Lists are a common ecommerce feature; it’s rare to find a retail site without at least one of the following list types:
- Saved items
- Gift list
However, what is rare to find is personalisation within user-controlled lists. Typically the list is left as the customer’s personal content, with simple tools to give them control, including:
- Share via social networks
- Email to friends
- Edit g. remove an item
Amazon is perhaps the best example of taking lists to the next level of functionality, letting customers, amongst other things, add addresses, make lists public, share lists with a closed group and change list statuses.
What these lists represent is a significant data opportunity, with multiple touch points for collecting and using data to understand user product preferences.
This blog looks at some simple ideas for using list data to drive personalisation.
Being clear what’s a recommendation
First, when inserting personalised content it’s essential that you’re transparent about your recommendations. Personalisation works best when it’s perceived to add value and relevant to the context of the user journey.
A list is personal to a user; simply adding products you think they like could be intrusive. Instead, make recommendations clearly distinct from the user’s list with clear wording like “Based on your list, we think you might also like…”.
It’s also important to test the impact of personalisation; you don’t want to kill your current list conversion through intrusive recommendations. So this means benchmarking KPIs like list shares, bag additions from the list, checkout conversion, revenue and average order value. You can then test elements of personalisation against a control and measure the impact. There’s no guarantee it will add incremental value, so testing lets you gauge the impact before rolling out.
The ultimate in user control would be to enable users to turn list recommendations on/off via a preference centre, or through a simple ‘Show/Don’t show me this’ switch on the site, storing this preference against the user profile.
Product recommendations are frequently found during core product browsing journeys, for example on product pages e.g. ‘Other customers also bought’. It’s not a major stretch of the imagination to apply the same logic to lists. Product recommendations can be made based on the current items in the list. If using a 3rd party merchandising tool like Rich Relevance or Fredhopper, this could simply be a new module. You can test different types of recommendation:
- Related items – if a white sleeveless shirt is in the list, show more white sleeveless shirts
- Accessories – for example, this hat and scarf go with this jacket
- Brand ranges – more from the same brand, or related brands.
Amazon, unsurprisingly, uses personalisation in the wishlist:
Frequently added items
When an item type or brand has been regularly added to lists, this is a better indicator of interest than a one-off addition. This data can also be correlated with customer order history to determine which product types convert the best. Product recommendations can be made using this data.
For example, Penguin is my favourite brand. I often create wishlists with a range of Penguin tops, to return at some point to make a purchase once I’ve narrowed down my decision. This is a strong indicator that my interest in Penguin as a brand is more than a one-off purchase. This data could be used to provide a product recommendation strip on the list page, such as ‘New by Penguin’, showcasing the latest products, personalised to the product types that I typically add to my basket: t-shirts, polo shirts and shirts.
When a user is buying an item regularly, this is a good indicator that they’ll be back for more. For persistent items (i.e. items that stay in stock and aren’t seasonal), you can create a ‘Frequently bought’ list for the user, giving them a quick buy option. You could then add an option to products in others lists to ‘Add to my quick buy list’.
Your wishlist can let users select the variant e.g. colour, size – see theexample from Farfetch below.
If you wanted to go further, you could give the user the option to flag if the item is ‘my size’, then store this data against their profile as a preference. If you offer a preference centre where users can save their preferred attributes, this can be linked from the list so the user can update at anytime.
This will help you automatically surface the most relevant product variant for the user, pre-selected for them for ease-of-use.
Data points can be used to improve the service provided to users. Product data is a good example and can be used to deliver different types of personalised message, for example:
- Stock availability – ‘Your list item X is trending – now only 5 left. Hurry while stocks last’
- New in – ‘Exclusive – check out the new Penguin range before it’s on public sale’
- Date specific – ‘Item X is available for pre-order on May 5th, set your reminder today’.
A user creating a birthday list can be encouraged to add the date and name of the recipient. This data can be stored and used to alert the user the following year X days/weeks before the birthday. Based on previous purchase history from the birthday wishlist, product suggestions can also be made.
However, a purchase made one year isn’t necessarily an accurate indicator of future gift needs; recommendations like this need to be carefully tested with clear wording like “Last year you bought X – <recipient name> may like these related items”.
Notifications can be sent by email and SMS, as well as shown in the user’s online account when they log-in. It’s important that notifications don’t overwhelm the customer; there should be control over the number of notifications a customer receives.
Although the primary purpose of lists is to provide quick access to products to add to the shopping bag, there’s no reason you can’t also use the data to test displaying relevant, useful content.
Taking the Penguin example above, what if the retailer has a video of the new Penguin range? Why not surface this on the list page with a “Hot off the catwalk” teaser.
Video used on product pages has been proven to uplift conversion. Testing techniques that work on other pages is a sensible way to see what types of personalisation increase list engagement and drive basket additions, checkouts and conversion.
To surface relevant content, this assumes that content meta data is aligned with product meta data so that the two data types can be associated. In the case of a brand, this would be easy as ‘brand’ can be used as an attribute for products and video assets.
Comments and questions
Of course personalisation based on browsing and purchase history is done elsewhere on ecommerce sites and in digital marketing e.g. tailored product strips in emails. I’m not saying this replaces it; rather it compliments a wider personalisation program. Lists are personal to a user and I believe (I say believe because I’ve not yet been directly involved in a test plan to validate this on a retail site) that ecommerce teams can enhance the user experience by adding personalised content.
What do you think?
Which retailers have you seen using personalisation in lists? Which retailers do you think provide the best list functionality?