How to Design an AI Marketing Strategy

Free illustrations of Artificial intelligence

AI is playing a big role in many industries and is having a lot of success. The most recent statistics demonstrate that AI can increase business productivity by up to 40%.

As new technologies are being developed and adopted at a rapid pace, many market leaders are feeling pressure to improve and become more efficient. They are turning to AI as the most powerful tool to help them achieve this goal.

Businesses that have AI will have an advantage over their competition. Many companies are investing in artificial intelligence to change the way marketing is done.

What is AI Marketing?

What is AI?

AI is an interdisciplinary science with multiple approaches. There is no one answer to the question “What is artificial intelligence?” because there is no one definition of artificial intelligence that is accepted by everyone.

Artificial Intelligence includes many different technologies that can perform tasks that usually require human intelligence. When used for regular business tasks, these technologies can gain knowledge, take action, and work with human-like intelligence. Artificial intelligence can help automate tasks which would normally be completed by humans, making processes more efficient and potentially saving businesses money.

Historically, four different approaches have been explored to Ai are:

The USPTO states that technologies within the AI landscape include (but are not limited to) machine learning, knowledge processing, AI hardware, natural language processing, evolutionary computation, computer vision, speech recognition, and planning/control.

Artificial Intelligence Marketing (AI marketing) is a rapidly evolving field in the digital era.

So what exactly is AI marketing?

In other words, AI marketing is a way of using smart technologies to get information about customers, what they might do next, and make decisions about marketing that take these things into account. In marketing, AI is usually used in speed-critical situations. According to a report from McKinsey, AI can boost the ROI of marketing by up to 50%.

Marketers can use AI to understand their customers’ behaviors and know what actions and indications they are likely to take. This allows them to be more efficient with their time and target the correct strategy to the appropriate person.

Firms are increasingly using AI to handle a variety of tasks, from narrow tasks like digital ad placement, to broad tasks like enhancing predictions, to structured tasks like customer service. There are many AI applications in marketing that can support common activities.

Firms employ AI at every stage of the customer journey, from the initial contact to post-purchase follow-up. If potential customers are considering purchasing a product, AI can target ads at them and help guide their search. The online furniture retailer Wayfair uses AI to determine which customers are most likely to be persuadable and, on the basis of their browsing histories, chooses products to show them. Bots that use AI to understand customers can help marketers increase their engagement and steer them towards specific pages or products. If needed, these bots can also connect customers to human sales agents via chat, phone, video, or even by sharing a screen to help them navigate.

AI can make the sales process faster and more efficient by using very detailed data on individuals to create customized product or service offers. As the journey continues, AI will help to sell more products and services, and make it less likely that customers will give up and leave their digital shopping carts. You’ll love how this changes your life.” After a customer fills a cart, AI bots could provide a testimonial to help close the sale, such as “Great purchase! You’ll love how this changes your life.” The initiative mentioned in the text can lead to a five-fold increase in conversion rates, or even more.

If a customer needs help after a sale, they can contact AI-enabled service agents from Amelia or Interactions. These agents are available 24/7 and are better equipped to handle a high volume of requests than human agents. They can handle simple queries about, say, delivery time or scheduling an appointment, and they can escalate more-complex issues to a human agent. Sometimes, AI can help customer service representatives by analysing the customer’s tone and suggesting different responses. AI can also coach customer service agents on how to best satisfy customer needs or suggest when a supervisor should intervene.

The Framework

AI can be sorted by how intelligent it is and if it works alone or as part of a larger system. The classification of some technologies, such as chatbots or recommendation engines, can be determined by how they are implemented within a specific application.

Let’s look at the two types of intelligence first.

Task automation.

These applications are designed to perform repetitive, structured tasks that do not require a high level of intelligence. They are not able to deal with complex issues such as requests that have many different aspects to them. A system that automatically sends a welcome email to each new customer would be an example. Other chatbots that are simpler, such as the ones provided by Facebook Messenger and other social media platforms, are also included in this category. The customer service robots can provide help to customers during basic interactions and take them down a defined decision tree, but they cannot discern customers’ intent, offer customized responses, or learn from interactions over time.

Machine learning.

These algorithms are designed to make predictions and decisions based on large quantities of data. Such models can recognize images, decipher text, segment customers, and anticipate how customers will respond to various initiatives, such as promotions. Machine learning models can help you automate decisions and processes to improve efficiency and effectiveness. Programmatic buying in online advertising, e-commerce recommendation engines, and sales propensity models in customer relationship management (CRM) systems are all driven by machine learning. The technologies of it and deep learning are quickly becoming powerful marketing tools. Although machine-learning applications can narrow tasks, they still need extensive training using a lot of data.

Now let’s consider stand-alone versus integrated AI.

Stand-alone applications.

Each AI program is designed to complete a specific task. They are not the same channels that customers use to find out about, buy, or get support for using a company’s products, or the channels employees use to advertise, sell, or provide service for those products. Essentially, customers or employees would need to go out of their way to use the AI.

Behr, a paint company, created a color-discovery app. The application provides recommendations for Behr paint colors based on the emotions detected in the text using IBM Watson’s natural language processing and Tone Analyzer. The app allows customers to choose two or three colors for the room they want to paint. The sale of paint is executed outside the app, although it does allow users to order from Home Depot.

Integrated applications.

These AI applications are often less visible to the customers, marketers, and salespeople who use them because they are embedded within existing systems. An example of machine learning would be a platform that is able to make split-second decisions about which digital ads to offer users. This platform would be responsible for the entire process of buying and placing ads. Netflix’s machine learning offers customers video recommendations that appear in the menu. If the recommendation engine were not stand-alone, they would not need to go to a dedicated app and request suggestions.

A Stepped Approach

We believe that marketers should focus on integrated machine-learning applications, as they offer the greatest value. While rule-based and task-automation systems can enhance highly structured processes, they don’t offer as much potential for commercial returns. Task automation is becoming more common with the use of machine learning to help with extracting data from messages, making complex decisions, and personalizing communications.

Even though stand-alone applications have their advantages in certain situations, they cannot be used in every instance. It is recommended that marketers gradually incorporate AI into existing marketing systems, rather than using AI for isolated tasks. Many companies are planning to integrate AI into all enterprise applications within three years, according to a 2020 Deloitte survey.

Getting Started

One way for firms who have limited experience with AI to get started is by creating or purchasing basic applications that follow a set of rules. Many companies use a “crawl-walk-run” approach, starting with a task-automation app that is not customer-facing and is used by human service agents.

companies that have acquired basic AI skills and have a lot of customer and market data can start moving from task automation to machine learning.

Challenges and Risks

Implementing even the simplest AI applications can present difficulties. Task-automation AI that is not integrated with other systems can be difficult to configure for specific workflows and may require a company to have AI skills in order to be able to use it effectively. If you want to integrate AI into your work process, you need to be careful about how you combine human and machine tasks. The AI should help people with their work, not cause new problems. Many organizations rely on rule-based chatbots to automate customer service; however, thesechatbots are often less capable, which can lead to frustration for customers. It is better to have bots assist human agents or advisers instead of interacting with customers.

As companies adopt more sophisticated and integrated applications, they must also consider other factors. It can be difficult to incorporate AI into third-party platforms. An example of this is Procter & Gamble’s Olay Skin Advisor, which uses deep learning to analyse selfies customers have taken, to assess their age and skin type, and recommend appropriate products. The Olay21 app is integrated into an e-commerce and loyalty platform, It has improved conversion rates, bounce rates, and average basket sizes in some geographies. Although Olay has been doing well, it has been difficult to integrate it with retail stores and Amazon, which make up a large percentage of Olay’s sales. The lack of the Skin Advisor on Olay’s Amazon store site hinders the brand’s ability to provide a smooth, AI-assisted customer experience.

Finally, companies must keep customers’ interests top of mind. The more advanced and integrated AI applications become, the more customers may worry about privacy, security, and data ownership. Some customers may be concerned about apps that collect and share location data without their knowledge. Others may be worried that their smart speakers could be eavesdropping on them. Most people are willing to give up some personal data and privacy in order to use apps that provide value.

A final thought

Artificial intelligence has been making great strides for a long time, and it is already influencing the future of marketing. You are responsible for using this technology in your business. It is certain that AI is the future. To succeed in running an online business in the next few years, it is essential to use marketing and tools that are powered by artificial intelligence.


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