If you're an e-commerce industry expert, you already know the importance of providing a personalized experience to your customers. Effective ecommerce personalization strategies helps businesses increase customer engagement, improve conversion rates, and drive sales across the customer journey.
Yet the term “personalization” is so ubiquitous, it can be hard to know where to start and what really matters.
We've compiled a concrete checklist of best practices for e-commerce personalization. This list of best practices will help you create a personalized shopping experience for your customers, matching their unique preferences. Not every form of personalization is relevant for every store. But selecting relevant personalization strategies can improve your customers' shopping experience, increase loyalty, and drive growth.
One of the most common and effective forms of personalization is to recommend a relevant category on your homepage. By capturing data like browsing and purchasing history, you can quickly guide customers to relevant products when they land on the homepage.
For example, let’s say a customer has previously shown interest in fitness. Your homepage should display recommended categories such as yoga pants, supplements, and activewear. This can help customers quickly find the products they are interested in, and increase the rate of conversion.
Exit intent popups are a proven way to increase engagement. While they may feel intrusive to some e-commerce leaders, they have become a staple for many e-commerce sites worldwide.
Traditionally, exit intent popups have featured a deal: 15% off your first purchase. However, an offer doesn’t necessarily solve the problem at at hand. Customers may be leaving because they didn’t find anything they liked.
That’s why a best practice is to include product recommendations in exit intent popups. Proactively serving relevant products to each individual customer is a sure way to increase conversions and engagement.
Upsell recommendations are quite simple: Show the most similar items that have a greater price. While these traditional upsells are better than nothing, there are two best practices for upsell recommendations:
• Combine with personalization. Smart recommendation engines are able to decipher which upsells each customer is most likely to convert on, due to the fact that they are powered by AI. AI learns from previous upsell decisions and serves each customer the upsell recommendations that are most likely to convert.
• Show upsells as quickly as possible. Many merchants make the mistake of adding upsell recommendations simply in the cart. However, there are many diverse UI options for making sure each customer sees the “You Might Also Like” upsell recommendations immediately after adding to cart.
For many consumers, the Product Listing Page (PLP) is a nightmare. Scrolling through hundreds of items that meet filter criteria isn’t a great experience. Especially when the most relevant one is buried at the end.
Help customers discover relevant products faster by sorting the PLP by recommended products. This dynamic filter responds to what each customer is most likely to purchase and surfaces those products first.
For e-commerce email marketing, the traditional practice is simply to remarket products that have already been viewed. The strategy was basic: A customer views a product without purchasing it, and a few days later they receive an email in their inbox with that product in it.
However, there was one key flaw: They didn’t purchase that product once, so why would they at another moment?
For most use cases, a better option is to proactively show new items that the customer hasn’t seen before. When including product recommendations in email & SMS, use a behavior-based recommendation engine that understands what a customer is most likely to buy.
Bundle recommendations use to be fueled by intuition and manual if-then rules. E-commerce and Merchandising leaders would manually create rules: “If a customer likes this shirt, then they should get a bundle recommendation for these pants.”
While intuitive bundle recommendations are a great start, a much more effective bundle recommendation is with AI. AI-powered bundles learn from every purchase ever made, leading to the most statistically accurate recommendations possible. While intuition may sometimes be right, tailored AI will always pick up behavioral customer data that go undetected by intuition.
Amazon has introduced a new add to cart confirmation page that shows a large carousel of product recommendations immediately after you add a product to cart. Depending on the item, these recommendations may be similar products, upsells, or bundle recommendations.
Surfacing those products helps customers find items they may have missed, or complementary accessories they need to consider. Retailers with enormous merchandise inventory must take product discovery seriously; that way, each customer will be more likely to purchase an item they love.
Immediately after a customer checks out on your site, add a secondary page that provides recommendations that would align with what was in their order. After having converted already, customers often feel better about adding additional products to their cart. If they decline, they move to the order confirmation page as normal.
To navigate a large catalog of items, customers often turn to the search bar to find products they’ll like. But each e-commerce stores should ask a key question:
Do customers know exactly what they want before they arrive? Or are they open to suggestions?
This is the epitome of need-based vs. taste-based buying, exemplified by how we shop very differently at Auto Zone or Macy’s.
For taste-based purchasing, e-commerce companies benefit significantly from adding a recommendation carousel inside the search bar. This is proactively asserting what customers should see, and can help them discover products they didn’t know how to search for.
In the fashion and apparel industry, one of the reasons customers return items is because they didn’t like how the item looked on them. In this case, the reason for return is taste-based.
When the customer submits the return form online, merchants can suggest products that may match their taste better. Those product recommendations should relate to the customer’s browsing history, showing products that are negatively correlated with the returned product.
This is a proactive way to drive customer loyalty, salvage a customer relationship, and recover the revenue generated from that purchase. All driven by successful personalization.
When shoppers land on your homepage, they should be able to find relevant products immediately. This benefits conversion rate optimization on a tactical level and brand perception en masse. One way to quickly help people find those relevant items is by responsively changing your banner image & CTA.
Dynamic banner imaging is a personalization tactic commonly used by e-commerce stores in the fashion & apparel industry. Displaying a large image of a relevant product or lifestyle image is a fantastic way to initiate an engaged browsing session.