Tag: machine learning

How to Use AI for Lead Generation, Qualification, and Conversion
Article is about how AI can be used in Lead Generation, Qualification, and Conversion

How to Use AI for Lead Generation, Qualification, and Conversion

Posted on | by Danni White

Artificial Intelligence is a massive part of our lives, from social media ads to campaigns and different advertisements. It’s spread everywhere, and since it’s a huge part of our lives, it’s due time that we make the most of it and incorporate it in generating tools in our Business, start from today!

AI is the perfect tool you need to make far-reaching changes in your sales. In a recent report published, approximately 80% of people expect AI to play a supporting role in revolutionizing marketing in the years to come.

Let’s find out how businesses can incorporate Artificial Intelligence and machine learning in their business model to generate leads, qualifications, and conversions.

5 Ways to Use AI in Your Organization

1. Achieving Personalization in your Business

Quite a few people sometimes aren’t satisfied with the customer service of different brands. Hence, they do not prefer purchasing from them. Fortunately, with AI, you can make a massive difference in improvising your customer’s experience. How can that be done? You can track your customers on the website, their likes, and dislikes, and develop a good insight regarding customer behavior.

By using different tools, different data is gathered, and you can engage visitors on the site. Artificial Intelligence allows you to greet your old customers with their names, personalizing their experience and making them feel welcomed. You can wish them on their birthdays by sending an email and a small discount code to make their experience the best with your brand. By going through their browsing history and services or products, they like you can build a personalized list of products they may want on your website. Artificial Intelligence helps you achieve this!

2. Development an Accurate Lead

Your Business must have a proper and accurate lead segmentation, including division of your target audience according to age, demographics, their different behaviors. This will help come up with messages of a group of people who have the same interest. AI assists you in building a segment based on your buyer according to their behavior patterns and makes it highly personalized. AI helps you provide your customers with the best experience and helps your brand grow too!

3. Reduction in Repetitive Tasks

A recent study shows that AI and machine learning will save businesses up to $8 billion by 2022. You can use different chatbots to help reduce the burden on teams responsible for responding to customers at additional hours during the day. These chatbots can reduce the costs spent on customer support by 30% and handle most of the queries customers ask.

Today, you’ll find chatbots made for conversations only, and they can learn from every new interaction to help improve the performance. These chatbots help can help and guide customers through the sit and suggest different items according to the visitors browsing history. They enrich your business’s query resolution and keep proper records and updates. This makes the work so much easy for you!

4. Repetition in Success

Artificial Intelligence is a powered platform assisting in lead generation. Machine Learning uses different algorithms to achieve marketing intelligence. They search and scan various organizations and find the organization whose description is similar to yours according to the criteria instilled in the system. Through this system, you can easily lead and grow towards success as the AI creates a continuous pipeline.

5. Machine-Driven Email Campaigns

You can easily set up automated email services through AI, which can be driven by your database’s customer data and behavior. This technology is vital in using the history of the user, their interests, and browsing behaviors. AI creates and sends automated emails before or after an event, such as after purchasing, abandoning the cart, etc.

These emails have a high rate of opening because of the attractive subject lines and the content. They are a pretty convenient way of grabbing the customer’s attention.

Final Thought

We have established that AI is an integral part of our lives and supports our brands in making everything better. From customer experience to campaigns. But the real question is how to achieve all of the above? That’s when Byonic.AI comes in place. The experts at Byonic can help you accomplish all of the above through their know-how in machine learning and artificial intelligence. What are you waiting for? Click on their website and take your business up the notch!

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.

	


	





	
	

		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 AI and ML Will Affect Organizations in the Future	


	

		ai and ml	

	

		
How AI and ML Will Affect Organizations in the Future
		

			Posted on  |  by Danni White		

			

	

		

According to a published research paper by Accenture Institute for High Performance, artificial intelligence can double the annual economic growth rates in several developed countries by 2035. This growth is proof that the world is about to experience a phase where tremendous technology-driven change through AI and ML will help us remedy a plethora of challenges in achieving desirable growth. AI and ML can be applied in several ways to build outstanding infrastructure for next-generation education systems, agriculture, healthcare systems, power, and telecoms in the future.





This article will offer some ideas to consider on how artificial intelligence will change the future.





  • Market and customer insights
    




    Machine learning and artificial intelligence will play a crucial role in analyzing your customers and target market. Predictive analytics can integrate data generated from social media, web matrix, and system matrix to design a new and improved product. Through customer insights, you can maximize customer experience as much as possible.
    




    Artificial intelligence offers an incredible benefit for start-ups. Start-ups will explore several opportunities to operate on various thought processes and develop innovative solutions for their business growth. Predictive maintenance will enable start-ups to reduce maintenance costs via regular quality checks.
    




    Most customers will probably be on social media, and many businesses must take place online. Organizations can utilize artificial intelligence and machine learning to understand essential measures of online social networks. Companies can use data mining strategies in the analysis of many forms of social media traffic. What makes AI and ML so incredible are that they can learn, unlike purely statistical approaches. The learning aspect makes it possible for them to evolve with any changes in the market behaviors in the future and continually improve performance as you generate additional data.





  • The growth of virtual assistance
    




    Many organizations are either utilizing or planning to adopt AI for virtual assistance. There are several reasons why companies deploy chatbots. The most prominent area is customer service. Although many individuals are not convinced of the concept of customers speaking with a robot as it might malfunction at specific points, regardless, there is a massive potential in machine-based assistance with human-based customer service.
    




    For instance, in the case of airline inquiries, an AI application can help answer fundamental questions such as the flight’s timing, alternative flights, and flight status, among other things. This will allow human personnel to concentrate on more complex issues. Marketers can effectively use chatbots to interact with their prospective customers seamlessly. Virtual assistants and chatbots will play a significant role in the way customers interact with technology. In the future of AI, virtual assistants would become a substantial component of our business lives, and many organizations are going to leverage this to move customer service to a greater level.





  • Efficient process automation
    




    Automation is one thing that has been on a steady rise for the last couple of decades. Many machine learning automation tools are being developed and upgraded, facilitating the agility of business processes. Today, advanced machines are collaborating with humans in different industries.
    




    Industry giants have predicted that with the tremendous rise of ML and AI, we are about to witness a new era of automation. AI is rapidly automating repetitive cognitive procedures. Intelligent algorithms are becoming more and more useful in different industries such as financial institutions, hospitality, and retail. These algorithms or machines will not only execute a highly proficient task but also will operate 24/7 nonstop.





  • Enhanced data unlocking
    




    In the past, the volume of data an organization generated was a relatively small set, which determined how it was structured. Capturing and storing the data in a database was easy. Business owners could obtain insights from the data mined for the organizational needs.
    




    However, the situation has changed today. There is no longer structured data, and the more significant segment of the available information is unstructured data. In fact, approximately 80% of the data received from online platforms is unstructured. Hence, the most impactful factor for any organization over the subsequent decade will be analyzing unstructured data.
    




    Understanding consumer conversations is another aspect that has great potential to make your organization a global giant. Your organization can be among the few businesses that utilize their consumer conversations to determine their personality types with Artificial Intelligence’s help so that those consumers receive compatible services. AI and ML will help many organizations to unlock massive data in the future.





  • Better personalized customer experience
    




    Artificial Intelligence and Machine Learning offer organizations the opportunity to drive a personalized experience to their consumers/customers. AI can be used to analyze extensive data far more efficiently. It can easily identify threads in the information – like previous purchase history, credit scores, purchase preferences, and other common patterns. Transactions can be examined daily to facilitate personalized services to individual customers.
    




    The use of actionable sales intelligence will help businesses analyze a particular buyer before, during, and after purchase behavior. This enables organizations to develop a highly personalized experience and initiate customer engagement at every point of interaction. Predictive intelligence has the potential to help organizations engage their customers authentically and effectively. An AI-based application can also help to personalize the sales cycle, enabling enterprises to engage the proper customers with the right content at the appropriate time.





In Summary





Machine learning and artificial intelligence across various industries have helped both small and enterprise businesses execute tested and effective methods to realize better business goals. Start-ups continuously have a competitive advantage through AI and ML, while big organizations are paving the platform to develop innovative solutions.





From working as a robot in a production plant to autonomous-driving vehicles and voice-activated resources in complex medical procedures, AI and ML have become a crucial part of reality. The future of AI and ML promise tremendous business solutions.





Want to start implementing machine learning and artificial intelligence based tools into your marketing and demand generation strategy? Contact Byonic.AI today for a demo and see how we can streamline your marketing and sales process and get results. Schedule a call today.