Common pitfalls of using Smart Shopping

Smart digital marketers use Upp’s powerful AI platform to acquire more customers and hit their targets.

Google smart shopping pitfalls
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    Did you know that Digital Marketers are now committing over 80% of their Google spend to Google Shopping?

    And did you also know that 90% of that budget is allocated to Google’s Smart Shopping?

    That’s because, on average, marketers who have adopted Google Smart Shopping are seeing an average 24% uplift on account performance. Yet, despite it being an incredibly powerful channel, it feels like Smart shopping leaves us with more questions than answers.

    For those new to Google Shopping, it can be challenging to know where to begin, and for more experienced marketers, you are often left wondering how to improve or make sense of your data. This article goes through the typical challenges you may face when using Google’s Smart Shopping, and how to overcome these.

    What is the best practice with Smart Shopping, and how should I set up my account?

    Since the launch of Smart shopping back in 2018, there has been a lack of concrete information regarding the ideal account setup for Smart Shopping.

    Some of us have chosen to create a single “smart campaign”, whilst others have decided to split campaigns by product type. But what is the best setup?

    Fortunately, Upp was recently invited to ‘Google’s Smart Shopping Campaigns: Optimisation, Best Practices and Use Cases for 2021’ webinar to gain insights around campaign best practices.

    Google highlighted that Smart Shopping is now doing a lot more data gathering in relation to product pages and the merchant centre feed. This means that we no longer have to provide and keep on top of our “negative keyword” list (Thank Goodness!). However the most interesting insight was what Google had to say about how to structure campaigns to maximise performance.

     


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    Google suggested that you should structure your Smart campaigns based on; your goals, product performance and ROI. This makes a lot of sense as products in the same category often perform very differently, and so should be treated differently too.

    Here are some examples of how we’ve adopted this methodology successfully for Upp’s clients, and how our AI recommends campaigns are split:

    • Products that have a similar gross margin (ensuring no under or overspend)
    • Similar spend ratios to drive conversions (products within a campaign should all have a similar CTR and CPA).
    • Segment your products by the different goals you have as a business.

    I’ve hit my ROAS target. Now what?

    Your campaigns are hitting their ROAS target. Great! But, have you ever considered how that might be restricting your account’s growth?

    If you have, you’re not alone. More often than not, we’ll find that retailers are hitting their ROAS targets, because that’s what Google does. In one case, we had a client with a 20:1 ROAS. However, after blending retailer performance data and further investigation, Upp identified that Google was promoting just 11 products in total, in order to hit the 20:1 target.

    This is where ROAS targets can limit your growth. Not every product can achieve a target ROAS, so that’s why Google just won’t promote those products. This clearly highlights the importance of splitting campaigns by performance, taking a blended ROAS performance approach.

    In order to optimise campaigns by performance, this requires both additional data sets outside of Google, and a helping hand from an automated process to ensure a balance between ROI and % of inventory advertised is optimised.

    It’s tricky to determine how much of your inventory will reach the ROAS threshold you’ve set. It leaves you wondering what % of inventory will Google now be confident to advertise based on your ROAS target. The most frustrating part is that Google doesn’t provide you with insight into how setting these goals will impact Google confidence in advertising your product lines.

    What does all this data mean?

    Analytics around performance remains fundamental to any digital marketer. Whilst the reports in Google Ads & Analytics are fantastic for delving into your data, they are incredibly detailed and require a considerable amount of time for you to figure out what’s most important to track.

    It’s essential to ensure your reporting provides you with the whole story. For example, if you’re looking to determine how many new customers you’re acquiring from Google Shopping, you can do just that.

    However, a report that only tells you the volume of new customers being acquired still leaves you with several unknowns; can I do better? What’s the difference in volume between old and new customers? Why are we all of a sudden doing well?

    So when creating reports to take action from, it’s always vital for us to question how we understand the entire goal, and not just the result.

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    Our Solution

    Upp has developed an AI neural network capable of identifying pattern opportunities outside of what Google can provide. Closing the gap between your entire organisation and Google, Upp can automatically maximise your performance for Smart Shopping ads whilst still giving marketers strategic control and providing full data transparency.

    Our solution helps retailers achieve maximum ROI and get 75-100% of all products advertised/visible by Google.

    Overall, Smart Shopping has been a game-changer! Any Digital Marketer working with Google Shopping should be using it. But like all things, to make the most of Google’s offering, you need to spend time fine-tuning your account to get the best return from it.

    Book a free AI Audit with us today to learn how to maximise your marketing spend and take your Google Shopping Campaigns to the next level.