If you own an ecommerce site, staying ahead of the competition can be difficult. You must continue to innovate and offer an incredible shopping experience to your customers.
So, what is the best way to attract customers and increase sales on your ecommerce site?
While there is a multitude of factors that influence sales, this article is going to talk about how product recommendations can help you increase online sales. Today, online shoppers value suggestions and expect ecommerce sites to personalize their shopping experience.
According to a Forrester study, 15% of visitors admit to buying recommended products.
Ecommerce sites should take into account visitor location, social context, browsing behavior, and other key signals to show context-rich suggestions. In this post, I've highlighted five ways an online recommendation engine can dramatically increase sales using the power of a personalized recommendation engine.
Offer a personalized shopping experience to your visitors
Personalization plays a crucial role in showing relevant products to visitors. It offers a compelling shopping experience, leading to greater conversions.
"76% shoppers get frustrated when this (personalization) doesn't happen. Ratcheting up the pressure on companies, if consumers don't like the experience they receive, it's easier than ever for them to choose something different." - Mckinsey
It's necessary that your recommendation engine efficiently analyzes your visitors' on-site behavior and understands their preferences. Manual recommendations cannot deliver accurate recommendations to the extent an automated recommendation engine can.
At most, a manual recommendations engine can allow site owners or merchandisers to map categories to complementary categories or specify recommendations per product. However, these recommendations will never be as accurate as those delivered by a personalized recommendations engine.
On the other hand, automated systems can provide more relevant recommendations by tracking visitor interactions, current behavior, location, etc., and analyzing their preferences.
For example, Unbxd helps you show accurate recommendations to your visitors by analyzing how they interact with your site in real time and understanding their product preferences. It uses personalized widgets such as 'You may also like,' 'Viewed also viewed',' and other widgets to show recommendations on key properties/pages of your site.
Here's an example of Amazon showing related products, such as sandals, handbags, etc., as dress recommendations.
Large retailers like Amazon attribute almost as much as 35% of their conversions to product recommendations. Recommendation widgets like 'Bought also bought' show complementary products to visitors, which enhances product discovery and boosts conversions.
Consumers can be motivated by content sharing (reviews, testimonials, etc.) more than by price or brand. Social proof is effective in today's post-modern internet-savvy generation as it relies more on what your peers say about a product and how they feel about it.
Recommendation widgets like 'People who viewed this also viewed' show recommendations based on the wisdom of the crowd and use social proof to engage visitors.
On a lighter note, Jeff Bezos once said, "When you have a bad experience offline, you tell six people; online, you tell 100 people."
Online shoe and clothing retailer Zappos offers social proof with this handbag recommendation.
Recommendation tools now consider location/weather to move further in their personalization efforts. Location-based recommendations make product suggestions more relevant and contribute to higher & faster conversions.
For example, it makes sense to show recommendations for summer dresses during summer. You would show different top sellers in Miami than the ones you show in Alaska primarily because of location/weather differences.
Unbxd personalizes the top seller widget based on location so you can cater to visitors from different locations with the same widget.
Suppose you’re selling mobiles in your store, and a visitor is viewing a specific model. You can then showcase recommendations for high-end mobiles or cell phones with better features. This solves two critical problems:
More products and better options can be shown to visitors, which they may not have discovered otherwise.
A high-end mobile phone with better features will be priced higher, hence encouraging visitors to spend more and will help increase the Average Order Value across your site.
Recommendation widgets like 'More like these' or 'Similar products' are often used to upsell products. These widgets, along with personalization, can help increase conversions significantly.
For example, Asos, an apparel and beauty store, upsells with the 'We recommend' widget to show similar products.
Leveraging personalized recommendation engines is paramount for ecommerce success. By analyzing customer data and behavior, these engines provide tailored suggestions, increasing sales significantly. Techniques such as cross-selling, social proof, geo-targeting, and upselling are proven strategies to enhance customer experience and boost revenue.
Have these recommendation techniques helped you increase your online sales? Discover how Unbxd Recommendations can help you drive conversions faster.