Introduction

As an omnichannel business, one of the hardest challenges is to know how you can maximize both your online and offline revenue together. Times are changing quickly and we saw an enormous uplift of e-commerce when COVID-19 struck. In 2022 we see a returning trend of some people preferring to shop in stores again rather than online.

Since the launch of Performance Max in Google Ads, it is possible to adjust the value of your conversions (store visits & store sales) based on geographic location, device and audiences at auction time bidding in real life.

How do conversion value rules work?

For an omnichannel client, we noticed that the AOV (=average order value) differed quite a bit from store to store. Now if we compare these values per region, we can see that a store visit in West-Flanders has an AOV of €125, which is 25% higher than the AOV of a store visit in Namur (€100). Therefore we can conclude that on average a store visit in West-Flanders will generate 25% more revenue than a store visit in Namur. With this information we changed the conversion value of a store visit in West-Flanders with +25%.

Conversion value rules allow you to feed the algorithm this information and optimize in real time towards these values. As a business it’s valuable to make this exercise regularly so you can update the conversion values more accurately.

To get the best out of the algorithms, one principle is very important: give the algorithm as much room as possible to optimize. This is also called liquidity

How do you implement the rules?

For the step-by-step guide of implementing these conversion value rules, Google wrote an extensive tutorial which you can find here.

Conclusion

Without these rules your campaign could have optimized towards 1,000 store visits with an AOV of €100. After implementing these rules, you can achieve 1,000 store visits but the algorithm preferred the more valuable store visits so your AOV is actually €125. At first sight your Cost/SV will be exactly the same but in reality, the business had an uplift of 25% in total revenue by using conversion value rules.


publication auteur Jonas Geiregat
AUTHOR
Jonas Geiregat

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