Optimizations to Improve Campaign Performance
We operate hundreds of data feeds in dozens of countries. Optimizing these feeds is an ongoing process; we make changes to our feeds every day. We are optimizing our channel feeds also to have a direct impact on the ROI of our campaigns. Here are a few examples:
Better Categorization [Improve Conversion Rate]
Each shopping channel has its own product categories. It is important to match each product with the most appropriate channel-category; this will help the channels to match our products with the search queries. As most comparison shopping sites also enable consumers to search by (sub-)category, it is important that our products show up in the right sub-category.
DataFeedWatch has created an additional field ‘enhanced product type’ that combines values of several input-fields; the data in this field is relevant enough to properly map our products to the matching category on Google or other channels.
Add missing values [Reduce Disapproval]
Having complete data for all products is crucial; the products that are missing a few fields will result in lower quality ads or worse: disapproval.
Some fields in our source feeds have no values for certain products. We are using rules in DataFeedWatch to ensure that all products have a proper value. Examples: add 'unisex’ to all products without a gender or ‘men’ if that is included in the product type.
Custom Labels [Optimize bids]
Sometimes we would like to adjust our CPC-bids for certain products. This can only be done with Custom Labels. We have created custom labels for several attributes that are important to us, for example: for which sport is a certain product used, what type of garment is it, etc. This enables us to tweak our bids on Google Shopping very specifically.
Removing unprofitable products [Optimize ROI]
We are managing our campaigns primarily on (sub)category. We know that there are products within each category that perform better than others. We just don’t know exactly which products are not performing well. Or for some channels we do, but then still; with tens of thousands of products per store, it is just not feasible to optimize.
With DataFeedWatch-Analytics, we can review the cost and revenue of each individual product on every channel. Unprofitable products can be removed from our shopping feeds with a single click: products with many clicks and no conversions, with a CPA that is too high or a ROAS that is too low, etc.
Some of the data optimization happens on regional level. adidas Western Europe, for example, contains dozens of online stores that often need similar adjustments. It is very time consuming to make simples changes a dozen times, but DataFeedWatch has an Enterprise functionality that enables us to apply a single change to multiple shops and channels.
- Some products need to be (temporarily) removed from all Reebok feeds. This can be done with a single action for all countries and channels.
- Some channels in multiple countries have the exact same ‘mapping’. If something needs to be changed or optimized, this can be applied to all feeds at the same time.
- Categorization is often the same or very similar in countries with the same language (e.g. Latin America). Categories can be copied between channels in different countries.
Making changes to all feeds at the same time saves a lot of time (both on regional and on local level), gives us a higher level of control and enables us to move a lot faster.
PPC-managers on 5 continents are now optimizing their data feeds. For many of them, this was a new experience. It was crucial that the data feed tool be intuitive, so the implementation could be swift and effortless. DataFeedWatch proved to be very easy to use; training was done online and in 30 minutes; that made for a very quick roll out. Support was available almost around the clock to answer questions from adidas’ campaign managers and to advise them on best practices.