Digital marketing is currently the most dynamically developing marketing segment. It is aimed at both reaching and building relationships with clients. That’s why it is extremely important to optimize your activities on the internet. Optimization allows more frequent and significant changes to the campaign. This is where AI in digital marketing steps into the game. Additionally, it allows more effective use of collected customers data such as:

    • clicks,
    • impressions,
    • time spent on the website,
    • the number of transactions made,
    • and many more.

Systems based on continuous analysis of up-to-date data will improve the results of your marketing performance in the internet

Marketing campaigns are more than just delivering messages. Time and the manner of their delivery are also important. Without a data-driven approach, campaign-related opportunities can easily be overlooked. Problems may arise when launching a new campaign. Machine learning in marketing is an ally of everyone who wants to maximize results when putting the same amount of work into tasks and marketing projects.

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AI in Digital Marketing for Cost, Time, and Profit Optimization

The aim of Machine learning is to take advantage of the achievements in the field of artificial intelligence (AI). With its help, we are able to create an automatic system that improves performance. Thanks to systems that use machine learning, you can optimize time and costs, make decisions faster, and in a more profitable way. Algorithms create better communication tailored to each customer. They allow them to run more effective campaigns that can be monitored and modified on an ongoing basis.

Machine learning could also help to improve content marketing, advertising, and sales processes. This type of software connects to other internal systems such as CRM or marketing automation. Thanks to such solutions, advertising campaigns are conducted more precisely. Additionally, collected data allows you to better target your potential clients.

Optimizing campaigns by using machine learning improves cooperation with potential clients and generates revenue. It can also generate a high return on marketing investment (ROI). Feel free to ask us about data science consulting.

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AI in Digital Marketing – Use Cases

AI in Digital Marketing – Application for Customer Churn Prediction

Retention is the main problem in all industries. Retaining a customer is 5 times cheaper than acquiring a new one. By analyzing the changing behavior of customers, algorithms are able to detect the riskiest customers. Those algorithms are able, in an automatic way, to predict which customers are most likely to stop using your product or services. That information would help you react accordingly and retain customers who are planning to leave.

Customer Lifetime Value Prediction (LTV)

Customer Lifetime Value (LTV) measures all the potential profits a particular customer can bring to your business. Machine learning algorithms for marketing will help you to understand the patterns and categorize your customers according to their LTV predictions. In this way, you will know which categories are likely to spend the most money. Which group will respond to your offers and discounts with a greater frequency? And which group will influence revenues in the future?

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Recommendation Engine (recommender system)

AI algorithms can predict customer preferences and propose personalized offers based on the behavioral data of every customer. With the acquired data on the behavior of customers with similar preferences, the algorithm can predict what kind of products or services may draw the attention of the particular buyer in the future. It will definitely support and personalize your cross-sell and up-sell activities.

You may also find it interesting – product recommendation system.

Conversion Prediction (lead scoring)

This can be divided into 2 rates. First is the probability of purchase (mainly for e-commerce, retail and similar industries). The second one is the probability that leads will convert into an opportunity/customer.

  1. Based on historical sales activity AI algorithms can determine the probability of a particular lead convert into an opportunity or customer. The sales team can use that information for better lead segmentation and prioritization. The gained knowledge will help them to understand why leads are likely to convert or not.
  2. Machine learning on the basis of users’ demographic data, activity on the online shops, loyalty program activity, preferences, and interests of each user can predict the probability of purchase in a particular time or event. That can be used for real-time marketing (show discount when customer hesitates) and support the product recommendation system.

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Customer Segmentation

AI algorithms in combination with big data from marketing campaigns can be used for finding your customer segments. Auto-segmentation algorithms will automatically learn on various behavioral data points and give you insights. Using such segments you can understand better what types of people are close to each other. That will help you to understand customer groups within your business. Later, you can use this information to improve customer satisfaction and user experience.

Dynamic Pricing

Price optimization enables you to adjust the price of products by a particular customer’s ability and willingness to pay. It will give you an opportunity to maximize conversion and revenue. Because people are more likely to buy goods that fit within their budget, AI algorithms will also analyze prices, previous sales, and revenue transactions to set prices, which would maximize your profits and purchase probability.

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Messages Personalization

Personalization in the creation of messages that correspond to the specific needs of a particular customer. Machine Learning based on demographic data, interests, and behavioral data can create a personalized message. This message for a particular user will be delivered with the right topic at the right time. If the user receives a marketing message with content that is thematically related to the problem he is looking for, he is much more likely to make a purchase decision.

Personalized Content on Website

Appropriately personalized content on the website increases content popularity and raises customer involvement. Machine learning can automatically find patterns and match them with the personal preferences of a particular customer based on past activity. That will help you to improve customer experience and customer satisfaction.

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How Does It Work in Practice? – AI in Digital Marketing

In practice, the above-mentioned solutions are integrated into your internal business applications, CRM systems, or e-commerce platforms or websites. You are gaining the real value using those algorithms together with an automated flow. That flow sends results from the AI model to your systems where direct actions are being performed.

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If you have more questions concerning Machine Learning integration and use of it in your particular digital project, feel free to ping us a message. Our data science experts will be happy to tell you more.

Check our case studies to see how marketing & loyalty companies from other industries use machine learning and deep learning.

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