How to cope with Google Smart Shopping campaign and take it to stable results
Google Smart Shopping Campaign is a real deal for a PPC specialist. This campaign can produce brilliant results with minimum actions from an expert, it is as automated as possible. But it’s not that simple. We had to go through a tortuous path of testing hypotheses, adjusting strategies, and exploring all kinds of ways to optimize the campaign to achieve the target goals. We will share our experience in this article, as well as the tools that helped us achieve the result.
What is Google Smart Shopping Campaign?
It works just like a regular shopping campaign – it shows products from the feed that you upload to the Merchant Center. What makes the campaign Smart is Google machine learning. Bids and ad placements are automatically selected to attract the most valuable conversions within a provided budget.
Smart Shopping ads appear on Search, Display Network, YouTube, and Gmail. So it covers more placements comparing to a regular shopping campaigns.
Example of a shopping ad below the Google search bar
Example of expanded shopping ad form on the Gmail
And this is one of the options for displaying the ads on YouTube
Machine learning in a campaign causes limitations on the management possibilities.
Here’s a list of what you can not do in a Smart Shopping Campaign:
- Work with search queries – exclude negative keywords;
- Exclude locations from the campaign. Though the location targeting can be set in the Smart Shopping campaign settings. Let’s say you have a feed in the United States, and you want to appear in all states except California. In this case, you can specify all states manually in the campaign settings, except for California. It turns out that you cannot actually exclude California, but you set all the states except this one;
- Adjust bids or exclude ad delivery on certain devices;
- Set up audience targeting;
- Manage ad scheduling.
Here is what you can manage in a Smart Shopping campaign:
- Budget (but total budget is not supported);
- Select a New customer acquisition (NCA) conversion goal. This feature is located in the Conversion goals section of the campaign settings. With this goal, you can set your own customer acquisition cost;
- Set and change the value of the target ROAS in the auto strategy settings;
- Product titles and descriptions. Product titles and descriptions are handled at the feed level. So you can do it via the service / app you use for creating and managing your feed, or in the Merchant Center using feed rules.
- Products. You can filter products by name, by ID number, by product category, or by any other attributes that you can set in the labels. There are no restrictions on working with product groups in a Smart Shopping campaign. As you can manage the product groups, you can build the account structure according to your tasks. For example, you can create an Alpha campaign for the best-selling products, so that you can set a larger budget for this campaign.
- Geographical targeting. As it was mentioned earlier, you can set specific locations within the country specified in the feed.
Getting started with a project
Our client is an online store powered by Shopify. Product types: everything for fishing, hunting, outdoor activities. The client does not sell items like fishing rods, spinning rods, or shotguns, but there is a lot that can be needed for camping – ice screw for winter fishing, multi-tool shovel, and hats with Led Llghts.
The client was getting a good amount of sales on Amazon and eBay. Google Shopping campaigns produced the results as well, but the client was not happy with the sales volume.
We have defined 500% ROAS as the target KPI. We divided the process of achieving this goal into several stages. The first stage is to achieve 200% ROAS consistently.
Step 1 – segmentation by the groups
The statistic was already accumulated in the account and showed that different product groups were selling with different efficiency. We decided to include the effective product groups in separate campaigns so that we can “push” them with a budget. First, we identified the most effective groups in separate campaigns. Then, groups with average efficiency.
From time to time, we received requests from the client to promote the required products. Then we included those products into separate smart shopping campaigns.
Depending on the group efficiency, we set different campaign budgets – from $5 to $20.
The dynamic was not stable, campaigns accumulated different numbers of conversions and had different ROAS, in some campaigns, it was less than 100%.
Step 2 – segmentation by results
While processing an ongoing work, we found that the beach umbrellas are the best-sellers. To enhance this effect, we have moved all of these products into a separate campaign. We set a daily campaign budget of $50. Target ROAS was not set.
The umbrellas sold well in a separate campaign until the season was over.
We went further and transformed the umbrella campaign into the Alpha campaign. We added other high-demand products to it. We set the budget to $60, and 300% ROAS as a target.
The campaign did not spend its limit. The amount of sales was small and not regular.
Step 3 – come back to a unified campaign
We did not receive positive results with product segmentation, so we put forward several hypotheses:
- The problem is in the feed. The product feed can be generated by one of many apps for Shopify stores. We used a standard free application with minimal control options. We assumed that the problem might be in the application itself;
- The problem is segmentation. A unified Smart campaign was driving more conversions than individual campaigns we segregated by category or by the product performance.
We changed a Product Feeds app
We switched to the Feed For Google Shopping app. It is not expensive, but quite functional. In the new application, it’s easier:
- To set a product category in Google,
- To edit Titles and Product Descriptions right in the app (no more feed conversion rules!). Titles and descriptions can now be edited directly in the application in the product card. You can always roll back to the original value from Shopify with one click.
Set up a new Smart Shopping campaign
Using the new feed, we set up a campaign with all products in it. All other campaigns were disabled. We set a budget of $ 80 and a target ROAS of 200% for the new campaign.
At the same time, we changed the attribution model in conversion from Last Click to Position based. Now, if several ad campaigns involved in a chain of interactions, conversions were distributed among these campaigns (in addition to Smart Shopping campaign, we also used other types of campaigns. We will talk about it later).
3 conversions in 1.5 weeks. 258% ROAS.
On the one hand, we achieved our target ROAS, but on the other hand, we were unhappy with the number of conversions. With this volume of conversion data, further growth and scaling of results was impossible.
Therefore, once we received this result, we decided to remove the target ROAS in the strategy settings. The vertical line on the chart below marks the day of the change. After that, traffic and the ad spend increased dramatically. Conversions have become more regular. The number of conversions per day has increased. ROAS dropped to 127%.
Working with a budget
The campaign is giving stable conversions, but the result is still insufficient. There is a feeling that we are missing something fundamental.
We assumed that the campaigns based on machine learning may not receive enough data to train successfully. So we came up with the idea to test the increased budget. Based on the recommendation of our personal Google manager, we have calculated the optimal budget: CPOx10. In our case, it turned out to be $330.
While we’ve been waiting for the new strategy approval, we had a feeling of inner resistance. In our field, you normally increase the budget once you achieved the goal and ready to scale the obtained result. But we had nothing to scale yet.
The other part of the resistance was that we were afraid the campaign will spend the entire budget available to it. Then we had a risk to fail in terms of profit.
To minimize the resistance, we decided that we will:
- “not think for the client”. We won’t be thinking if the client will agree on budget increases or not. Instead, we organized a meeting, proposed the strategy, and showed the reason for increasing it. We clearly showed that we need this much of daily budget;
- set 140% ROAS in the campaign as a target. Based on our experience, specifying target ROAS has always limited campaign costs;
- Agree with the client to test the new settings for 2 weeks. At the same time, it was important to make no changes with the campaign, give it time to receive data, and learn from it.
The time period of two weeks was based on the recommendation of our personal Google manager. She suggested that based on their data, this period is enough. In this case, in the first week, the campaign collects data for training, and from the second week, it optimizes itself based on this data. A positive result can be considered as increasing the number of sales per day starting from the second week.
The results were summed up after 2 weeks of the test: we got 28.26 conversions with 135% ROAS. With a daily budget of $330, the campaign spent $934 in 2 weeks.
Since the first week of the campaign was a week of data collection and training, we did not count it for a final test conclusion.
Result for the second week of the test:
11.85 conversions, 192% ROAS. $376.71 of ad spend, revenue $721.94. We got a positive dynamic in terms of ROAS and sales.
Based on the test results, we decided to keep the settings and give the campaign a time to increase the number of conversions per day.
Reconfiguring event parameters for dynamic remarketing
We set such event dynamic parameters as Page Type, Product ID and Total value at the different stages of the work process with this project. It was running great until we got a warning about Google Ads tag audience source error in Google Ads account. We got the same warning message in several e-commerce projects we run at the same time. We solved this problem by renewing the script for dynamic remarketing (we borrowed the fresh release from https://www.digitaldarts.com.au/).
That was the last day of the test when we solved the issue with dynamic parameters. This day is shown as the rightmost red vertical line on the chart. Once we fixed the issue, the average daily number of conversions increased, the campaign started spending more and getting more conversions. We’ve got much more conversions in the sequence of interactions after multiple visits from a Smart Shopping campaign. That made us think that the Smart Shopping campaign with the dynamic remarketing finally works.
By the time the test ended, the campaign was spending 18% of its daily limit per day and getting 2-3 conversions per day. We decided to keep the settings and give the campaign a chance to increase the average daily sales.
We made the changes 3 weeks after the end of the test.
Results during 3 weeks:
106 conversions, ROAS was 198%. The campaign was already spending $100 per day. The BFCM sale played an important role here. These days we’ve got a significant increase in daily sales and campaign costs.
Once we received such results on ROAS, we decided to increase the target value in the campaign settings and raised it from 140% to 150%. We gave the campaign a week to work on this setting. During a week the campaign got 153% ROAS, we consistently received at least 10 sales per day, and the daily ad spend has almost reached the limit.
We decided to increase target ROAS again, now we did a radical step – from 150% to 215%. Our KPI – consistently receive at least 200% ROAS.
210% ROAS. We have reached our ROAS target. But the dynamics changes 2 weeks after the last ROAS increase, and the number went down, as well as the number of conversions. This result was influenced by the seasonal change of demand as the Christmas shopping season ended.
After the Christmas holidays, the situation got stable, and we reached 324% ROAS in January. Today, we continue to work with adjustments to the target ROAS value in the Smart Shopping campaign and carry out regular optimization.
Working in other directions
Throughout the entire period of work with the project, we tested other types of advertising campaigns – Smart Display, Search by keywords, Brand campaign, Dynamic remarketing in GDN, DSA, YouTube video campaign, cent shopping campaign, Smart campaign. We’ve got the conversions and interaction sequences in Brand campaign (4.60 conversions, 3441% ROAS) and a cent shopping campaign (4.55 conversions, 488% ROAS). The Cent campaign in a nutshell is a regular Google Shopping, and it displays all the same products as the Smart campaign. The campaign runs at minimum bids – a few cents. The task is to get residual cheap traffic.
The rest of the campaign types either did not bring the results, either it is still early to make a conclusion as the campaigns are getting tested.
We are regularly optimizing the titles and product descriptions in the feed. Here are the titles principles that we are testing:
- The first five words should contain the key information about the product;
- Brand name included / not included;
- Include important characteristics in the heading, in our case, these are dimensions, important qualities of the product;
We constantly monitor the products status and errors in the Merchant Center:
- Wrongly rejected products;
- Images with overlays (captions) that are automatically pulled from the Shopify store. We turned on auto image change in the Merchant Center. But it needs to be controlled periodically to check what Google included in the ad;
- Products for dynamic remarketing. Sometimes products are mistakenly banned, for example, wrongly assigned with ‘only for adults’ mark. And 18+ products cannot be advertised in remarketing for ethical reasons. It is getting fixed by sending rejected goods for re-moderation.
During our work with the Smart Shopping Campaign, we went from one unified campaign and back to it, by segmenting products by category and performance. The key decision was to give the Smart campaign time and the ability to accumulate data on a large budget. Before the test, campaigns did not spend even small limits, so it seemed that the increased budget would not help. We also had concerns that a large budget means a big risk. It was also mentally difficult to keep the campaign working for 2 weeks without any changes. Silently monitoring the campaign with a large budget feels too luxurious when the time is limited.
Now we know that a Smart Shopping campaign powered by machine learning can successfully learn and start performing well on a large budget when it has the ability to get at least 10 conversions per day (budget = CPOx10). It is important to specify the target ROAS in the auto strategy settings. Don’t overestimate it: do not highlight something that is very different from your actual ROAS, especially at the initial stages of work.
We hope this article helps you get the results you want with Smart Shopping campaigns. You can do it yourself or order E-commerce PPC management from our experts.
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