We turn your unique business situation into a math problem, then we solve it. We have distilled keyword bid evaluation down to a science.
For setting keyword level bids, we use our own proprietary bid management software called BetterBid co-developed with Frederick Vallaeys, a lead engineer on the team that created the AdWords quality score algorithm.
This is the desired cost per action (phone call, lead, signup, etc) before quality is taken into consideration.
This is the rate at which clicks convert into the desired action: a lead, a phone call, a signup, or a sale.
This is where we apply quality data gathered by SPARC to zero in on the best quality keywords for your business.
This is how much we'll pay for a click from a particular keyword. It can be much higher or lower than it would have been without taking quality into consideration.
This is typically very important. We nearly always see big differences in performance between regions. Clients with physical locations often get better performance in their local market. We can also target specific areas with higher or lower income.
Available geographic bid adjustments include: Region, Nielsen DMA Region, Congressional District, State, County, Municipality, City, Postal Code, City Region, Borough, Neighborhood, University, District
Depending on the desired outcome (calls, web forms, online sales, etc.), mobile devices could be the best or worst performer and need to be adjusted according to performance. For example, with call-generation campaigns, we frequently increase the bid adjustments on mobile devices to generate more calls.
It used to be that mobile device call quality was lower than desktop call quality, but that began to change in 2015. Today, the data shows us that mobile call quality actually exceeds the quality of desktop callers for almost all call-focused clients. When you also take into account the naturally higher call conversion rate of mobile phones, in almost all cases our calculations dictate a strong positive bid adjustment for mobile devices.
Available device type bid adjustments include: Desktop, Mobile, & Tablet
Some businesses have very specific demographic focus, such as a women’s clothing store or a product for retirees. For these business, being able to target and/or exclude certain demographic groups is a core part of their campaign strategy.
For most businesses, there are often meaningful differences in performance between men & women, younger & older, and even between specific age/gender combos. Parental Status & Household Income are also useful. Even Known vs Unknown (signed into Google or not) can sometimes reveal major differences. We exploit these performance differences with demographic bid adjustments.
Available demographic bid adjustments include: Age, Gender (male vs. female), Age & Gender combined, Parental Status, Household Income, Known vs Unknown
There is a noticeable difference in the quality and conversion rate of traffic on a daily, weekly and monthly basis. The specifics often reveal patterns that can be used to optimize ad spend, for example by bidding more during office hours or during peak periods of the day. Even specific days of the week can perform differently and need to be bid on accordingly.
Available time of day/day of week bid adjustments include: Time of Day, Day of Week, Day of Month, Time of Year
This feature uses a list of previous site visitors or even customers that have completed specific actions, such as making a purchase, visiting key pages, or abandoning a shopping cart. By tracking behavior, we can bid more to reach searchers who are more likely to convert in the future.
With this specific client, the cost per lead was originally $73 and we determined the cost per “qualified lead” was $784. After applying our Keyword Evaluation Formula, the cost per lead rose to around $196, but the cost per qualified lead fell to $408, nearly a 50% drop. This in turn translated into a nearly 50% lower cost per sale, and much better ROI.
For one drug rehab client who is focused on generating phone calls, we analyzed conversions and quality throughout the day on an hour by hour basis. We found that although clicks in the morning and afternoon converted at similar rates, the quality of the calls during the early part of the morning was significantly higher. We added a positive bid adjustment to buy more of those high-converting early morning calls.
In order to target the parents of teens with eating disorders, we found a correlation between conversion rate and call quality by age. Conversion rates and quality of calls were best in the 35-44 and the 45-54 age groups. We increased bids appropriately. Interestingly, parental status was unreliable and never outperformed the average, but males did outperform, which was counterintuitive. We were able to adjust bids positively for both age and gender successfully.
One of several demographic factors that proved meaningful for this particular client were Google’s demographic categories: “Known vs Unknown”. This client was targeting higher income users looking for “private rehab” and “executive rehab”. It turns out “Unknown” users who didn’t want to be tracked by Google were even more interested in private rehabs than “Known” users who were signed into Google. We were able to adjust bids higher for these users to capitalize on their higher conversion rate.
Unsurprisingly, with this specific client, we found that both quality of phone calls and likelihood of calling are higher for mobile device users than for desktop users. By increasing the bid adjustment on mobile devices, not only did we bring this client more calls, but we brought them higher quality calls that resulted in more admissions.
In our work for an addiction treatment center in the Midwest, we found some interesting demographic differences between males and females. While females had a higher CTR and higher overall spend vs. males, they had a lower phone call conversion rate and were less likely to become a client. We used this data to set a positive bid adjustment for males and successfully lowered the client’s average cost per admission.
For this client that was focused on targeting leads in a large metropolitan area, New York City in this case, we found stark performance differences when comparing specific city neighborhoods. Because of this, we made positive and negative bid adjustments for neighborhoods based on their phone call conversion rate and likelihood of becoming a sale. We slashed spending in lower performing neighborhoods, leaving a larger budget for the parts of the city that were performing well. As a result, we made our client’s dollars work harder, and positively impacted their bottom line.
Due to premium pricing structure of their business, this client was facing the challenge of getting too many unqualified leads. They needed to focus their efforts on leads that were willing to pay more. After reviewing the data, we targeted specific geographics based on zip codes. We were able to identify and target specific specific zip codes that demonstrated a higher qualification and conversion rate, while also blocking underperforming zip codes. As a result, we not only saved the client significant ad dollars, but also eased the burden on their call center by significantly reducing the amount of low quality phone calls.