Cybersecurity, Data Science and Machine Learning Expert for Sales and Marketing Analytics

Discover how I can help you construct a data driven marketing solution using Artifical Intelligence and Machine Learning to maximize your ROI and turn your online advertising into profit

20+ Years of Experience in B2B Sales, Marketing and Supply Chain Analytics

About Garry Mclachlan

Hi there my name is Garry Mclachlan, from Toronto Canada

I am a results-oriented leader and versatile Ryerson BCom graduate, an M.I.T. data scientist and machine learning graduate, an advanced cybersecurity graduate from Stanford combined with 20+ years of sales, marketing, and business to business development experience across various industries.

My specialty is designing behavioral-driven lead generation and conversion campaigns that target the perfect customers online for minimal costs and maximum revenue using artificial intelligence technology and SEO to optimize: Marketing Funnel Conversion, Reputation Mgmt. , eLearning Products, & Ecommerce Stores, to generate record revenue growth, maximize advertising ROI, and improve lifetime customer value and retention.

I've helped high-level experts, entrepreneurs, and business owners turn their expertise and passions into highly profitable businesses. (I do this by building "marketing automation vehicles" that help folks get to their income goals 6-24 months faster than they would if it was all on their shoulders).

I do this by using machine learning algorithms that use real-time analytics to optimize custom Facebook, Youtube, and Google PPC campaigns

I am open to working with a dynamic team of entrepreneurial business leaders and innovators in the cybersecurity, data science, and machine learning sector to generate record revenue growth, maximize advertising ROI and improve lifetime customer value and retention.

How can machine learning marketing metrics and analytics can reduce your ad spend and improve your revenue and allow you to scale faster than you ever thought possible?

Some Examples of what Machine Learning and Artificial Intelligence can do

Predictive Modeling

I can use your data to make predictions and model different scenarios and outcomes. This will help to find patterns in your existing or new data such as customer analytics, advertising metrics, and expected ROI

Machine Learning

I can implement and build machine learning applications that look for patterns, anomalies, or insights that can be used to build analytical models, evolve with deep learning algorithms and improve ROI.

Data Visualization

Building on the data analysis capabilities of machine learning and deep learning systems, we can identify customer or users behavior and use this for various marketing initiatives and split test results.

Problem Solving

Artificial Intelligence helps to solve complex problems using data science and machine learning to find information needles in very large haystacks by using different hypotheses and validate with super-smart unbiased data analysis.

Why Data Science for Marketing?

With data science, simply put you can make better data driven decisions.

Why? Because they are made not on someone's opinion but on a much more reliable source. Only data science and machine learning systems can analyze millions of bytes of the given data within seconds.

It is a massive simplification, but more or less it works like that: you have a problem to solve -> you input that into the data science examples-> the application analyzes all the data available -> you receive the best solution and the algorithm continues to evolve and know your customers better and offer relevant options that produce more revenue.

Do you see the difference?

You may have your instinct, but it can be unreliable. Data science and machine learning systems do not have this problem. They work without emotions and rush.

Examples of Artificial Intelligence Marketing

Case Study #1

How does Google Analytics use data science? -
Why do you care? Well, Data analytical systems, such as Google Analytics, deliver you accurate data about who visited your website or e-commerce, when, from where what was he or she interested in, and many more.

If you have been using Google Analytics already, you know how powerful a tool it is.

It helps you to suit your target audience's needs, and that is done by modifying your advertisements, your website's layout, or even offers too! Using data science may cause your company to implement some changes, because with the data-based solution probably you will see some new and unexpected possibilities. But the results of implementing the data-based strategy can also be surprisingly good.

How can machine learning algorithms be used for collecting, analyzing, and integrating data?

The term big data refers to extremely large sets of structured and unstructured data that cannot be handled with traditional methods.

Big data analytics can make sense of the data by uncovering trends and patterns. Machine learning can accelerate this process with the help of decision-making algorithms. It can categorize the incoming data, recognize patterns and translate the data into insights helpful for business operations.

Machine learning algorithms are useful for collecting, analyzing, and integrating data for large organizations. They can be implemented in all elements of big data operation, including structured and unstructured data labeling and segmentation, data analytics, and scenario simulation.

Market research and segmentation.

The reason enterprises need to carry out market research that can delve deep into the minds of potential customers and provide insightful data for recommendations and remarketing.

Exploring customer behavior

Machine learning does not stop after drawing a picture of your target audience. It also helps businesses explore audience behavior and create a solid framework for their customers.

Predicting trends

Machine learning algorithms use big data to learn future trends and forecast them for businesses. With the help of interconnected computers, a machine learning network can constantly learn new things on its own and improve its analytical skills every day.

Personalizing recommendations

Businesses need to offer personalization to their customers. Be it a smartphone or a web series, companies need to establish a strong connection with their users to deliver what's relevant to them.

Examples of Machine Learning

Case Study #2 - Recommendation systems in marketing & advertising

This one is fantastic because, for marketers, it is very valuable to analyze user behavior on their websites. Therefore, using data science in marketing, companies can determine: what are the tastes and preferences of the customers what kind of knowledge or help do they seek what are they interested in what do they want to buy how much do they want to pay for it?

Customer Journey analysis allows you to create more and more perfect recommendation systems which on the basis of this information indicate time-specific products that customers are willing to buy.

Furthermore, the implementation of such systems helps stores to be closer to the customer and thus drive their business. The data science algorithms can help sales representatives in deciding between products or services eligible to suggest to the potential client. Or they can indicate what discount would be reasonable.

Data science in the company is fast, accurate, and irreplaceable support. Data science can indicate which prospects sales representatives should focus on, which prospects have the biggest chance to close the deal. There's plenty of options.

Smarter Supercharged Marketing Analytics

Marketing is the key to the customer acquisition kingdom, and metrics are critical to that success. Building analytics into your marketing strategy empowers your marketing and sales teams by providing the ability to measure the impact of each marketing investment.

Data Science and Machine Learning enable marketers to confidently identify which parts of the marketing efforts deliver the optimal return on investment (ROI), including the performance of channels, specific calls-to-action (CTAs), and individual pieces of content, such as blog posts or gated resource guides.

With the right marketing analytics, you can accurately forecast results and measure the progress of each marketing activity against defined milestones. You’ll be able to optimize your marketing in real-time, accurately plan out your future marketing long-term, and overall, frame, justify, grow, and defend all your marketing activities and budgets.

What are some examples of Artificial Intelligence SAAS Marketing Machines?

I've always been a big fan of automation and innovation when it comes to SAAS software. Over the past decade, technology has come a long way to make complicated things like building websites, making compelling videos, or segmenting email marketing lists.

As with any business transformation, the success of your marketing measurement program depends on how well you implement it and what its running on, and what data is being collected. This requires you to establish the right team, process, and technology

These are some of the best SAAS software that is presently using AI in their respective tool suites when it comes to building a brand online.

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Need help building an Automated Online Customer Acquisition Marketing Funnel?

Discover my 6 Step Process to Automation

Try to imagine a perfect scenario where every lead that is captured by your website is responded to immediately, along with a marketing and sales automation system that keeps your client in the loop, the company leaders informed, and your team on the ball.

Imagine a perfect scenario where every lead is never sent more information than they’ve requested.

Imagine a perfect scenario where important geographic and demographic information about your target client is automatically mined by your system.

Imagine a perfect scenario where your web traffic doesn’t take immediate action but is re-targeted in other social media platforms and brought back to the point of action.

Imagine a perfect scenario where automated tasks are created for you only if the automation is not followed by the prospect or lead.

Imagine a perfect scenario where every lead is put through a simple automated qualification process, saving you the time of sifting through the criteria yourself for a good target client.

More Examples of Machine Learning

Case Study #3 - Supply chain optimization in the logistics industry

The world is a complicated place with risks emerging at every conceivable turn. Anticipating, planning, and responding to these changes and risks are critical to the longevity of any operation. Big data has proved to be helpful across the risk management spectrum, providing early visibility to potential risks, helping to quantify the exposure to risks and potential losses, and helping to expedite the response to major changes.

Risk models based on big data have proved their benefit across a range of industry applications, from customer and market risks to challenges emerging from government shutdowns to natural disasters. Companies can digest information from a wide range of disparate data sources and synthesize that information to provide greater situational awareness and understanding of how to allocate resources to deal with emerging threats.

Supply chain management and logistics firms have been particularly adept at using big data to help identify and mitigate potential risks. From closures that can result in supply shortages to unexpected demands due to natural disasters or pandemic-induced changes in buying behavior, supply chain and logistics firms have used big data to understand how best to apply limited resources to mitigate these evolving risks. In addition, security companies, both physical and cybersecurity firms, are making increased use of big data to inform their threat and risk assessments and thus mitigate new and evolving risks.

It is also of huge importance in the logistics industry. Optimization algorithms are able to shorten the delivery time and select the optimal route for the vehicles thus reducing operating costs and speeding up the work! Transport service providers are also able to predict the demand for services with high accuracy by combining historical data with information on consumer profiles and macroeconomic indicators.

Data science can also optimize the warehouse sector. Also, it saves time, space, and resources while reducing errors in managing the warehouse.

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