How Artificial Intelligence (AI) Can Evolve Your Marketing Strategy

Artificial intelligence (AI) makes it possible for machines to learn from experience, adjust to new data inputs, and perform human-like tasks, while relying on deep learning and other technologies to optimize over time.  It sounds complex, but AI is rapidly giving way to marketing innovations that expand reach and engagement exponentially for businesses of all types. It’s an incredibly useful tool that can be used to build modern brands and offer a better user experience than ever before.

Nearly all businesses can benefit from evolving their marketing strategies through the implementation of various AI technologies, and it’s not as difficult as it may seem. Here are just a few examples of how brands can use AI to take their marketing to the next level.


More and more frequently, consumers expect personalized online experiences with brands. In fact, according to a new Epsilon study, 80% of consumers are more likely to make a purchase when they have a personalized interaction with a brand, and according to Statista, 90% say they find personalized content “somewhat” to “very” appealing.

Personalization helps brands gain insights into their customers’ preferences and intent through data, so they can offer more tailored experiences. With personalized content and experiences, visitors and customers are able to see what is relevant to them more quickly, and they can easily determine if a product or brand is a good fit. This helps boost sales and engagement.

Using AI, brands can develop algorithms that generate a personalized content feed for users. It can also be used to gather and analyze critical data on customers that can then be applied to target ideal clients even more specifically, and make the best real-time recommendations based on their patterns, interests, behaviors, and preferences.

Pay-Per-Click Advertising

The pay-per-click (PPC) advertising model is a great example of AI in marketing, as it’s already widely used. For example, AI helps to support:

  • Dynamic search ads: In these search ads, headlines are dynamically generated to target relevant searches depending on the user’s search and the text that’s most relevant to your landing page or domain.
  • Smart bidding: These automated bid strategies use AI to optimize for conversions or conversion value in every auction. Examples of smart bidding strategies include:
    • Target CPA
    • Target ROAS
    • Maximize conversions
    • Maximize conversion value.

During bidding, AI algorithms analyze data at a massive scale to help you make more accurate predictions and decisions about how different bid amounts might impact your ads.

  • Price optimization: The right price can make or break a brand. Price optimization requires a large amount of data to ensure you’re finding the sweet spot of pricing for a product or service. AI can help digest consumer and market data to find that balance between value and profit.
  • Responsive ads: Responsive ads automatically adjust their size, appearance, and format to fit available ad spaces. AI can be used to help deliver relevant, valuable ads in the correct size and format for a particular audience.


Recent reports show 87.2% of polled consumers have had a neutral or positive experience with a chatbot. Chatbot marketing is a great way to promote products and services and perform customer service tasks, with AI supporting these conversations.

Chatbots understand basic requests and are able to showcase certain products. They also allow for customers to quickly get answers to questions in real time. Not only does this greatly improve the customer experience, but it can also boost sales.

In fact, chatbots are predicted to save businesses 2.5 billion hours by 2023, and businesses can then allocate employee time to other tasks that was previously spent fielding phone calls and emails, and handling other customer service scenarios.

Additionally, chatbots can make use of natural language processing (NLP) to communicate with visitors on a site. This allows for more informed and realistic recommendations for products or quickly answering questions.

Natural Language Processing

Natural language processing (NLP) is a branch of AI that’s concerned with enabling computers to understand text and spoken word, similarly to the way humans can. NPL can include:

  • Speech recognition, which enables a computer to identify and interpret words and phrases in spoken language and convert them into texts.
  • Part of speech tagging, which refers to categorizing words in a text with particular parts of speech, depending on the definition of the word and its context.
  • Named entity recognition, which can automatically scan articles and pull out fundamental entities in a text, classifying them into predefined categories.
  • Sentiment analysis, which is used to determine whether data is positive, negative, or neutral. This can help brands monitor product sentiment in customer feedback, as well as better understand customer needs.
  • Coreference resolution, which is the task of finding all expressions that refer to the same entity in a text. This can help understand things like document summarization, question answering, and information extraction.

Predictive Analytics 

Reading and analyzing large amounts of data on customer interactions with websites and marketing materials can be overwhelming. However, AI allows for optimal use of data via predictive analytics. This takes away the need to manually analyze the data yourself, with the goal of going beyond knowing what happened to providing the best assessment of what will happen in the future.

Machine learning can use your customer and prospect data to predict future behavior based on current user behavior. Examples include time on site, pages visited, and interactions with content. AI makes collecting and analyzing this data instantaneous. This eliminates the need for manually parsing through the data and dedicating company time to analysis.

It also offers a greater level of accuracy. This can increase conversion rates and improve customer satisfaction due to the more customized experiences and relevant content delivered.

Overall, predictive analytics is important in marketing because it can:

  • Automatically optimize marketing campaigns
  • Help businesses attract, retain, and grow their most profitable customers
  • Improve operations and enable efficiency
  • Reduce risk

Contact Us

If you are looking to enhance your digital marketing strategy through AI technology, contact Dragonfly Digital Marketing today. We can help you accelerate your efforts towards expanding reach, driving revenue, and achieving your organizational goals.

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