I recently dove into Google Ads to explore their new customer acquisition goals. With fresh capabilities like high-value customer bidding and retention targeting, I was curious about how they could boost my marketing efforts.
Many strategies still assume new customers are the most valuable, but this breaks down rapidly. Not every new customer is worthwhile, and ignoring existing ones can be a mistake. The crux is Google’s high-value customer and retention bidding goals.
Google uses predictive bidding to pinpoint high-value customers, but the key is the customer match list I upload. To tweak settings, I venture into the customer lifecycle optimization section under Goals > Summary and select Edit Goal.
Here, I set a higher new customer value to bid aggressively for high-value clients. Google usually suggests a value based on higher LTV, but I ensure it aligns with my strategy before making adjustments.
Once adjusted, Google’s reports reflect the added conversion value alongside the actual sale or lead value. If using cost-per-conversion models, the discrepancy is less impactful. However, it can skew ROAS in a ROAS-based model. Luckily, Google introduced a column to separate true and additional values for clarity.
Building high-value customer audiences means adding an audience list of high-value customers. I think about what makes my customers valuable, whether due to high order values or interest in premium services.
Once I compile and upload the list, I need at least 1,000 active members on YouTube or Search networks to serve effectively. Including additional data like phone numbers and addresses improves my match rates.
If I want a streamlined approach, tools like Klaviyo can integrate audiences directly into my Google Ads account, often yielding high match rates.
With everything set in the customer lifecycle optimization section, it’s time to optimize my campaigns. I can’t apply both bidding goals to the same campaign, so I tailor my targeting and ad copy to different customer types.
For campaigns focusing on high-value new customers, I expand the Customer Acquisition segment and choose a bidding option to target specifically new customers.
It’s critical that my ad content resonates whether I’m aiming for new clientele or re-engaging past customers.
When it comes to re-engaging lapsed customers, I set bidding parameters for retention back under Goals. There, I find lists for lapsed and high-value lapsed customers, if I have the data to support them.
Google suggests values or lists, but accuracy is key before saving adjustments. In Performance Max campaigns, lapsed customers may see a variety of ads, making it essential my messaging speaks to them effectively.
Everything hinges on having reliable inputs like quality customer match lists and performance metrics. Used right, lifecycle bidding can prioritize valuable customers and revive lapsed ones, but careless usage just skews data without driving real results.
I’ve often marveled at high ROAS numbers during my campaigns, thinking they spell success. But, is this performance truly driving growth?
High ROAS numbers can be misleading, often masking mere demand capture rather than creation. To accurately assess growth, I focus on incrementality and marginal ROAS to guide more effective spending strategies.
An ecommerce company once collaborated with my PPC agency, eager to delve into the world of paid search. We crafted a robust plan that quickly led to impressive results: high conversion figures and a commendable ROAS.
It seemed like a strategy success story at first glance. However, when I took a closer look, I noticed something crucial.
Some conversions might have transpired naturally through direct or organic search channels, suggesting our campaigns perhaps weren’t spurring actual growth. This is a vital aspect that often remains unexamined. To gain genuine insight into performance, I examine incremental lift alongside marginal ROAS.
The truth about ROAS
I recall hearing about eBay’s paid search experiment. They heavily invested in brand PPC ads, only to later conduct controlled tests by pausing these ads for certain users, measuring their impact.
Much of the conversion was absorbed by organic traffic, scarcely affecting revenue. Yet, intriguingly, eBay reactivated the branded ads. Whether this was driven by fear or wisdom, I ponder the implications.
As automated search and multi-touchpoint customer journeys evolve, accurately attributing conversions to their channels becomes increasingly complex. Advert platforms often claim the credit, but adopting a skeptical view towards these reports is invaluable.
I comprehend that what these platforms report as attributed return doesn’t necessarily equate to causal lift. While ROAS indicates platform-influenced revenue, it falls short in revealing how much revenue would have materialized regardless of the ads.
With tools like Performance Max and Advantage+, platforms excel in optimizing conversion avenues, often not discovering new clientele but instead marking the costliest touchpoints in pre-determined conversion paths.
In the absence of incrementality assessment, automation tends to amplify non-incremental signals: capturing existing demand through brand search campaigns, retargeting nearly-converting users, and creating falsely “safe” channel reports.
Incrementality tells you whether marketing created something extra
By analyzing incrementality, I can determine how the campaign wrought changes it wouldn’t have caused otherwise, typically through comparisons of exposed groups with control groups. This reveals the actual organizational impact of the campaign.
Recognizing this might feel uncomfortable, yet it serves as a more precise lens for budget allocations than superficial platform attributions.
Sometimes, even a seemingly successful channel in-platform ROI might not equate to impactful incremental growth. Often, it merely realizes existing demand rather than inventing it.
If I truly wish to ascertain if a campaign drives genuine growth, the incrementality factor must become my focal question.
Despite being vital, incrementality only provides part of the picture. The necessity for marginal ROAS to chart subsequent steps can’t be overstated.
An incremental channel alone doesn’t specify where the next budget investment should proceed. Understanding marginal ROAS is essential here.
The marginal ROAS examines the revenue from an additional unit of spend, surpassing the average ROI across all expenses. Often, initial budget allocations perform well but subsequently deliver diminishing results.
As investments continue, dollars spent towards the end become disproportionately less efficient. This principle also holds true for CPA metrics: a blended CPA might appear satisfactory while the terminal dollars spent demonstrate poor efficiency, luring advertisers beyond optimum bidding zones.
I consider an example where an initial $10,000 budget generates $50,000 in revenue (500% ROAS). Deciding to expand, I then invest an additional $5,000, only to generate an incremental $5,000 revenue.
Your new average ROAS: 366%
Your marginal ROAS: 100% (Essentially a $1-to-$1 trade.)
In such instances, the final $5,000 expenditure was ineffective, despite overall acceptable “average” performance on dashboards.
This highlights the folly of focusing solely on average ROAS. It can obscure the genuine scalability that might only be viable at lower spend levels, misleadingly disguising profitable demand capture as flawed incremental expansion.
Informed decision-making requires peering deeper: platform ROAS aids in optimizing in-platform efforts, incrementality assesses campaign-generated value, while marginal ROAS indicates where the ensuing budgets should be directed.
A robust ROAS can reflect true efficiency or merely illustrate a platform ensnaring already-converting demand. Hence, incrementality tests form the cornerstone of my analysis.
My critical inquiry is not whether a channel is efficient per se, but whether subsequent dollars are sufficiently efficient. This understanding is essential for prudent scaling.
Embarking on incrementality testing doesn’t require a flawless measurement lab. Utilizing geo tests, holdouts, audience exclusions, and controlled spending reduction can enhance understanding far beyond another month spent in attribution debates.
Geo-split testing: Organize markets into dual comparable geographic groups, maintaining ad runs in a “test” grouping while halting them in a “control” group. Revenue disparities between these regions unveil the genuine incremental lift of your ads.
Search lift tests (holdouts): Leverage platform tools to generate holdout groups, excluding a small user fraction from ad exposure. The behavioral contrasts between them and exposed groups unveil Search or YouTube campaign direct impacts.
Furthermore, investigating remarketing, branding, awareness campaigns, or supplementary social channels can reveal additional insights.
The real shift: From reporting performance to allocating capital
For too long, marketing teams have restricted measurement to explaining past events. The optimal application lies in shaping future endeavors effectively.
Incrementality helps me discern value creation within a channel, while marginal ROAS justifies additional investments. Together, they elevate marketing measurement from mere reporting to informed capital allocation.
ROAS demonstrates credit allocation, incrementality pinpoints actual transactional changes, and marginal ROAS guides subsequent budgeting. It’s crucial to remember that incrementality differs from attribution. While attribution awards channel credit, incrementality evaluates whether this pursuit justified itself.
