Utilizing Revenue-per-Click to Calculate PPC Keyword Value
How do you determine a search keyword’s value? Are you making data-driven bid optimization decisions based on keyword data? Should you be focused on keyword revenue-per-click in your PPC campaigns?
Search keywords. They’re valuable. Some are really, really valuable! Others have little to no value. Others you simply don’t know the value of for one reason or another.
What we do know is this: keywords are the lifeblood of paid search. Building a keyword list and then collecting data about keywords is paramount in search engine marketing, especially if you’re looking to optimize PPC campaigns.
PPC Keyword Value
If a keyword is generating a lot of traffic and clicks, it’s seen as important – but it may or may not be valuable to your business. You need to understand if it actually drives revenue and conversions.
If a keyword is a “monetizer” – or a high-revenue driving keyword – you’ll want to evaluate how much impact the keyword has on your business. You would look at it and say: “If we pause this keyword, how much will revenue decrease? By how much? If we decrease the bid, will we lose a lot on conversions – or if we increase bids, will we still only generate the same number of conversions?” There are many questions to ask to determine how valuable a keyword can be to your paid search program.
Evaluating a keyword’s value can be a challenging task. Some methods are more simple; some get deeper but may not be actionable; some are totally automated and drive action automatically! What we’re really after is the revenue-per-click metric that truly informs a bidding strategy.
High-Level: What is the dollar value of a keyword?
Your keywords drive impressions, they drive ad-clicks, they drive conversions… and hopefully repeat purchases! They’re obviously worth quite a bit, and qualitatively and quantitatively are one of the most reviewed areas within SEM ad campaigns.
The dollar value of a keyword is, in short, the amount of money you can expect to generate from each click from a given keyword. A keyword may have only generated one click, or it may have generated hundreds of thousands of clicks; zero conversions, or dozens per day!
Conversions lead to dollar-driving outcomes. Dollars are our interest here. How much revenue can we earn from each click on each keyword?
Some of those dollar-values are actual and calculable (online or offline purchases for goods; lead form fills that lead to purchases), while others may need to be understood in aggregate based on metrics within your company (subscriptions or lead forms that don’t have a purchase, but create other dollar value).
Google Ads makes it easy to report on keyword performance and rank by impressions, clicks, costs, conversions, and even the aggregate revenue for certain conversions that can be tracked and directly tied back to the Google Ads platform. However, estimating the value of a keyword for a more specific use case (such as using that value for PPC bid optimization) requires a different view of keyword value, which we’ll dig into shortly.
Going Deeper on Valuation
To analyze a more nuanced value of each keyword on your business, you may need to do some serious spreadsheet wrangling. Why? To understand the impact of historical bid changes on conversions over time! (Those time-dependent reports are always hard, aren’t they?)
A method we’ve witnessed involves exporting large change history reports and large historical keyword performance reports, and then stitching them in Excel to look at the trend analysis. To do this, you would map the dates of bid changes on specific keywords to the keywords historical performance on given days on or after that bid change. Then you can get a historical understanding of the impact of that keyword’s bid on revenue, and see how changes impact your business.
Now, is that actionable? Depending on your team’s level of sophistication, it may or may not be – but with sufficient analysis you should have an understanding of how increases or decreases in bids create more efficient CPA or ROAS numbers on your keywords.
The keyword’s value can be understood in many ways, but ultimately you want a keyword valuation model that gives you something actionable to work with. Even more ideally: something that can drive action without you needing to apply all those changes.
In a large program with many keywords, the data export we’ve described may be fairly arduous. Without a program or tool to calculate these keyword values for you automatically, you would need an internal process for calculating the revenue per click on each keyword, which is a significant investment.
Actionable, Data-Driven Keyword Value Estimation
Keyword value estimation requires data. Having your conversion data (with revenue numbers) tied back to clicks and keywords is a prerequisite to being able to meaningfully calculate an “estimated keyword value”. However, with more and more data tied back to those keywords, on multiple dimensions, and over time, the true value estimation gets more and more accurate!
When you map all the data about costs and clicks and conversions back to each keyword, you can build a graph of revenue values–what we call “revenue-per-click” (RPC). The RPC graph provides a visual representation of the clicks vs revenue for that keyword. Automated bidding automation tools calculate RPC graphs for every keyword regularly to review data and discern if the value has changed enough to merit a bid change. The RPC calculation at scale enables a mechanism for taking that estimated keyword value and immediately putting it to work in a bidding calculation process.
Revenue-per-click is calculated in a meaningful way only because of large data sets and machine learning models. Without a machine learning powered process, these calculations – and the actions they drive – would lack serious optimization potential.
Using Keyword Value in Optimizing PPC Bids
What is significant about a very granular and specific keyword value? So glad you asked!
When all the conversion data (whether immediate click-to-sale, or LTV from latent and repeat purchases) is used to model a highly accurate keyword value, then acting in a “data-driven” way naturally follows. The data provides a story of how much this particular keyword is worth across other variables, so that the information can be plugged into cost, volume, and bid landscape data to model the optimal bid calculation for any conceivable segment.
For instance, when person A from North Carolina searches for your exact match keyword in the morning on a Tuesday at work using a desktop computer and makes an immediate purchase… the bidding decision made for other similar search queries are valued in a similar light. On the other hand, person B from Los Angeles searching for your keyword in the late evening on a Saturday using a mobile device, and then purchases the next week, has a completely different value. The similar attributes should be accounted for in bidding decisions based on the dollar value calculated from those correlations in the data points.
Estimating the value of a keyword is an important and fundamental step in the process of calculating optimal PPC bids on keywords.
Learn More About the Bidding Process
At the start of the process for any bidding optimization solution, the dollar value of each keyword must be determined. There are two notable pieces at play here: the “machine” that builds the model, and the data you feed that machine.
In the highest caliber optimization tools, the calculations for this revenue-per-click model are generated using various types of machine learning algorithms. Clearly processing at this magnitude is an issue with any standard approach (a multitude of keywords x a multitude of data points), so the infrastructure and scalability of the tool is important.
The data fed-in is likely even more of a secret weapon than the algorithms and modeling. The more sources the better, particularly as they relate to stages of the customer journey and revenue. (Imagine not only online purchases and lead forms, but also delayed transactions and CRM information being utilized).