Google Ads ECommerce Case Study: 6X ROAS & 4M Revenue in a Year

In this Google Ads Case study for eCommerce, I want to focus on how I managed to scale an account to the ROAS of 6X ROAS and bring in 4M revenue in one year.

Problem Statement

  • Poor campaign structure & messy account
  • Many Google Ads parameters not updated for a long time

Google Ads ECom Case Study

Google Ads Strategy for the Case Study

Restructuring DSA Campaign: Many to One

Initially, multiple product categories were running in a separate campaign with their own individual budgets. Though we could tell which particular product categories were performing better, but, it was hard to make budget shifts depending upon the performance for two reasons:

  • the daily budget shift would put a lot of manual work
  • Daily or even weekly budget change would not let any of the DSA campaigns stabilize properly in terms of the campaign budget

So, we brought in all the product categories as individual ad groups in a single campaign. That enabled us to let the Google ads automatically allocate/ deallocate budget to different ad-groups at the campaign level.

Another restructuring

After 3-4 months of solid, we categorized product categories into high-selling and low-selling items based on the AdGroup performance. After that, we segregated all the AdGroups into two campaigns from one campaign. One of the segregated campaigns was for high-selling items with high-selling AdGroups and the second one was for the low-selling items.

DSA Campaign Categories

ProTips: Have a separate negative keyword list for the DSA campaigns and keep them updating at least on a weekly basis. I have also written a separate blog on DSA campaign optimization.

From Manual to Automatic

Another big decision that we took was moving away from manual to smart or more automatic bidding since the account already had enough data. The changes were not only at the bidding strategy level but also at the campaign type level. Here are a few examples:

  • Moving away from Manual CPC/ECPC to Maximize Conversions after enough conversion data
  • Moving away from regular shopping to smart shopping
  • Moving away from dynamic remarketing to smart shopping
  • Moving away from standard display campaign to smart display campaign

Our Learnings

  • Some of the campaigns even stopped delivering when we ran some automated rules for budget changes. So, do not make frequent budget changes with any campaign running smart/ auto-bidding
  • It was hard for the top of the funnel smart display campaign to optimize first. After a call with a Google Ads Rep, we were asked to include more conversions at the account level, so I added conversions such as add to cart, Initiate checkout, etc and smart display campaign picked up

Account Behaviour

I have not seen many marketers do the overall account analysis in terms of audience behavior, but, it was a game-changer for us. We unearthed a lot of facts which made our decisions easier. Here are a few and based on them what decision we took:

  • Weekends had the highest CTR and session duration but the lowest conversion rate. So, we used to launch new offers just before the weekends.
  • The shopping prime time was from 6 PM to 9 PM
  • The mobile device had much lower compared to Desktop across all age groups and gender. We regularly used Mobile landing page Script to improve our performance but we could not crack that.
  • Cities with lesser spending have performed much better in terms of ROAS compared to larger cities with higher spending. Always keep on discovering the new geographic locations from the campaign location report. It helps saves a lot of ads spend.
  • We had to eliminate the unknown gender from the targeting because of their poor performance across the board.
  • Try to gain more search impression share for the top-performing products and keywords. I regard that as a move to become the market leader for that particular product range or keyword range

Attribution Model Comparison

Attribution model comparison will give you a lot of amazing insights about your current marketing efforts across different channels. Some of the best practices that I have learned and implemented on this Google Ads case study for eCommerce are:

  • Make the best use of UTM tags to compare different sources and mediums
  • By default, some channels might be producing a lot higher ROAS compared to others. But, for example, just a single comparison of first-click vs last-click will give you enough insights into what source is the best source for the top of funnel audience and what campaigns are the best for the middle/ bottom of funnel campaigns. I have also written a guide on attribution model in marketing
  • It will also help you coordinate your effort on different channels and platforms for different audiences along with the right budget.

Attribution model comparison ECom Case study

For example, the above model comparison gives the following insights:

  • Organic search brings in a lot of fresh traffic and the same goes for paid search & email.
  • Social media is doing okay for middle of funnel traffic and maybe, the bottom of funnel traffic.
  • Surprisingly, the display is doing better for the bottom of funnel traffic too (because of smart shopping?)

Based on the above insights, we would

  • Focus more on SEO effort
  • Display remarketing & shopping
  • Top of funnel paid search traffic (DSA?)

Case Study Outcome

  • Amount Spent: ~$702,569
  • Conversion Value:  ~$4,226,580
  • ROAS: 6.02

Resources for you:

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One Reply to “Google Ads ECommerce Case Study: 6X ROAS & 4M Revenue in a Year”

  1. Winfinity Media Australia

    Your article post is very help full and easy understand the Google Ads and E-Commerce. Looking at Google shopping ads for eCommerce can overwhelm users with their options. Denver PPC creates a winning strategy to boost sales. Thanks for the sharing this article.


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