When optimizing our hotel metasearch campaigns, many of us get caught up in the day-to-day of bid changes, statistical analysis, and UI experiments as the main ways we affect the performance of our meta portfolio. However, one thing we often forget to do while working hard to achieve our goals, is to – you know – reevaluate our goals.

This process of reevaluation is actually incredibly important, and can have just as many tangible performance benefits as your well-thought out bid strategy. I’m going to talk about one of these here – maximizing your booking volume at a ROAS goal – but to be specific, let’s make a nice thesis statement:

If you want to maximize booking volume on a hotel metasearch campaign while maintaining a certain return on investment, you rarely should maintain a single ROAS goal across your portfolio of channels.

Instead, you should optimize your ROAS goal at a channel- and publisher- level, so that maximum volume can be driven in certain channels, and optimal ROAS can be driven in others.

Let’s look at a hypothetical scenario with the following assumptions:

• You’ve been given $100k to spend on Meta for the month
• You’re planning to spend this money across four meta channels
• You have a return ratio you’re trying to hit – coming in the form of ROAS – of 10 to 1 for this meta spend
• You have an average revenue per booking of $100 dollars

Now, we’ve seen that not all publishers return optimally at the same volume levels. With this is mind, you might break down this spend in the following way:

Publisher	Spend	Return	Return on Spend	Bookings
A	$55,000	$550,000	10	5,500
B	$25,000	$250,000	10	2,500
C	$10,000	$100,000	10	1,000
D	$10,000	$100,000	10	1,000
Total	$100,000	$1,000,000	10	10,000

 

This is a pretty standard breakdown, and at first glance, it makes sense. All channels are performing at goal = your aggregate is performing at goal. However, there might be a better way.

Let’s suppose that Publisher A isn’t fully utilized at $55k. In fact, let’s say that we’re about $10k  underutilized, and could spend a total of $65k at a 10 ROAS. But you don’t have this extra $10k in your budget, and shifting $10k over from one publisher with a 10 return to another with a 10 return wouldn’t affect overall booking volume, right?

Here’s where the idea of optimizing your return goals per publisher come in to play.

If we can spend less on Publisher B, C, or D at a higher ROAS, and shift the now-uncommitted incremental spend into Publisher A, you can return more revenue at a higher ROAS on the same spend. If Publisher B is where we can shift funds from in the example above, our new example, with optimized ROAS goals can be found below:

Publisher	Spend	Return	Return on Spend	Bookings
A	$65,000	$650,000	10	6,500
B	$15,000	$210,000	14	2,100
C	$10,000	$100,000	10	1,000
D	$10,000	$100,000	10	1,000
Total	$100,000	$1,060,000	10.6	10,600

 

The reason for this is that the incremental return on this $10k, when channeled through Publisher B, would be $40k (a ROAS of 4). However, if we channel these funds through the underutilized Publisher A, we would get an incremental ROAS of 10, and return $100k.

Therefore, keeping the higher ROAS goal with lower spend on Publisher B while reinvesting the incremental spend on Publisher A will produce a greater return, on the same investment, as our original plan with a flat 10 ROAS.

So take some time away from the CSVs, and think through the following questions before you decide your budget for the next period.

• Do I have any publishers that are underutilized with my current budget? Could I realistically push for more volume at the same ROAS, if I had more spend?
• Do I have any publishers that are poorly utilized, i.e. publishers that are receiving spend with a poor incremental ROAS compared to if that spend were spent elsewhere?

If your the answer to either is yes – congratulations, you have opportunity! Now it’s just a matter of accurately gauging the incremental spend opportunity and projected marginal ROAS of each of your channels, placements, silos, search types, and attributes. This is a really basic example, but there are a lot of interesting and sometimes counterintuitive effects that come in to play when optimizing on a campaign that we’ll cover in future posts.