E-commerce product recommendation technology blurs the lines between merchandising techniques, making it difficult to distinguish between cross-selling and personalization.
We are in the middle of a software revolution. In just a year, machine learning and, in particular, generative artificial intelligence have moved from the domain of data scientists to the realm of mundanity, and these technologies are changing the way e-commerce sites operate.
![Screenshot of BestBuy.com from "Frequently purchased with" items on a MacBook Pro product details page. Screenshot of BestBuy.com from "Frequently purchased with" items on a MacBook Pro product details page.](https://www.practicalecommerce.com/wp-content/uploads/2023/11/Best-Buy_Bought-with.jpg)
This “Frequently Bought With” section on BestBuy.com is an example of cross-selling. It appeared in the bottom section of a MacBook Pro’s product page. Click on the image to enlarge.
AI everywhere
Since the release of ChatGPT on November 30, 2022, generative AI has become so common that it can be found in almost every editor, app, or online search result. It’s even made its way into e-commerce product recommendations, with cutting-edge AI tools poised to adjust the copy used to describe a product to match what it knows about the product. a buyer’s browsing history.
This level of customization is incredible. But does this change what it means to market an online store? If every recommendation, every part of the navigation and even the products displayed following a search are personalized and manipulated by AI, does cross-selling make sense?
To answer, let’s think about how e-commerce cross-selling and personalization were defined before the widespread use of machine learning and AI.
Ecommerce Cross Selling
Cross-selling is a merchandising technique in which a website offers shoppers complementary items as they browse and visit product detail pages.
Classic examples include batteries with electronic components or a case with a laptop. So ecommerce cross-selling often makes suggestions based on items frequently purchased together or what can logically complement the main product.
Even before the rise of AI, cross-selling suggestions could be tailored to the individual’s interests or behavior.
Cross-selling was typically done in a “frequently also purchased with” section at the bottom of the product detail page or during the checkout process.
From a merchant’s perspective, cross-selling aims to increase average order value by encouraging customers to purchase more in a single transaction.
Ecommerce Personalization
Personalization changes the shopping experience based on an individual customer. Ecommerce personalization is based on that buyer’s unique preferences, past behavior, and data.
While most marketers use personalization to increase profits, this tactic should make the buyer feel understood and valued. Customers who are satisfied and comfortable with their purchasing journey will likely purchase repeatedly and therefore have a relatively higher lifetime value.
Personalization requires data collection and analysis. It then uses algorithms and AI to understand customers’ behavior patterns, preferences and potential needs. Personalization can manifest itself anywhere on an ecommerce site, including navigation, category pages, search results pages, and product detail pages.
Ride
Both cross-selling and e-commerce personalization require analytics, although personalization relies on deeper analysis. And cross-selling can be a form of personalization when recommendations are based on individual user data.
The better AI gets, the blurrier the line becomes, although one could argue that cross-selling is more transactional, focusing on increasing the immediate value of a purchase, while personalization aims to foster a long-term relationship.
Contrasts
An increase in average order size can measure cross-selling success. In contrast, most online marketers measure personalization over time using metrics like customer retention rates, repeat purchase rates, and lifetime value.
Thus, cross-selling can be considered a point-of-sale strategy, while personalization is a holistic approach that influences every customer interaction.
AI Takeover
By 2023, AI-based e-commerce recommendation software already handles both cross-selling and personalization for many, if not most, online stores. From an ecommerce merchant’s perspective, there is little difference between the two.
So why bother making a distinction? That’s it.
- Different metrics. While e-commerce cross-selling and personalization aim to improve the shopping experience and increase revenue, the techniques work on different principles and have different metrics.
- Single order relationship or global relationship. Cross-selling increases the value of a single order, and personalization deepens the customer relationship over time.
- Mixture of the two. An effective ecommerce strategy often combines the two, leveraging their unique advantages to maximize immediate and long-term gains.
Marketers shouldn’t put on-site merchandising on autopilot and allow an AI to take over. This might have short-term benefits, but when every e-commerce store has AI-based personalization, personalization will no longer be a competitive advantage.
In fact, the more automatic algorithms and AI become, the more important the art of marketing, as opposed to the science of it.
Understanding the nuance between cross-selling and personalized product recommendations might lead to knowing that cross-selling should not be personalized in some cases.
It’s always a good idea to gift batteries with a small car that needs them or a case with a laptop rather than gifting something that is completely unrelated in terms of behavior.