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# How to Calculate Bid Adjustment by Placement

Recently a listener of our Amazon PPC Den Podcast asked us a poignant question on bid adjustment by placement, which has been growing more and more popular.

He specifically want to know how to funnel his traffic to the ad placement with the highest conversions (top-of-search) while reducing his bids for the other areas (product pages and rest-of-search).

That’s right, not all ad placements are created equal, and these three locations can have drastically different conversion rates, click-through-rates (CTRs) and cost-per-clicks (CPCs). Fine tuning your ad spend between all these placements can be tricky without some help.

In the past, we’ve touched on the basics of adjusting bid modifiers when they first released. Today we’re going to walk through a simple, proven formula to calculate your bid adjustments by placement.

By the end of this, hopefully you’ll have all the skills you’ll need to tackle your own placement bid modifiers head-on! Here’s what we’ll be covering:

1. The ad placement performance gap (Data doesn’t lie)
2. The only ‘Bid Adjustment by Placement’ formulas you’ll need

## The Ad Placement Performance Gap (Data Doesn’t Lie)

Before we start, let’s create a simpler situation with devices instead of ad placements to better understand how everything works.

Say you had two devices mobile bids & conversion rates and desktop bids & conversion rates. Your most recent placement reports shows that your mobile bids are converting 200% better. In a perfect world, you could directly set your mobile bids to be 200% greater to match its 200% greater performance. Unfortunately, it’s a little bit more complicated in real life.

To properly adjust your bids, you first have to figure out what your desktop bid should be without a modifier. Then you need to decrease it and increase your mobile bid by that same amount.

Still confused? It’s a complicated topic, so don’t worry we have another example for you but instead of devices we have ad placements.

Here’s data from a real Ad Badger account showing the performance gap between the Amazon ad placement categories:

Let’s breakdown what this means.

First you want to find out which of your placements has the best conversion rate (hint: it’ll usually be top-of-search). Here you can see top-of-search has a conversion rate of 13.51% which is much better than than the 2.95% and 1.39% the other two categories are posting up.

Insanely enough, most of the time the majority of the ad spend is going to the ad placement with the worst conversion rate, highest CPC, and highest ACOS!

In this example, product pages are receiving \$200 of spend with it’s measly 1.39% conversion rate while top-of-search only receives \$32 of spend even though it has a much better conversion rate.

This is what we’re trying to fix. We want to funnel that nonoptimal spend to it’s better performing brother and calculate the precise benefits of doing so with no guesswork.

You might be tempted to just turn off product pages because of its poor statistics and call it a day. But wait!

Product page ads are getting conversions, and they are driving sales, they’re just doing it at a really high ACOS. What we need to do is redistribute the spend and not necessarily axe it completely.

## The Only ‘Bid Adjustment by Placement’ Formulas You’ll Need

Let’s run through everything you need to know with a simple example. Top-of-search has a 10% conversion rate, rest-of-search has a 5% conversion rate, and product pages have a 7% rate.

(Quick note: You can calculate your conversion rates by using the formula: Total Orders / Total Clicks).

### The Increase Equation

The real magic begins now. To find the performance difference between your ad placements you’re going to do (Best Conversion Rate / Worst Conversion Rate) – 1.

For this example, you take your best rate (top-of-search: 10%) and divide it by your worst rate (rest-of-search: 5%) – 1. 10/5 -1 = 1. This is equal to a 100% performance difference in percentage terms.

A 100% performance difference means the same thing as performing twice as good. This makes sense, 10% is twice as good as 5%. We now have our top-of-search modifier of 1.

We could apply the same math to find our product page modifier. You do 7/5 -1 = 0.40. This is a 40% performance difference and our product page modifier is 0.40.

### The Decrease Equation

All we have left is rest-of-search. If you remember from our device example, if we increase top-of-search, then rest-of-search must decrease by a proportional amount.

The decrease equation is 1 / (1 + Increase %).

In our example we would do 1/ (1 + 1) = 0.50. We now have all our modifier values. The next step is to multiply all of our bids by 0.50 to effectively decrease rest-of-search to half of its original value.

Then when we multiply top-of-search by 2,  it alone will go back to its original value. This will redistribute your ad spend optimally across the different ad placements.

Boom! That’s your step-by-step on how to calculate bid adjustment by placement. Be sure to go over the steps a few times to really cement that knowledge you learned.

In the future we’re planning on releasing an online bid calculator to automate this whole process for you. Stay tuned for it…

So is this the end? Not exactly there’s still one more elephant in the room to address.

You can only set your bid modifiers on the campaign level, and we all know that different keywords inside the campaign might have different conversion rates for top-of-search and the rest of the ad placements.

In essence, they would each need their own unique modifiers, but we can’t do this.

Fair warning, our solution is a bit experimental, and there still isn’t a perfect way to solve this problem inside of Amazon. That being said, here’s our take at a work-around.

Let’s say you have Keyword A with a 15% conversion rate on top-of-search and a 5% rate on product pages. Keyword B has a 45% top-of-search conversion and a 1% product page conversion.

The only way to have separate modifiers for these two drastically different keywords is to break them out into separate campaigns.

If you’ve been following us, you know that this is very similar to the Single Keyword Brand Ad Strategy. You’ll be able to separate your modifiers this way, but is it worth the effort to implement this strategy?

Separating these keywords manually can be time intensive and exhausting. Instead, we recommend you do this through bulk file operations inside of a spreadsheet like Excel.

This will allow you to quickly create 20 new campaigns with a single keyword inside each ad group. (Yeah, bulk file operations are that good).

It isn’t the most elegant solution, but it gets the job done and allows you to track the performance of these keywords with their unique bid modifiers.

## Key Takeaways

There you have it, a sure-fire method for calculating bid modifiers by placement.

To recap, we’re doing this to funnel nonoptimal ad spend away from poor converting ad placement types (Looking at you product pages) and towards your most profitable ad placement type (typically top-of-search).

As always if you have any questions, comments, or thoughts on how to adjust bids by placement, sound off in the comments below.

This entire topic was inspired by a podcast  listener’s question (shoutout Adam). Who knows, maybe next post will be about your question?