I wrote an article a few months ago about attribution, and how which model you choose can have a significant impact on which channels and campaigns you invest into.
If this isn’t done in a sensible way – if you attribute too strongly towards lower funnel channels, for example – you risk strangling your pipeline and losing future volume.
Similarly, if you underinvest in conversion-led campaigns, you could see competitors stealing your potential customers, or continue to run quite ineffective upper funnel activity.
The overarching point I was trying to convey was that you need to try to find a way to view all of your digital activity in the context of what’s best for your business overall.
One aspect of this that is currently massively neglected in the retail industry is how to take returns into account.
It’s reasonable to expect that some search terms will generate more revenue, but also more returns, meaning the ROAS, and the net benefit to the business, is a lot lower than visibility in Google Ads would suggest.
For example, in 2019, a selection of retailers in the UK, most noticeably ASOS, made changes to their returns policy to deter mass returns offenders.
If a retailer has not set up a way to integrate returns data with their Google Ads account, the only way to identify which searches might be linked to returns behaviour is through conjecture.
And that conjecture is likely to completely oppose the data you are seeing. For example, both large ticket items and high basket value orders could be indicators that returns are imminent, but also produce the best ROAS.
You can read more about the wider implications of returns to ecommerce here, but you can see, given the example above, the havoc you could accidentally wreak in your Google Ads account if you don’t have solid data on your returns.
Below are the four ways in which we would suggest dealing with returns in your PPC accounts:
- Using Google Analytics’ Enhanced Ecommerce plug-in and refund upload system
- CRM integration if you use a CRM system
- A less exact but still somewhat data-driven method for calculating which campaigns ought to receive more or less funding
- A bonus idea for managing high-cost high-return customers
Alternatives to these would include using third-party software as part of your ecommerce platform, but it’s hard to find any that actually integrates the data back into Google Ads.
1. Upload your refund data in Google Analytics – once you’ve switched to the Enhanced Ecommerce plug-in
You can read more about switching over to using enhanced rather than standard ecommerce tracking with Google Analytics here, but for our purposes, the important thing to note is that it gives you access to the Refund Data Import function in Google Analytics.
Refund Data Import allows you to record full or partial refunds, using a transaction ID, and upload that into your Google Analytics account.
Google Analytics gives you insight into where the refund has been attributed in terms of PPC channels, but if you want to have oversight of data within Google Ads, you need to make sure you’ve linked the two channels.
There is an article here that takes you step-by-step through setting this up, but in summary, to issue full refunds:
- Switch to enhanced ecommerce tracking if you haven’t already.
- Within Google Analytics, select “Admin”, then “Data Import” within Property.
- Follow the guide to download a template spreadsheet and fill it in with your transaction IDs.
- Re-upload within Google Analytics.
There are limitations to this approach:
- It is not a seamless integration, and so require manual maintenance and uploads of your refund data. It would be a good idea to do this on a set day of the week, month or quarter, depending on the volume of transactions you see. After this point, your PPC managers ought to be able to optimise appropriately.
- Partial and full refunds need to be processed separately and so require separate databases and uploads.
- Currently, it works by attributing the return to the last customer interaction, rather than the purchase interaction, so the attribution is better than nothing, but not 100% accurate.
2. Integrate your CRM system into Google Ads
If you are using a CRM system to record your sales, a very effective way of optimising towards actual sales is to integrate this into Google Ads.
In essence, it’s very simple. You would need to:
- Enable auto-tagging in Google Ads.
- Re-upload sales data in Google Ads, including the gclid.
In order to account for refunds, you would need to reflect this in your CRM system and upload – for example, through attributing negative revenue.
Alternatively, you could create a return conversion action in the Google Ads interface, and use this as a conversion in your CRM.
That would then need to be taken into account when analysing the data – i.e. treat it as you would conversion lag when making bidding decisions.
3. Identify the high return rate products, create specific campaigns for them, and set your targets and strategy accordingly
For example, if you sell a range of ball dresses, and you’ve noticed that 25% of orders get returned, it makes sense to create ball dress-specific search campaigns.
You can then set more appropriate ROAS targets (i.e. higher than account average) for these products, taking into account that only 75% of the revenue accounted for in Google Analytics will be realised.
It is possible to take this a step further, with specific brands or styles that are particularly sought after but not often kept.
This logic can also be very easily applied to Shopping campaigns using custom labels in the feed. Create a label for high return items, and you can control bids within Shopping campaigns on them accordingly.
The issue with this approach is that you don’t see the benefit within Google Analytics if the returns data is not being recorded there.
However, it ought to be reflected in your business’s bottom line, as well as your return rate for those products.
4. Create a remarketing list using customer match for serial returners
Presuming that you send refund confirmation emails for everything returned, you ought to be able to use this list to create a customer match remarketing list within Google Ads.
You can then apply that as an audience to your campaigns and apply a moderate negative bid modifier.
The best advanced way of implementing this strategy would be to create separate lists depending on that customer’s net value.
That is, if a customer has bought a lot of high ticket items but returned the majority of them, they may be worth the same as a customer who spends less on average, but also returns fewer items.
As you can tell from the above, there is not yet a perfect way to optimise towards refunds in your PPC account. However, the above tips should hopefully improve your situation if you are not currently taking this into account.