Our client, Blue Bungalow, is one of the leading online fashion shopping stores for women’s clothes like linen dresses and accessories with more than 3,000 fantastic styles to choose from. The store stocks over 150 different brands.
Fashion is hyper-competitive in advertising as an increased number of businesses bid on the same search terms causing auction prices to rise.
As a result, if all else stays equal, to maintain the volume or grow you can increase the cost per click for product groups in shopping and search terms in search which decreases profit, or you can continue as is while click volume decreases to cause a drop in revenue. The client approached us to identify the issues and problems faced.
Our goal with Blue Bungalow was simple: scale through profit.
You can only scale with positive cash flow that comes from generating profit. Increased traffic doesn’t matter, more transactions don’t matter, and even more, revenue does not matter. Any business will eventually die without profit.
Digital Darts audited their current ad account, identifying various features not used and a lack of segmentation that meant there was little bid discrimination to optimize for scaling decisions. Smart shopping campaigns are all too common in Shopify-run businesses because they are easy to create and manage.
Agencies love the hands-off nature as it saves them time. But, it’s short-term gain for long-term loss. The campaign type lacks search term data showing you what queries led to sales, which means you get no insights to drive profit decisions or conversion data from shopping to fuel ideas in search campaigns.
Google Ads Cart Tracking
In 2020, Google Ads released a beta version of their cart conversion tracking code. I wrote about how to get set up on this in another blog on DataFeedWatch called Google Ads Conversion Tracking with Cart Data.
We used the new feature which provided us with eCommerce data, like the number of items per purchase, cost of goods sold, and profit on purchases. Cart data is important as it adds another layer of relevant information on top of each conversion.
Leveraging cart data, you can see what items, such as pants and leggings, are purchased through ad clicks and which products convert better. You can also see what items, such as sneakers, are top sellers and the amount of profit made.
With the regular Google Ads Conversion Tracking (GACT) tracking, if you segment out various product groups in its shopping campaign, the most you can know and evaluate is what products were clicked on and the amount of revenue that came from the purchase.
Segmentation with DataFeedWatch
Now using DataFeedWatch and cart data, we knew what products were purchased even if the SKU clicked was different from the one in the shopping ad.
With the help of the Cost of Goods Sold (COGS) field in the shopping feed, we can see the profit. Using this valuable data gave the client and our team a much better and holistic idea of how profitable their shopping campaigns were. This helps us optimize campaigns even further.
Previously in Shopify, it was possible to gather cost information with meta fields you created. Managers and store owners had to input the cost per item themselves in meta fields then DataFeedWatch was able to extract and download this information.
However, Shopify introduced a beneficial cost per item field that we can more easily use in DataFeedWatch. Most merchants use this field now given it affects various reports within the platform.
To set up the Cost of Good Sold field for Google Ads, in DataFeedWatch, we created an internal field called cost per item:
This gives you the flexibility and ease to use the same data across other channels like dynamic product ads on Facebook Ads.
Next, for the Google Shopping feed, we mapped Google’s cost_per_goods_sold attribute to the internal field:
In my Google Shopping For Shopify: The Definitive Guide book, the strategy and various fields are discussed more in-depth to optimize shopping campaigns.
Google Ads’ system is extremely subjective and automated. It is subjective regarding what it deems is the most effective way to optimize your ad campaign and spend your valuable advertising dollars.
However, we believe in objective strategies and recommendations based on factual data and aligned with specific goals.
Dynamic Search Ads with DataFeedWatch
DataFeedWatch was also used for dynamic search ads (DSAs) as a strategy to collect search data not captured in other search campaigns. The higher the SKU count a store has, the more important an automated strategy is to maintain the data.
- We created and maintained a DSA strategy with DFW for Blue Bungalow by:
- creating a custom channel,
- choosing a comma separator format,
- renaming the page URL to use Shopify’s variant URL,
- and using a custom label to suit the brand.
The CSV file is then uploaded as business data and can be regularly fetched to keep DSA campaigns up-to-date. Bids can be customized to meet profit.
Other strategies implemented involved segmenting branded and unbranded traffic across all campaign types. Plus building in-depth manual search campaigns and eventually cold acquisition through display as conversion data continued to increase.
- Total ad spend has increased by 2000% and revenue by 3000% while gross profit continues to climb.
- Blue Bungalow’s Google Ads campaigns are more profitable than in the past.
- Cold display campaigns now net more profit than all campaigns previously managed before we came on-board.
- With the client’s increased number of returning visitors as well as increased customer lifetime value, the revenue from other channels is growing from the top of the funnel, generic term traffic.
If you are a Shopify brand and want to optimize your various paid marketing campaigns, we highly recommend DataFeedWatch as your feed management tool.