The Best AI Tools I’m Using Right Now

The Best AI Tools I’m Using Right Now

And Why They’re Changing Everything

Most businesses are dangerously unaware of what AI is doing to their industry. For several decades, Google has consistantly sent customers to local and national websites without fail, but these last few months, something has changed and it has local chamber of commerces worried.

I’ve spent 27 years watching the internet evolve — from dial-up to social media to mobile-first everything. Nothing has moved as fast, or hit as hard, as AI.

As an AI SEO consultant and web designer, I live and breathe digital tools. And right now, artificial intelligence isn’t just a trend I’m watching from the sidelines — it’s embedded in how I work, how I build, and how I help my clients grow. If you’re still wondering which AI platforms are worth your time, I’m breaking down the ones I actually use and trust in 2025.

What Is AI, Really?

Artificial intelligence refers to technology that enables computers to learn, reason, and perform complex tasks in ways that once required human intelligence. But that clinical definition doesn’t capture what it feels like to use it. AI feels like hiring a brilliant assistant who never sleeps, never complains, and never runs out of ideas.

The tools below are the platforms reshaping how individuals, businesses, and entire industries operate. I’ve tested them all. Here’s what you need to know.

1. Claude by Anthropic — The Best AI for Deep Thinking

Claude is quickly becoming my go-to for complex, high-stakes work. Built by Anthropic with safety and accuracy as core design principles, Claude handles nuanced tasks that other AI tools stumble on — long-form analysis, technical writing, coding, and strategic planning. If you need an AI that actually thinks through a problem rather than just pattern-matching a response, Claude is where I consistently land. It’s the AI I trust when I can’t afford to be wrong.

2. ChatGPT — The Everyday Powerhouse

ChatGPT from OpenAI is the platform that put AI on the map for most people, and for good reason. It’s fast, versatile, and remarkably good at everyday tasks — drafting emails, brainstorming content ideas, explaining complex topics in plain language. I use it as a first-pass research and ideation tool. Whether you’re a student, entrepreneur, or marketer, ChatGPT lowers the barrier to entry for AI-powered productivity in a way few platforms have matched.

3. OpenAI — The Research Engine Behind the Revolution

Before there was ChatGPT, there was OpenAI — the research organization whose mission is to ensure artificial general intelligence benefits all of humanity. OpenAI’s work on large language models, image generation, and AI safety has set the standard for the entire industry. If you want to understand where AI is headed at the frontier level, following OpenAI’s research is essential reading. Their developer tools also power a massive ecosystem of AI applications used by millions of businesses worldwide.

4. Google Gemini — Google’s Answer to the AI Race

Google didn’t sit quietly while the AI revolution happened around them. Gemini is Google’s flagship AI assistant, built directly into the Google ecosystem. What sets Gemini apart is its deep integration with Search, Gmail, Docs, and the broader Google Workspace. For anyone already living in Google’s tools — which is most of us — Gemini feels like a natural extension rather than a separate app. I especially appreciate its ability to pull real-time information, giving it an edge for research-heavy tasks.

5. Google AI — The Bigger Picture

Beyond Gemini, Google AI represents Google’s broader commitment to enriching knowledge and solving complex global challenges through artificial intelligence. From healthcare applications to climate modeling to accessibility tools, Google AI is working at a scale that goes well beyond chatbots. For anyone interested in how AI is being applied to real-world problems at an institutional level, this is a fascinating window into what the technology can actually do when pointed at meaningful challenges.

6. Perplexity AI — The Search Engine Reimagined

Perplexity is one of the most exciting AI tools I’ve started recommending to clients who are tired of sifting through ten blue links to find an answer. It’s a real-time AI-powered answer engine that pulls from live sources and cites them directly — so you can trust what you’re reading and trace it back to the original. For research, competitive analysis, and staying current on fast-moving topics, Perplexity has genuinely changed how I start my research process. It’s free, fast, and remarkably accurate.

7. Z.ai — The All-in-One AI Agent Worth Watching

Z.ai is a newer name in the space but one that’s caught my attention. Powered by advanced GLM models, Z.ai positions itself as a free AI-powered assistant that can build websites, create slides, analyze data, and answer questions in real time. The breadth of what it aims to do in a single interface is ambitious — and for users who want one tool to handle multiple types of tasks without paying multiple subscription fees, it’s worth exploring. Keep an eye on this one as it continues to develop.

Why This Matters for Your Business

The AI platforms above aren’t toys — they’re infrastructure. The businesses and professionals who learn to use them strategically right now will have a compounding advantage over those who wait. I’ve already seen this play out with my own clients: the ones who leaned into AI tools a year ago are producing more content, ranking faster, and scaling with smaller teams than ever before.

As someone who has built websites and SEO strategies since 1999, I can tell you: this shift is bigger than mobile. It’s bigger than social media. And the entry point has never been lower — most of these tools are free to start.

The question isn’t whether AI will change your industry. It already has. The question is whether you’ll let it work for you.

Q: What is artificial intelligence (AI)?

Artificial intelligence is a set of technologies that enables computers to learn, reason, and perform advanced tasks that previously required human intelligence — including understanding language, recognizing images, making decisions, and solving complex problems.

Q: How does AI actually work?

AI systems are trained on massive datasets, learning patterns and relationships within that data. When you ask an AI a question or give it a task, it uses those learned patterns to generate a response or take an action. Large language models like those behind ChatGPT and Claude are trained on billions of text examples to understand and produce human-like language.

Q: Is there any AI I can use for free?

Yes — several leading AI platforms offer free access. ChatGPT (https://chatgpt.com), Claude (https://claude.ai), Perplexity AI (https://www.perplexity.ai), Google Gemini (https://gemini.google.com), and Z.ai (https://z.ai) all have free tiers that give you meaningful access without a subscription.

Q: What are the main types of AI?

The five primary types of AI are: reactive machines (respond to inputs with no memory), limited memory AI (learns from past data, like ChatGPT), theory of mind AI (still in development, understands emotions/intent), self-aware AI (theoretical, not yet achieved), and narrow vs. general AI — most tools today are narrow AI, designed for specific tasks.

Q: What can I actually use AI for in my daily life?

AI has practical applications across almost every area of life: writing and editing, research and summarization, coding and debugging, scheduling and task management, creative brainstorming, data analysis, customer support, language translation, image generation, and even medical diagnosis support.

Q: What are the benefits of artificial intelligence?

AI saves time, reduces human error, enables personalization at scale, accelerates research, improves accessibility, and opens opportunities for small teams and solo entrepreneurs to compete with much larger organizations. For businesses, AI can triple productivity in areas like content creation, customer service, and data analysis.

Q: What are the ethical concerns around AI?

Key ethical issues include bias in training data leading to discriminatory outputs, job displacement, privacy and data security risks, spread of misinformation via AI-generated content, lack of transparency in AI decision-making, and questions around accountability when AI systems cause harm. Responsible AI development — like Anthropic’s safety-first approach with Claude — is actively trying to address these challenges.

Q: Which jobs are safest from AI automation?

Jobs requiring deep human empathy, complex physical dexterity, creative judgment, and high-stakes interpersonal relationships are considered most resilient. These include healthcare providers, skilled tradespeople (plumbers, electricians), therapists, teachers, and creative directors. However, even these roles are being augmented — not necessarily replaced — by AI tools.

Q: Does the Bible mention AI?

No — the Bible predates modern technology by thousands of years and contains no direct reference to artificial intelligence. However, many theologians and philosophers are actively exploring AI through ethical and spiritual frameworks, examining questions of consciousness, soul, and what it means to create intelligent systems in the image of human thought.

Q: What is the difference between ChatGPT, Claude, and Gemini?

All three are large language model-based AI assistants, but they differ in design philosophy and strengths. ChatGPT (OpenAI) excels at everyday versatility and creative tasks. Claude (Anthropic) is designed with a strong emphasis on safety, nuance, and handling complex, long-context tasks. Gemini (Google) integrates deeply with Google’s ecosystem and has strong real-time search capabilities. Choosing the right one depends on your specific workflow.

Q: What is generative AI?

Generative AI refers to AI systems that can create new content — text, images, audio, video, or code — rather than just analyzing existing data. Tools like ChatGPT, Claude, Gemini, and image generators like DALL·E are all examples of generative AI.

Source:

Google AI

https://ai.google

Claude

https://claude.ai

Z.ai

https://z.ai

Perplexity AI

https://www.perplexity.ai

Google Gemini

https://gemini.google.com

Google Cloud

https://cloud.google.com/learn/what-is-artificial-intelligence

ChatGPT

https://chatgpt.com

OpenAI

https://openai.com

How to build your own AI

How to build your own AI

What Does It Cost to Build an AI Assistant for a Small Business in 2026?

Real Pricing for HVAC, Plumbing, Electricians, Landscapers, Window Cleaners & Medical Offices

Small business owners are increasingly asking:

“Can I get an AI assistant for $300 per month that runs my entire business?”

They want something that:

  • Responds to leads automatically

  • Books jobs in Housecall Pro

  • Organizes Google Docs

  • Manages Google Calendar

  • Writes emails

  • Handles HR tasks

  • Screens job applicants

  • Sends follow-ups

  • Conducts research using Google Gemini

What they’re imagining isn’t a chatbot.

It’s a fully integrated AI operations system.

This article breaks down:

  • Real-world AI assistant development costs

  • Monthly infrastructure expenses

  • Industry-specific pricing

  • Required third-party APIs

  • Ongoing maintenance fees

  • Example build costs by industry

If you’re searching for “AI chatbot development cost,” “how much to build a Gemini assistant,” or “AI assistant pricing for small business,” this guide gives you transparent numbers.


What an AI Business Assistant Actually Includes

A real AI assistant for a service business typically includes:

  • Lead intake automation

  • Two-way SMS messaging

  • Missed call auto-response

  • CRM integration (Housecall Pro, Jobber, ServiceTitan)

  • Google Calendar scheduling

  • Email drafting & responses

  • Estimate follow-ups

  • Review automation

  • Hiring workflow automation

  • Internal documentation organization

  • Research assistant functionality

This requires multiple APIs, hosting infrastructure, monitoring systems, and ongoing optimization.

This is business infrastructure.

Not a plugin.


Core APIs Required (And Why)

1. Google Gemini API

Used for:

  • Writing emails

  • Responding to customer inquiries

  • Research tasks

  • Internal documentation summaries

  • HR drafting

Monthly cost: $50–$300 depending on usage.


2. Twilio (SMS API)

Used for:

  • Two-way texting

  • Missed call text-back

  • Appointment reminders

  • Review requests

Monthly cost: $25–$150+


3. Zapier or Make (Automation Layer)

Used to:

  • Connect CRM with Google Workspace

  • Sync hiring forms

  • Trigger workflows

Monthly cost: $30–$150+


4. Google Workspace API

Used for:

  • Gmail drafting

  • Calendar scheduling

  • Google Docs organization

Cost: $12–$25 per user/month.


5. Hosting & Monitoring

Used for:

  • Backend automation server

  • Error tracking

  • Logging

  • System reliability

Monthly cost: $45–$130.


Typical Baseline Infrastructure Cost:

$150–$500 per month (before maintenance fees)


AI Assistant Cost by Industry (2026)

Below are realistic example build costs when designed and implemented by an experienced systems architect like Sandy Rowley.


HVAC Company (6–12 Technicians)

What the AI Handles:

  • Emergency intake triage

  • Quote follow-ups

  • Seasonal reminders

  • Dispatch note automation

  • Review generation

  • Hiring intake assistance

Monthly Infrastructure: $250–$600
One-Time Build Cost: $15,000–$25,000
Monthly Maintenance Fee: $750–$1,200

Why?
HVAC companies often generate $1M+ annually. A 10% improvement in close rate can mean six-figure revenue gains.


Plumbing Company

AI Responsibilities:

  • Emergency dispatch intake

  • Membership renewal reminders

  • Invoice follow-up

  • Hiring automation

Monthly Infrastructure: $250–$600
Build Cost: $15,000–$30,000
Maintenance: $750–$1,500

Higher urgency and liability increase system complexity.


Electrical Contractor

AI Responsibilities:

  • Permit tracking reminders

  • Project updates

  • Invoice follow-up

  • Hiring automation

Monthly Infrastructure: $200–$500
Build Cost: $12,000–$20,000
Maintenance: $600–$1,000


Window Cleaning Company (5–8 Employees)

AI Responsibilities:

  • Lead qualification

  • Estimate booking

  • Weather rescheduling

  • Unsold estimate follow-ups

  • Review automation

Monthly Infrastructure: $150–$400
Build Cost: $8,000–$12,000
Maintenance: $497–$750


Landscaping Company

AI Responsibilities:

  • Recurring maintenance scheduling

  • Seasonal upsells

  • Photo documentation organization

  • Review requests

Monthly Infrastructure: $150–$400
Build Cost: $7,500–$12,000
Maintenance: $397–$750


Medical Office (Private Practice)

AI Responsibilities:

  • Appointment reminders

  • Intake screening

  • Insurance FAQ

  • Secure messaging

  • HR documentation drafting

Monthly Infrastructure: $400–$1,000
Build Cost: $25,000–$60,000
Maintenance: $1,000–$2,500

Compliance requirements significantly increase cost.


What Monthly Maintenance Covers

Sandy Rowley’s monthly maintenance includes:

  • Error monitoring

  • Prompt refinement

  • Workflow adjustments

  • API updates

  • Integration troubleshooting

  • Feature improvements

  • AI training optimization

  • Security monitoring

AI systems require ongoing refinement.

They are not “set it and forget it.”


Why $300 Per Month Isn’t Realistic

If infrastructure costs alone range from $150–$500 per month, then $300 total does not cover:

  • Hosting

  • SMS usage

  • Monitoring

  • Ongoing training

  • Development support

That price point only works if:

  • The system is heavily standardized

  • Customization is extremely limited

  • It serves dozens of businesses simultaneously

Custom operational AI systems require professional implementation.


Build Timeline Expectations

Basic Lead Capture Assistant:
1–2 weeks

Booking + Follow-Up Automation:
3–4 weeks

Full AI Operations Assistant:
4–8 weeks


Final Takeaway

An AI assistant that:

  • Reduces admin workload by 40–60%

  • Increases booked jobs by 10–20%

  • Prevents missed calls

  • Improves follow-up

  • Assists with hiring

Is not a $20 task.

It is operational leverage.

And when built correctly, it becomes one of the highest ROI investments a service business can make.

A Step-By-Step Guide on How to Build an AI

Published by Lilit Melkonyan at February 22, 2026

Since the 1940s, when the digital computer was developed, it’s been clear that computers could be programmed to complete extremely complex tasks. For example, they could discover proofs for mathematical theorems or play chess. In fact, computers or computer-controlled robots can perform tasks typical of humans. That’s where artificial intelligence comes into play.

Are you interested in how to build an AI? This article provides a basic understanding of artificial intelligence, its application, and the steps necessary for making an AI.

What Is Artificial Intelligence

Build AI.

Artificial intelligence (AI) is the ability of a digital computer or computer-controlled robot to carry out tasks that intelligent beings perform. AI represents a branch of computer science. Siri, Alexa, and similar smart assistants, as well as self-driving cars, conversational bots, and email spam filters, are examples of AI.

Mathematician Alan Turing’s paper, “Computing Machinery and Intelligence,” and the Turing Test express AI’s fundamental goal and vision. Turing wrote his paper on artificial intelligence, arguing that there isn’t any convincing argument that machines can’t think intelligently like humans. Similarly, the Turing Test is a method of determining whether a machine can “think.”

Based on the information theory, intelligence is one’s ability to accept or transfer information and keep it in the form of knowledge. The information theory mathematically represents the conditions and parameters that affect how information is transmitted and processed

According to Shane Legg, co-founder of DeepMind Technologies, intelligence is the agent’s ability to set goals and solve different problems in a changing environment. If the agent is a human, you deal with natural intelligence, and if the agent is a machine, you deal with artificial intelligence.

AI Operation and Application

Increasingly, building AI systems is becoming less complex and cheaper. The principle behind making a good AI is collecting relevant data to train the AI model. AI models are programs or algorithms that enable the AI to recognize specific patterns in large datasets.

The better you make AI technology, the more wisely it can analyze vast amounts of data to learn how to perform a particular task.

The process of analyzing data and performing tasks is called machine learning (ML). For example, Natural language processing (NLP) gives machines the ability to read, understand human languages, and mimic that behavior. The most promising AI apps rely on ML and deep learning. The latter operates based on neural networks built similarly to those in the human brain.

Real-world applications of AI systems are wide-ranging. Below, you can find the most common examples of AI in daily life:

  • Speech Recognition

Also known as automatic speech recognition (ASR), computer speech recognition, or speech-to-text, is a capability that uses NLP to process human speech into a written format. For example, Siri utilizes speech recognition to conduct voice searches.

  • Customer Service

Increasingly, more companies are turning to online virtual agents for customer service, thus replacing human agents. According to Servion Global Solutions, 95% of all customer interactions will involve artificial intelligence by 2025.

  • Computer Vision

In this case, AI technology allows computers and systems to derive meaningful information from digital images, videos, and other visual inputs. You can see its application in photo tagging on social media.

  • Discovery of Data Trends

AI algorithms can use consumers’ behavior to discover data trends, allowing companies to build effective cross-selling strategies. As a result, companies can offer relevant add-on recommendations during the checkout process. That’s where predictive analytics software steps in.

Such software allows real-time decision-making with your data. For instance, the software can generate risk assessment models, such as fraud and risk detection, targeted advertising, and product recommendations.

  • Fraud Prevention

One of the primary problems that artificial intelligence tackles are payment and sensitive information fraud. Companies utilize AI-based systems to detect and prevent this type of fraud effectively.

  • Automated Stock Trading

AI-based high-frequency trading platforms make thousands or, sometimes, millions of trades each day. As of 2020, half of stock market trades in America were automated. According to Allied Market Research, the global algorithmic market size is forecast to account for $31.2 million by 2028.

How to Build an AI: What Is Required to Build an AI System?

Gartner, Inc. predicts that worldwide AI software revenue will reach $62.5 billion in 2022, growing by 21.3% from 2021. So, how to build an AI? Let’s go through the basic steps to help you understand how to create an AI from scratch.

Step 1: The First Component to Consider When Building the AI Solution Is the Problem Identification

Before developing a product or feature, it’s essential to focus on the user’s pain point and figure out the value proposition (value-prop) that users can get from your product. A value proposition has to do with the value you promise to deliver to your customers should they choose to purchase your product.

By identifying the problem-solving idea, you can create a more helpful product and offer more benefits to users. After you’ve developed the first draft of the product or the minimal viable product (MVP), check for problems to eliminate them quickly.

Step 2: Have the Right Data and Clean It

Now, when you’ve framed the problem, you need to pick the right data sources. It’s more critical to get high-quality data than to spend time on improving the AI model itself. Data falls under two categories:

  • Structured Data

Structured data is clearly defined information that includes patterns and easily searchable parameters. For example, names, addresses, birth dates, and phone numbers.

  • Unstructured Data

Unstructured data doesn’t have patterns, consistency, or uniformity. It includes audio, images, infographics, and emails.

Next, you need to clean the data, process it, and store the cleaned data before you can use it to train the AI model. Data cleaning or cleansing is about fixing errors and omissions to improve data quality.

Step 3: Create Algorithms

When telling the computer what to do, you also need to choose how it will do it. That’s where computer algorithms step in. Algorithms are mathematical instructions. It’s necessary to create prediction or classification machine learning algorithms so the AI model can learn from the dataset.

Step 4: Train the Algorithms

Moving forward with how to create an AI, you need to train the algorithm using the collected data. It would be best to optimize the algorithm to achieve an AI model with high accuracy during the training process. However, you may need additional data to improve the accuracy of your model.

Model accuracy is the critical step to take. Therefore, you need to establish model accuracy by setting a minimum acceptable threshold. For example, a social networking company working on deleting fake accounts can set a “fraud score” between zero and one to each account. After some research, the team can decide to send all the accounts with a score above 0.9 to the fraud team.

Step 5: Opt for the Right Platform

Apart from the data required to train your AI model, you need to pick the right platform for your needs. You can go for an in-house or cloud framework. What’s the main difference between these frameworks? The cloud makes it easy for enterprises to experiment and grow as projects go into production and demand increases by allowing faster training and deployment of ML models.

  • In-house Frameworks

For example, you can choose Scikit, Tensorflow, and Pytorch. These are the most popular ones for developing models internally.

  • Cloud Frameworks

With an ML-as-a-Service platform or ML in the cloud, you can train and deploy your models faster. You can use IDEs, Jupyter Notebooks, and other graphical user interfaces to build and deploy your models.

Step 6: Choose a Programming Language

There is more than one programming language , including the classic C++, Java, Python, and R. The latter two coding languages are more popular because they offer a robust set of tools such as extensive ML libraries. Make the right choice by considering your goals and needs. For example:

  • Python is a good choice for beginners as it has the simplest syntax that a non-programmer can easily learn.
  • C++ boasts a high level of performance and efficiency, making it ideal for AI in games.
  • Java is easy to debug, user-friendly, and can be used on most platforms. In addition, it works well with search engine algorithms and for large-scale projects. As a rule, Java is used to build desktop applications.
  • R is developed for predictive analysis and statistics. Thus, it’s primarily used in data science.

Step 7: Deploy and Monitor

Finally, after you’ve developed a sustainable and self-sufficient solution, it’s time to deploy it. By monitoring your models after deployment, you can ensure it’ll keep performing well. Don’t forget to monitor the operation constantly.

Sum Up

“How to build an AI” is a question many are interested in these days. To make an AI, you need to identify the problem you’re trying to solve, collect the right data, create algorithms, train the AI model, choose the right platform, pick a programming language, and, finally, deploy and monitor the operation of your AI system.


Lilit Melkonyan

Lilit Melkonyan

Content WriterLilit Melkonyan is a Content Writer with a background in Philology. Lilit loves Research and Analysis and has covered various topics such as Science and Technology, eCommerce, Marketing, and Finance.


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by Sandy Rowley AI Developer

How has AI changed digital marketing for the better?

How has AI changed digital marketing for the better?

How has Artificial Intelligence(AI) influenced Digital Marketing?

social-media-and-ai-tools

Artificial intelligence has widespread use in the digital marketing space. Since its inception, Artificial Intelligence has become increasingly relevant to digital marketing strategy and execution. From making predictions, constructing simulation models, and even customizing the purchasing process by offering suggestions based on previous purchases, the importance of Artificial Intelligence in digital marketing is undeniable. With technology constantly changing ad developing, digital marketing will benefit significantly from using Artificial Intelligence.

What is Artificial Intelligence(AI)?

Artificial Intelligence is the development and theory of computer systems able to perform tasks that require human intelligence like decision-making, translating languages, recognition of speech, visual perception, and problem-solving. The ideal feature of Artificial Intelligence is its ability to identify its environment, analyze the environment, solve problems, and take actions that have the best chance of success.

Types of Artificial Intelligence

Reactive Machines

These are the most basic type of AI. They only react to current situations and cannot create memories, store information, or use past experiences in decision-making.

 

Limited memory

This type of AI can store previous data and use it to learn, train, and make better predictions.

 

Theory of mind

This concept of AI can perform the functions of limited memory machines and have the cognitive capacity to understand and respond to human emotions.

 

Self-aware

These machines are aware of their emotional states and that of others. This AI has human-level intelligence and can process, mimic and respond to human emotion.

social-media-marketing-expert-reno-nv

What is Digital Marketing?

Digital marketing is advertising a brand, product, or service using the internet and other digital channels like email and social media. With the advent of the internet in the 1990s, digital marketing has revolutionized the marketing industry allowing businesses to reach a larger consumer base. Digital marketing campaigns appear on social media, tablets, and computers and can measure their impact throughout the customer journey.

 

Alt Text: Digital marketing is essential when reaching out to potential customers on social media platforms.

 

Digital Marketing channels

 

Digital marketing is conducted through a variety of channels.

 

  • Affiliate marketing: affiliates earn a commission for marketing a company’s product. Commission can be from traffic generated or purchases made.
  • Social media marketing: marketing on social media channels like Facebook, Twitter, Instagram, and Youtube.
  • SMS marketing: sending promotional messages via SMS.
  • Content marketing: the creation of written material that will engage, attract and retain an audience to create a customer base.
  • Video Marketing: using videos to promote and create awareness of a product or service.
  • Email marketing: sending customized content to your subscribers. The content can include discounts or information on new products.
  • Pay-per-click advertising: the advertiser pays every time their ads are clicked.
  • Website marketing: promoting a company’s website to increase traffic.
  • Search engine optimization: making your website page rank higher on search engines.

 

Impact of Digital Marketing on Business

 

In a world that is constantly moving towards the digital space, digital marketing has become essential to most businesses. Over 3 million people in the world use social media channels daily and a majority are more likely to follow brands than celebrities. Good digital marketers utilize the digital space to promote brands and increase awareness of the products or services that they offer.

 

Here is how digital marketing has impacted businesses that use it:

 

  • Lead generation

 

Leads are generated when customers show interest in a product. Social media marketing allows users to show interest in your product without having to purchase it; this generates more leads for your business.

 

  • Sales increase

 

Digital marketing exposes your brand or product to a large audience maximizing the purchasing potential. This increases your sales volume.

 

  • Increase in brand awareness

 

More than half of the world’s population is on social media today; making it the perfect tool to connect with potential customers and increase your brand awareness. More than 50% of social media users get to know about new brands through this platform.

 

  • Connecting with customers one-on-one

 

Unlike traditional forms of marketing which involve one-way communication, digital marketing is a dialogue. The audience can directly interact with the brand or business. Customers can give reviews on products and services directly to the business.

 

  • Improves customer service

 

Customer service in digital marketing can be automated making responses to queries quicker. Fast answers to questions about your products will help you retain and grow your customer base.

 

  • Cuts down on marketing expenses

Digital marketing is cheaper than traditional forms of marketing. This is because most processes are personalized and automated. 

 

  • Variety of marketing outlets

 

Digital marketing opens up marketing opportunities that would otherwise be unavailable with traditional forms of marketing. There is a wide variety of marketing outlets to choose from. Businesses can pick the outlets that best suit their needs and use them. They can even decide to optimize their results by using all the marketing outlets available.

 

  • Global reach

 

Traditional forms of marketing are usually limited to a specific geographical zone/region. However, digital marketing has a global reach; it delivers your marketing message to a worldwide audience. Global exposure to your brand increases sales volume.

social-media-marketing-expert-reno-nv social networking

How has Artificial Intelligence changed digital marketing for the better?

 

Automated and personalized marketing

Automated marketing is great but most of the time it is not personalized; this could be the difference between making a sale or not. AI and machine learning can collect customer data by analyzing their preferences and sending personalized messages and emails. 

 

AI also can predict what type of content will appeal to a specific customer at a specific time and create an attractive message persuading them to buy. AI can automate social media marketing, email marketing, search engine optimization, and pay-per-click advertising. In a world where businesses are competing to get to clients first, personalized marketing gives a company an upper hand over its competitors.

 

Targeted marketing

Convincing someone you do not know to purchase your product or service can be difficult. This is a hurdle that traditional forms of marketing have not been able to overcome. However, AI can create customer segments based on the occurrence of behaviors and future events. This technique is known as predictive consumer segmentation and is usually automated. Using the data from predictive consumer segmentation, marketers can estimate whether the consumers will be interested in their products before asking them to buy, saving time and increasing sales.

 

Boosting customer relationship management(CRM)

 

A customer relationship management system allows a business to maintain a good relationship with customers and increase sales.

AI-based customer relationship management gives companies insights into how their customers interact. It can also monitor customer data and predict who is likely to make a purchase. AI can also, study different sales scenarios where deals were rejected or closed and provide useful information that can assist the sales rep to take the right steps to close a deal. AI can also identify which customer has the intention of purchasing the product or service thereby quickening the sales process. Chatbots also offer knowledge that is critical to expanding your client base. With AI-based CRM, businesses close more deals and lose less money.

 

AI-based content marketing

 

Alt Text: Artificial intelligence conducts content research allowing content marketers to deliver customized content to potential buyers.

 

Marketing largely relies on getting the right message to consumers at the right time. This requires market research to identify consumer trends and craft the right content to persuade consumers to buy.

 

AI has already started revolutionizing content marketing. A lot of work usually goes into content marketing; content writing, search engine optimization, trends, and analytic and keyword research. With chatbots, predictive analysis, and custom feed algorithms, AI can perform content research and development allowing content marketers to focus on the writing. It also assists in predicting topics that will be of interest in the future providing content marketers with a head start in the content writing process.

 

In addition to conducting content suggestions and research, Artificial Intelligence can integrate data from its predictive analytics into content like blogs and articles creating content that can generate leads and increase sales.

 

Identifying micro-influencers

Influencers are currently playing a huge role in the marketing industry. With their large number of followers and subscribers, they are one of the best options to use in social media marketing. The majority of influencers are usually celebrities; they are quite expensive to hire. However, there are smaller influencers(micro-influencers) who have created an audience of people who value their opinions. They are also cheaper than celebrities.

 

AI algorithms enable marketers to identify a micro-influencer who will best promote their products or services by analyzing their audiences and the content they post. AI will also assist the marketer in deciding whether it’s best to hire an influencer or several micro-influencers. Choosing the right influencer using AI is better and more efficient than the marketer deciding on their own which influencer is best.

AI MARKETING IN RENO

Customer service chatbots

 

A chatbot is a computer program that uses AI and natural language processing to simulate human conservation. It can understand customer questions and send automated responses to them. 

 

Many customers prefer to communicate with companies via messaging apps like WhatsApp, direct messaging, and Facebook messenger. It is difficult and expensive to maintain customer care staff on these platforms. Some businesses use chatbots to respond to customer inquiries decreasing the workload and providing instant responses. Chatbots can also be programmed to give predetermined answers to frequently asked questions. In addition to this, they can also forward complex queries to a human operator.

 

Enhanced security

 

Transferring large amounts of data securely were previously seen as difficult to achieve. However, with biometric authentication that uses AI technology, it is now possible to gather and transfer data securely. Digital marketing data is paramount in customizing customer experiences, and safeguarding it will ensure that you maintain your customer base. Customers are also more likely to purchase if the safety of their data is guaranteed.

 

Predictive analytics and behavioral analysis

 

Data on the behavioral patterns of customers can easily be found on the internet. Analyzing these huge amounts of data is impossible for humans. Artificial intelligence, on the other hand, uses machine learning and large data analysis to analyze customer data and give information on the current and future behavior of customers. This information can be used in future marketing campaigns.

 

Alt Text: Predictive analytics can predict future customer behavior and purchasing patterns.

 

Why use Artificial Intelligence in Digital Marketing

 

  1. It identifies market trends and opportunities.
  2. Makes marketing more effective in driving revenue.
  3. Provides in-depth and precise analysis of campaigns.
  4. Helps to pinpoint potential customers.
  5. Helps to identify new marketing opportunities.
  6. Identifies current and future marketing opportunities.

 

Benefits of using AI in digital marketing

 

  • It saves on costs.
  • Personalization of the digital marketing processes to fit customer behavior.
  • Understanding your customers better.
  • Increase in sales
  • Reduced human error.
  • It has enhanced user experience.
  • Improvement of recommendations and suggestions of future trends and personalization of the marketing strategy.
  • Search Engine Optimization.
  • Optimization of pay-per-click advertising.
  • Content curation and creation.

 

Will AI take over digital marketing?

 

There has been a lot of debate on the future of AI in digital marketing with some marketers thinking that Artificial Intelligence will end up replacing them. However, AI adds value to marketing teams by helping to analyze large data sets, enabling personalized marketing, and using predictive analysis to assist in future campaigns. AI is a co-partner, not a replacement.

 

Artificial Intelligence and Digital Marketing

Artificial Intelligence assists in several tasks in digital marketing like optimizing campaigns(email and social media), analyzing customer data, and predicting future trends and consumer behaviors. It can also automate tasks like customer service(using chatbots) and email marketing campaigns. Incorporate Artificial Intelligence into your digital marketing strategy to enjoy the above benefits and more!

 

 

How has AI changed digital marketing for the better?