Definitive PR & Marketing Guide to Machine Learning / AI

machine learning

Definitive PR & Marketing Guide to Machine Learning / AI

Machine learning is now a familiar concept and even a buzzword when it comes to marketing. That is to say, this form of artificial intelligence (AI) is employed more than ever, but at the same time, many business owners seem desensitized to buzzwords and somewhat uneducated as to the meaning or inner workings of machine learning.

In short, machine learning uses cutting-edge technology to create new opportunities and the affordable nature of this AI is just as attractive as the effectiveness of the process. With this in mind, we have put together an informative article and marketing guide to machine learning.

The Origin and Important of Machine Learning

In case you might be asking yourself, machine learning is nothing new. In fact, this term was first coined way back in 1959 by Arthur Samuel – one of the first known computer pioneers. At that time, machine learning was described as a science which gave computers the ability to learn, without being programmed for this same purpose.

Since then, new technology and media have inevitably steered this process in unexpected directions, but in truth, the overall concept is no different to when it first appeared.

As already mentioned, given the dramatic rise of AI in marketing, many business owners and professionals are often desensitized to the actual benefits of terms such as “machine learning”. Ironically, this can make the marketer’s job a lot more challenging, for they now need to convince startups regarding the purpose of this process before they can even put it into action.

Needless to say, this explanation is worth the effort as marketing would seem highly inadequate in the modern day without the assistance of AI.

What can Machine Learning Do?

Although aside from sounding complex, what does machine learning actually do?

Machine Learning is applicable in almost any industry, and its presence is likely to increase as soon as smaller businesses understand the many use-cases of AI. For example, in finance, machine learning can identify patterns and be used as a defense mechanism against fraud. In e-Commerce, AI is extremely fast when it comes to establishing a churn rate, while even dating websites are using this feature to create the perfect match. On the other hand, machine learning is increasingly prominent in the health industry, where data can be used to predict patient diagnostics and identify genetic patterns which could potentially lead to serious illness.

In fact, experts in every field are in agreement that machines are set to take on the responsibility of extracting and holding all the most critical information in every industry. When you understand the method behind this process, it would be hard to have any argument against it.

How is this Even Possible?

Sure, the concept of machine learning sounds promising, but how does it actually work?

Essentially, machines can learn by picking up on patterns within a particular system. As with people, when a machine receives more date, they become more “knowledgeable” although AI is less likely to forget this information than a person.

That being said, one of the tricky aspects of gathering this data relates to relevance. In some cases, data may not be appropriate or even misleading; something a machine can have trouble picking up. For this reason, machine learning is in the midst of a challenging period during which programming AI to choose the right information is most important.

In the end, once the necessary information is collected, the machine can make predictions based on probability or historical data.

Types of Machine Learning

Supervised learning, unsupervised learning, and reinforcement learning are the three types of machine learning but rest assured we are not trying to complicate matters for you.

Simply put, supervised learning is based on data labeled by humans while the opposite refers to unlabeled data which is interpreted by the machine according to patterns or previous data. On the other hand, reinforced learning is focused on a reward system.

As you might expect, the most common and straightforward of these is supervised learning which accounts for the vast majority of machine learning in every industry.

Using Machine Learning for Startups

As you may know, the above is not nearly enough to explain the impact or role of machine learning and for this reason, let us considered a brief example:

Have you ever wondered how advertisements for products or services find their way into your newsfeed, right after you spend time viewing or searching for these same items? Maybe they suddenly appear or stick out in the advertisement section on search engines?

Indeed, this is no coincidence but rather a direct result of machine learning which is used by almost every major platform from Bing and Google to Facebook and YouTube.

However, machine learning is not merely for the major brands, and this process is increasingly popular with savvy marketers. After all, one of the main objectives in marketing is to gather as much information as possible, which can then be used to create content and establish the most suitable strategy.

Here are some of the ways in which machine learning can benefit any business:

Personal Ads

When you consider Facebook, Google Ads or one of the many more platforms which allow adverts, you will see the perfect example of machine learning in action. As you know, these platforms collect endless information about users and behaviors which is then used to help paid advertisements target the right people.

Gaining Insights

Marketing is heavily reliant on information while research and reports are some of the most critical parts of the process for any product or service. In this respect, machine learning can extract vital information for performance, and this can be tweaked or changed to improve upon the initial strategy. At the same time, machine learning is also invaluable to research as it allows businesses to interpret the needs and wants which can eventually lead them to create the best product or service.

Formulating Mass Data

Data entry and related data studies are incredibly time-consuming. In fact, this is one reason for which automation is so widely embraced and accepted in many industries – people are tired of these unrewarding or monotonous tasks. Either way, machine learning can take full responsibility for accumulating this data and do this with more efficiency and accuracy than a human.

The Benefits of Machine Learning

Unfortunately, for many marketing specialists, they spend far too much time on selling the features of machine learning as opposed to putting them into practice. And we do not mean that these experts are lazy, but rather that AI can still seem like such a potent selling point that get caught up in the sales process as opposed to demonstrating the actual benefits.

Gathering Data – Machine learning is capable of accumulating mass information. Furthermore, this new technology can extract insights from this data which can be used almost immediately. Needless to say, it would not be possible to match the potential of machine learning without an incredibly large team of humans to concentrate on the same task.

Real-Time – While extracting insights from this data is crucial, one of the highlights is the capability of machine learning to put this date into action. In this sense, big data can be gathered, interpreted and used all at once. An example of this can be seen on social media platforms which offer to place adverts in front of a very specific audience.

Niche and Targeted Information – One of the main frustrations in marketing is the amount of waste when it comes to advertising. In the modern era, this often relates to clicks and impressions as in many cases, the adverts kept landing in front of the wrong people. Machine learning is changing all of this in a dramatic fashion, and Google Ad Words is a prime example of how behavioral data and purchase patterns can define a very effective target market for almost any product or service.

Decision Making – As you can see in the previous benefits, machine learning is responsible for gathering immense amounts of information. Quite often, this information is factual, accurate and based on real consumer trends. With this in mind, machine learning has made the decision-making process a lot easier for the fact that these decisions can now be made with informed data rather than blind projections based on nothing more than theory.

The Role of Machine Learning for Marketing AI Startups

Defining the underlying purpose of any brand, product or service is never an easy or quick task. When it comes to professional marketers, this is certainly true, and most experts will spend weeks or months trying to craft the perfect message. After all, this product is not only a first impression but also something which should stay with the organization for the foreseeable future.

Defining the Message – Machine learning is the perfect tool for marketers who need to gather ample information and condense this down to the most relevant data. Although this is sure to save time, the very specific nature of machine learning is designed to capture market sentiment and not just the statistics. With this in mind, copywriters or brand experts have a lot more to work with regarding defining the right and best message for a market.

Increased Efficiency – We would mention the speed of machine learning a lot more, but in truth, we expect that businesses will know about this already. After all, automation of any kind is rapid next to the alternative and can take responsibility for many of the most menial or frustrating tasks facing the business.

Affordable Cost – One of the most enticing aspects of machine learning is the cost, and this is most often a fraction of what it would take to employ people to do the same job. Furthermore, machine learning drastically reduces the cost of communication and other marketing-related expenses.

Improved Communication – Aside from the affordable nature of emails or instant chat functions, marketers can use machine learning to identify the most effective communication channels. Furthermore, many applications are available to integrate these mediums and form one main channel of communication.

What to Consider When Integrating AI and Machine Learning

While it may seem like a straightforward process on the face of it, integrating machine learning and AI can be a rather complex task. If the technology is not integrated correctly, this will obviously result in a poor performance but also consequences in the long term.

Selling Your Story

We mentioned previously how many marketers tend to sell the features of AI without putting it into action. This point really needs to be emphasized and made entirely clear, for it is crucial to understand why the customer is here in the first place.

In other words, it is crucial to realize that the client wants you, and not the AI. With this in mind, we can see why smart marketing strategy will always focus on the story behind the brand. After all, most services are almost identical to the next, and the only real difference between them is the underlying philosophy of the team in question.

Recognizing the Pitfalls of Reliance

When you integrate machine learning, it is also important to know that AI should never be the primary focus of a product or service. If a brand relies solely on machine learning, there is always the risk of losing ground when the relevance or impact of machine learning subsides. And this is a real possibility as marketing and technology continue to change at an exponential rate which can often make methods and strategies redundant.

Moral of the Story; the quality of a product or service should always be the main priority and if in doubt, consider how it might be perceived without the aid of machine learning or AI.

Research and Due Diligence

As with any new venture, research should be the very first step and one which is given a sufficient amount of attention. In a highly technical environment, many teams are capable of producing the most impressive products and services. However, one of the main downfalls in such cases can be the negligence of this team when it comes to performing research.

With this in mind, it needs to be understood that businesses should seek to know if a problem exists before creating a “solution”. Furthermore, when a business is fully aware of the specific details of this problem, they are inevitably able to create a much more accurate and relevant solution.

How to Integrate AI and Use Machine Learning

While we would like to tell you that the integration of machine learning is nothing more than the click of a button, this is simply not true. Every business should have a strategy for machine learning, and the best way to identify or understand the required algorithm is to begin with the overall objectives and then work backward.

In this sense, you may have a particular sales target in mind for a product or a certain number of page views for a website. If you know the current level of sales or page views, the sensible question to ask is “Why are you currently short of the objective?”

It may sound straightforward but unfortunately, the easiest of tasks can often become the most complicated. At this point, business owners or marketers should also consider putting themselves in the shoes of a customer and walk through the process with an open mind. Quite often, gaps and problems can be identified a lot faster during this walkthrough and provide a unique perspective on what needs to happen going forward.

With this in mind, chatbots are a great way to streamline a process and gather information at the same time. After all, this smart software is designed to provide customer service but then it also extracts email, feedback and other important information.

Steps to Integrate Machine Learning

As already mentioned, integrating machine learning involves some very specific stages but by breaking these down into more manageable steps, the process is a lot less overwhelming. Here are the main steps when it comes to understanding what is needed and then integrating machine learning into the business:

Step 1 – Know the Language

Algorithms are a bit of a mystery for most people but mostly for the fact that they seem far too scientific and mathematically complex. However, it is certainly worth putting time into learning about computer programming language such as Python, for having a reasonable understanding of data structure will be of great benefit to the programmers when they come on board to code the required algorithms.

Step 2 – Build the Team

Indeed, programmers will be the difference between excellent algorithms and substandard machine learning. For this reason, business owners should invest in creating a team with the strongest possible machine learning experience. Furthermore, this team will ideally consist of experts from various domains including business, data science, and technology.

In many cases, an organization will employ one vastly experienced individual to initiate the machine learning process. As time goes on, existing members of the organization can be tasked with assisting this person, and when the time comes, more professionals can be taken on board to roll out the rest of the process.

Step 3 – Deep Learning

Although this may be another word to add to the vocabulary for AI, deep learning is an effective strategy for machine learning. Simply put, this is an application which is powered by machine learning, and it is centered on creating algorithms to replicate the decision-making process in a human brain.

Sounds complicated, right?

Thankfully, marketers and programmers are usually adept when it comes to drumming up ideas for deep learning, and this strategy is mostly used in conjunction with video and image data.

Either way, deep learning is an option and one to consider.

Step 4 – Rolling everything out and Co-Existing with AI

When the time comes to roll everything out, there is also a responsibility to educate others and inform as to why machine learning should be embraced.

Indeed, as automation takes over the role from cashiers in supermarkets around the world, more and more people seem to fear the emergence of AI. However, machine learning is not intended as a replacement for humans but rather as a device to aide and improves the overall process.

To integrate machine learning, businesses need to adapt this mentality and ensure that every individual is fully aware that AI is not only here to stay, but also here to help improve their experience in the workplace.

Conclusion

It’s true that the best marketing practices continue to change and what seems to be relevant in this era may not be practical or relevant in the future. However, machine learning is currently an integral concept in marketing, and there is every reason to believe that this dynamic process is here to stay.

As you can see, machine learning also reaches far beyond Google Ad Words or the newsfeed on Facebook. Gathering information is critical in this process but the capability of AI to interpret and provide insight on this mass of information is nothing short of invaluable. Furthermore, machine learning is still an incredibly new concept and still in what one might call its infancy.

Either way, when you package everything up, the process makes sense, and with the assistance of programmers and marketers, there is really no reason to feel overwhelmed. Machine learning is a relatively simple process in theory and one with very complex capabilities. And then there is the cost, for this new technology is much more affordable than any alternative and one of the most proficient marketing tools on the planet.

Indeed, machine learning drives the algorithms and at a time when every major platform is already integrating these features, it would seem that embracing and adopting this AI is the only way to catch up.

Do you need any assistance with integrating machine learning? Maybe you need some guidance as to what you need to get things moving? Please do not hesitate to get in touch and we can get your product or service ready to thrive. You can contact info@press.farm or rhiannon@press.farm for more information.

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