5 Examples of AI Marketing

Artificial intelligence has made progress in leaps and bounds, and many things that used to be pure Sci-Fi are now a reality. For years, the world has tried to become more digitized, and only in the past few years have we started to grasp the limitless advantages of everything surrounding AI correctly.

Below listed are 5 examples of AI marketing.

  • Deep Learning
  • Content Benefits and Search Recommendations
  • Ad Targeting and Marketing Automation
  • Chat-Bots
  • Dynamic Pricing
AI Marketing

Deep Learning

Humans can do not everything; for instance, we can’t monitor and operate every part of a business by ourselves. Even having a team of individuals dedicated to a particular job is still overwhelming. Apart from a range of tasks that could be pointed out, having a computed way of handling marketing procedures is necessary.

Deep learning is an A. I function that is the center of algorithms in computerized systems. These mechanisms have advantages such as recognizing speech, detecting objects, making decisions, and translating languages. Therefore, the system itself can understand your audience because of the Deep Learning function in it.

Deep Learning is the center for marketers in today’s world. It teaches computers and inculcates them with skills like understanding text, speech, or photos, delivering answers, clarifying queries, offering suggestions, and even improving filters on posts and ads you create. These are the things that help marketers target their desired audience. If that’s not a benefit for marketers, then what is?

Content Benefits and Search Recommendations

The entire internet and everything that comes with it is connected. There are layers and layers upon one another, which are responsible for each other, yet can even act separately on their own for whatever purpose they’re entitled to. This means thinking about content and searches on the web.

They’re connected to the internet and come from the internet. Yet, it is a segment of the whole system responsible for its actions but still related and affected by whatever comes before it. That thing that comes before is (another layer) called Deep Learning, which comes from (another layer) Machine Learning, which comes from algorithms which are a part of the computers infrastructure.

That’s just putting it mildly, so you get the idea. Each layer can get complex in its own way, and each segment is getting smarter and smarter, thus making the whole system and everything from it smarter.

The content you see in all forms everywhere, and the searches you use and everything you experience from them, all come from the same place. Deep learning runs it all, and Deep Learning is also an AI machine that analyzes large chunks of data every sec. The data this mechanical system collects all the time is on people’s behavior on the internet.

The reason for this is simple; it teaches you to give you what you want. Algorithms analyze your likes and dislikes, thus giving you only what you like. This awesome mechanism is a marketer’s greatest gift.

Thanks to this system, marketers know what their audience wants from them and do everything to please them. People are satisfied with this because they get understood and get more of what they want. All your searches are analyzed from this AI system, and products are recommended to you accordingly. Based on what you search, click, and buy, the system teaches you, thus offering you exactly what you need.

Google is perhaps one of the best examples to use here when it comes to machine learning. We generally know it as a search engine, but it is a smart computer with was smart yesterday, but today it is super smart. It is so smart that it detects your words, however, is pronounced/misspelled as they may be and suggests the most likely matches for your search keys while producing results based on your misspelled/mispronounced words.

Google’s algorithm and its updates, such as Hummingbird, Penguin, etc., are all significant chunks of machine learning and operative code introduced in large segments with holistic capabilities that on their own are added to an existing powerhouse of intelligence.

This huge electronic entity comprises an ultra-enormous amount of organized data and is segmented in various configurations at unimaginable speeds to make suggestions that meet user needs. The basis of all this is not just leveraging keywords, their variations, contextual occurrences, etc., but also user intent, user behavior, user search history, etc. And this is just the tip of the iceberg, as we’re stating here includes the bare fundamentals.

While there is a vastness of numbers, alphabets, symbols, and whatnot, that code is what delivers the results you see and makes users go wow! Tailored results, suggestions, etc., are only some of the things that lead you to your desired goal eventually, if not immediately. After all, there’s a reason why Google and a couple of top-rated search engines are the ones we turn to when we need answers.

Ad Targeting for Marketing Automation

Ad targeting is a process of carefully selecting which ads to show to which audience. However, the overall processes of creating and selecting can become complex and overwhelming due to these complaints A. I technology has created a system for marketing automation called (RNN.) Recurrent Neural Networks to make the process of tracking website visitors and Ad targeting more efficient and smoother.

Whether you take Facebook or Google as an example, the idea is the same; they and other search engines tend to show you advertisements based on your search keys and user behavior. This feature is a huge help because it helps consumers and brands too. Both don’t need to work too hard to achieve their objectives. For brands producing their ads, their efforts are an investment that search engines favor for their users’ benefit.

Every time a brand funds an advertisement and runs it, provided that everything is configured correctly, Facebook, Google, etc., will direct the ads to show up for users whose user behaviors match the services and products in those ads. Brands need to be careful when configuring their ads because things like location, gender, age groups, etc., are essential factors in targeting an audience. Search engines won’t help them drive the leads brands are looking for if the configurations are wrong.


Many marketers use chat-bots that are AI automated that can interact with humans – to answer customer queries, assist customers when making purchases, and much more. Chat-Bots have a crucial role to play. They engage with customers and lead them down a trail through a sales cycle by suggesting certain content through their frequently asked questions, engaging with them across platforms, or tailored email campaigns.

Chatbots are increasingly becoming renowned for their accuracy when you need direct answers. They are by their nature alert and will give you precisely what you need if they have it in stock. They don’t beat around the bush and hand you accurate data based on what they have in their data warehouse. With memory access being even more readily available than before with advanced cloud tech, users are pleased with the speed they get their answers and relative accuracy.

In most cases, when most people chat with customer service online, they can’t be sure it’s a human being or not due to the level of customer-friendly engagement. However, with relatively no mistakes or miscommunication, customers suspect there’s a polite machine behind those professional exchanges. Brands report higher sales since they have adopted chatbots, which is only the beginning.

Dynamic Pricing

Dynamic pricing benefits your audience and you as a marketer. It is a benefit for the whole supply chain process of your products. It is based on real-time changes in product supply and demand. It’s an essential procedure for marketers in their product advertising and supply benefits for their customers.

Dynamic pricing allows a company selling goods or services online an edge over price adjustment on the fly in response to market demands, apart from other extensive benefits.

The price adjustments that are done on a run-time basis are handled through pricing bots. These are software agents responsible for collecting data and applying algorithm calculations to adjust pricing. Aspects taken into consideration include the customer’s location, the time of day, the day of the week, the level of demand, and competitors’ pricing.

These bots collect extensive data to analyze and target your audience and refine the pricing benefits. By collecting and analyzing individual customer data, pricing bots project current, and possible pricing soon, customers would be willing to pay.

It is a legal action, and it is realistic, too, as consumers mostly feel that they are fair. The best use of this implementation is probably demonstrated when you purchase airline tickets or reserve hotel rooms online. The approach of dynamic pricing is sometimes termed a personalization service. This approach contrasts with fixed pricing, an approach to setting the selling price for a product or service that does not fluctuate.

Market price fluctuation, competitor activity monitoring, and product demand and supply are some of the things that are considered when adjusting prices. The most logical approach that this mechanism leverages is lowering prices to remain in the competition and manage internal stock levels. This allows you to remain competitive in the market.

Bottom Line

Artificial Intelligence benefits us in more ways than we can imagine; it is being harnessed for curtailing global warming problems, pollution problems and is even handy in education for all kinds of individuals. As for businesses, it serves financial procedures and management, helps to mitigate and handle work-time losses in various ways, eases business processes smoother, and makes marketing procedures more effective.