How many times browsing the eCommerce of your favorite fashion brand have you come across a selection of products designed just for you?
Surely so many: already because the personalization of the browsing and purchasing experience in an eCommerce goes primarily through the activation of dynamic product recommendations.
Showing customers and potential customers products that are in line with their needs, interests, and expectations is, in fact, key to increasing shop sales through the use of dynamic up-selling and cross-selling strategies.
If in the former case, the goal is to stimulate the potential customer to spend more on the product he or she is looking for by proposing alternatives at a tendentially higher price, in the latter case the value of the shopping cart is raised by proposing products related to the one viewed or chosen.
Marketing automation for fashion eCommerce: Amazon docet!
If the functionality of dynamic product recommendations is, without a shadow of a doubt, the one most widely used in a marketing automation strategy for eCommerce fashion and web personalization, the reason is, undoubtedly, attributable to their effectiveness in terms of increasing sales and enriching the browsing and purchasing experience of the users themselves.
It is no coincidence, in fact, that Amazon has stated how 35 percent of its revenue comes precisely from recommendation algorithms and, how on average, these manage to bring in a 12 percent increase in revenue, with peaks, in some cases, of 30 percent.
From the home page to the product page, from the category page to the shopping cart page: each eCommerce section can be customized with dynamic product recommendations.
The goals?
- Increase profits: this is the ultimate goal of any marketing strategy, which is to increase ROI.
- Customer loyalty: if we can solve one of their problems or needs, we earn their trust.
- Increase the value of the average receipt: making a first sale is already a great step, after we have established initial contact with the customer, why not try to increase the volume of the sale.
- Increasing CLV: Considering the difficulty in building customer loyalty and increasing supply, in some cases it pays to target existing customers and stimulate them to make a second purchase. This means lengthening the customer lifecycle and thus creating more opportunities for a new conversion. If a customer perceives that we continue to take care of him and provide him with personalized offers, he will remain loyal more easily.
Product recommendation for fashion eCommerce: yes, but which ones?
We have well understood the effectiveness of product recommendations their value within an effective marketing automation strategy for fashion eCommerce, but, in the implementation phase, we must well evaluate which ones and how to use them.
In fact, there are different types of product recommendations that vary according to the type of algorithm they use.
Let us analyze the main ones:
- trending recommendation: the most popular products in the catalog are shown by click and view;
- personalized recommendation: users have the opportunity to see selected products based on their recent browsing history within the site;
- personalized trending recommendation: in this case, the algorithm proposes products by mixing the previous two, i.e., suggesting products from the user’s browsing history but more popular by clicks and views;
- browsing history recommendation: in this case, products are shown from those that the user has seen;
- personalized recommendation by sales: the algorithm allows showing suggested products from those that the user has recently purchased;
- shopping cart recommendation: with this type, recommended products are shown from those that the user has placed in the cart;
- remarketing recommendation: products that the user has viewed in the last few days but has not purchased are shown.
These are only the main types:they can be further customized and the products shown filtered, depending on the user’s own segment (e.g., spending thresholds, preferred categories, size worn).
The potential of a feature such as product recommendations is truly enormous if it is well exploited and responds to a well-planned strategy: as with any marketing automation activity, the advice, of course, is to make judicious use of it to avoid over automation effects that could annoy the user.
