In this post we’ll look at ways in which retailers can provide a personalized experience for customers. 

Personalization is about all about relevance for visitors. It’s about using what you know about a visitor to your site to give them the best possible experience.

Data such as the visitor’s location, the source of their visit, the campaign they responded to, as well as their previous history with the site, can be used to tailor a site to the customer.


The Benefits of Personalization

Effective personalization in ecommerce is all about relevance.

It’s not simply a case of using the customer’s name, but of using the data they share with you to create a better and easier shopping experience for them.

This could be making the on-site experience easier by remembering settings and preferences from previous visits, or by using data to surface products and services that they’re likely to be interested in.

According to stats from Accenture, 75% of shoppers are more likely to buy from sites which use some form of personalization.

Personalization can be a challenge for retailers to implement effectively, as it requires the technology and resources to implement, as well as the need for marketing and product teams to have access to the data required.

In many cases, the real-time behavioural data required for effective personalization is held by data teams and not easily accessible.

For these reasons, personalization is seen by many companies as hard to implement in-house.

Indeed, 34% of companies in Econsultancy’s 2017 CRO Report rated personalization as ‘very difficult’ to implement, more so than any other CRO method.

There may be challenges in implementation, but personalization works when used well. The vast majority of companies using it reported an uplift in conversion rates.

Q: Have you experienced an uplift in conversion rates through any of these channels since implementing personalization?


Examples of Personalization in Ecommerce

There are many ways to use personalization, both on and off site using a range of different data sources.


Weather and Customer Location

Very uses personalized homepages which greet returning customers with products and content which reflect their interests and previous behavior.

It can add other factors in too, such as the weather in the visitor’s current location.

The pages can be adjusted according to the weather, and combines this with existing personalization of product recommendations and addressing the user by name.


Personalized Site Search Results

Footwear Etc saw a 10% increase in revenue per visitor to personalized search and navigation pages.

The site search presents different results for different people using the same search term, based on their previous search and buying behavior.

One simple example is serving up results for a term like ‘leather boots’ and showing only men’s boots as that’s what the customer has previously bought.


Size and Fit

Fashion retailers face challenges in reducing returns rates, more so than most sectors. Personalization can help retailers to address the fitting issues which are behind many returns.

ASOS asks customers to enter their height, weight and preferred fit so it can deliver more accurate recommendations for the size to choose.


Personalized Homepages

Amazon has a lot of data on customers, and uses it wherever possible to provide product and category recommendations.

The homepage alone contains lots of examples. I’m welcomed by name, while it provides quick links to my account and reminds me of the items I left in my basket.

In addition, it recommends video and music for me, as well as plenty of other product recommendations further down the page. This ensures that at least 50-60% of the page contains recommendations that are relevant to my previous on-site behavior.


Ask Customers About Their Preferences

It can take time to collect enough customer data to be able to personalize the on-site experience, and one shortcut here is to ask for it directly.

Thread takes the relatively bold step of asking customers to answer a series of questions about their clothing preferences, size and fit details, as well as their budgets.

It’s asking customers to do some work (the questions take a few minutes) but the result is a lot of data from which to create personalized recommendations for shoppers.


Post-Purchase Emails

Personalization can be used to create more relevant emails, in this case Matalan uses it in post-purchase emails.

Customers are sent cross-sell recommendations based on products they have previously browsed or purchased.


Browse Behavior

The products people view when on our site can be used to create personalized recommendations.

Browse abandonment emails can then be sent to users with the products they viewed without buying, as well as similar products.


Mobile Commerce Apps

Apps are a great way to build a personalized experience for users, and reward customers for their loyalty.

Customers using apps can generate a wealth of data for retailers, allowing them to understand product trends and individual customer preferences.

This information can then be used to create a personalised shopping experience, as Polyvore does here, using customer likes and preferences to recommend products.