Top Machine Learning Algorithms Explained: How Do They Work?

A botnet is a collection of several compromised systems that are connected to the central controller called a botmaster. As long as botmasters are coming up with new ways to attack, sophisticated solutions for botnet detection are very essential. To illustrate how to use these tools, this paper will discuss several tools and processes involved in developing a Botnet detection system. Different libraries like Scikit Learn, Pandas, Theano, Matplotlib, Pickel, and NumPy are used.

• On the other hand, our initial weight is 5, which leads to a fairly high loss.
• Deep learning relates to neural networks, with the term “deep” referring to the number of layers inside the network.
• In simple terms, a machine learning algorithm is like a recipe that allows computers to learn and make predictions from data.
• Machine learning also performs manual tasks that are beyond our ability to execute at scale — for example, processing the huge quantities of data generated today by digital devices.
• Each time we update the weights, we move down the negative gradient towards the optimal weights.

You can learn machine learning and develop the skills required to build intelligent systems that learn from data with persistence and effort. Learn more about this exciting technology, how it works, and the major types powering the services and applications we rely on every day. Machine learning is a deep and sophisticated field with complex mathematics, myriad specialties, and nearly endless applications. The algorithms and styles of learning above are just a dip of the toe into the vast ocean of artificial intelligence. Usually, the availability of data is considered as the key to construct a machine learning model or data-driven real-world systems [103, 105]. Data can be of various forms, such as structured, semi-structured, or unstructured [41, 72].

We cannot predict the values of these weights in advance, but the neural network has to learn them. With neural networks, we can group or sort unlabeled data according to similarities among samples in the data. Or, in the case of classification, we can train the network on a labeled data set in order to classify the samples in the data set into different categories. The design of the neural network is based on the structure of the human brain. Just as we use our brains to identify patterns and classify different types of information, we can teach neural networks to perform the same tasks on data. Machine learning is a powerful technology with the potential to transform how we live and work.

Additionally, the processes for utilising these tools are illustrated in this paper. The features are extracted like packet size, packet byes, source address, destination address, length, and corresponding protocols. Feature extraction requires a significant amount of domain expertise and manual work from professionals in current machine learning-based botnet detection systems.

Types of Machine Learning Algorithms

Thus, to intelligently analyze these data and to develop the corresponding real-world applications, machine learning algorithms is the key. The learning algorithms can be categorized into four major types, such as supervised, unsupervised, semi-supervised, and reinforcement learning in the area [75], discussed briefly in Sect. The popularity of these approaches to learning is increasing day-by-day, which is shown in Fig. The x-axis of the figure indicates the specific dates and the corresponding popularity score within the range of \(0 \; (minimum)\) to \(100 \; (maximum)\) has been shown in y-axis.

Besides, the deep learning, which is part of a broader family of machine learning methods, can intelligently analyze the data on a large scale. In this paper, we present a comprehensive view on these machine learning algorithms that can be applied to enhance the intelligence and the capabilities of an application. We also highlight the challenges and potential research directions based on our study. Overall, this paper aims to serve as a reference point for both academia and industry professionals as well as for decision-makers in various real-world situations and application areas, particularly from the technical point of view.

A machine learning algorithm is a set of rules or processes used by an AI system to conduct tasks—most often to discover new data insights and patterns, or to predict output values from a given set of input variables. Many classification algorithms have been proposed in the machine learning and data science literature [41, 125]. In the following, we summarize the most common and popular methods that are used widely in various application areas. Data is any type of information that can serve as input for a computer, while an algorithm is the mathematical or computational process that the computer follows to process the data, learn, and create the machine learning model. In other words, data and algorithms combined through training make up the machine learning model. Logistic regression, also known as “logit regression,” is a supervised learning algorithm primarily used for binary classification tasks.

Artificial Neural Network and Deep Learning

Accordingly, the values of z, h and the final output vector y are changing with the weights. Some weights make the predictions of a neural network closer to the actual ground truth vector y_hat; other weights increase the distance to the ground truth vector. Deep learning models tend to increase their accuracy with the increasing amount of training data, whereas traditional machine learning models such as SVM and naive Bayes classifier stop improving after a saturation point. All recent advances in artificial intelligence in recent years are due to deep learning. Without deep learning, we would not have self-driving cars, chatbots or personal assistants like Alexa and Siri.

First, the dataset is shuffled, then K data points are randomly selected for the centroids without replacement. In the below, we’ll use tags “red” and “blue,” with data features “X” and “Y.” The classifier is trained to place red or blue on the X/Y axis. In sentiment how do machine learning algorithms work analysis, linear regression calculates how the X input (meaning words and phrases) relates to the Y output (opinion polarity – positive, negative, neutral). This will determine where the text falls on the scale of “very positive” to “very negative” and between.

Therefore, the challenges that are identified create promising research opportunities in the field which must be addressed with effective solutions in various application areas. In the following section, we discuss several application areas based on machine learning algorithms. In general, neural networks can perform the same tasks as classical machine learning algorithms (but classical algorithms cannot perform the same tasks as neural networks). In other words, artificial neural networks have unique capabilities that enable deep learning models to solve tasks that machine learning models can never solve. A general structure of a machine learning-based predictive model has been shown in Fig. 3, where the model is trained from historical data in phase 1 and the outcome is generated in phase 2 for the new test data.

Any new data point that falls on either side of this decision boundary is classified based on the labels in the training dataset. Naive Bayes leverages the assumption of independence among the factors, which simplifies the calculations and allows the algorithm to work efficiently with large datasets. Linear regression is a supervised machine learning technique used for predicting and forecasting values that fall within a continuous range, such as sales numbers or housing prices. It is a technique derived from statistics and is commonly used to establish a relationship between an input variable (X) and an output variable (Y) that can be represented by a straight line. Machine learning is a subfield of computer science that emphasizes the development of algorithms and statistical models.

Deep learning provides a computational architecture by combining several processing layers, such as input, hidden, and output layers, to learn from data [41]. The main advantage of deep learning over traditional machine learning methods is its better performance in several cases, particularly learning from large datasets [105, 129]. Figure 9 shows a general performance of deep learning over machine learning considering the increasing amount of data.

The concept of machine learning has been around for a long time (think of the World War II Enigma Machine, for example). However, the idea of automating the application of complex mathematical calculations to big data has only been around for several years, though it’s now gaining more momentum. Neural networks enable us to perform many tasks, such as clustering, classification or regression.

Logistic Regression

It uses a systematic approach to achieve its goal going through various steps such as data collection, preprocessing, modeling, training, tuning, evaluation, visualization, and model deployment. This technique is widely used in various domains such as finance, health, marketing, education, etc. You also need to know about the different types of machine learning — supervised, unsupervised, and reinforcement learning, and the different algorithms and techniques used for each kind.

It’s the number of node layers, or depth, of neural networks that distinguishes a single neural network from a deep learning algorithm, which must have more than three. Cluster analysis, also known as clustering, is an unsupervised machine learning technique for identifying and grouping related data points in large datasets without concern for the specific outcome. It does grouping a collection of objects in such a way that objects in the same category, called a cluster, are in some sense more similar to each other than objects in other groups [41]. It is often used as a data analysis technique to discover interesting trends or patterns in data, e.g., groups of consumers based on their behavior. In a broad range of application areas, such as cybersecurity, e-commerce, mobile data processing, health analytics, user modeling and behavioral analytics, clustering can be used.

You can foun additiona information about ai customer service and artificial intelligence and NLP. It operates by segmenting the data into smaller and smaller groups until each group can be classified or predicted with high degree of accuracy. K-means is an unsupervised algorithm commonly used for clustering and pattern recognition tasks. Similar to K-nearest neighbor (KNN), K-means clustering utilizes the concept of proximity to identify patterns in data. Many machine learning systems we use daily, such as face detection, speech recognition, object detection, and more, are all types of machine learning, not AI. AI, which originally referred to human-like intelligence in machines, now refers to any aspect of technology that partially shares attributes with human intelligence.

Reinforcement learning is explained most simply as “trial and error” learning. In reinforcement learning, a machine or computer program chooses the optimal path or next step in a process based on previously learned information. Machines learn with maximum reward reinforcement for correct choices and penalties for mistakes. Resembling a graphic flowchart, a decision tree begins with a root node, which asks a specific question of the data and then sends it down a branch depending on the answer.

Artificial intelligence is a general term that refers to techniques that enable computers to mimic human behavior. Machine learning represents a set of algorithms trained on data that make all of this possible. Machine learning is a type of artificial intelligence that involves developing algorithms and models that can learn from data and then use what they’ve learned to make predictions or decisions.

Several different types of machine learning power the many different digital goods and services we use every day. While each of these different types attempts to accomplish similar goals – to create machines and applications that can act without human oversight – the precise methods they use differ somewhat. Clustering algorithms are common in unsupervised learning and can be used to recommend news articles or online videos similar to ones you’ve previously viewed. In classification in machine learning, the output always belongs to a distinct, finite set of “classes” or categories.

Supervised Learning Algorithm

A common way of measuring the usefulness of association rules is to use its parameter, the ‘support’ and ‘confidence’, which is introduced in [7]. Machine learning algorithms typically consume and process data to learn the related patterns about individuals, business processes, transactions, events, and so on. In the following, we discuss various types of real-world data as well as categories of machine learning algorithms. Unsupervised Learning is a type of machine learning algorithms where the algorithms are used to find the patterns, structure or relationship within a dataset using unlabled dataset.

These challenges can be dealt with by careful handling of data, and considering the diverse data to minimize bias. Incorporate privacy-preserving techniques such as data anonymization, encryption, and differential privacy to ensure the safety and privacy of the users. In two dimensions this is simply a line (like in linear regression), with red on one side of the line and blue on the other. Compare your paper to billions of pages and articles with Scribbr’s Turnitin-powered plagiarism checker.

For starters, machine learning is a core sub-area of Artificial Intelligence (AI). ML applications learn from experience (or to be accurate, data) like humans do without direct programming. When exposed to new data, these applications learn, grow, change, and develop by themselves. In other words, machine learning involves computers finding insightful information without being told where to look.

Typical results from machine learning applications usually include web search results, real-time ads on web pages and mobile devices, email spam filtering, network intrusion detection, and pattern and image recognition. All these are the by-products of using machine learning to analyze massive volumes of data. After each gradient descent step or weight update, the current weights of the network get closer and closer to the optimal weights until we eventually reach them.

It helps organizations scale production capacity to produce faster results, thereby generating vital business value. Reinforcement learning happens when the agent chooses actions that maximize the expected reward over a given time. This is easiest to achieve when the agent is working within a sound policy framework. In this case, the unknown data consists of apples and pears which look similar to each other. The trained model tries to put them all together so that you get the same things in similar groups. Each time we update the weights, we move down the negative gradient towards the optimal weights.

At that point, the neural network will be capable of making the predictions we want to make. To understand the basic concept of the gradient descent process, let’s consider a basic example of a neural network consisting of only one input and one output neuron connected by a weight value w. The input layer receives input x, (i.e. data from which the neural network learns). In our previous example of classifying handwritten numbers, these inputs x would represent the images of these numbers (x is basically an entire vector where each entry is a pixel). While learning machine learning can be difficult, numerous resources are available to assist you in getting started, such as online courses, textbooks, and tutorials. It is also beneficial to put theory into practice by working on real-world problems and projects and collaborating with other learners and practitioners in the field.

Main Uses of Machine Learning

Imagine the above in three dimensions, with a Z-axis added, so it becomes a circle. Much as a teacher supervises their students in a classroom, the labelled data likewise supervises the algorithm’s solutions and directs them towards the right answer. The academic proofreading tool has been trained on 1000s of academic texts and by native English editors.

Still, most organizations either directly or indirectly through ML-infused products are embracing machine learning. Companies that have adopted it reported using it to improve existing processes (67%), predict business performance and industry trends (60%) and reduce risk (53%). But you don’t have to hire an entire team of data scientists and coders to implement top machine learning tools into your business. No code SaaS text analysis tools like MonkeyLearn are fast and easy to implement and super user-friendly. Originating from statistics, logistic regression technically predicts the probability that an input can be categorised into a single primary class.

In this article, you will learn about seven of the most important ML algorithms to know and explore the different learning styles used to turn ML algorithms into ML models. Most often, training ML algorithms on more data will provide more accurate answers than training on less data. Using statistical methods, algorithms are trained to determine classifications or make predictions, and to uncover key insights in data mining projects. These insights can subsequently improve your decision-making to boost key growth metrics. With Machine Learning from DeepLearning.AI on Coursera, you’ll have the opportunity to learn practical machine learning concepts and techniques from industry experts. Develop the skills to build and deploy machine learning models, analyze data, and make informed decisions through hands-on projects and interactive exercises.

Your learning style and learning objectives for machine learning will determine your best resource. Like any new skill you may be intent on learning, the level of difficulty of the process will depend entirely on your existing skillset, work ethic, and knowledge. In DeepLearning.AI and Stanford’s Machine Learning Specialization, you’ll master fundamental AI concepts and develop practical machine learning skills in the beginner-friendly, three-course program by AI visionary Andrew Ng.

Once the model has been trained well, it will identify that the data is an apple and give the desired response. At a high level, machine learning is the ability to adapt to new data independently and through iterations. Applications learn from previous computations and transactions and use “pattern recognition” to produce reliable and informed results.

Zendesk Support app Help Center

Zendesk also packs some pretty potent tools into their platform, so you can empower your agents to do what they do with less repetition. Agents can use basic automation (like auto-closing tickets or setting auto-responses), apply list organization to stay on top of their tasks, or set up triggers to keep tickets moving automatically. Use ticketing systems to manage the influx and provide your customers with timely responses. When it comes to advanced workflows and ticketing systems, Zendesk boasts a more full-featured solution. Due to our intelligent routing capabilities and numerous automated workflows, our users can free up hours to focus on other tasks. Provide self-service alternatives so customers can resolve their own issues.

However, some users have reported issues with the platform’s customer support and pricing. One of the standout features of Zendesk’s user interface is the ability to view customer interactions in a timeline format, which can help track the progress of a customer’s support request. Additionally, Zendesk offers a wide range of customization options, allowing users to tailor the user interface to their needs.

SAP Concur vs Saasu: for streamlining business finances

A collection of these reports can enable your business to identify the right resources responsible for bringing engagement to your business. Zendesk is another popular customer service, support, and sales platform that enables clients to connect and engage with their customers in seconds. Just like Intercom, Zendesk can also integrate with multiple messaging platforms and ensure that your business never misses out on a support opportunity.

• Our integration with Intercom enables bi-directional contact and case synchronization, so you can continue using Intercom as your front-end digital experience and use Zendesk for case management.
• You could say something similar for Zendesk’s standard service offering, so it’s at least good to know they have Zendesk Sell, a capable CRM option to supplement it.
• Additionally, the platform allows for customizations such as customized user flows and onboarding experiences.

Zendesk is primarily a ticketing system, and its ticketing capability is overwhelming in the best conceivable manner. All client contacts, whether via phone, chat, email, social media, or any other channel, land in one dashboard, where your agents can quickly and efficiently resolve them. In today’s hyper-competitive, hyper-connected globalized economy, customer experience has become a fundamental differentiator.

Email marketing, for example, is a big deal, but less so when it comes to customer service. Still, for either of these platforms to have some email marketing or other email functionality is common sense. Zendesk can also save key customer information in their platform, which helps reps get a faster idea of who they are dealing with as well as any historical data that might assist in the support. Zendesk Sunshine is a separate feature set that focuses on unified customer views. The best help desks are also ticketing systems, which lets support reps create a support ticket out of issues that can then be tracked. When it comes to self-service portals for things like knowledgebases, Intercom has a useful set of resources.

Zendesk has also introduced its chatbot to help its clients send automated answers to some frequently asked questions to stay ahead in the competitive marketplace. What’s more, it helps its clients build an integrated community intercom and zendesk forum and help center to improve the support experience in real-time. The platform is evolving from a platform for engaging with consumers to a tool that assists you in automating every element of your daily routine.

Pricing: Zendesk Vs. Intercom

Intercom has a unique pricing structure, offering three separate solutions, each intended for a distinct use case. We wish some of their great features were offered in multiple plans, but none features overlap among plans. When a customer asks a question in the Messenger widget, the Operator automatically suggests a handful of relevant articles based on keywords to help customers resolve their own issues. In fact, agents can even add customers to private messaging chats when necessary, and the customer will receive the whole conversation history by email to ensure they’re up to date. Collaboration tools enable agents to work together in resolving customer tickets and making sales. You need a complete customer service platform that’s seamlessly integrated and AI-enhanced.

Since both are such well-established market leader companies, you can rest assured that whichever one you choose will offer a quality customer service solution. Today, both companies offer a broad range of customer support features, making them both strong contenders in the market. Zendesk offers more advanced automation capabilities than Intercom, which may be a deciding factor for businesses that require complex workflows. Some people like Intercom’s conversational support tool, which lets customers talk to you in a more personalised and interactive way. Users like that the platform lets them have talks in real time, which makes it easier to answer customer questions quickly and correctly. People have also said nice things about Intercom’s proactive message features, which let businesses talk to users before they even complain, which improves the overall customer experience.

Importing Zendesk data into Intercom

The overall sentiment from users indicates a satisfactory level of support, although opinions vary. Intercom is a customer support platform known for its effective messaging and automation, enhancing in-context support within products, apps, or websites. It features the Intercom Messenger, which works with existing support tools for self-serve or live support. Zendesk is a customer service software offering a comprehensive solution for managing customer interactions. It integrates customer support, sales, and marketing communications, aiming to improve client relationships.

Founded in 2007, Zendesk started as a ticketing tool for customer success teams. It was later that they started adding all kinds of other features, like live chat for customer conversations. They bought out the Zopim live chat solution and integrated it with their toolset. One of the standout features of Zendesk’s customer support is its ticketing system. Users can submit support tickets through the platform, and customer support teams can manage and track those tickets to ensure they are resolved promptly.

Zendesk vs. Intercom: Features comparison

Zendesk was founded in 2007 by Mikkel Svane, Morten Primdahl, and Alexander Aghassipour. The platform was created to provide a simple and effective way for businesses to manage customer support tickets. Over the years, Zendesk has expanded its offerings to include features such as live chat, knowledge base, and customer feedback. While the company is smaller than Zendesk, Intercom has earned a reputation for building high-quality customer service software. The company’s products include a messaging platform, knowledge base tools, and an analytics dashboard.

That not only saves them the headache of having to constantly switch between dashboards while streamlining resolution processes—it also leads to better customer and agent experience overall. Intercom built additional tools to aid in marketing and engagement to supplement its customer service solution. But we doubled down and created a truly full-service CX solution capable of handling any support request. Here are our top reporting and analytics features and an overview of where Intercom’s reporting limitations lie. Intercom is great at making it easy for businesses and their customers to talk to each other instantly and back and forth.

It will allow you to leverage some Intercom capabilities while keeping your account at the time-tested platform. Though the Intercom chat window says that their customer success team typically replies in a few hours, don’t expect to receive any real answer in chat for at least a couple of days. You can foun additiona information about ai customer service and artificial intelligence and NLP. If you’d want to test Intercom vs Zendesk before deciding on a tool for good, they both provide free trials for 14 days. But sooner or later, you’ll have to decide on the subscription plan, and here’s what you’ll have to pay. Well, I must admit, the tool is gradually transforming from a platform for communicating with users to a tool that helps you automate every aspect of your routine.

Using the existing knowledge base functionality, they can display self-help articles in the chat window before the customer approaches your team for support. You can create these knowledge base articles in your target audience’s native language as their software is multilingual. Zendesk also offers digital support during business hours, and their website has a chatbot.

Users can also access a resource library to stay updated on the latest trends, product announcements, and best practices. Intercom regularly hosts webinars that are recorded and stored for future reference. What better way to start a Zendesk vs. Intercom than to compare their features? After switching to Intercom, you can start training Custom Answers for Fin right away by importing your historic data from Zendesk.

In today’s world of fast-paced customer service and high customer expectations, it’s essential for business leaders to equip their teams with the best support tools available. Zendesk and Intercom both offer noteworthy tools, but if you’re looking for a full-service solution, there is one clear winner. Intercom is an excellent option for businesses prioritizing personalized communication and customer engagement.

This keeps any data sent private and stops people from getting to it without permission. On the other hand, it is absolutely necessary to investigate the nature of these integrations in order to ascertain whether or not they are relevant to the criteria that you have in mind. It is essential to evaluate the compatibility of the connectors offered by each platform with the tools and workflows that you already have in place. During this phase, you will determine the essential features, functionalities, and tools that are essential to the operations of your firm. When choosing the right customer support tool, pricing is an essential factor to consider. In this section, we will compare the pricing structures of Intercom and Zendesk.

• Intercom has a full suite of email marketing tools, although they are part of a pricier package.
• This can be a valuable resource for users looking for solutions to specific problems or wanting to learn more about the platform.
• Intercom also has a mobile app available for both Android and iOS, which makes it easy to stay connected with customers even when away from the computer.
• Intercom has a dark mode that I think many people will appreciate, and I wouldn’t say it’s lacking in any way.

Operator, Intercom’s automation engine, empowers Intercom chatbots to gather key information from each website visitor to qualify leads and route customers to the right destination. Zendesk for Service transforms customer queries and conversations from all channels–call, web chat, tweet, text, or email–into tickets in the Agent Workspace. You can create dozens of articles in a simple, intuitive WYSIWYG text editor, divide them by categories and sections, and customize with your custom themes. You can even improve efficiency and transparency by setting up task sequences, defining sales triggers, and strategizing with advanced forecasting and reporting tools. Starting at $19 per user per month, it’s also on the cheaper end of the spectrum compared to high-end CRMs like ActiveCampaign and HubSpot. Using this, agents can chat across teams within a ticket via email, Slack, or Zendesk’s ticketing system.

Intercom also has a community forum where users can help one another with questions and solutions. For Intercom’s pricing plan, on the other hand, there is much less information on their website. There is a Starter plan for small businesses at $74 per month billed annually, and there are add-ons like a WhatsApp add-on at $9 per user per month or surveys at $49 per month.

Intercom vs Zendesk: Availability of Customer Support

This exploration aims to provide a detailed comparison, aiding businesses in making an informed decision that aligns with their customer service goals. Both Zendesk and Intercom offer robust solutions, but the choice ultimately depends on specific business needs. While both platforms have a significant presence in the industry, they cater to varying business requirements. Zendesk, with its extensive toolkit, is often preferred by businesses seeking an all-encompassing customer support solution. Understanding the unique attributes of Zendesk and Intercom is crucial in this comparison. Zendesk is renowned for its comprehensive range of functionalities, including advanced email ticketing, live chat, phone support, and a vast knowledge base.

On the other hand, Zendesk is a more comprehensive customer support tool that offers a broader range of features, including ticket management, knowledge base creation, and reporting and analytics. Its robust ticketing system and automation capabilities make it an excellent option for businesses with high-volume customer support needs. Additionally, Zendesk’s customizable dashboards and reporting features provide valuable insights into customer support performance.

Intercom is a customer relationship management (CRM) software company that provides a suite of tools for managing customer interactions. The company was founded in 2011 and is headquartered in San Francisco, California. Intercom’s products are used by over 25,000 customers, from small tech startups to large enterprises. Intercom’s solution aims to streamline high-volume ticket influx and provide personalized, conversational support.

Use HubSpot Service Hub to provide seamless, fast, and delightful customer service. Intercom puts a lot of effort into making a sleek and easy-to-use interface. They want to make a space that makes it easy for people to find their way around and quickly adopt the app. The design philosophy is based on keeping things as simple as possible so that even people who have never used the site before can quickly figure out how it works. After an in-depth analysis such as this, it can be pretty challenging for your business to settle with either option.

To select the ideal fit for your business, it is crucial to compare these industry giants and assess which aligns best with your specific requirements. However, the right fit for your business will depend on your particular needs and budget. If you’re looking for a comprehensive solution with lots of features and integrations, then Zendesk would be a good choice.

It is also not too difficult to program your own bot rules using Intercon’s system. In the category of customer support, Zendesk appears to be just slightly better than Intercom based on the availability of regular service and response times. However, it is possible Intercom’s support is superior at the premium level.

There is a simple email integration tool for whatever email provider you regularly use. This gets you unlimited email addresses and email templates in both text form and HTML. Help desk SaaS is how you manage general customer communication and for handling customer questions. If you’re not ready to make the full switch to Intercom just yet, you can integrate Intercom with your Zendesk account. This will provide live data on who your users are and what they do in your app.

From the inbox, live agents and chatbots can refer to and link knowledge base articles, to elaborate on replies and help customers locate answers. Zendesk is billed more as a customer support and ticketing solution, while Intercom includes more native CRM functionality. Intercom isn’t quite as strong as Zendesk in comparison to some of Zendesk’s customer support strengths, but it has more features for sales and lead nurturing.

If a customer starts an interaction by talking to a chatbot and can’t find a solution, our chatbot can open a ticket and intelligently route it to the most qualified agent. HubSpot helps seamlessly integrate customer service tools that you and your team already leverage. Picking customer service software to run your business is not a decision you make lightly.