Product Recommendation for Shopify


Shopify, as you know, is an ecommerce platform where you sell your products, grow your buyer base and make more money. You can multiply your profit with the right product recommendation and increase customer loyalty.
Almost every ecommerce website needs good product recommendation tools. With these tools in place, shoppers can browse through many items without leaving your store soon. Meaning they’ll stay longer, know more about your products and buy some from the recommended ones!
If you’re looking for one such product recommendation tool, this guide will walk you through the many benefits of using it and how to get started using the right tool that will absolutely fit your needs.

How do the product recommendation tools for Shopify work?

Ecommerce platforms use AI-based product recommendation engines to analyze shoppers’ purchasing behaviour (in technical terms, this is known as data collection). Then the AI filters out (or you can say it recommends) a list of products based on the shopper’s likeliness of buying them.

Data Collection:

There are several kinds of shopper’s data collection methods used by ecommerce companies of the world, each with its own set of benefits and challenges that guide product recommendation strategies. But majorly there are four kinds:

1- Third-Party data collection:

Third-party data is purchased from an aggregator. These aggregators do not collect data themselves but rather acquire it from different companies and combine it into several sets. This means that the data could be obtained from multiple sources, such as a DSP (demand side platform) or a DMP (data management platform).

There are also third-party data marketplaces such as Acxiom, Nielsen, Google and OnAudience.
Third-Party data collection is the most popular data collection method used from some old times of the internet, but there are several drawbacks to it:

  • its reliability or accuracy is always questioned because you don’t exactly know the source of this data as there are multiple parties involved.
  • you could not be sure enough if the privacy regulations are followed.
  • there are usually very large datasets, so segmentation and usage are always challenging and sometimes require a large team to handle.

2- Second-Party data collection:

Unlike Third-Party data, the Second-Party data is reliable because it is sourced from trusted partner(s) with whom you can check if they are following privacy regulations like the GDPR and the CCPA. Grocery stores selling their customer loyalty data to credit card companies are the most popular example of Second-Party data.

The Second-Party data in ecommerce works along with the First-Party data in most cases.

3- First-Party data collection:

Data you acquire directly from your customers on your own platform, like websites, apps etc., is known as First-Party data. This data comes from customer purchases, support and customer success and marketing programs. All the purchase history, email engagement, sales interactions, support calls, and customer feedback programs fall under First-Party data.

First-Party data is highly reliable because you collect this data directly from your customers. The only challenge here is data management. You are sorted if you know how to handle and manage incoming data and segment it according to your current and future needs.

4- Zero-Party data collection:

There is a new kind of data in the ecommerce world, and since its inception, it has become the talk of the town. Although it mostly looks similar to the First-Party data, the biggest leverage it has over the First-Party data is that the shoppers proactively share their data with you, giving all their consent.

Coined by Forrester Research, zero-party data is defined as

“data that a customer intentionally and proactively shares with a brand, which can include preference center data, purchase intentions, personal context, and how the individual wants the brand to recognize her.”

The greatest examples of platforms which gather Zero-Party data is the Selfie Quiz TM platform, the product recommendation tools etc.

Data Filtration:

There are largely four kinds of data filtering processes ecommerce companies are using lately.

1- Collaborative Filtering:

This is how it works. Once a buyer gets one product and consecutively adds another, the new product falls under similar product category. If more shoppers repeat the same process, the algorithm will recommend the second product to everyone who is purchasing the first product. Similarly, if the shoppers get the second product, the first product will be lined as a similar product.

This means that the algorithm works by pairing off products based on previous shopper behaviour. As you can see, this automatically falls into the First-Party data collection methodology. Some ecommerce companies also use Second-Party data to increase the accuracy of the product recommendation process in their Shopify stores, especially if the store is new or the user base is too small.

2- Content-Based Filtering:

Have you ever felt that you are shown a product on Amazon similar to the blog article you read last night? It’s purely a Content-based filtering method. Content-based filtering method uses Third-Party data such as browser cookies to track shopper behaviour, likes etc., to recommend products. In this method, algorithms recommend products similar to what the user has visited or liked in the past.

For this method to work, tags, keywords, and categories are used by ecommerce businesses.

3- Hybrid Filtering:

Hybrid is just the combination of Collaborative and Content-based filtering methods. Here businesses use all kinds of data they can source information from to show you the right product you want to purchase.

4- Need based Filtering:

This is an emerging method used by ecommerce businesses to personalize customers’ shopping experiences to increase loyalty. The Need-based filtering method uses Zero-Party data sourced directly from the users through interactive quizzes.

For further personalization in Need-based product recommendations, you can send an email to each user who has expressed interest in your products. When they open the message, they’ll be presented with a list of recommendations based on their past purchases and the data they shared with you.

For example, at Tangent, we use this Selfie QuizTM platform to collect Zero-Party data for our ecommerce clients.

foundation quiz by Tangent AI

Then send personalised emails like this to help increase their AOV.

Adoratherapy selfie quiz personalised page

The data that goes into these recommendations includes:

  • What products you’ve purchased over time (e.g., which ones you liked or didn’t like).
  • Which items are similar to those purchased by other users who have purchased similar items.

and much more based on the business requirement.

How to activate product recommendations in your Shopify store?

Here we will describe step by step method to use product recommendation feature of Shopify. But first, you need to ensure that you are using a theme that is compatible with Online Store 2.0.

Now you can customize the appearance of product recommendations by changing the title, colour, font, tags, and background.