When we are onboarding a brand, we almost always see clients get excited about the Price Bucket reporting within Koddi. When participating in an auction, HPA advertisers have the ability to see how competitive their rate is for a specific hotel versus the other advertisers participating. Clients love this metric because it allows them to:

1. understand when and where to bid more or less aggressively,
2. get a sense of their pricing vs. competitors at granular and broad levels,
3. expose potential issues with their rate and availability data, and
4. share helpful business intelligence with teams that manage hotel relationships.

What is a Price Bucket?

When determining what ads to show a user for a specific search, Google groups advertisers rates together into sets. These sets of rates are the Price Buckets, and Google makes this data available to advertisers via the reporting API. Koddi consumes this data and makes it accessible from the search level all the way up to the total campaign level.

Price Bucket Values

• 0: Lowest/cheapest price
• 1: Competitive with lowest price
• 2: Not competitive with lowest price
• -1: Bid is below reserve

When generating reports for bidding or price parity analysis, it is important to understand how frequently Price Buckets of -1 are showing up in your data, and may be worth filtering out those instances. Consider the following scenario:

A property with a true Price Bucket of 2 has been performing extremely well on the mapresults Search Type, but not so well on hotelfinder. In response you may have bid hotelfinder down using a multiplier of 0.1. You then receive 2 impressions on hotelfinder, and 1 impression on mapresults. If you roll your Price Bucket up to the property level, you will end up with a Price Bucket of 0, the average of -1, -1, and 2.

(If the Search Types above aren’t familiar, you might want to read our explanation of HPA traffic sources.)

There are certainly instances where you might want to report this weighted average, but by understanding exactly where and when this may be happening you can avoid unintentionally polluting your data set. In Koddi you have the option to look at pre-aggregated data or to export data to your desired level of granularity using the Dimensions filters and Custom Reports.