New Feature: Simplified Recommendations Fallback to maximize upsell opportunities

Certain Recommendations widgets require user data to populate. As such, it’s important for merchants to have the ability to implement a fallback mechanism, in the eventuality that the data for their preferred widget is not yet generated. Now, Findify has made this process even easier. ​

A Recommendations solution is a powerful ecommerce tool that helps merchants drive incremental revenues and higher margins, allowing them to maximize their online store’s upsell and cross-sell capabilities.

And at the core of every great Recommendations suite is the ability to tailor - but without losing the ability to get the Recommendations widget in place quickly and easily.   

Findify’s Recommendations solution includes a number of widgets, including ‘Hot Sellers’, ‘Recently Viewed Items’, ‘Newest Products’, ‘Frequently Purchased Together’, ‘Customers Who Viewed This Also Viewed’, and ‘Customers Who Viewed This Ultimately Bought’. 

Now this offering has been improved even further, with the addition of an automated Recommendations Fallback Mechanism.

A Findify Recommendations widget in action on a product page of Findify client United by Blue.

What is a Recommendations Fallback mechanism?

The aforementioned categories of Recommendations widgets can be split into two main groups - those that require user data to populate, and those that do not. 

Take Newest Products, for example. This widget can be applied without personalization and will then be populated based on the parameters the retailer specified, and not on user behaviour. As such, the same products will be shown to all shoppers. 

If we then look at the Recently Viewed Items widget, this is a good example of the other type, which requires user data to populate. This widget retains information about the current and previous sessions of the individual user, and populates the section with items that that specific shopper has already viewed. 

So what happens if there is no data for the widget to draw upon? Perhaps it is the shopper’s very first time on the site, and hasn’t viewed any items at all yet. Or perhaps the product itself is new, and there is no data to populate the Users Who Viewed This Ultimately Bought widget, because nobody has yet viewed that product and gone on to make a purchase. 

In this scenario, it is important that there is a fallback mechanism in place, so that the space occupied by the widget does not lay idle, potentially representing a missed selling opportunity. 

Previously, merchants using Findify could implement a fallback mechanism directly in the frontend for this eventuality. Now, they don’t have to. They can simply select a fallback option within the merchant dashboard. ​

A Recommendations Fallback can be set in seconds within the Findify Merchant Dashboard.

“This is an improvement we are very happy to roll out, and one I think will really help out clients. A fallback mechanism is especially valuable for tailored widgets and, in our quest for merchant simplicity, this addition will make life even easier for our clients to quickly select their preferred fallback option,” explained Findify’s Vsevolod Goloviznin. 

“In our opinion, the best fallback mechanism is usually Hot Sellers, but of course merchants can make that call for themselves very easily now within our dashboard. The whole thing only takes a few clicks now, which is great.”

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Within the analytics side of Findify’s merchant dashboard, retailers can view how the widget is performing, but also how the fallback option is performing, and data like how many times the fallback was rendered. 

“We would advise our clients to check their analytics regularly. Yes, it requires setting aside a portion of time each week or month to go through the stats, but it’s such valuable data that’s being generated. It’s a great way for merchants to gain some truly actionable insights. 

For more information on Findify’s powerful ecommerce tools, including personalization software and solutions such as Personalized Search, Smart Collections, and Recommendations, book a demo here.

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