Effective bidding systems set bids based on expected revenue, and do so at the most atomic level possible. We set bids at the ad level when there are multiple versions of a keyword running—representing different geographies, match-types, syndication settings, ad copy, landing pages, whatever—if we have enough data to distinguish between them. When the system doesn’t have statistically significant data at the most granular level it must cluster ads together, aggregating data to predict the revenue generated from a click on any of those ads.
Since a typical campaign has few ads that generate statistically significant traffic within a reasonable window of time, the clustering mechanisms turn out to be enormously important in the overall performance of a PPC program.
The goal is to set bids for each cluster that best predict the value of traffic from each ad in the cluster, so the more closely related the ads are the better the model works.
So how do we know that keywords are related to each other? Furthermore, how do we know which relationships are actually predictive and which aren’t?
There are two ways to think about keyword relationships.
The first and most commonly used is the taxonomy approach. Keywords are related to certain product categories, sub-categories, manufacturer brands, etc. The most closely related keywords are in the same adgroup, the most closely related adgroups share a campaign, and some go the extra mile to have multiple engine accounts, with each account holding campaigns that relate to one another.There are a number of problems with this approach.
- First, by tying the analysis to the account structure you’re forced to pay a great deal of attention to which keywords go in which adgroups and how those adgroups are laid out (i.e., is an adgroup defined by a sub-category and manufacturer brand or sub-category, manufacturer brand and gender? etc).
- Creating new keywords becomes incredibly laborious because finding the right spot to put them takes time. It’s kind of like taking a random stack of books and having to put them in the right place on the library shelves (dating myself, here!).
- Finally, because at most there are only three levels, the clusters mechanisms only have two or three levels. This is kind of like having dresses that only come in three sizes; they won’t fit like a tailor-made dress with the result being overspending on poor performers and missing opportunity on winners.
There is a more fundamental problem with the taxonomy approach, however: it’s rigid. Even if you had infinitely many layers, the connections between clusters are set in stone by the hierarchy. Say, for example, you sell consumer electronics. You might have a separate campaign for each product category, a separate adgroup within the television campaign for each manufacturer brand, but what do you do with the different types of televisions (flat screen, lcd, plasma, projection, etc)? What about the different sizes?
Since the adgroups define the ad copy, the right approach is split up the adgroups into small clusters, like “Sony flat screen – big”, “Sony flat screen — medium” etc. But now you have a different problem when you start doing data aggregation. If the adgroups are tightly defined, the next level up will encompass too much (all TVs lumped together). You also lose the ability to cluster data by just manufacturer and sub-category. You don’t have the ability to track the performance of large, flat-screen TVs across all brands, which might all get a bump near the Superbowl, or just a particular manufacturer brand across all product categories.
The right way to classify terms is not with a taxonomy, but with flexible attributes. Keywords in reality can fit into an infinite number of groups, and while the ad copy clusters might need to be one way, the analytics might suggest that other clusters are more predictive for bidding purposes. For an apparel retailer attributes of keywords might reflect: the type of clothing, gender, material, color, designer/manufacturer, style, discount related (“cheap,” “discount,” “outlet”) etc. For an electronics retailer in addition to the obvious, tagging keywords as SKU-specific or not can prove enormously informative for bidding.
Splitting the account structure into micro adgroups doesn’t solve the problem. You need to be able to analyze performance across any dimension or combination of dimensions, and rigid hierarchies simply don’t permit this.Because the account structure is inadequate to capturing all attributes of keywords, that information must be databased separately by the PPC agency or your in-house team, in such a way that keywords can have any number of attributes, and attributes can be defined and created after the fact: “Oh, shoot, I’d like to tag seasonal offerings as a separate set of attributes so I can see at a glance how all my Valentine’s day related terms are performing, or how the subset of V-day discount terms are doing.”
With this detailed information about keywords an advanced bidding system can essentially learn which combination of attributes best define a close relationship, and how to set bids correctly on the middle-to-low traffic ads. The structure also allows smart analysts to easily tune bids up or down in anticipation of performance changes that the algorithm doesn’t know are coming: promotions, stock positions, co-op advertising dollars on certain brands, seasonality (birthstone months across different types of jewelry), etc.
Without attribute tagging, analytic power is limited, bidding algorithms have less information to use for clustering, and analysts have problems fine-tuning to anticipate conversion rate changes. The details matter. Make sure your team has the right data structures in place to maximize campaign performance.
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