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 internet. Optimization allows more frequent and significant changes to the campaign. Additionally it allows more effective use of collected customers data such as:
- time spent on the website,
- the number of transactions made etc,
- and many more.
Systems based on continuous analysis of up-to-date data will improve the results of your internet marketing performance.
Marketing campaigns are more than just delivering messages. Time and the manner of their delivery is also important. Without a data-driven approach, campaign-related opportunities can easily be overlooked. Problems may arise when launching a new campaign. Machine learning is an ally of every marketer who wants to maximize results when putting the same amount of work into tasks and marketing projects.
Machine Learning for cost, time and profit optimization
The aim of Machine learning is to use the achievements in the field of artificial intelligence (AI). With it’s help we are able create an automatic system that improve performance. Thanks to systems that use machine learning, you could optimize time and costs, make decisions faster and in more profitable way. Algorithms create better communication suited to each customer. They allow 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. Additionaly, collected data allows you to better target your potential clients.
Optimizing campaigns using machine learning improves work with potential clients and generates revenue. It can also generate a high return on marketing investment (ROI).
Machine Learning in marketing – Use Cases
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 most risky customers. Those algorithms are able in automatic way predict, which customers are more 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 you business. Machine learning algorithm 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 more money than the others. Which group will respond to your offers and discounts with a greater frequency. And which group will influence revenues in the future.
Recommendation engine (recommender system)
AI algorithms could predict customer preferences and propose the personalized offers based on behavioral data of every customer. With the acquired data on 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.
Conversion prediction (lead scoring)
This could be devided into 2 reates. First is probability of purchase (mainly for e-commerce, retail and similar industries). Second one is probability that lead will convert into opportunity/customer.
- Based on historical sales activity AI algorithms can determine probability of a particular lead to convert into an opportunity or customer. Sales team could use that information for better lead segmentation and prioritization. Gained knowledge will hepl them to understand why leads are likely to convert or not.
- Machine learning on the basis of users demographic data, activity on the online shops, loyalty program activity, preferences and interests of each users could predict probability of purchase in particular time or event. That could be used for real time marketing (show discount when customer hesitates) and supports product recommendation system.
AI algorithms in combination with Big Data from marketing campaigns could be used for finding your customer segments. Auto-segmentation algorithms will automatically learn on various behavioral data points and give you insights on your customer segments.Using such segments you could better understand what persons are close to each other. That will help you to understand customer groups within your business. Later you could use this information to improve customer satisfaction and user experience.
Price optimization enables you to adjust price of products by a particular customer’s ability and willingness to pay. It will give you opportunity to maximise 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.
Personalization in creation of messages that correspond to the specific needs of particular customer. Machine Learning based on demographic data, interests and behavioral data could create personalized message. This message for particular user could be delivered with 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.
Personalize content on the Website
Appropriate personalized content on the Website increases content popularity and can raise up customers involvement. Machine learning could automatically find patterns and match it with personal preferences of particular customer based on past activity. That will help you to improve customer experience and customer satisfaction.
How does it work in practice?
In practice above mentioned solutions are integrated into your internal business applications, CRM systems or your ecommerce platforms or websites. You are gaining the real value using those algorithms together with an automated flow. That flow sends results form AI model to your systems where direct actions are performed.
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 company from another industries use machine learning and deep learning.