We continue our problem solving series, this time looking at how to create and optimize ads for the clothing industry.
In this short article, WakeupData CEO Dennis Cassøe looks at common problems which we've solved multiple times for clothing companies and how they give great value to clients' marketing efforts.
"PPC prices have dramatically increased and it is therefore difficult to reach the same high ROI/ROAS numbers as earlier."
There are multiple paths available to take to improve ROAS:
Improving the data quality
To improve which items get displayed on, for example Google Shopping, the ability to send correct and optimized data is of huge relevance.
For clothing, this includes adding valuable information in the right places.
For instance, by making sure that high-value brands are included in the product’s title along with individual attributes such as color, size, material, etc.
This alone will lift the average performance, but there are other vital aspects such as product descriptions, proper categorization and choosing the right images that can add a noticeable increase in the value of ads.
Adding a brand in front of a title is usually a simple fix, but often the data for color and materials do not exist as separate data attributes in the webshop, usually being included in the description.
The solution for this problem is to use WakeupData's expression engine to extract the relevant information, and then add it to the correct fields in the various exports.
The functionality needed is all included in the expression engine, or can be done with the rules engine. It may take some time, but is often of huge value! This can be done by the user (see help video, above) or through our CSM department.
"Differentiate bidding for different price ranges pr brand or clothing type"
A classic case is that the company bids the same for every brand or clothing type.
Instead of doing it this way, it will generate better ROAS if bids are placed differently on cheap products, mainstream and high end items pr brand/clothing type. This can be done by doing a categorization pr brand or/and clothing type and then divide it based on either sales price.
At WakeupData this can be achieved by using the rules section, where you make bidding rules by setting up rules for products with a given brand and price below X or above Y.
If you are already using WakeupData, there is no additional charge.
Differentiate bids based on recommendation-popularity
For the advanced user a big value gain lies within bidding differently based on how popular the given items are in the online store.
This will allow the store to prioritize those items within a brand or product category that are the high flyers, whilst downscale those which are not getting the users attention.
An example is to display the white shirt from Hugo Boss in medium instead of the pink small shirt that is on sale from the same brand. And this can be done automatically.
By merging recommendation data from Raptor Smart Advisor or Clerk.IO (or a 3rd provider if such is used) on top of the feed, we can automatically score each item based on its popularity. This can then be used in bidding strategies and in promotion on Facebook or affiliate channels
If already using WakeupData, the additional cost will be a merge, which cost 249 kr pr month. For Clerk.io it is extremely simple to setup, while other tools like Raptor can cost an hour or two of support time.
Check out more tips, case stories and resources here.
Want to start implementing these changes right away? Talk to us or signup for a free trial here: