AI makes it possible to use collected customer data more effectively.
Systems based on the continuous analysis of up-to-date data will improve the results of marketing campaigns.
Marketing campaigns are more than just delivering messages. The timing and the way they’re delivered also matter. Without a data-driven approach, campaign opportunities can easily be overlooked. Problems can arise when launching a new campaign. Machine learning in marketing is an ally to anyone who wants to improve results while putting the same amount of work into tasks and marketing projects.
AI for optimizing costs, time, and profits in digital marketing
The goal of machine learning is to make use of advances in artificial intelligence (AI). It can be used to create an automated system that boosts productivity. Systems that use machine learning make it possible to optimize time and costs and make effective decisions. They enable more effective campaigns that can be monitored and adjusted continuously.
Machine learning can also help improve content marketing, advertising, and sales processes. This type of software connects to other internal systems, such as CRM or marketing automation. These solutions make advertising campaigns more precise. In addition, the data collected makes it possible to target potential customers more effectively.
Optimizing campaigns using machine learning improves work with potential customers and generates revenue. It can also deliver a high return on marketing investment.
Artificial intelligence (AI) in digital marketing – use cases
1. An AI-based application for predicting customer churn in digital marketing.
Customer retention is a major challenge across all industries. Retaining a customer is 5 times cheaper than acquiring a new one. Algorithms can automatically predict which customers are most likely to stop using a product or service. This information can help you respond appropriately and retain customers who are planning to leave.
2. Predicting customer lifetime value (LTV).
LTV measures all the potential profit a particular customer can bring to a business. Machine learning algorithms for marketing can help identify patterns and classify customers according to their projected LTV. This makes it possible to find out which categories of people might spend more money. Which group will respond more often to offers and discounts. And which group will influence revenue in the future.
3. Recommendation system.
AI algorithms can predict customer preferences and offer personalized recommendations based on each customer’s behavioral data. Once it has data about the behavior of customers with similar preferences, the algorithm can predict which products or services may catch a particular buyer’s attention in the future. This will definitely support and personalize cross-selling.
4. Conversion prediction (lead scoring).
This can be divided into 2 parts. First, there’s the probability of a purchase (mainly for e-commerce, retail, and similar industries). Second, there’s the probability that a potential customer will become a regular customer.
Based on historical sales activity, AI algorithms can determine the likelihood that a particular lead will become an opportunity or a customer. The sales team can use this information to segment and prioritize leads more effectively.
Machine learning based on user demographics, online store activity, loyalty program activity, and each user’s preferences and interests can predict the likelihood of a purchase at a specific time or event. This can be used for real-time marketing (showing a discount when a shopper is hesitating) and supports the product recommendation system.
5. Customer segmentation.
AI algorithms combined with big data from marketing campaigns can be used to identify customer segments. The algorithms will automatically study various behavioral data points and provide insight into customer segments. Using these segments, you can better understand which people are similar to one another. This will help you understand the customer groups in your business. Later, you can use this information to improve customer satisfaction and user experience.
6. Dynamic pricing.
Price optimization makes it possible to adjust product prices based on customers’ ability and willingness to pay. This can increase conversions and revenue, because people are more likely to buy products that fit their budget. AI algorithms will also analyze prices, previous sales, and revenue transactions to set prices that increase profits and the likelihood of a purchase.
7. Website content personalization.
Relevant personalized content on a website makes the content more popular and can increase customer engagement. Machine learning can automatically identify patterns and match them to a particular customer’s personal preferences based on their past activity. This will help improve customer service and satisfaction.