In order for addwish to generate accurate product recommendations to your visitors, it’s important that we have the conversion data available.
This will allow addwish to generate statistics and analytics on how the personalisation of your webshop are performing. addwish can show whenever a click on a product recommendation or search led to a conversion. Once you’ve logged into your addwish admin account, you’ll be able to see this all this information in easy and understandable graphs.
In order to provide addwish this information, you’ll need to add the addwish conversion span to your webshop.
What’s the addwish conversion span?
The addwish conversion span is a small snippet of code that you paste in your website’s HTML. You’ll need to paste it into the HTML template of your checkout page – the page customers see, once they have completed a purchase.
There are two spans you can use – one providing the minimum required information:
- The order number (required)
- The total amount of the order, including VAT excluding shipping (required)
- The email address of the customer (optional)
To provide extended information, you can add a nested span – which provides the addwish algorithms, even more details to use in future recommendations:
- The canonical URL of each product
- The product number or ID of each product
- The quantity of products included in an order (optional)
How do I add it to my website?
You’ll need to paste the conversion span into the HTML template of your checkout page (the page customers see after they have completed an order).
Here’s the code you need to copy and paste:
<span class="addwish-conversion" style="display:none;" data-ordernumber="[The order number. Required]" data-total="[Total amount for the order, including VAT, excluding shipping. Required]" data-email="[Email address of the customer. Optional]" > </span>
To add the additional information you’ll need to add this section too. This is the nested inner span:
<span class="addwish-product" data-url="[Absolute canonical url of the product. Required*]" data-productnumber="[Product number. Required*]" data-quantity="[The quantity of products. Optional]" ></span>
This nested inner section should be added underneath the first one but before the final closing
Note: It’s very important to replace the values in the [brackets], including the brackets themselves with the actual attributes. (Tip: Ask the developer on your team to implement this, so values are automatically generate).
- If you don’t want to fill out one of the optional categories, just delete the whole line.
- Prices should be filled in without including the currency. It’s assumed that they are in the default currency of your webshop. You should use a dot (“.”) to mark the decimal place and include two digits of decimal precision e.g. 12.50. Don’t use any marker to separate thousands in the prices. So type 12500.50 instead of 12,500.50.
- For the inner span, prices should be for only one unit of the product, even if the customer has bought multiple units.
- At least one of
data-productnumbermust be present in the extended version.
Here’s an example including only the minimum required attributes:
<span class="addwish-conversion" style="display:none;" data-ordernumber="20323" data-total="6198.45" ></span>
Here’s an example including all the available attributes:
<span class="addwish-conversion" style="display:none;" data-ordernumber="20323" data-total="6198.45" data-email="firstname.lastname@example.org" > <span class="addwish-product" data-url="http://example.com/shop/UltraBlender-2000.html" data-productnumber="10-800-1234" data-quantity="1" ></span> <span class="addwish-product" data-url="http://example.com/shop/NoFall-Solid.html" data-productnumber="8-304-9876" data-quantity="2" ></span> </span>
If you get stuck at any point, please just give one of our team members a call – we’ll be happy to guide you through the process.
Submitting historical sales data via an order-feed:
Here you can learn more about how to share historical conversion data with addwish.
This can be beneficial, in terms of building data for popular products based on purchases as well as data for products to recommend, based on what has been purchased together – for up-selling opportunities.