There are many scenarios in which Google Shopping campaign managers would love to have a special Bid Management strategy that allows them to influence product ad impression levels for certain products, brands or categories.
For example, you may want to give an added push to products that have a surplus inventory or pull back on products that are delivering low ROAS results.
At the moment, you can do this manually at the campaign level, but that will apply the change across all products in that campaign. You can also do it at the product level, but that would be an awful lot of manual work. Neither are ideal solutions.
So we’ve developed a Camato feature that gives you the power to customize bids down to a granular level. Introducing…Custom Bid Strategies.
Feature goal: Execute custom Bid Management strategies that take performance aspects into account
When would you use a Custom Bid Strategy?
There are typically two scenarios where you would want to increase your product bid:
- For temporary promotions which are usually tied to business goals defined outside of PPC departments.
- To clear stock levels or to promote new collections/brands.
In both of these, typical performance KPIs like Cost of Sale (COS), are often allowed to perform below average. They may even be combined with additional metrics such as yield, to account for the effective value of incremental sales.
In addition, PPC marketers can also exploit meta-knowledge. For example, if you sell certain products with competitive advantages, like low prices or fast delivery, you’ll want to bid slightly higher than average to get them in front of more people and make more efficient sales. Eventually, based on the tracking data, the Camato Bid Management system will identify these products and bid up on them by default. But, with your expert knowledge and Custom Bid Strategies, you can get a jump start on this process by bidding proactively instead of reactively.
Similar scenarios hold true when bidding lower. Low stock levels and competitive disadvantages (like high prices) are certainly reasons to pull back bid aggressiveness.
Why you shouldn’t rely on regular bid modifiers
You recognize the need to bid up (or down) on certain types of products, but how should you go about it? Usually, it’s done by either adding a few cents to the bid or adding a fixed-percentage bid modifier on top of the existing bid.
In this example, we want to increase ad impressions for products A, B and C. We’ll start by adding a +10% bid modifier on top.
Table 1: Bid modifiers push everything up
|Product||Cost||Revenue||ROAS||Old Bid||New Bid|
Of course, adding 10% across the board, means that product C – although performing poorly – gets a higher bid as well as products A and B.
The problem with this approach is that it does not take the relative performance of each product into account. In a perfect world, we would push A stronger than B, and B stronger than C. Even if the old bids weren’t the same to start with, using this bid modifier method is like using a hammer. There is only one bid direction, and that direction is up. Regardless of performance.
How Custom Bid Strategies solve the problem
Instead of adjusting the bid directly, what if we could tie bid adjustments to another metric? One that typically influences the bid calculation decisively – e.g. revenue. If revenue isn’t your main KPI, you can use another metric, like margin or customer lifetime value.
A simplified version of this theory would look like this, if our target ROAS was 5 and we didn’t do any additional bid modifications.
Table 2: Simplified new bid calculation without additional pushes
|Product||Cost||Revenue||ROAS||Old Bid||New Bid|
|A||100||1000||10||0.5||Above target: bid up (e.g. 0.55)|
|B||100||500||5||0.5||On target: leave bid as is|
|C||100||100||1||0.5||Below target: bid down (e.g. 0.4)|
If you still want to more heavily push A, B and C, you can pretend these products performed better than they really did. You simply calculate an adjusted Revenue and, derived from that, an adjusted ROAS.
In this example, we’re pretending that these products performed 20% better than they really did:
Table 3: Adjusted new bid calculation with additional pushes
|Product||Cost||Adjusted Revenue||Adjusted ROAS||Old Bid||New Bid|
|A||100||1200||12||0.5||Even more above target: bid up a little stronger
|B||100||600||6||0.5||Slightly above target, bid up a little (e.g. 0.52)|
|C||100||120||1.2||0.5||Below target, Bid down, but not as strongly as
without adjustment (e.g. 0.44)
The big difference is that now, we’re taking A, B and C’s performance figures into account more forcibly. We bid up A more than B, and we still bid down C, although not as strongly as in Table 2.
Please note that the examples we’ve given are basic. There are other factors to consider, such as when you should stop bidding up due to the S-Curve dynamics in Google Shopping, or the fact that there are many long tail products, for which revenue or ROAS need to be estimated.
Adjusting the bid calculation influencers, instead of the bid itself, allows you to take past performance into account. You can do this manually – or you can use Camato’s new Custom Bid Strategy feature and advanced Bid Management techniques to remove the extra setup and monitoring work that bid customization would otherwise create.
What you gain from the extra customization is the ability to be hyper-targeted in your bidding, even though it’s done automatically. This saves you money and increases overall account performance.
Our ultimate goal when we created Camato was to give more control of Shopping campaigns to the people who need it most – Digital Marketers. And, we wanted to do it in a way that would take different performance levels into account and didn’t require a ton of extra work.
After creating Campaign Segmentation, allowing marketers to bid more for high-value search queries, and Bid Management, allowing them to optimize bid amounts for different KPIs, Custom Bid Strategies was the next logical step.