Search by Algolia
How to increase your ecommerce conversion rate in 2024
e-commerce

How to increase your ecommerce conversion rate in 2024

2%. That’s the average conversion rate for an online store. Unless you’re performing at Amazon’s promoted products ...

Vincent Caruana

Senior Digital Marketing Manager, SEO

How does a vector database work? A quick tutorial
ai

How does a vector database work? A quick tutorial

What’s a vector database? And how different is it than a regular-old traditional relational database? If you’re ...

Catherine Dee

Search and Discovery writer

Removing outliers for A/B search tests
engineering

Removing outliers for A/B search tests

How do you measure the success of a new feature? How do you test the impact? There are different ways ...

Christopher Hawke

Senior Software Engineer

Easily integrate Algolia into native apps with FlutterFlow
engineering

Easily integrate Algolia into native apps with FlutterFlow

Algolia's advanced search capabilities pair seamlessly with iOS or Android Apps when using FlutterFlow. App development and search design ...

Chuck Meyer

Sr. Developer Relations Engineer

Algolia's search propels 1,000s of retailers to Black Friday success
e-commerce

Algolia's search propels 1,000s of retailers to Black Friday success

In the midst of the Black Friday shopping frenzy, Algolia soared to new heights, setting new records and delivering an ...

Bernadette Nixon

Chief Executive Officer and Board Member at Algolia

Generative AI’s impact on the ecommerce industry
ai

Generative AI’s impact on the ecommerce industry

When was your last online shopping trip, and how did it go? For consumers, it’s becoming arguably tougher to ...

Vincent Caruana

Senior Digital Marketing Manager, SEO

What’s the average ecommerce conversion rate and how does yours compare?
e-commerce

What’s the average ecommerce conversion rate and how does yours compare?

Have you put your blood, sweat, and tears into perfecting your online store, only to see your conversion rates stuck ...

Vincent Caruana

Senior Digital Marketing Manager, SEO

What are AI chatbots, how do they work, and how have they impacted ecommerce?
ai

What are AI chatbots, how do they work, and how have they impacted ecommerce?

“Hello, how can I help you today?”  This has to be the most tired, but nevertheless tried-and-true ...

Catherine Dee

Search and Discovery writer

Algolia named a leader in IDC MarketScape
algolia

Algolia named a leader in IDC MarketScape

We are proud to announce that Algolia was named a leader in the IDC Marketscape in the Worldwide General-Purpose ...

John Stewart

VP Corporate Marketing

Mastering the channel shift: How leading distributors provide excellent online buying experiences
e-commerce

Mastering the channel shift: How leading distributors provide excellent online buying experiences

Twice a year, B2B Online brings together America’s leading manufacturers and distributors to uncover learnings and industry trends. This ...

Jack Moberger

Director, Sales Enablement & B2B Practice Leader

Large language models (LLMs) vs generative AI: what’s the difference?
ai

Large language models (LLMs) vs generative AI: what’s the difference?

Generative AI and large language models (LLMs). These two cutting-edge AI technologies sound like totally different, incomparable things. One ...

Catherine Dee

Search and Discovery writer

What is generative AI and how does it work?
ai

What is generative AI and how does it work?

ChatGPT, Bing, Bard, YouChat, DALL-E, Jasper…chances are good you’re leveraging some version of generative artificial intelligence on ...

Catherine Dee

Search and Discovery writer

Feature Spotlight: Query Suggestions
product

Feature Spotlight: Query Suggestions

Your users are spoiled. They’re used to Google’s refined and convenient search interface, so they have high expectations ...

Jaden Baptista

Technical Writer

What does it take to build and train a large language model? An introduction
ai

What does it take to build and train a large language model? An introduction

Imagine if, as your final exam for a computer science class, you had to create a real-world large language ...

Vincent Caruana

Sr. SEO Web Digital Marketing Manager

The pros and cons of AI language models
ai

The pros and cons of AI language models

What do you think of the OpenAI ChatGPT app and AI language models? There’s lots going on: GPT-3 ...

Catherine Dee

Search and Discovery writer

How AI is transforming merchandising from reactive to proactive
e-commerce

How AI is transforming merchandising from reactive to proactive

In the fast-paced and dynamic realm of digital merchandising, being reactive to customer trends has been the norm. In ...

Lorna Rivera

Staff User Researcher

Top examples of some of the best large language models out there
ai

Top examples of some of the best large language models out there

You’re at a dinner party when the conversation takes a computer-science-y turn. Have you tried ChatGPT? What ...

Vincent Caruana

Sr. SEO Web Digital Marketing Manager

What are large language models?
ai

What are large language models?

It’s the era of Big Data, and super-sized language models are the latest stars. When it comes to ...

Catherine Dee

Search and Discovery writer

Looking for something?

facebookfacebooklinkedinlinkedintwittertwittermailmail

Do you want to know what your users are doing as they search and browse your online catalog? Do you want to suggest recommended products as they shop? 

If you are not yet capturing your customer behavior, and not using Recommend – Turn it on today!

We’ve all seen and clicked on recommendations while shopping online. As soon as we add an item to the cart, we are prompted to continue shopping for items Frequently Bought Together.  

Take a closer look. Those recommendations may have been powered by Algolia Recommend – which in turn is powered by Algolia’s out-of-the-box analytics. 

By capturing user behavior with Algolia Analytics, and feeding that data into Algolia Recommend, you can start offering meaningful recommendations to your customers that will increase their satisfaction and conversion rates.

Consumers appreciate it when an online store suggests relevant items in a non-invasive and timely manner. Consider Jennifer, who is looking for a bed for her sick dog, Luna. She finds the perfect bed and adds it to the cart. Then she notices a “Frequently Bought Together” section that recommends dog covers, pillows, and other accessories that could comfort a sick dog. Jennifer may also see items that other dog owners have bought for their beloved pets. 

Recommendations increase basket size

This is how Algolia Recommend increases your customers’ basket sizes and values while improving their experience and loyalty.

But how does Algolia know what to recommend? Does Algolia know that Jennifer is a pet owner or that Luna is sick?

The simple answer is no. Algolia doesn’t know anything about Jennifer except that she is following a consumer pattern based on similar purchasing behavior by similar customers. Many customers like Jennifer have searched for, clicked on, and bought similar products.

And while it may sound simple, to mix and match what customers want, it actually takes a lot of analytics data and a careful implementation of AI-driven machine learning to get it right. 

Recommend works by collecting user behavior (event-based analytics) and training  models to display features such as: Frequently Bought Together, Trending Items, and Those Who Bought This Also Bought. These models have been carefully designed to align with a customer’s original purchasing intent, so as to guarantee a greater likelihood that the customer will continue shopping. As you can imagine, only the most relevant recommendations drive financial growth and customer loyalty.

With Algolia Recommend you can significantly improve your business performance

Before telling you how to set it up, let’s see how the right recommendations can lead to more upsells, cross-sells, and overall customer loyalty (Ask Amazon!)

Gymshark has boosted its revenue by adding Algolia Recommend to its Algolia Search implementation. Gymshark’s success metrics:

  • 150% increase in order rate and 32% “add to cart” rate with new users on Black Friday
  • 13% higher order rate and 10% higher “add to cart” rate from returning customers
  • 1.4 clicks per user vs. 1.1 with previous solution

Gymshark adds Algolia Recommend to handle the crucial Black Friday period. We’ve discovered that training your recommendations models during big shopping events such as Black Friday or Christmas Holidays will help you understand your shopper’s behaviors in time for the next big event.  

Setting up recommendations – All you need to do is start training the model!

Here are the four steps to Recommend.

  1. Capture user activity (events)
  2. Send the events data to Algolia
  3. Enable Algolia Recommend on the Dashboard and have it start generating recommendations in the background
  4. Display recommendations to your users

Step 1 – Capturing quality data

Data quality is essential. Steps 1 & 2 are entirely in the control of the customer. Customers build up their analytics dataset by capturing significant user activities (events). This means that you must be careful to capture actions that are “significant” to your business. Of course, purchase history is almost always relevant, but what about the shopping cart? Or category pages? Facets? 

Essentially, you want to track all user actions that you believe will have a significant likelihood to lead to a conversion. 

The good news is that Algolia comes with an Analytics Dashboard that you can use to test the quality of your data.

Step 2 – Sending events to Algolia

How do you send Recommend events? Like most analytics, your engineers need to add a line of code to the pages in your website where you want user events to be captured. For example, on the search results page, you’ll add a line of code to collect every click on a result and send it to Algolia. On the purchase page, or the view product page, you’ll add a line of code to capture the product just purchased or viewed, and then send it to Algolia. 

In sum, whenever you want to capture an event, you need to (1) add a line of code that captures the information that your user is doing and (2) send it to Algolia via the Algolia’s API. One line of code per event.

Steps 3 & 4 – Enable recommendations, and you’re good to go

Once you’re sending significant user actions (here’s a big list), then all you need to do is enable recommendations in Algolia’s Dashboard (step 3) and let the machine roll. As you send events, Algolia does the rest – it feeds those events into its machine learning models and generates a robust set of recommendations. 

After a short time – could be weeks, depending on the activity of your website –  you can start displaying those recommendations to your users (step 4). Here are some powerful ways to leverage recommendations, and an overview on how to display recommendations.

The bottom line

If you can provide great recommendations, ensuring that your customer journey is enjoyable and stress free, you’ll be on your way to building customer loyalty that will transform your business bottom line.

Our powerful Recommend API and AI-driven technology will quickly and seamlessly improve your user’s experience and the conversion rates on your website and mobile applications.

To learn how leading ecommerce retailers leverage the capabilities of an AI-powered recommendations engine to exceed and transform their digital merchandising goals, contact us, and let’s get rolling!

About the author
Luigi Castellano

SR Product Marketing Manager

Recommended Articles

Powered byAlgolia Algolia Recommend

How Algolia Recommend can increase basket size & value and build customer loyalty
product

Luigi Castellano

SR Product Marketing Manager

Introducing Algolia Recommend: The next best way for developers to increase revenue
product

Matthieu Blandineau

Sr. Product Marketing Manager

Why we recommend Recommend to make recommendations
product

Pauline Lafontaine

Sr. Product Marketing Manager