Contact Byonic.AI today for a demo and get assistance in prioritizing AI-based solutions for greater results. Schedule a call today.

	


	





	
	

		5 Enterprise AI Best Practices	


	

		ai best practices	

	

		
5 Enterprise AI Best Practices
		

			Posted on  |  by Danni White		

			

	

		

Artificial intelligence is a massively growing aspect of the technology industry. At conferences and trade shows, artificial intelligence is forming the foundation for new products and services.





Companies integrate products and features to create virtual assistants, and chatbots answer customer questions on a range of tech support issues to product information. Simultaneously, many companies are working to integrate AI as an intelligence layer across their entire tech stack. 





Machine learning, computer vision, and natural language processing have all progressed in recent years, making it simpler than ever to incorporate an AI algorithm layer into your app or cloud platform.





Practical AI applications for companies can take several forms, depending on your organization’s needs and the BI insights obtained from the data you collect. Companies can use AI for various tasks, including mining social data, driving interaction in customer relationship management (CRM), and optimizing logistics and productivity in asset monitoring and management.





Machine learning has and will continue to have a pivotal role in artificial intelligence development. It incubates AI startups and helps companies incorporate AI on top of their existing products and services.





Currently, recent progress in machine learning is driving the interest and use of artificial intelligence. There is no single breakthrough you can point to, but the business value we can extract from ML is now off the charts. What is going on right now regarding enterprise business processes such as planning and control, scheduling, resource allocation, and reporting may be disrupted. We’ve gathered some expert advice to clarify the steps companies should take to incorporate AI into their operations and ensure effective implementation.





Top 5 Enterprise AI Best Practices





1. Get Familiar With AI





Modern AI can do a lot to grow your business, but it requires time to understand AI’s scope and how you can implement it. The TechCode Accelerator offers startups an array of resources through partnerships with Stanford University and AI-focused corporations. Take advantage of the wealth of online information, remote workshops, resources, and courses to gain familiarity with AI’s basic concepts. Doing this can be an easy way to start integrating predictive analytics within your organization.





2. AI Can Solve All of Your Problems Quickly





Once the basics are straightforward, any company’s next step is to begin exploring different ideas. Think about ways you can incorporate AI into your new products and services. More importantly, your business should consider particular use cases in which AI might solve business problems or bring value. 





When working with any organization, we start with an overview of its vital tech programs and issues. We want to show how natural language processing, image recognition, machine learning, and other technologies integrate into such products, typically through a workshop with its management. The details vary from industry to industry. If the company does video monitoring, applying machine learning to the process will add value.





3. Prioritize Concrete Value





You must evaluate the market and financial potential of the numerous AI implementations you’ve found. It is easy to get caught up in “pie in the sky” AI discussions; the researchers emphasized the importance of explicitly linking the initiatives to business value.





To prioritize, build a 2×2 matrix with the dimensions of potential and feasibility. This matrix should allow you to prioritize near-term visibility and determine the company’s financial value. Managers and top-level executives must usually take responsibility and accept these moves.





4. Acknowledge the Internal Capability Gap





There’s a big difference between what you want to do and what you can accomplish in a given time if you have the right organizational skills. Before diving into a full-fledged AI implementation, a manager should know what it can and cannot do in terms of technology and business processes.





The first step in resolving the internal capability gap is identifying what you need to acquire and processes you need to build before you start. Established projects or teams, depending on the company, may assist specific business units in doing so organically.





5. Call-In Experts to Set Up a Pilot Project Easily





It’s time to start developing and integrating the company until it’s ready from an operational and technological perspective. Starting tiny, having project objectives in mind, and, most importantly, being mindful of what you do and don’t know about AI are the most critical factors here. Bringing in outside experts or consultants who know the topic well can be highly beneficial in this situation.





Typically, 2-3 months is a good range for a pilot project. Companies need to build insufficient bandwidth for storage, the graphics processing unit (GPU), and networking to achieve this balance. Security is an oft-overlooked component as well. AI requires broad access to swaths of data to perform the task well. Ensure that you understand the types of data involved and usual security safeguards such as encryption, VPNs, and anti-malware.





In Summary





Bringing external experts and internal team members together and a tighter time frame will keep the team focused on business goals. After completing the pilot, you should decide the longer-term aspects of the project and whether it is valuable enough to make sense for your business. It’s also essential that experts from both sides—the people who know about the company and the people who know about AI—are merged on your pilot project team.





Want to start implementing some of these best practices for artificial intelligence into your enterprise? Contact Byonic.AI today for a demo and get assistance in prioritizing AI-based solutions for greater results. Schedule a call today.