May 25, 2018 Why You Should Integrate a Similar Items Feature Into Your eCommerce Recommendation System
During the past decades, Internet has given customers access to a greater choice of goods. Fashion has been one of the top industries affected by this major shift. Nowadays, online selling techniques in fashion involve a complex set of strategies. They range from emailing campaigns to flash sales, and include recommendation systems and visually similar items.
Tracking visitors to narrow their profile
eCommerces use the so-called recommendation engines to track all visitor movements. Consequently, they adapt their offering to personal details, such as age, gender, items in which the visitor has shown prior interest, etc.
All this data is collected and used to tailor content for a specific potential customer. It also analyses events in customer’s shopping journey. This way, it can determine their level of interest in particular or generic items.
Knowing what customers wish for
We all know customer purchases are not always completed out of a need. Many times, customers buy something because they’ve fallen in love with it, not because they need it. Therefore, knowing what your customers may like can be a game-changer and help you boost sales. This is where personal recommendations really make a difference.
They are, without a doubt, the best way to show your customers relevant offers from your eCommerce catalog, and meet their expectations. In fact, 70% of those who surf the net would like to see personalized content.
Tailoring content without user data: similar items
Smart product suggestions can optimize your eCommerce journey and merchandising by increasing conversion rates and reducing cart abandonment or bounce rates. However, as we’ve seen, they strongly depend on the data that recommendation engines gather.
Good news: there are alternative or complementary feature. They allow fashion retailers to show relevant products to customers throughout all their journey without relying on customer data.
Artificial Intelligence-powered similar product suggestions work this way. Catchoom’s solution, for instance, focuses on providing alternatives based on visual similarity, instead of user behavioral data. Whether it helps promote higher-priced items to boost upselling, or display matching accessories, the final value of purchases can be easily increased with such a feature.
Better content for better conversion
Let’s say a customer types “red floral dress” in your website. The results will be as good as your eCommerce data. That means, if you have no products labelled as such, the visitor will probably see a “No results found” message on the screen.
Same happens if you are having stock issues with a specific item. Customers will probably leave your website if the item they are looking at is not available and they see no relevant alternatives.
Providing products with a detailed meta description and displaying visually similar items helps increase conversion. Moreover, it also reduces site abandonment caused by these kind of problems.
An Artificial Intelligence solution based on Image Recognition is capable of analysing and understanding images. It then retrieves and assigns relevant and highly specific categories and attributes. This is what Privalia, a marketplace part of Vente-Privée Group did with Catchoom’s solution.
You can then use this rich data to suggest the closest items, after a specific search. This can be done inside a product page or even in transactional emails. This is how retailers can provide the best possible fit to their customers’ needs.
Merging predictive merchandising with similar product suggestions
As we’ve seen, both user behavior based recommendations and similar items suggestions can add value to your eCommerce catalog.
Combining both can be an even better approach. You can adapt content to the specific user’s profile (such as shopping history or past interests) and also suggest a set of items that might be relevant to the specific product the customer is looking at. This way, you, as a retailer, can make sure that you provide relevant suggestions. These recommendations belong to categories and involve relevant attributes to the specific query. This will provide your potential customers with exactly what they personally want in the exact moment they need it.
If you want to bring your fashion eCommerce to the next level, check out how to enable product recommendations.