10 Ways Artificial Intelligence is Impacting Marketing

By Kirsten Oelrich 5 min read

Artificial Intelligence has become integral to many industries. For marketers, the impact of AI has been nothing short of revolutionary. Surveys reveal that marketing professionals across industries agree that the arrival of AI has brought about a seismic shift in modern marketing practices. According to a recent study, around 72% marketers view Artificial Intelligence as a business advantage. AI and machine learning are being increasingly used to leverage customer data and anticipate the customer’s next move and enhance customer journey. This gives customers a highly personalized experience.

While the hype about AI in marketing is real, it’s important to understand how Artificial Intelligence is transforming day to day marketing activities. Here are 10 examples of AI in marketing.

Product categorization

AI and machine learning have helped online retailers and aggregators in the process of categorizing and tagging products more efficiently. Despite of different languages being used to describe a single product; the algorithm is able to understand and identify the product based on its context. Text based classification tools are being used to train various models. For instance, when a customer searches for a product, the piece of data provided is not the title of the product, but the model has classified it correctly because of the surrounding context; thus, giving the customer apt results for the search.

Audience targeting & segmentation

Marketers use AI to target their audience by giving them a personalized experience. Basically, AI draws the pre-existing data of the customers and are trained to identify important variables and common properties. They can even recognize incorrectly identified contacts. Marketers can segment their consumers’ data in both simple and complex ways. Simple as is age, gender or complex as in buying personas or past behaviors. Dynamic segmentation considers the fact that customer’s behavior is constantly changing or is fixed; they can take on different personas at different times for various reasons. For instance, if a customer browses for a gift for someone, dynamic segmentation will categorize them with the segment most appropriate to their present purchasing behavior, by using real-time data; and sending relevant offers for targeting.

Recommending products and content

Businesses are using AI for gathering and interpreting information pertaining to customer behaviors to predict their future behaviors. Basically, information gathered is paired and interpreted with demographics and profile information. The AI based systems enable customer recommendations for customers by using ‘collaborative filtering.’ It can now adapt to the likes and dislikes of the customer and offer new recommendations instantly. Successful digital companies are sending highly relevant and personalized product or content recommendations that are built around their products offerings. Now major brands are also jumping into the bandwagon. Some machine learning models are designed to recommend content according to the consumer’s mood. These days, customers are looking for brands that are providing similar experience. AI powered content recommendation widgets are also being implemented by publishers. It can identify related content and recommend it to the readers, as well as personalize it based on their browsing habits.

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Chatbots & conversational AI

Initially, chatbots were a big hype, but now its value has gone as it did not work as well as expected. However, many companies are successfully using chatbots to give information and respond to natural language queries. Now brands have started building conversational voice experiences for customers. The future of brand interaction will be through voice interfaces and voice enabled devices, and it will respond with the help of machine learning.

Analyzing and filtering data

Data is very important for marketing as its essential for improving customer experience, targeting, personalization and more. It is very difficult and tedious to manually analyze the vast amount of data that has been gathered. Therefore, AI is being integrated into business strategies, as it can take on complex analysis and organizational tasks. For instance, it can be used to enhance account selection in account-based marketing, as it is carried out at a large scale. Businesses can also filter out customers that can create a loss for them or identify customers that are beneficial to them.

Optimizing search engines

Many ecommerce websites have incorporated AI into their search engines. It makes product searching easier and faster. It is basically based on machine learning algorithm. With new innovations like semantic search and natural language processing, search engines can now determine the links between products or services to similar items suggested, auto-correct it and even find relevant search results.

Visual search & image recognition

These days, visual search is in its primary stage. AI image recognition and analysis have advanced and are being used for visual search. It is being used in the same way text-based search is being used. It helps find results for similar topics. Visual search is an asset for retail and marketing. It can be used to enhance merchandizing and personalize the shopping experience. It can recommend relevant products according to how they look. This helps customers to find products which are similar or match it. Customers use platforms like Pinterest Lens or Google Lens to identify products or brands related to the photo taken. Social media has helped marketers in identifying the visual trends, logos and finding the uses of brand products. Visual social listening has also helped marketers to identify how and where customers are interacting with their product, brand and logo, completely without text search.

According to a survey 51% of marketers are already using AI, let us look at how digital marketers use AI||  Incorporate Artificial Intelligence in your digital marketing plan today to create a more enjoyable customer experience.

Predictive analytics

Customer experience and service is improving for those businesses that have incorporated AI. Information is actually extracted from data sets and analyzed for predicting future trends. It is being used in ecommerce to analyze the customer’s purchase behavior, whether they would repeat the purchase or go for something new. Predictive analytics has helped marketers to deconstruct the customer’s experiences and actions to decide which marketing strategies would bring a positive result.

Computer vision & augmented reality

Computer vision is the programming of computers to see the world around them. Digital videos and images help them understand and gain insights. Information is automatically extracted, analyzed and understood from an image or video. This is possible only through machine learning, AI and loads of datasets, as they are trained to identify and recognize objects. Computer vision is very important for augmented reality (AR). It has to be accurate, especially for its application in marketing. It is relevant and not intrusive, as AR advertising integrates with the customer’s surroundings. It has the potential for giving customers product insights and offers, business information and interactive shopping experience. As of now furniture companies are using this to allow users to visualize how a furniture will look in their room. Cosmetic brands are also using AR to engage their customers. They can virtually try out products and then make the purchases. AR can also provide personalized recommendations to customers, educate them about the product and instruct them how to use it.

Speech recognition

Speech recognition technology has considerably advanced, especially natural language processing. There is a variety of voice-activated devices and their potential has increased. Speech recognition gives customers a good voice experience and it ensures that voice interfaces and interactions function without any complications. It also ensures that the customer’s requests are interpreted correctly.

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Kirsten Oelrich

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Kirsten Oelrich