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6 great Marketing Strategies That Use Artificial Intelligence.

6 Marketing Strategies That Use Artificial Intelligence.


It is inevitable that artificial intelligence (AI) will soon be an essential tool in the modern marketer’s toolkit. In fact, the ability to use AI-related technology will likely be a competitive advantage in the coming years. There are already AI-powered technology tools available, like chatbots, which can help marketers connect with customers, and machine learning (ML), which can analyze marketing data to create new strategies.

1. Chatbots:

Chatbots are probably the most common application of artificial intelligence in marketing. They’re used to automating customer service, and many businesses find that they’re more efficient and cheaper than traditional customer service options.

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Chatbots are also used for marketing in other ways that aren’t customer service related. Chatbots can be used to provide self-service information, which can save your customers time.

The possibilities for chatbots are endless – you could set up your chatbot to collect data on customer preferences, or to provide targeted and automated recommendations based on customer preferences.

Chatbots can also be used to engage customers in other ways. For example, they can handle small requests quickly and easily. Think of the bots available on Facebook Messenger – they can provide answers to simple questions, like “How do I change my contact details?”

2. Personalized Ads:

Ads are becoming more and more personalized as artificial intelligence allows marketers to target customers with greater accuracy. This improves the customer experience and increases the effectiveness of ads.

Marketers can serve relevant ads to their customers, making them more effective.

Personalized ads are the most effective form of digital marketing because they are directly related to customer interests and preferences. As a result, these ads will be much more likely to resonate with customers than non-personalized ads.

3. Predictive Analytics:

Predictive analytics is a form of artificial intelligence that uses data mining and machine learning to make predictions about the future. Predictive analytics allows businesses to make better decisions by using historical data and mathematical models to predict what will happen in the future.

Predictions can be made about customer behaviour, product performance & market demand. A good example of predictive analytics is Amazon’s “customers who bought this item also bought” feature. The idea is that people who buy a particular item are likely to buy other items that are

4. Content Creation marketing strategies using AI:

Automated content curation and aggregation: There are a number of tools like Jasper ai, that uses artificial intelligence and natural language processing to automatically gather and curate content from a variety of sources for use in blogs, social media posts, or newsletters. This can help to save time and ensure that you’re always sharing the most relevant content.

Content personalization: This is especially useful if you are running a large website or enterprise-level business. By using the customer’s personal information to tailor the messaging they see, you can increase engagement and conversion rates with every customer.

Automated social media management: So, you want your business to be on social media. While you can still use Facebook and Twitter manually by posting to your own accounts, there are tools that will do this for you.

Tools include services like Buffer, Hootsuite, and others, which will share your content for you and help you schedule when and where it will be posted.

Automated tagging: There are several tools that will help you tag your content correctly, and also help you find content to be tagged.

5. Churn prediction and smart customer engagement:

One of the biggest challenges for businesses is reducing customer churn. Churn prediction and smart customer engagement can help to reduce churn and increase customer loyalty.

There are a number of different ways that churn can be predicted. Statistical models can be used to identify patterns in customer data that indicate who is likely to churn. Machine learning models can also be used to predict churn. These models use algorithms to learn how to predict churn from past data.

Once churn has been predicted, businesses need to take action to prevent it. Smart customer engagement involves using customer data to personalize the customer experience. This can be done through smart segmentation, where customers are divided into groups based on their level of loyalty.

With the right customer segmentation strategy in place, businesses can tailor their engagement efforts to specific segments and optimize marketing and service efforts accordingly.

Customer churn prediction is just one part of the equation for a successful customer engagement strategy. It’s equally important to provide a positive experience throughout the customer lifecycle, whether it’s in product usage or in post-purchase interactions. A customer that is given a bad experience can be very quick to churn, and even the best prediction model won’t help.

Customer lifecycle management is also important for managing customer engagement in a consistent way across channels. Many companies are adopting omnichannel strategies, with customers interacting with them through different channels at different points in the lifecycle. It’s critical that each channel’s experiences are consistent with one another so that the customer has a seamless experience throughout the process.

6. AI-powered customer insights for marketing strategies:

As artificial intelligence (AI) starts to play a more significant role in business, one of the most important applications for it will be in understanding customers. AI can help companies to gain insights into customer behaviour that would otherwise be impossible to obtain. With these insights, businesses can create a more personalized experience for customers and improve their overall customer satisfaction.

The results can include higher revenue, better customer retention and larger market share. With these benefits in mind, it’s easy to see why AI is an appealing option for marketers.

Yet, it’s important for companies to remember that these benefits can only be achieved if decisions about AI are made carefully. With the wrong approach, AI systems might accidentally do more harm than good.

In order to make use of AI, it’s important to have access to high-quality data. This is something that many retail and financial services companies have already started to leverage.

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