Personalization is all about providing greater relevance for your visitors and customers to give them a better experience, and to encourage them to buy.

In ecommerce, this can apply to on-site or in-app experiences, as well as contacts through marketing channels.

On-site, recommended products and categories can be personalized using a customer’s browsing and purchase history, while marketing such as emails can be more relevant to the customer, by recommending products related to previous history.

If it’s done well, personalization improves the experience for the user which in turn makes it more likely that they’ll make a purchase.

According to recent research from Eagle Eye quoted in Econsultancy, 81% of consumers say relevance is a key in whether or not they redeem promotions, while 75% are not happy to receive generic offers.

It can work for retailers too. Respondents to Econsultancy’s 2017 CRO Report reported an uplift in revenue for any channels they’ve personalized.

For example, 94% of companies experience an uplift in conversion rates by using website personalization.

It can be a challenge to personalize effectively though, as the mixture of technology, data and content involved can be complex.

Personalization requires data to be used effectively alongside content to be truly effective, to recommend products based on a customer’s previous purchase behaviour for example.

Gaining access to different data sets across a company, then integrating systems to gather and process that data and deliver the relevant content to each visitor can be a major challenge.

Personalization should also go way beyond simply using a customer’s name. That’s all very nice, but there’s more to it than that.

These are just a few ways to personalize:

  • By traffic source
  • Visitor location
  • Purchase history
  • Previous browsing behaviour
  • Aggregated customer data

Here are eight top-notch examples of retail personalization done well…

ASOS

A simple example, but a good way to improve relevance for repeat visitors. Having visited the site before and looked at menswear, ASOS remembers this and directs me to the men’s version of the page when I type in the URL.

Thread

This fashion retailer has built its website around personalization and tailoring the content people see to their preferences.

New visitors are asked about their favourite brands, what kind of looks they like, the prices they typically pay for different clothing items, size information and more.

In practice, this means people have to take time to select their preferences, which is asking a lot of a new visitor, but the outcome is that Thread acquires a lot of data it can use to personalize the experience.

Tesco

Tesco, through Clubcard and its online shoppers, has lots of customer data and uses it to create a personalized experience.

Previous purchase history is used to deliver relevant coupons and offers for Clubcard users. Tesco knows I have a cat because I buy cat food and litter, so I’ll get an offer now and then for the brands I buy.

Personalization can also be used to make repeat purchasing more convenient. By remembering shoppers favourites and using data to recommend more relevant products, it means shoppers can fill their shopping baskets more quickly.

Asda

Asda sends a Daily Alert email for 10 days leading up to pay day each month. These contain product and content recommendations.

The emails are simple enough in design and content, but it’s the way they’re created that’s impressive. Each email is unique to the recipient, with the product-offers personalized for each user according to their propensity to purchase.

These are automated emails which use customer data to select the relevant products and content. It’s an effective way to personalize on a larger scale.

Amazon

It would be hard to write a post like this without mentioning Amazon, as its use of personalization is widespread around the site.

The homepage alone contains plenty of examples of personalization:

  • By using the phrase ‘Graham’s Amazon’ it tells me it’s tailored to me.
  • There’s a personal hello and link to my account – hovering over this provides more examples, such as the ability to re-order from recent purchases.
  • It reminds me of recent orders (none since Christmas) and popular categories.
  • It suggests I continue watching a video I’d been viewing on Prime.
  • It recommends other videos based on my viewing history.

There are plenty more product recommendations based on both purchase and browsing history, and I’ll see these throughout the site.  

Thanks to Christmas gifts and my kids using Prime video, they may not all be relevant to me, but Amazon throws enough out there that some is bound to match my interests.

Graze

Graze is built around data, and uses customer data to continually adjust its product selections. For example, millions of customer ratings are used to determine the contents of Graze boxes, and the development of new lines.

This works on a broad level, but also helps to personalize the product and marketing to each customer. Direct customer feedback tells Graze which products people like and don’t like, and this determines the contents of each box sent out to customers.

It also helps Graze to create personalized emails such as these reminders to reorder snacks.

Henri Lloyd

Fashion retailer Henri Lloyd uses personalization to help customers find the best fit for them. Customers can enter details of height, body shape, weight etc so the site can recommend the best size to buy.

The details entered are then used on other product pages, so customers see personalized size recommendations throughout the site.

It’s a great way to make the shopping experience easier for customers, while finding the right size should help to reduce the number of returns the retailer receives.

Busted Tees

Retailer Busted Tees used personalization to optimise the send time for its email campaigns.

The retailer used to send all its emails at the same time, meaning that some received emails at times of day when they were less likely to notice or respond to them.  

Having first segmented send times so each email arrived with customers at 10AM whatever their local time zone, it then went further and created a personalized send time for individual customers based on their data.

The result was a 17% increase in email response rates, and an 8% increase in revenue from email marketing.