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June 21, 2024

Up-Selling and Cross-Selling: 5 Reasons to Use Machine Learning


Artur Haponik

CEO & Co-Founder

Reading time:

8 minutes

The probability of selling a product to an existing customer is 60-70%[1], while for new customers it is only 5-20%. Quite logically, there are numerous strategies focused exactly on existing clients, and cross-selling and upselling are among them. If used effectively, they can increase your sales and, therefore, profits. And if backed with machine learning for marketing, cross-sell and upsell campaigns can significantly improve your business’ position on the market.

But why and how to use machine learning in the case of cross-selling and up-selling? We are here to answer these questions.

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Cross-Selling and Up-selling: What is the Difference?

Before, we will tell you how to use machine learning in finance and marketing to increase your sales and customer loyalty. Let us quickly explain to you the difference between cross-selling and upselling.

So, cross-selling focuses on complementary products. For instance, if a customer buys a bicycle, they may also be interested in buying a helmet or knee pads.

The McDonalds famous phrase “Would you like fries with that?” is a great example of cross-selling. It allows the company to sell more than 4 million kilograms of fries per day[2].

In turn, upselling is about reaching existing customers and offering them an opportunity to upgrade the product they have already purchased or to buy a more expensive item.

The upselling campaign of Spotify is just brilliant. Customers clearly see the benefits of the Premium account in comparison with the Free one, and are more likely to upgrade.

How to increase sales using Machine Learning (cross-selling and up-selling)

Now, when the difference is absolutely clear, it is time to tell you why to use machine learning algorithms for marketing to boost your sales. And what to do to reach much better results with using machine learning for cross-selling and up-selling.

Interested in machine learning?
Read our article: Machine Learning. What it is and why it is essential to business?

1. Machine Learning is a key to personalized recommendations and improves cross-selling and up-selling opportunities

You have already collected more than enough data about your customers. You have data about your customer’s age, location, gender, hobbies, buying history, marital status, etc. And even did customer segmentation for the better offering. That’s great, but machine learning algorithms can significantly improve your offer personalization.

Thanks to data-driven recommendations, customers will get the right offers at the right time, and, therefore, purchase more products. Here is an example for you — Amazon identifies which items are often purchased together. After that shows for users potential complementary products. This trick ensures a better customer experience. As users receive exactly those recommendations which they may be waiting for.

Machine learning algorithms are usually divided into two categories: collaborative filtering and content-based filtering. However, combining both of these approaches is also a popular way of building a recommender system. In case you are not familiar with these methods or do not feel confident enough when using them, we highly recommend you look for an expert. Otherwise, your real-time product recommendation system may turn out to be not really efficient.

2. Machine Learning sharpens sales prediction

One of the best things about machine learning is that it never stops learning from new data. Sorry for a little tautology, but this ability allows you to forecast your customers’ behavior and expectations in the future. On the basis of historical and new data, a machine learning model can increase the accuracy of the sales forecast.

This is especially important and useful when you need to predict how your customers will perceive new products or services. You will understand how to work on your cross-selling and upselling strategies and reduce the risk of inefficient marketing.

Apart from this, using machine learning and customer predictive analytics, it is possible to identify the most effective sources of contact with clients. Again, this can help you to polish your selling strategy and gain more profits.

3. Machine Learning makes dynamic pricing possible

Dynamic pricing is among the latest pricing trends — it implies continuous altering of product prices, in reaction to real-time demand and supply. This model allows better control of the pricing strategy, gives flexibility without reducing the brand value, and saves budget over the long run. And, obviously, dynamic pricing can be a great help when you are cross-selling or upselling your products.

However, it is virtually impossible to efficiently monitor other items and follow the real-time demand and supply manually, since there is too much data to check and analyze. But machine learning can solve the problem — a properly built model will take into account a lot of factors. It will take much more than you will be able to consider without such an algorithm, provide you with precise data, and ensure much faster responses to demand fluctuations.

4. Machine Learning is more efficient than automated A/B testing

A/B testing is also known as split testing — it is an experiment implying dividing the audience, testing a number of variations, and defining which of them works better than the others. For instance, the first group of customers receives a special upsell offer with a certain discount, while the second group gets the same offer, but with a different discount. Comparing the results and understanding which offer triggered more upsells, you will figure out the most efficient way to build your upselling campaign.

It sounds inspiring, but machine learning algorithms are much better than A/B testing. Instead of focusing only on two options, they allow testing thousands of variations. Besides, A/B testing is a rather time-consuming process, but machine learning has no problems like this.

You and your team will be able to spend the time saved, for instance, on improving the selling strategy or working on new products. In this way, with machine learning methods, you will be able to bring your campaign to perfection in a very short time.

5. Machine Learning simplifies churn analysis and prediction

Making your business stable can be very challenging if you somehow forget about churn analysis. It refers to the customer attrition rate, and helps to define the reasons of churn. After that you could develop effective strategies for dealing with this issue.

Applying machine learning to churn models allows using much more variables than you could ever process manually. Besides, this trick may also expose those correlations and patterns which you would never notice on your own. And, logically, the results of churn analysis and prediction based on machine learning would be much more accurate than the ordinary ones. So, again, you will be able to build more efficient cross-selling and up-selling strategies.

Implement cross-selling and up-selling solutions with machine learning

Now you know the exact difference between cross-selling and upselling. You know how to use machine learning for up-selling and cross-selling. You understand how machine learning can help you to deliver better customer experience and increase your sales and customer loyalty. But if you still have any questions or need help with developing a customized machine learning services and solutions, feel free to get in touch with us.

Using Machine Learning for Cross-Selling and Up-Selling – FAQ

How can machine learning improve cross-selling and up-selling?

  • Personalized Recommendations
  • Sales Prediction
  • Dynamic Pricing
  • Efficient A/B Testing
  • Churn Analysis and Prediction

What are the benefits of using machine learning for cross-selling and upselling?

  • Enhanced Personalization: Customers receive tailored recommendations, improving their experience and increasing the likelihood of additional purchases.
  • Accurate Forecasting: Better sales predictions help businesses understand customer needs and adjust their strategies accordingly.
  • Optimized Pricing: Dynamic pricing ensures competitive and flexible pricing strategies without compromising brand value.
  • Faster Campaign Optimization: Machine learning accelerates the testing and optimization process, saving time and resources.
  • Improved Customer Retention: Accurate churn analysis helps retain customers by addressing issues proactively, thus supporting long-term business growth.

How can businesses implement machine learning for cross-selling and upselling?

  • Data Collection: Gather comprehensive data on customer demographics, behavior, and purchase history.
  • Algorithm Selection: Choose appropriate machine learning algorithms (collaborative filtering, content-based filtering, or a hybrid approach) for building recommendation systems.
  • Model Training: Train models on historical data to improve prediction accuracy.
  • Continuous Improvement: Regularly update models with new data to refine recommendations and strategies.
  • Expert Assistance: Consider consulting with machine learning experts to ensure efficient implementation and avoid potential pitfalls.

Why is machine learning more effective than traditional methods?

Machine learning offers several advantages over traditional methods:

  • Scalability: It can handle vast amounts of data and complex variables more efficiently.
  • Speed: It processes and analyzes data faster, providing quicker insights.
  • Accuracy: It provides more precise recommendations and predictions due to continuous learning and adaptation.
  • Resource Efficiency: It frees up time and resources that can be redirected towards strategic planning and innovation.

This article is an updated version of the publication from Apr 12, 2019.


[1] Matt Mansfield. Customer Retention Statistics – The Ultimate Collection for Small Business. Oct 25, 2016. URL: Accessed Apr 12, 2019.
[2] Recruitingtowin.“Would you like fries with that?” – What McDonalds can teach us about cross-selling. Sep 19, 2017. URL: Accessed Apr 12, 2019.


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