Accelerating Revenue Growth With Keyword Parity

As campaigns mature, keywords evolve from experiments to proven revenue drivers. Remembering to add a keyword to Bing after a successful trial in Google or remembering to expand a new top performing keyword across its other match types is easier said than done.

With so much focus these days on the next best thing — generating the most compelling creative or discovering the next diamond-in-the-rough keyword — search marketers often ignore the keyword gaps that slowly accumulate over time.

Maintaining publisher and match type keyword parity is one of the most important campaign management strategies available to a search marketer. Unfortunately, it’s also one of the most difficult strategies to scale and implement successfully. Sifting through hundreds of thousands of keywords and toggling between multiple publishers to identify keyword disparities can prove to be a daunting task.

Today, we’ll discuss several best practices for maintaining keyword parity across match types and publishers. We’ll also help identify where potential keyword gaps reside in your account and provide the necessary tools for filling them.

Publisher Parity

Imagine you’ve just added a new keyword into your Google account that’s expected to acquire more revenue for your business. Now two weeks, 100 clicks and 10 conversions later, your keyword is a hit. In the fog of excitement, you’ve decided to research additional keyword expansion opportunities, neglecting to explore the full potential of your newly discovered revenue driver.

Adding your new, proven keyword into your Bing accounts has slipped your mind. As day-to-day optimization strategies take your account forward, this and other keyword gaps continue to accumulate. Unfortunately, many search marketers fail to maintain keyword parity across publishers, even when the failure to do so can result in missed revenue opportunities.

Locating publisher keyword gaps can prove to be a daunting task. Implementing tracking prior to tackling keyword expansion addresses the parity issue at its core. (Third-party solutions, like Marin Software, can help track and report on these keywords at scale.) Take detailed notes on when, where and why these keywords were added to an account. These notes are essential to assessing performance and critical for locating revenue driving keywords to copy across publishers.

In order to retroactively assess publisher parity, download a keyword performance report and apply an Excel pivot table to compare publisher keyword sets by key performance indicators.

Once you’ve identified the gaps, add these keywords to the appropriate publisher on a weekly or monthly basis. Remember to set competitive bids, generate compelling creative and deploy proper negative keywords.  Missing out on an important negative keyword when achieving parity can result in reduced effectiveness for the keywords you add.

Match Type Parity

Ensuring match type parity by adding keywords across all three match types is a common best practice. This can be approached in two different directions—expanding from broad to phrase to exact match type or from exact to phrase to broad match type.

Copying successful exact and phrase match keywords to broad match type is a quick and easy approach for reaching a broader audience and discovering new keyword opportunities. However, the resulting increase in traffic doesn’t always correspond to an increase in performance. Be cautious when expanding keywords to broad match type and aggressively mine for negative keywords.

Improving keyword efficiency often requires the expansion of broad match keywords to phrase and exact match type. This strategy is an effective means of segmenting keyword performance. Keep in mind that broad match keywords already capture the traffic for its phrase and exact match counterparts. Consequently, cost and conversion metrics are attributed to a single keyword.

Maintaining match type parity is a simple way of segmenting keyword performance by match type, allowing for the implementation of match type specific bids, creative or both.

Match Type Silos

Consider this common scenario. A keyword on Google is active across all three match types. However, the broad match bid is set higher than the phrase and exact match bids.

As a result, the broad match keyword cannibalizes impressions and clicks that might best be served on and captured by the phrase and exact match keyword. This undesired behavior creates a reporting and optimization nightmare; performance data for the phrase and exact match queries is attributed to the broad match keyword.

To properly expand keywords across broad, phrase and exact match types, and segment performance based on match type, match type silos must be deployed. To implement match type silos, start by creating a separate group for each keyword match type. Within the broad match group, add the phrase match negative keyword. Within the phrase match group, add the exact match negative keyword.

Match type silos are not only easy to implement, but ensure that publishers properly match your keywords to user search queries. Furthermore, the segmentation of keywords by match type provides greater visibility into performance, resulting in a more effective reporting and bidding strategy.

As you work through keyword expansion opportunities, be mindful of keyword gaps and maintain publisher and match type parity. Once you’ve filled these gaps, remember to optimize. Setting appropriate keyword bids, generating relevant creative and researching negative keywords are just a few strategies to in mind.

What has and hasn’t worked for your campaigns when it comes to maintaining parity across publishers and match types? We’d love to hear from you in the comments below.

Opinions expressed in the article are those of the guest author and not necessarily Search Engine Land.

Related Topics: Channel: SEM | Enterprise SEM


About The Author: was the Vice-President of Marketing at Marin Software, a leading platform for digital advertising management, and held a variety of executive roles at Coremetrics, one of the early innovators in web analytics.

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