When making bidding decisions, many PPC Account Managers will look at data not just on an individual keyword level, but also by evaluating keyword categories. If you sell power tools, how do keywords related to power drills perform vs. power saws? If you sell cloud services, how do your root keywords around online storage vs. web hosting vs. dedicated servers perform from an ROI standpoint? If account structure is sound, these keyword categories, or root terms, are easy to evaluate and bid appropriately.

 

However, all too often I see advertisers miss out on gains from evaluating the other thematic components of your keywords. At Metric Theory, we call these elements modifiers. Think of the words that make your root keywords long(er) tail terms:
– sale
– discount
– reviews
– best
Or on the B2B side:
– system
– solution
– software
– service

In most accounts, we see big discrepancies in the performance of these modifiers across keyword categories.

How do we do this evaluation? I prefer to take a look at the search terms report for the entire account under the dimensions tab in AdWords. Make sure that you’re looking at a nice solid chunk of data and have included all relevant columns.

Once you’ve exported the data, apply filters and exclude all rows that contain branded terms. We want to evaluate non-brand queries only. Next, apply subtotal figures for clicks, impressions, cost and conversions. Apply formulas in this top row for CTR, average CPC, CPA & conversion rate. This will allow you to subtotal the data for different modifier filters.

modifier analysis

After that, you’re set to go! Start evaluating the data by selecting the search term filter in column A and selecting “contains” then type in the different modifiers that you’d like to test.

For one B2B client, we saw that the modifier “software” had a CPA that was 2.5x the CPA of the modifier “platform”. On one eCommerce account, we saw a 150% delta in the ROI of “cheap” vs. “discount”.

So what do we do with all of this data? Whether you’re using manual bidding, bid algorithms or rules-based bidding, you’re likely concentrating your bid adjustments on keywords that are converting effectively (bid up) and keywords that are high-spend and non-converting or have high CPAs/low ROIs (bid down). Since only a fraction of your keyword inventory convert in a given period, this bidding methodology usually only affects a fraction of your overall keyword inventory.

Modifier analysis helps you implement more intelligent predictive bidding. Predictive bidding allows you to put yourself in a position to get more conversions at lower CPAs by making calculated bets on keywords that haven’t converted yet. Predictive bidding allows you to bid up the aggregate of long-tail keywords likely to convert, rather than just your root terms.

For instance, if I have an account average CPA of $80 and I see the following modifier CPAs:

– sevices – $118
– solution – $112
– system – $104
– SaaS – $54
– Web-based – $52

I’m going to bid up lower volume, low position, non-converting keyword terms with “SaaS” and “Web-Based” in them, while bidding down non-converting services/solutions/systems terms.

Note, Google recently allowed you to filter by search terms in the search query report:

sqr filter