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Product recommendations are an indispensable tool for driving sales in eCommerce stores. Presenting shoppers with the right products at the right time fuels the creation of personalized experiences, leading to more purchases. For fashion stores, nailing product recommendations not only drives sales but can also cement your brand as being a trustworthy fashion-forward source.
Knowing which types of product recommendations to use and when can be difficult, especially as there are so many types to choose from. So, in this article, we’ll break down the best product recommendation strategies fashion stores need to be using.
What Are Product Recommendation Strategies & Why They Are Needed?
Before we lay out the best product recommendation strategies, let’s first define what they are and why they are important for fashion stores.
Product recommendation strategies are techniques used to suggest relevant products to visitors to your eCommerce store. They aim to drive personalization by predicting what shoppers are likely to be interested in at different stages of their journey. These predictions are made using algorithms and with the help of product and customer data to display relevant products automatically or manually.
A key benefit of product recommendations is that they are proven to drive sales and increase average order value (AOV). Research has found that product recommendations can result in a 5x higher per-visit spend. What’s more, on average, product recommendations account for 31% of a store’s revenue. This effect seemingly balloons on fashion stores, with one retailer reporting a 533% conversion rate lift from product recommendations.
Visits with product recommendation clicks drive a sizeable amount of revenue (Source: Salesforce)
Product recommendations also enhances the shopping experience for customers. This will undoubtedly bolster engagement and keep customers coming back to your store. But don’t just take our word for it. Product recommendations have been proven to lift time spent on a store by 344%. Plus, shoppers that engage with recommendations are twice as likely to come back to your store.
These impressive figures are totally achievable for your fashion store, but it comes with a caveat - product recommendations must be done right. So, let’s get down to business and show you how to effectively recommend products in your fashion store.
8 Product Recommendation Strategies For Fashion Stores
Make use of a recommendation system
Recommendation systems are a key piece of technology many eCommerce stores employ to surface relevant products. They use customer data and machine learning to make predictions and display what a customer is likely to have the most affinity with.
These recommendation engines typically make predictions in one of three ways:
- Collaborative-based filtering: uses data on what other similar users have purchased and displays them to a shopper
- Content-based filtering: this looks at the similarities between items to make recommendations
- Hybrid: combines collaborative and content-based filtering approaches
As these are data-focused and use machine learning, the relevancy of the product recommendations is pretty high. For Shopify merchants, there are two options for making use of a recommendation system - Shopify’s native one or an app.
The Shopify algorithm uses purchase history and product descriptions to make recommendations automatically on English-language stores. For non-English storefronts, only purchase history is used. In other cases, such as stores with more than 7000 products, it displays related collections. This is quite limited.
(Source: Shopify)
This will likely be too limiting for fashion stores, so the use of a product recommendation app is our best suggestion. Apps like Boost AI Search & Discovery use AI to further personalize product recommendations and give you access to a range of different types of recommendations.
Search as a product recommendation tool
Search is a prime spot for product recommendations. With 43% of shoppers heading straight to the search bar on retail sites, it’s easy to see the important role it can play in recommendations.
There are a few simple yet effective tactics you can employ to turn your store’s search experience into a product recommendation tool. A great one to start with is recommending popular or trending search terms the moment the search bar is clicked. You could even place links to your bestsellers or use past behavior to dictate suggestions like in the Bata case study.
An unsuccessful search that produces no results is another key opportunity for you to flex your recommendation muscles. Rather than presenting these searchers with a blank page, recommend products that are new in or trending.
Boost Product Filter & Search (will soon become Boost AI Search & Discovery) offer advanced site search engine, multiple product filter, various merchandising rules, and insightful analytics. Try it for FREE now and wait for the release of Product Recommendation.
Exploratory recommendations
Not every shopper will know exactly what they’re looking for. You’ll need to cater to these consumers by implementing recommendation strategies that are exploratory. The aim here is to showcase products most fashion consumers would be interested in. You can gauge this using real-time data and historical sales.
Salomon displays the products they have that are new for the season
To attract the impulsive, fashion-conscious shopper, gather your best-selling, currently trending, and just-in items to display on the homepage. For collection pages, display the best sellers in the category. If you really want to show your brand’s fashion prowess, consider using style guides. These can be based on seasonal trends or popular styles.
Recommend products that form a look
Complete the look product recommendations are wildly popular in the fashion world for a reason. They do a great job at giving shoppers a true-to-life idea of how different items can be styled together and provide great context for your products. It’s also a surefire way to position your brand as fashion-forward.
Red Dress recommends the shoes, bag, and belt seen in the main product image for a dress
These types of recommendations commonly sit on product pages but can also be displayed on your homepage.
When piecing together your inventory to form looks, you need to take into account current trends. What types of sets have been dominating the catwalk or are among your target audience?
On the product pages for each “complete the look" item, make sure you include an image of a model wearing the whole outfit. As for homepages, image hotspot linking is a great way to surface these recommendations.
Suggesting similar items
For first-time visitors, suggest fashion items that are similar looking on product pages. The idea is to give these customers a wider scope of what you have to offer that closely matches what they’re currently looking at with minimal steps.
On the product page of a stiletto heel shoe, Nine West places 3 others as recommendations right in the middle
There are a couple of venues for surfacing these recommendations.
If you’re using Shopify’s in-built recommendation algorithm, you’ll need to add similar products manually. This is because related recommendations will be a mix of similar and complementary items. Make sure your product descriptions and tags are up to date so you can easily customize the similar items section. Try to select the similar products that are most popular with other customers.
Shopify Product recommendation apps, on the other hand, will have a more sophisticated algorithm. When you use Boost AI Search & Discovery, for example, similar products will be found automatically. This means you’ll get relevant and high-converting similar items recommendations in no time.
Suggesting similar items is also an excellent opportunity to upsell and boost your AOV. With this technique, you need to make it clear that the pricier product is superior and worth its price. Such products should be best sellers or hugely popular with other customers. You can show this with star ratings or a hot-selling badge.
Leveraging UGC
User-generated content (UGC) is a powerful force in the fashion world. You may be surprised to read that 65% of consumers have purchased apparel or fashion items based on it. UGC is extremely influential as it’s more trustworthy than the word of a brand. It can also give visual styling context that is more relatable to customers. You can even have your modern logo design generated by the users.
Social media is the leading influencer of fashion shopping purchases, so sourcing UGC from social media to form product recommendations will be an instant hit.
Shoppable UGC on Red Dress homepage
The first step is to create a branded hashtag for your customers to use in posts of them wearing your fashion items. It’s a good idea to incentivize this with a % off voucher or free shipping so you can have a steady flow of content. Once you have a healthy stash, you can use an app like Instafeed or Socialwidget to add a Shop our feed section to your store.
As this product recommendation type is more on the exploratory side, you should reserve this section for your homepage.
Shopping cart recommendations
Product recommendations on the cart page open another chance to bump up AOV with product recommendations.
Fashion Nova suggests additional items on the cart page and also lets shoppers know how much more to spend to unlock a promotion
The shopping cart is a good spot for suggesting frequently bought together items. Product recommendation apps can do this for you automatically. Money-saving bundles are an excellent way to display these complementary items and give them an extra boost in the minds of your customers.
Exploratory recommendations can be combined in the shopping cart in the form of triggering an offer or unlocking a promotion. Examples include free shipping when you spend x amount or a % off. You could also remind shoppers of the products they viewed during their current session.
Recommending products in marketing communications
Last but not least, let's move on to recommendation strategies for marketing communications. This can be incorporated in emails, SMS notifications, web push notifications, pop-ups, and more.
The key here is to ensure that you add a layer of personalization. The wealth of data you have at your fingertips can easily make a personalized shopping experience through recommendations a reality.
Start with asking questions based on historical data. What has your specific customer purchased in the past? What new items can you present to them that match them? After that, an all-in-one marketing app like Omnisend or Automizely can help you automate these communications.
Set up post-purchase emails and SMS communications that feature products that complement what has been purchased. For example, if someone has purchased a party dress recently, you could feature a selection of statement handbags or heels in post-purchase communication.
You should also set up targeted email campaigns that display new gems from a sub-category they’ve purchased from before. Remember, fashionistas want to be on trend, so using personalization in this way is a great hook to draw them back to you.
(Source: Mailchimp)
Abandoned cart communications are an excellent opportunity to showcase personalized recommendations. Almost 88% of shopping carts are abandoned, and the reasons for doing so vary. So, it’s a good idea to hedge your bets by showing just how much your products resonate with your customers’ styles. New in and best-selling items in the same category as what’s in a customer’s cart are a good call. You could also recommend similar items.
Wrapping Up
Product recommendations can work wonders for your Shopify fashion store. They help to enhance the shopping experience for your customers whilst simultaneously thrusting your store into the fashion-savvy territory. Exploratory recommendations, search, UGC, complete the look, and other data-driven product recommendation strategies are surefire ways to ace product recommendations. So be sure to give them a try!
Install the Boost app now to get a change to become early-bird users of the new Product Recommendation feature.
