How To Plan PPC Campaigns For SaaS Marketing & Think Strategically via @sejournal, @timothyjjensen

Planning a SaaS PPC strategy can be daunting. It often involves lengthy buying cycles, complex products to explain, and high competition.

According to Gartner, B2B SaaS buyers spend 27% of their time in the buying process doing independent research online.

Being visible across channels is crucial to keep your brand top-of-mind during this process.

In this article, we’ll dive into thinking through PPC for SaaS marketing, from audience planning to measurement.

Your Target Audience Is Wider Than You May Think

Who’s your target for a software product that will be used in an enterprise-level company?

While a director or C-level individual may sign off on a purchase, the people using the product on the ground are more likely to influence the decision-making process.

For instance, if you’re selling marketing automation software, a marketing operations manager may be fed up with their current solution and ask senior management to consider a new tool. However, a CMO may still need to approve the contract.

Particularly when using platforms such as LinkedIn, which allow incredible granularity with job title targeting, you’re likely shooting yourself in the foot if you only target select executive suite titles.

Job functions and groups can be a better route to reach a broader pool of individuals with a say in the purchase process.

Use data from your organization wherever possible to inform targeting. For instance, look at existing titles for individuals engaging with lead forms and sales processes.

Additionally, you can use LinkedIn Audience Insights on the website audience segments you’ve created to view demographic criteria that can help identify what job roles and types of companies are actually most engaging with your content.

Identify Your “Hook”

How are you getting the attention of prospects for their initial touchpoint with your brand?

Keeping in mind the breadth of the potential target audience, you’ll likely want to consider testing multiple “hooks” that may appeal to those in different roles.

For instance, a call to action (CTA) emphasizing bulk seat discounts may appeal to a chief financial officer (CFO) or someone primarily accountable for cost savings. A CTA emphasizing efficiency may resonate with an operations manager.

Testing multiple hooks against different audience segments can also be a helpful way to determine what will drive the most prospective interest at the most efficient cost.

This can be done using testing capabilities within ad platforms, such as Google’s Ad Variations or Campaign Experiments, or Meta Experiments, to ensure that separate individuals are exposed to the appropriate variant.

Next, while planning what messaging to use to entice new prospects, take into account what competitors are running and how your offer compares.

Analyze Your Competition

SaaS can be a highly competitive niche in paid search and other channels, with other players bidding aggressively and attempting to outdo each other’s offers.

Take some time to document the advertising efforts of competitors in your space, including ad messaging, creative, and landing pages.

Realize that those who actually compete with you most often in search auctions may not necessarily be the same as those who top the list of competitors for company leadership.

Use Auction Insights in Google and Microsoft to note which advertisers most often overlap for particular campaigns.

You can then use ad libraries such as Google’s Ad Transparency Center, Meta’s Ad Library, and LinkedIn’s Ad Library to view ads that these competitors have run.

Your analysis should help to answer questions such as:

• What audience do they appear to be speaking to?
• How attractive are competitor offers compared to yours?
• What CTAs are they using?
• Are there ad formats they are running that you’re not currently using (for instance, LinkedIn Lead Gen Forms)?
• How many creative variants do they appear to be testing? Are there types of images that you’re not incorporating (for instance, vector graphics vs. stock photos)?

Additionally, go to competitor sites and initiate their lead processes by filling out forms or starting to sign up for an account. See what follow-up measures they implement.

Are you seeing retargeting ads encouraging you to complete account signup or offering additional resources? Are you receiving email follow-ups?

Share your findings with your team, not only to inform paid media tactics but also in thinking about landing pages and marketing automation flows.

Additionally, bidding on competitor names can be an effective paid search tactic to target people who are in the market for your product, especially when first entering the foray of search.

You can latch onto familiarity with a larger competitor that may have more search volume than your own brand.

While you shouldn’t directly mention your competitor by name in an ad (for trademark and ethical reasons), feel free to highlight your brand’s differentiators.

Research your competitors to keep tabs on areas where they may be weak and you are strong.

For instance, if a competitor has recently increased their pricing, current users and those in research mode may be more open to other options.

You can capitalize on mentioning your more efficient pricing, if that is your brand’s selling point.

You may also discover that a competitor receives frequent complaints about their customer service. If your brand is known for positive customer service, highlight that aspect of your business in ads to stand in contrast against your competitor.

Set Measurement Goals

SaaS marketing often involves long sales cycles with multiple steps of interaction before a user or business becomes a paying customer.

Establishing realistic expectations for cost per acquisition (CPA) and conversion rate goals at each stage is crucial.

Additionally, only measuring at the beginning or the end of the cycle is not beneficial in the long run.

If you’re purely focused on tracking and bidding toward CPA and conversion rate for a final paying customer, you’re likely not getting enough data back to the ad platforms to inform campaigns focused on those earlier in the awareness stages.

Conversely, if you’re strictly optimizing to an initial touchpoint such as a whitepaper download, you’re likely not providing the platforms enough information about lead quality to drive conversions from those most likely to become paying customers.

Consider your lead cycle and how your sales team nurtures prospects when establishing these goals.

For instance, you may categorize individuals as marketing qualified leads (MQLs) after they’ve downloaded a resource and attended a demo and mark them as sales qualified leads (SQLs) if they’ve completed those actions and started a free trial.

Each stage further down the “funnel” is likely worth a higher value, which should inform one’s willingness to pay a higher CPA to obtain it.

Connect Ad Platforms To Your CRM

Connecting your ad platforms to CRMs and/or marketing automation platforms, such as HubSpot or Salesforce, can help with complete conversion tracking and audience creation.

First, you can pass back conversions when users complete specific actions, such as completing a demo or signing up for a trial, and associate them with the same user.

Enhanced Conversions for Leads can ensure you track these actions more accurately based on individual contact information.

You can also use your CRM or marketing automation platform to sync audiences to ad platforms.

For instance, you may have a list of people that have opted in from an initial touchpoint with a webinar or whitepaper download.

Use the lists that you build for audiences to target ads based on the buying stage where they are.

Additionally, you can use lists of high-value prospects or customers to seed lookalike audiences for targeting and reaching people with similar demographic characteristics and interests.

Meta allows for lookalike targeting across campaign types (with the exception of some sensitive industries), while Google lets you build lookalike audiences for Demand Gen campaigns.

LinkedIn offers Predictive Audiences built from first-party data as a method to reach similar individuals.

You can also use customer lists to exclude existing customers from campaigns or to exclude those who already have a particular product from upsell campaigns.

Use Account List Targeting

In addition to targeting specific individuals, you can also use account-based marketing (ABM) to reach select companies you’d like to target.

The advantage here is that you don’t need explicit opt-ins to upload a list. A sales team may have a list compiled of “dream” target accounts, or you may have access to a list of major companies within a particular industry.

Out of the major self-service ad platforms, LinkedIn is the primary route for uploading account lists.

In addition, you can also work with reps to sync account lists in native advertising platforms such as Taboola or Outbrain, and for larger buys, you can look into dedicated ABM platforms.

You can also overlay additional targeting onto the account lists to ensure you’re reaching the right decision-makers in the organization.

For example, you can overlay an IT job function with job seniority of director and upwards to put your ads in front of people likely to make IT buying decisions.

Making PPC Work For Your Saas Marketing

Planning and executing a PPC strategy for a SaaS product can be a complex but rewarding process when you start to see qualified leads come through and turn into paying customers.

Start with understanding your audience and competition, and work through setting clear measurement goals and targeting strategies.

As you move forward with your campaigns, you can continue to test and refine based on the data you can gather, especially as you have time to analyze lifetime value associated with customers obtained via various channels.

More Resources:

Featured Image: Andrii Yalanskyi/Shutterstock

Budget Allocation: When To Choose Google Ads Vs. Meta Ads

Choosing between Google Ads and Meta Ads isn’t about which platform is better. It’s about which makes more sense for your goals, audience, and spend.

As both platforms continue to change with smarter automation, stricter privacy rules, and rising ad costs, advertisers need a framework for deciding where their budgets go.

Here’s how to think it through.

Google Ads Vs. Meta Ads: The Core Difference

Google Ads is built around user intent.

Whether it’s [best CRM for real estate agents] or [emergency plumber near me], that intent translates into higher conversion potential because people come to Google actively looking for solutions.

Meta Ads (Facebook and Instagram) are driven by discovery.

You’re placing ad content in front of users who weren’t searching for your product but might be persuaded to click, browse, or buy.

This makes Meta stronger for brand awareness, lifestyle products, and impulse-driven purchases.

In short, Google wins when users know what they want. Meta wins when you want to influence what they want.

When Google Ads Make More Sense

Google Ads is the platform to choose when search volume and purchase intent are high.

Legal services, home service providers, and B2B companies often perform better in Google because they solve specific problems people are actively researching.

The cost-per-click (CPC) is especially high in competitive industries like home services or law, but the quality of traffic and the high payout often justifies the spend.

CPCs in home services verticals can exceed $6.50 and legal can exceed $8.50 (most up-to-date numbers from 2024).

It’s also a strong fit for ecommerce brands.

Someone searching for [black corset sundress] or [best gaming laptop under $1,500] is closer to buying than someone casually swiping through Instagram.

Google Shopping Ads and Performance Max campaigns can be great campaigns to streamline the purchase path.

Local businesses also benefit from Google’s ecosystem, especially with Local Services Ads.

When Meta Ads Outperform

Meta shines when you aim to build demand, but it’s not limited to just awareness or engagement.

For many ecommerce brands, Meta is a primary driver of direct conversions, especially when the product is aesthetically pleasing, impulse-friendly, or supported by strong creative assets.

Campaign types like Advantage+ Shopping, paired with dynamic product ads, won’t just help your brand get noticed; they can drive sales right away, too.

What makes Meta effective is how it is able to blend product discovery with fast action, which makes it a great tool for new product launches, lifestyle products, and visually driven goods like fashion, beauty, or home decor.

Its creative formats, Reels, Stories, and Carousels offer brands the flexibility to tell a compelling story and convert in the same swipe.

It’s also the better choice for lower-budget campaigns. The 2024 Facebook Ads benchmarks show a $1.88 CPC across all industries compared to Google’s $4.66.

Meta also leans into AI to adapt to the post-iOS 14 landscape. Advantage+ Shopping Campaigns and the Conversions API help automate targeting and placements while making up for lost third-party data.

Lead generation for B2C brands can perform well on Meta, too, especially with strong creative and clear calls to action.

With the right mix of assets, like product demos, influencer content, and user-generated content, Meta can drive results well beyond just awareness.

What’s Changing In 2025 And Why It Affects Budget Decisions

Rising ad costs are forcing marketers to be more deliberate with every dollar.

Google’s Smart Bidding and Meta’s Advantage+ now automate most of the optimization process, from placements to bidding strategies.

However, they’re not one-size-fits-all. Without a clear structure, reliable creative inputs, and regular human oversight, automation can just as easily waste budget as performance scales.

The dominance of short-form videos has changed ad creatives. Formats like YouTube Shorts, Instagram Reels, and Facebook Stories capture more attention and often outperform static ads in both reach and cost efficiency.

First-party data is now essential for advanced targeting.

Google’s Customer Match and Meta’s Conversions API offer better performance if you have the data to feed them – this is where scale matters.

Large brands with thousands of customers can activate precise targeting strategies. For small businesses, platform-led targeting or broader lookalikes often remain the better bet.

How To Allocate Your Budget

Unfortunately, there’s no universal formula for dividing the budget between Google Ads and Meta Ads. What works for one brand may fail for another.

Instead, I would suggest evaluating your budget not just by platform but by objective, funnel stage, product type, and customer behavior.

Start With Intent

If your target customer is actively looking for a solution, whether it’s a personal injury lawyer, an enterprise SaaS tool, or a plumber at 3 a.m. Google Ads is where you’ll see the highest return.

Paid search captures high-intent traffic at the moment of need, and in these scenarios, it’s often smart to allocate 70% or more of your ad budget to Google.

Meta simply isn’t built to catch bottom-of-funnel demand like that.

Here’s a good way to frame this:

Ask yourself, “Does my customer know they need this?” If the answer is yes, test Google first.

If, instead, you’re thinking, “I need to tell my customer why they need this,” test Meta first.

If you’re not sure whether your customers know they need your product or service, start with keyword research. If there’s a high search volume, Google likely deserves a larger share of your budget.

If You’re Building A Brand, Lean Into Meta

Meta Ads does great in categories where brand identity, lifestyle associations, and storytelling drive consideration.

This makes Instagram and Facebook ideal for brands launching new products, entering crowded markets, or selling aesthetics-first items like skincare, fashion, or home decor.

In these cases, it’s common to see brands allocate 70% of their budget to Meta, especially when early-stage awareness or engagement is a priority.

Ecommerce Brands Need A Dual Strategy

For product-driven businesses, the platform split often comes down to product price points and customer buying behavior.

High-ticket or research-heavy products, like fitness equipment, electronics, or furniture, usually perform better on Google.

Lower-priced, impulse-friendly products, like jewelry, apparel, or novelty gifts, often convert faster on Meta, where users aren’t actively searching but are open to discovery.

A 50/50 split is a good starting point, but the performance data should quickly tell you whether to skew heavier toward search or social.

Lead Generation Requires Funnel-Specific Planning

For B2B lead gen, Google Ads is the platform where you’ll receive higher-quality leads due to the higher intent of the query.

If your sales process is long or consultative, Google is worth the majority of your spend.

However, Meta can be cost-effective for B2C lead gen, especially offers like product waitlists or newsletter opt-ins.

Meta’s audience targeting and creative tools can nurture users through the early stages of the funnel.

Budget splits here can range from 60/40 to 40/60 depending on your customer and conversion goals.

Testing Is Your Real Allocation Strategy

Budgeting isn’t a set-it-and-forget-it model.

Allocate a small portion of your budget to test both platforms. Validate which creative, copy, audiences, and formats drive the best results.

Don’t just test platforms. Test offers, price points, landing pages, and funnel sequences.

A $250 test campaign split evenly across Google and Meta can give you more insights than a bloated campaign stuck on a single channel.

Make Your Budget Flexible, Not Fixed

Most successful brands don’t work with rigid splits. They start with a hypothesis, test quickly, and reallocate monthly (or weekly) based on data.

Seasonality, promotions, creative fatigue, and even news cycles can impact which platform is more effective at any given time.

If your entire budget is tied to one platform year-round, you’re probably leaving money on the table.

Final Word: Don’t Pick One. Build A Hybrid Strategy

The most effective advertisers aren’t choosing between Google and Meta. They’re building strategies that leverage both.

Use Google Ads when you need to convert high-intent search traffic. Use Meta Ads when you’re building demand, launching a product, or nurturing an audience.

Invest in creative work that works across platforms, especially video. Lean into automation, but keep a close eye on performance.

Most importantly, stay flexible. What works in Q1 may fail in Q3. Let data, not assumptions, shape your budget decisions.

More Resources:

Featured Image: Whiskerz/Shutterstock

Navigating Time Zone Differences: Scheduling Ads For Maximum Impact via @sejournal, @brookeosmundson

Ad scheduling is a fundamental setting in Google Ads and Microsoft Ads, but when managing campaigns across multiple time zones, it becomes more complex.

Standard scheduling tactics may not cut it if you’re advertising internationally or running campaigns across regions with different peak engagement times.

Poorly timed ads can lead to wasted budget, lower conversion rates, and missed opportunities.

This article goes beyond the basics to cover next-level strategies for scheduling ads effectively across different time zones.

We’ll explore techniques such as localized scheduling, data-driven adjustments, and automation to maximize campaign performance.

Understanding Time Zone Challenges In PPC

When advertising across multiple regions, time zone discrepancies can create challenges that impact ad delivery, engagement, and conversions.

A common pitfall is assuming that a single campaign schedule will work universally. In reality, what works in one location might be completely ineffective in another.

For example, if your Google Ads account is set to Eastern Time but your target audience is primarily on the West Coast, your ads might be running during off-hours, leading to suboptimal performance.

International campaigns require even more diligence to consider local business hours and consumer behavior patterns.

Another factor is peak engagement hours. While lunchtime or evening hours may be prime time in one country, those same hours could be completely irrelevant in another.

Understanding these nuances is essential for optimizing your ad scheduling strategy.

Advanced Strategies For Scheduling Ads Across Time Zones

Successfully managing ad scheduling across time zones requires a thoughtful approach that goes beyond the basics.

While many advertisers set simple schedules and hope for the best, the real wins come from leveraging automation, data-driven insights, and strategic segmentation.

Whether you’re running campaigns domestically across U.S. time zones or managing international PPC efforts, applying advanced techniques can help ensure your ads are served at the right time for the right audience.

Segmenting Campaigns By Time Zone For Better Control

If you’re running campaigns across multiple time zones, one of the best ways to stay in control is by creating separate campaigns for different regions.

This lets you adjust ad schedules, budgets, and bidding strategies based on local peak performance times rather than forcing a single schedule to work for every location.

For example, an ecommerce brand serving customers in the U.S. and Europe might run separate campaigns for each region.

The U.S. campaign can focus on morning and evening hours when engagement peaks, while the European campaign targets prime shopping hours in local time zones.

While this approach adds complexity, the benefits far outweigh the extra management effort. Automating adjustments with rules and scripts can help streamline this process, ensuring each campaign is optimized without constant manual oversight.

Leveraging Automated Bidding Over Fixed Schedules

Manual ad scheduling has its place, but automated bid strategies like Target ROAS or Maximize Conversions allow you to optimize bids dynamically rather than setting fixed hours.

These AI-driven approaches adjust bids in real time, ensuring ads appear when conversion probability is highest, regardless of time zone differences.

For instance, if data shows that users in one region convert at a higher rate between 9 a.m. and 11 a.m. but another region performs better in the evening, automated bidding will allocate more budget when it matters most.

Instead of manually adjusting bids every few weeks, let machine learning do the heavy lifting.

Optimizing Scheduling Based On Market-Specific Peak Hours

Different markets have different user behaviors, so it’s crucial to base your scheduling decisions on actual performance data rather than assumptions.

Google Ads’ ad schedule reports and Microsoft Ads’ time-of-day insights can help you identify when users in each region are most active.

For example, if analytics reveal that North American users are most engaged in the evening while European users peak in the morning, your campaigns should reflect that.

Instead of blanketing all markets with a generic ad schedule, tailor your approach based on real-time engagement trends.

Using Labels To Manage And Adjust Scheduling

One often overlooked yet powerful feature in Google and Microsoft Ads is the use of labels.

Labels let you group campaigns, ad groups, or keywords into easily manageable categories, making it simpler to track and adjust schedules.

For example:

• Tagging campaigns by region allows for easy bulk adjustments when shifting schedules due to seasonal changes or promotional events.
• Labeling time-sensitive ads ensures that you can quickly pause or resume campaigns as needed without sifting through dozens of settings.
• Using automation scripts with labels enables automatic bid adjustments or scheduling changes based on real-time performance.

By applying labels effectively, you can streamline scheduling changes without manually editing each campaign, saving time and reducing errors.

Automating Scheduling Adjustments With Scripts

If you’re managing multiple time zones, Google Ads scripts can be a game-changer.

Rather than manually adjusting schedules, scripts can dynamically modify bids based on real-time performance data.

For example, a script could be set up to boost bids by 20% during high-converting hours and reduce them by 10% when conversions drop. This keeps campaigns optimized while freeing up time to focus on strategy rather than daily bid adjustments.

Scripts also work well with labels. You can program scripts to modify bid strategies for campaigns tagged with specific labels, ensuring changes are applied only to relevant ads.

Adjusting For Daylight Saving Time Changes

Another scheduling headache is Daylight Saving Time (DST), which varies by country and can cause misalignment in ad schedules.

A campaign that ran perfectly last month might suddenly be off by an hour if a region switches to DST.

To avoid this, maintain a calendar of DST changes in key markets and adjust schedules proactively.

Another option is using automated rules or machine learning-based bid adjustments to handle these shifts without manual intervention.

Budget Allocation Based On Regional Performance Trends

Rather than splitting your budget evenly across all time zones, consider allocating more spend to the highest-performing regions based on historical data.

By analyzing performance reports, you can determine which locations deliver the best ROI and adjust budgets accordingly.

For instance, if your data shows that conversions peak in the late evening for Pacific time zone users but decline in the early morning for Eastern time users, shift more budget toward the stronger-performing time periods.

This approach ensures ad spend is being used effectively rather than wasted on time slots that don’t generate conversions.

Mastering Ad Scheduling For Global Success

Effectively navigating time zone differences in Google and Microsoft Ads isn’t just about setting a schedule and forgetting about it.

A winning strategy requires a mix of localized segmentation, automation, and continuous data-driven adjustments.

Instead of seeing time zone variations as a challenge, think of them as an opportunity to refine and optimize your strategy.

By leveraging campaign segmentation, smart bidding, labels, and scripts, you’ll gain greater control over when and where your ads appear – without unnecessary budget waste.

At the end of the day, great PPC management isn’t about simply keeping the lights on. It’s about making smart, strategic moves that maximize impact.

Test, tweak, and refine your approach, and you’ll see the results in both efficiency and performance.

More Resources:

Featured Image: tovovan/Shutterstock

Measuring PPC Performance In The Luxury Fashion Space

When it comes to luxury fashion, the likes of Louis Vuitton, Chanel, and Gucci are among the most well-known, and rightfully so. These three alone are valued at over $87 billion.

Over the last decade and a half, I have had the opportunity to work extensively within this market.

Managing PPC in this space is super interesting as it requires a shift in thinking away from cookie-cutter ecommerce PPC strategies.

It’s not just a case of the product value being above average, leading to longer, more considered decision-making processes.

Luxury audiences are unique. They differ by brand, and finding success with PPC requires careful consideration and a holistic understanding of digital performance – not just the data within the accounts.

Understanding The Luxury Fashion Consumer

Luxury consumers have unique characteristics and behaviors that mold their purchase motivations and preferences.

You may be thinking through a lens of tactical management and day-to-day operations, “But how does this really impact PPC?”

But it really does.

Broadly speaking, luxury fashion consumers can be divided into three groups:

1. Ultra-High-Net-Worth Individuals (UHNWI): Buyers with plenty of disposable income who frequently purchase luxury fashion without too much consideration for price.
2. Aspirational Consumers: Middle- to high-income buyers who occasionally purchase within their budget to match their lifestyle.
3. New Luxury Shoppers: Younger buyers, particularly Millennials and Gen Z, who engage with luxury brands online and purchase more infrequently, likely to have more of an affiliation with pre-loved luxury.

From a motivational perspective, elements such as social standing, quality, heritage, and storytelling play an important role. They can feed into ad copy and/or landing page messaging to test the impact on PPC.

Let’s say a brand is only bidding on new customers through Google Ads with a basic strategy of “brand keywords” (e.g. “louis vuitton”) and “generic keywords” (e.g. “luxury handbags”).

Focussing on generic keywords, these three audience groupings have to be considered – and segmentation is absolutely essential – as they all search using the same queries and are served the same SERP:

Screenshot from search for [buy luxury handbags], Google, February 2025

It differs on a case-by-case basis, but the customer lifetime value (CLV) of a “New Luxury Shopper” will likely be vastly different from users in the “UHNWI” grouping.

Aspirational buyers now make up 18% of the luxury fashion market across key economies. By 2030, Gen Z is projected to contribute 25-30% of luxury market purchases, with Millennials making up the majority at 50-55%.

There are plenty of opportunities for brands to benefit from when scaling up their PPC spend.

Simply excluding current customers, allocating a percentage of the budget to generic keywords/shopping listings, and then reporting on return on ad spend (ROAS) or customer acquisition cost (CAC) isn’t enough.

Segment performance data, get a clear view of which grouping demands the highest portion of media spend, and ask yourself whether this can be improved and how this data can be used to drive better results.

The Complexity Of Luxury Purchase Paths

Deliberated decision-making is a key element to consider when measuring PPC performance in luxury fashion.

A study found that two-thirds of Chinese luxury goods consumers undertake thorough product research before making luxury purchases, with over 40% of the respondents sharing that they used photos and pricing comparisons and compared to similar products before making the purchase.

Reporting week-to-week on ROAS/CAC may not be enough, and if decisions are made using windows that are too short/long, optimizations won’t align with the overarching goal.

I’ve seen growth stunted because of this, and access to purchase path data is a must.

The journey to purchasing luxury as a whole (not even considering the three audience groupings) involves many interactions.

A consumer insights study from NP Digital found that as the price point grew, the number of touchpoints for B2C increased.

For example, from $100 or less to $10,000+, the number of touch points moved from 8 to 23, and while price point doesn’t necessarily mean luxury, it shows a strong correlation.

This, combined with 80% of all luxury sales being digitally influenced, shows the importance of having connected data that can be fed into PPC strategies.

Take a scenario where a brand’s new customer acquisition through PPC is bulletproof, but social content is thin, and lead nurturing is non-existent.

This fractures the decision-making process, which ultimately impacts the results driven through PPC as demand will stay consistent and budget will be spent.

Still, the needs of the consumer are not met, which would mean fewer sales, a lower conversion rate, and declining efficiency.

A long-term view is essential for both reporting and optimization, and the necessity for brands to adopt an integrated marketing strategy has never been more critical.

The Influence Of Trends And Tradition

A survey found that 70% of consumers identify as trend-focused, and 77% value tradition.

Trend-focused consumers are driven by the latest movements in luxury fashion, whether this is the latest collab, a change in leadership, or a trending style – all of this will influence PPC.

These trends can occur steadily over time or, in some cases, emerge overnight due to the media, social influence, etc.

Depending on how an account is set up, the flow of search queries over time for “brand” keywords, “brand + product,” “product,” and “generics” will experience ebbs and flows based on a wealth of factors.

Knowing why certain products become more popular than others and being able to pull this data out of the accounts to share with the wider team is essential.

This should be reported as frequently as platform ROAS or blended CAC, not just on an ad hoc basis.

This can be done a few ways, one being to group search queries together, categorize them, and report using an index vs. the average, for example:

• Automate the export of all search queries on a daily basis (or longer, depending on the size of the brand and budget) with a date stamp.
• Create a script (or use Python/equivalent) – or find a partner to build one for you – to categorize these queries into groupings, or do this manually to get started.
• Build a dashboard that maps the groupings over time and benchmarks vs. the average to provide a clear view of increases/decreases in demand, sales, cost, etc.
• Map an index to each category and rank these based on the average.

It may take some work, but it is definitely worth it, considering how often trends and seasons change in luxury fashion.

With this data, brands can look at trends over time and react much quicker than simply seeing a better or worse ROAS one week vs. the previous week.

From a holistic perspective, this data can be shared across channels (e.g., SEO, PR, and content) and with the wider team, such as buyers, to share insights ahead of time on what users are searching for and why.

Holistic Performance Measurement

A robust measurement strategy is paramount when managing PPC in any capacity, and even more so for luxury fashion.

Gone are the days when teams were asked, “How much did we put in and how much did we get back?” at an ad platform level (well, almost gone).

Luxury brands are leaning into smart attribution modeling, focusing on CLV and personalization, to find the true value of their PPC activity.

This goes much further than an out-of-the-box attribution solution.

Unified Data Collection

Luxury brands spend millions annually on advertising, with 33% of this cost allocated to digital.

Measuring PPC performance through one or two lenses isn’t enough as it influences (and is influenced) by all other channels.

Data unification is essential to facilitate integration, understand the customer journey, and set budgets and key performance indicators (KPIs) for PPC.

Stores, pop-ups, and events are incredibly important for luxury fashion brands, and being able to target new and returning users at key touchpoints through PPC is a must.

Pulling together online and offline data across multiple markets, channels, and sources requires robust processes, ownership, and consistency.

And, as easy as it is for me to write this, it most likely isn’t a quick task and will take considerable time and resources, but the payoff is certainly worth it to have a clear view of PPC performance.

Attribution Modeling

In this day and age, models such as “last click” are a touchy subject, and with good reason.

Consider all the touchpoints, advertising channels, and budget invested in delivering great customer journeys that luxury buyers experience for billion-dollar brands to then turn around and say the last click before the sale gets 100% of the credit.

In-platform, brands use multi-touch attribution (such as Google data-driven attribution or DDA), which is the best of the bunch, but still very limiting.

To begin to bridge the gap between online and offline, Louis Vuitton and Gucci invested heavily in omnichannel data platforms that allow them to connect in-store activity with digital interactions.

Another way brands are moving towards a more connected view of performance is through Marketing Mix Modelling (MMM), a methodology that was first used in the 1950s.

This takes attribution a step further by measuring the impact of PPC (for example) on the broader marketing ecosystem, quantifying PPC’s role within the entire marketing mix.

The complex decision-making process for luxury fashion makes having this connected data a non-negotiable for effective PPC budgeting, optimization, and growth.

Customer Lifetime Value

This metric refers to the total profit a customer is expected to drive over the duration of their relationship with a brand.

New customer acquisition through PPC is often measured through in-platform/blended ROAS and CACs, both serving a purpose when analyzing platform performance.

However, luxury brands that adopt CLV are able to identify which customer segments are the most profitable and take a longer-term view of acquiring high-value customers while tailoring strategies to audiences that aren’t as profitable to them.

Let’s look at an example of Brand A, who sells luxury handbags:

• Current KPI for paid search: Account-wide Google Ads in-platform ROAS >5.

Brand A runs a data analysis project to find CLV by user segment and discovers that Audience 1 spends $11,000 over their lifecycle with the brand, and Audience 2 spends $4,000.

Their new KPIs would look vastly different, which feeds into budgeting, setting bidding strategy targets, promotions, and more.

Looking at the longer term, having this data gives luxury brands the benchmarks to work from to improve CLV numbers, which will, in turn, allow more freedom for scaling PPC spend.

Bringing It All Together

PPC doesn’t exist in isolation for any brand.

It’s influenced by many factors, and looking at one metric or platform for the answers isn’t enough.

Luxury fashion operates differently from a standard ecommerce retailer with unique audience profiles, extended decision-making processes, and an ever-moving flow of trends and traditions.

I’ve seen luxury brands dive headfirst into advanced data modeling, then go back to the drawing board to set accurate KPIs that fuel the fire of scaling media spend on PPC.

At the same time, plenty of luxury fashion brands use arbitrary in-platform modeling (e.g., last click) as the source of truth.

Aside from the obvious (cost, time, resource, etc.), there are only benefits from digging into the data to improve PPC performance from one end (e.g., setting bids, messaging, etc.) to the other (e.g., budgeting, market/network expansion).

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