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Depict Team
April 13, 2023
min read

Employing the power of AI-led E-commerce personalization in fashion



Personalization is becoming increasingly crucial in E-commerce due to the rapid growth and saturation of the fashion and lifestyle industry. With tens of thousands of fashion E-commerce stores and marketplaces competing for consumers' attention worldwide, personalization is a key differentiator that sets one store apart from the rest and keeps a shopper coming back for more. 

Tailoring the shopping experience to each individual customer means creating a more intimate connection with customers which fosters brand loyalty and trust. And, with more and more companies realizing that E-commerce personalization is the way forward if you’re not already offering a personalized shopping experience, you’re seriously at risk of being left behind.

Personalization engines can impact everything from conversion rate optimization to lifetime customer value. Luckily, at Depict, we know a thing or two about personalization engines. Below, we will explain why and how to implement AI-led E-commerce personalization in your E-commerce retail store.

Part 01

The rise of AI-led E-commerce personalization in fashion

Data-driven online shopping personalization and machine learning in E-commerce have both been exponentially growing over the past few years, with E-commerce personalization being one of the biggest fashion E-commerce trends for 2023.

The Google trends chart below shows that over the course of 2022, searches for AI personalization boomed.

Source: Google Trends

And, the market can attest to this boom. A study from McKinsey has shown that 71% of consumers expect personalization when they shop on an E-commerce site, and 76% get frustrated when this expectation isn’t met.

This has led to retail giants such as Nordstrom and Macy’s announcing a focus on their digital personalization strategy, as retailers begin to see this as a path towards E-commerce conversion rate optimization. 

When this expectation isn’t met, shoppers will switch to somewhere that meets their needs. But, when online shopping personalization is done correctly, customers are much more likely to convert, with Epsilon finding that shoppers are 80% more likely to convert if they’re offered a personalized shopping experience.

Purpose of this personalization-focused whitepaper

So, now you’ve got an idea of just how quickly personalization in E-commerce is growing, and the kind of results you can expect from implementing a personalization engine. 

In this whitepaper, we aim to give you a deeper understanding of:

How your customers want to see their online shopping experience personalized
How machine learning in E-commerce can help with this personalization 
How to implement a personalization engine in your fashion E-commerce business
Future-proofing your personalization engine strategy

Read on to start your personalization engine journey.

Part 02

The benefits of E-commerce personalization

Some of the E-commerce personalization benefits have been mentioned above, including higher conversion rates and better user experience. But, there’s plenty more where that came from.

Good E-commerce personalization can uplift several crucial metrics. Take Depict’s customers as an example. After implementing several different aspects of the Depict product, including product listing pages and AI-led product recommendations, these fashion E-commerce businesses saw:

  • Increased orders
  • Higher conversion rate
  • Reduced manual work
  • Lower bounce rate

See our customer success stories for more specific information on these metrics.

Three important things consumers want most from a personalized online shopping experience

Evidently, the switch to personalization is unavoidable if you’re looking for growth. But, what is it shoppers are looking for when they seek personalized online shopping experiences? 

At Depict, we’ve found the following to be the most essential when it comes to personalization.

1. Context is key for personalization

Personalizing your user experience isn’t an excuse to throw logic out of the window. As such, cross-selling items work best when there is context to what you’re cross-selling.

A study by Baymard institute found that shoppers found that product recommendations shown to customers in their cart, or straight after adding to card needed to match their expectations. This means they should, in some way, be relevant to a user’s interests or the item they added to their basket. If not, the cross-selling is likely to be seen as distracting or annoying, or simply ignored by users, which renders the cross-selling useless.

2. Steer clear of spam

The aim of personalization is to come across as less spammy, by providing more relevant product recommendations and a tailored online journey in general. So, to go overboard and begin providing recommendations at every step of the journey will defeat this point, and come across as spammy.

Providing the right recommendations at the right time is far better for customer experience (and therefore conversions) than just showing customers items at every available opportunity. Breaking this rule can actually lead to customers abandoning their carts, as shown in another study by Baymard.

3. Stay on track

Understanding and adhering to the customer journey is essential. This means your personalization should be done in line with the way customers shop. For example, search results can be personalized to match a customer’s taste, but they should always actually match what a shopper is looking for.

As well as this, alternatives to a product can be shown as a shopper considers the product, but shouldn’t be promoted once they have already made a decision and added their product to the basket. Then, complementary products should be recommended. 

How an AI-led personalization engine can help with all of the above

Achieving this high level of personalization, and ensuring that it is maintained and consistent, can in theory be done manually. However, it would require a lot of work and a huge number of employees to ensure that it was done right, especially in larger businesses with a high SKU count.

In reality, this just isn’t possible for most fashion E-commerce businesses. There are plenty of other tasks aside from personalization that requires your team’s time and attention. So, how do you solve this problem? The answer is automation. 

A personalization engine will understand every unique shopper's wants and interests, and tailor your E-commerce site to these needs.

What is a personalization engine in fashion E-commerce?

A fashion E-commerce personalization engine is a specialized type of personalization engine used in the fashion E-commerce industry. This type of personalization engine is used to provide personalized product recommendations, search results, and experiences to online shoppers. 

A fashion E-commerce personalization engine typically utilizes data analytics and machine learning algorithms to understand the preferences, behaviors, and styles of individual shoppers. The engine then tailors its product recommendations and surfaces accordingly. The engine can collect user data from various sources, such as behavior on site, browsing history, purchase history, and other online activity, to build a detailed user profile. 

This user profile can include information on the user's size, style preferences, purchase history, and browsing behavior, among other things. The engine then uses this data to suggest personalized product recommendations and show products a user is most likely to be interested in as they navigate through the site.

What features can an E-commerce personalization engine offer?

Different E-commerce personalization engines offer different features. However, the most crucial features are generally accepted as:

  • Personalized product recommendations
  • Personalized home pages
  • Personalized cross-sells

Other personalization engines may offer unique search results for each user, or tailored product listing pages. However, as mentioned, the offering often depends on the engine.

What are the benefits of implementing a personalization engine?

The benefits of implementing a personalization engine are the same as the benefits of E-commerce personalization listed above, paired with the fact it takes nearly no manual effort.

If you’re already doing your personalization manually, implementing a personalization engine will also free up a great deal more time. This time can then be used to pursue other methods of business growth.

An AI-led personalization engine is also continuously learning and growing with your customer, meaning you’re future-proofing your online shopping personalization efforts!

Check out our documentation here

How to implement a personalization engine in your E-commerce business

There are generally two main methods of implementing a personalization engine, such as Depict, into your E-commerce business. These methods are:

  • Using predefined user interface (UI) elements in a frontend software development kit (SDK)
  • It's important to note that styling can still be changed to match branding with most SDK integrations, such as Depict’s. The way this is done will change between providers.
  • Develop your own, fully customized UI, from the ground up and populate it with data from a personalization engine’s services using an application programming interface (API).

What are the differences between these two integration methods?

The main difference between using a customized integration and an SDK-based integration is the time and effort involved in these integrations.

Using an SDK to integrate a personalization engine into your E-commerce site is by far the fastest, most simple way to implement this type of product. In some cases, this can be done in as little as an hour, and the results can be felt on the same day.

A more customized integration can take notably longer but allows you to completely tailor different aspects of the engine’s UI to suit your specific needs and branding.

Part 03

How to integrate using an SDK: step by step

The first step of course, is to choose a personalization engine. There’s plenty on the market, each with different features. 

For ease and simplicity, we’ll detail how to integrate your chosen personalization engine using the SDK method, as a customized integration will have more steps depending on your needs.

Collect and feed your data

Firstly, any personalization engine is going to need the product data from your E-commerce platform. 

This data can usually be integrated via a product feed, or through a direct integration with your E-commerce platform. For example, Depict has a partnership with Centra that allows data to be ingested much more quickly and easily.

The data needed will vary between personalization engines, but usually includes:

  • Product data
  • Customer data such as 
  • Historical transaction data and (anonymized) user information
  • Some forms of customer behavioral data (e.g. past viewed products)

This data then helps the personalization engine give accurate, effective recommendations. 

For more detailed information on the best practices for data ingestion, including specific examples of how product data would be formatted to send to Depict, check out the Depict docs pages on data ingestion.

Integrate the SDK into your build system

As the SDK is predeveloped into a full product, minimal development work is needed. Following these general points will get you up and running.

  1. Read the documentation
  2. Add package via your package manager (npm/yarn/pnpm)
  3. Find a package for your framework
  4. Integrate the package into your code

It is important to remember that how much work this takes will depend on your chosen personalization engine, your current setup, and how many features you’ll be integrating.

Analyse and optimize 

A personalization engine is able to work effectively on its own. However, that doesn’t mean you’re unable to control aspects of it when needed. A good personalization engine will allow you to enter its portal and pin products when needed. This means the products will then show up where you want them to, whether this is product listing pages or recommendations.

Once you’ve implemented your personalization engine, doing this with any crucial products you might want to is a good idea. 

It is also worth taking learnings from your personalization engine over time, by checking what is working and what isn’t. This will allow you to improve profitability in the future.

What challenges are there with integrating an E-commerce personalization engine?

Every journey has its challenges, and you may run into a few when implementing your personalization engine. Some common challenges (and their fixes!) are listed here:

  • Your product data isn’t in a standard format
  • This can be easily avoided by either using a direct integration, such as Depict has with Centra, or exporting your product data directly from your platform provider as a CSV.
  • Your product feed isn’t up to date
  • Keeping your product feed up to date is essential, so ensuring this is updated before integrating your E-commerce personalization engine will help you get started on the right track.
  • Your chosen personalization engine isn’t compatible with your E-commerce platform
  • Most major E-commerce platforms integrate with personalization engines. If you aren't sure whether your E-commerce platform integrates with an engine, why not get in touch with our expert team at Depict for information?

The future of E-commerce personalization engines for fashion

As fashion E-commerce is rapidly changing, personalization in fashion E-commerce is expected to change along with it. Plenty of new features are being introduced every day, and if you want to stay updated on some of them, sign up for the Depict newsletter. 

You can expect expert insights into fashion E-commerce personalization, and more from the general industry, straight to your inbox.

Part 04

Part 05

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Transform your retail experience online with Depict.

Become a product discovery champion today.