Below are some of my favorite tactics to boost conversions and multiply revenues.

Bundles are a fantastic way to increase average order value.

Kickstarter campaigns are an excellent source of inspiration. Often, creators only have a single core product. To increase the likelihood of success, they create various tiers of participation.

Often, these add-ons are complimentary products that make the core experience better.

Here is one example. Below, the less expensive base game with a single expansion is offered at $22. It has 247 backers. 

In contrast, the "All-in" bundle with multiple map packs, expansions, and other goodies is offered at $90. It has 1,197 backers. 

Another example is Fashion Nova.

Here, they create dynamic bundles based on which product is being viewed, presenting them in a simple recommendation widget.

When a customer adds an item to a cart, it is an incredibly strong signal about product affinity. 

You can capitalize on this moment in various ways. One tactic is to present a pop-up with the option to continue to checkout or continue shopping. 


In this pop-up, you can also offer complimentary products. Target does this beautifully with their after add to cart action sequence. Here they present items that are frequently bought together. 

Buying trends offer an excellent opportunity to present more relevant offers even without knowing anything about the visitor. 

Amazon provides a great example.

While I am writing this update, we are two weeks away from Mother’s Day. Simulating a first time visitor, Amazon offers numerous Mother Day offers.

Above the fold they showcase a portal to their "Mother's Day Gift Shop", with a separate call to action positioned in the top right corner to "Shop for Mother's Day Jewelry".

As you scroll down, the next recommendation widget highlights a series of top selling categories in the Mother's Day Gift Shop.

Although Amazon doesn't know what this first time visitor is actually coming to their site for, they recognize there is a high chance they will want to  purchase a gift for Mother's Day. 

Personalization is the most effective tactic on this list. 

Your customers are diverse.

Some are price sensitive. Some care about this brand, while others care about that. There will be first time visitors and returning visitors.

Understanding that your customers are individuals is the first step toward effective product recommendations.

Below, we compare one store's experience implementing product recommendation personalization. 

Personalization often doubles how effective recommendations are.

Nordstrom showcases another advanced product recommendation tactic. 

Instead of using a more generic “Trending Products” recommendation widget, they use a “Trending Near You”.

This takes advantage of geographic differences, such as seasons and taste. Again, the goal is to create relevant offers without having access to previous behavior. 

Incorporating demographic data helps weed out non-relevant offers. For example, it’s doubtful their customers in San Diego shop winter coats, even in December.

Conversion depends on trust. 

While the concept of social proof is not new, nor "advanced", it surprises me how few companies use social proof elements in their recommended products. 

Amazon integrates social proof elements throughout their recommendation widgets. 

Product recommendations can also be used to help customers make the best decisions. Again, Amazon provides an instructive example of how to combine product recommendations and product attribute data in an easy to digest format. 

When combined with merchandizing strategies, brands can control what context they are comparing the current product, and help influence the ultimate purchase decision.

The purpose of product recommendations is twofold: first, to improve the shopping experience, and second to increase revenues.

Product recommendation systems do this by presenting shoppers offers they are most likely to want.

Engines sift through the tens, hundreds, or thousands of items a store carries, and decide which one best suits this particular user.

Generally speaking, there are three broad techniques engines use to filter through SKUs.

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Content based filtering focuses on the specific shopper. The product recommendation software tracks a users actions, such as web pages viewed, products clicked on, time spent on various categories, and items added to cart.

Based on this information, a customer profile is created. This profile is then compared to the product catalogue to identify which items to show.

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The best recommendation software actually combines both techniques to give the most accurate prediction. This is how Barilliance works.

By combining both techniques, product recommendation engines are able to apply the "wisdom of the crowd" to prospects before they gather much data. As more information is learned about that particular user, recommendations become more and more personalized based on their session and use history.

In brick & mortar, stores are forced to choose a single merchandising strategy.

ECommerce stores don’t have this limitation.

Retailers can use personalization technology to create specific merchandizing strategies for any segment of customers. One of the main tools retailers use to accomplish this are product recommendations.

By default, product recommendation engines work algorithmically. 

However, the best engines allow retailers to "overrule" the software's recommendations in lue of explicit merchandizing rules you set up. 

Exmaples include:

Retailers define what rules exist, and when these rules are triggered.

Again, using Barilliance as an example, you can determine customer segments that matter to your business. You can then selectively use merchandizing rules on these various segments. 

As you can see, retailers have the capabilitity to define exactly what audience they want to display particular product recommendation widgets for. 

One of the most profitable ways to segment your audience is through a solid RFM analysis. There are six key segments that you can (and should) be creating merchandizing rules for, including:

By combining merchandising rules and product recommendations, you can create highly targeted offers.

You can promote best selling items to new site visitors, recently viewed items to returning visitors, and related products based on previous purchase to returning customers. 

We performed an in-depth study of Barilliance customers who implemented our product recommendation solution.

The results were incredible.

Improving your product recommendations are the "low hanging fruit" of eCommerce personalization.

We host free demos for our product recommendation solution. If you would like to discover if we could improve your current recommendations with a hybrid, machine learning approach, click here.