Customer acquisition costs are rising for many online stores, meaning it is becoming increasingly expensive to get new visitors to your site and have them complete a sale. A lot of the growing costs have to due with SEO –– and the need to produce ample amounts of inbound marketing in order to rank highly. AdWords marketing has also seen a rise in competition –– and you could be spending your entire marketing budget on search engine advertising campaigns, and seeing very little return.
Let’s take a step back, though. Advertising isn’t your largest marketing channel –– your website is. By focusing on leveraging the experience of shoppers currently on your site, rather than potential net new shoppers, you can increase your:
All of these numbers are real success metrics from online stores investing in one particular facet of their site: product recommendations.
Product recommendations are about optimizing those customers already on your site and looking to purchase. Product recommendations market to shoppers on a 1-to-1 basis, and offer a more personalized shopping experience relevant to the tastes and interests of each individual user. Using product recommendations to increase AOV and customer loyalty, as opposed to spending money to increase site traffic, allows you to reduce marketing spend and provide a quick, and relatively permanent, remedy to increase cash flow.
Below are three ways to improve the integration of product recommendations on your online store to drive real business results and increase customer satisfaction.
Use Your Data
Data is the key component in delivering product recommendations that will increase AOV. Unfortunately, however, many online stores only implement basic recommendation functionality, thus failing to deliver recommendations that are relevant to the user.
As a merchant, you should leverage the details of users’ previous purchases and on-site searches to help recommend relevant products. Here are a few of the most common ways successful retailers power their product recommendations:
- Search Queries: Recommend products based on a customer’s search terms
- Purchase History: Recommend products based on a customer’s past purchases
- Shopping Cart: Recommend products based on the current contents of a customer’s cart or wishlist
- Social Behavior: Recommend products based on product rating, shares and likes
- Geographic Location: Suggest relevant products based on customer’s local climate or other regional considerations
- Customer Segments: Use purchase histories of customers with similar demographics to recommend products
There is no one-size-fits-all solution to creating effective product recommendations, as every online store offers a different mix of products and caters to their own particular demographic. But, all online stores do have the data you need to create effective product recommendations.
Dive into your analytics and reports and look for patterns of behavior. Then, use this information to intelligently configure the product recommendation functionality available to you from your ecommerce platform and other technology providers.
Limit Your Product Recommendations
Avoid over-cluttering the user experience and take an organized approach to recommendations. Quantity does not mean quality. Instead, focus on a small set of product recommendations that are highly relevant to the visitor.
Let’s look at an example here. Online retailer Chuck Levins utilizes product recommendations to increase AOV per customer. After visiting a few product pages on their website for acoustic guitars, the site will yield a “You Might Also Like” suggestion box for visitors. Highly relevant to the visitor’s initial search and browsing behavior, the website presents only a few highly similar products in varying colors.
Keep in mind here that while product recommendations do well on product pages, not every page needs to serve up recommendations. Always know what the end goal of each individual page experience is for your customer –– and then be sure that you elevate that particular CTA –– or call to action. On a product page, the main CTA is to add to cart. Ideally customers click on a product recommendation after adding products to their cart, subsequently staying on your site longer, liking more of your products and purchasing more items.
On your checkout page, however, the main CTA is to actually check out. This isn’t necessarily the best place to add product recommendations as it might distract the customer from finalizing their purchase.
Focus Your Product Recommendations on Complementary Products
Offering complementary product recommendations on each product page is the most successful approach to increase your AOV. By recommending the top parallel components that go with each main item, you’ll improve your ability to drive larger transactions. Customers usually respond well when presented with this type of specific offer as they often don’t know exactly what they want or need beyond the main product, and are looking for help finalizing their product selection.
CasinoSupply.com has successfully increased their average order value by recommending complimentary products as their visitors add products to their carts. In effect, they are asking their customers “would you like fries and a drink with that?”
Consider a “Complete the Look”
“Complete the Look” is an additional strategy to leverage product recommendations to increase average order value. These recommendations are more category-based (versus behavior-based), which is an easier approach for many retailers since it means that they can rely on their own experience to make effective recommendations.
An example of a site that is leveraging the power of product recommendations with a complete the look strategy is ASOS. The international retailer pays particular attention to the detail of what their customers are searching for and putting in their cart. Their “Shop the Look Button” quickly matches customers to complementary products on their website. As seen in the example below, the company recommends shoes and accessories to complement a dress in the shopper’s cart.
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