AI Performance Marketing for B2B SaaS Growth

AI performance marketing helps B2B SaaS teams make better paid media decisions across Google Ads, LinkedIn Ads, Meta Ads, Reddit Ads, attribution, budget allocation, campaign reporting, and revenue measurement.

Instead of relying only on platform recommendations or surface-level metrics, AI helps growth teams identify wasted spend, analyse campaign performance, improve pipeline quality, reduce CAC, measure ROAS, and connect paid media activity to revenue outcomes.

For SaaS founders, CMOs, demand generation leaders, and performance marketers, the goal is not to replace strategy with automation. The goal is to use AI to make performance marketing more structured, measurable, and connected to business growth.

If you want to understand where your current paid media spend is working, start with a free ads audit. If you are ready to improve paid media execution across channels, explore our paid media services.

What is AI performance marketing?

AI performance marketing is the use of artificial intelligence, automation, campaign data, and revenue signals to improve how paid media is planned, launched, optimised, and measured.

For B2B SaaS teams, this means using AI to analyse audience segments, keyword intent, ad copy, landing page performance, budget pacing, conversion quality, and CRM outcomes. The focus is not just on getting more clicks or leads. The focus is on understanding which campaigns create qualified pipeline, efficient CAC, stronger ROAS, and better revenue outcomes.

AI can support planning and optimisation across paid media campaigns by helping teams identify patterns in channel performance, audience behaviour, search intent, creative tests, and conversion quality. It can also simplify AI PPC reporting by turning scattered campaign, CRM, and revenue data into clearer insights.

The simplest way to think about AI performance marketing is this: it helps teams make smarter paid media decisions faster, but it still needs strategy, clean tracking, and human judgement to work well.

Why B2B SaaS teams need AI in performance marketing

B2B SaaS performance marketing is difficult because the buying journey is rarely linear. Sales cycles are longer, buying committees are larger, and the person who clicks an ad is not always the person who becomes the buyer.

That creates a gap between platform performance and business performance. A campaign may generate leads at a low CPL, but those leads may not become SQLs, opportunities, or closed-won revenue. CAC can rise quickly when budgets are scaled without understanding lead quality. Attribution also becomes messy when the same account interacts with Google Ads, LinkedIn Ads, Meta Ads, Reddit Ads, landing pages, demo forms, retargeting campaigns, and sales touchpoints before entering pipeline.

AI helps B2B SaaS teams analyse these patterns faster. It can highlight which audiences are producing better SQL rates, which campaigns are creating weak leads, where budget is being wasted, and where pipeline quality is improving. When paired with B2B marketing attribution, AI can help teams move from surface-level reporting to revenue-aware optimisation.

The goal is not just to reduce CPC or improve CTR. The goal is to understand how paid media affects CAC, ROAS, opportunity creation, and pipeline efficiency. Tools like a CAC calculator and ROAS calculator can help SaaS teams evaluate whether paid media is creating growth that is actually sustainable.

The AI performance marketing framework

AI performance marketing should start with strategy, not automation. Before a team uses AI to write ads, change bids, build reports, or recommend budgets, it needs a clear view of the ICP, channel role, campaign structure, offer strategy, conversion quality, and revenue measurement.

A strong AI performance marketing framework has six parts: audience intelligence, channel strategy, campaign structure, creative testing, budget allocation, and revenue attribution. Each part helps B2B SaaS teams move from platform-level optimisation to pipeline-aware decision-making.

Audience intelligence

Audience intelligence starts with understanding who the campaign is built for. For B2B SaaS teams, this includes ICP fit, buyer roles, pain points, objections, job titles, industries, company size, buying triggers, and competitor alternatives.

AI can help analyse customer conversations, CRM notes, sales objections, search terms, ad performance, and website behaviour to identify patterns that are difficult to spot manually. This helps teams build paid media campaigns around real buyer intent instead of broad assumptions.

Channel strategy

Each paid media channel has a different role. Google Ads is usually strongest for demand capture. LinkedIn Ads works well for B2B targeting, account-based marketing, and persona-specific messaging. Meta Ads can support creative testing and retargeting. Reddit Ads can help with niche audience discovery, community insight, and message-market fit.

AI can help compare these channels by intent, cost, audience fit, conversion quality, and pipeline contribution. A clear paid media strategy for B2B SaaS helps decide where each channel should sit in the funnel and how budget should be allocated across them.

Campaign structure

Campaign structure affects how clearly a team can analyse performance. Campaigns should be organised by intent, ICP, funnel stage, offer, keyword groups, audience segments, and conversion quality.

AI can help review whether campaigns are too broad, whether ad groups mix different intent levels, whether offers match the funnel stage, and whether budget is being spent on the right audience segments. Better structure makes optimisation easier because performance data becomes easier to interpret.

Creative and message testing

AI can support creative and message testing by generating ad variations, analysing hooks, grouping persona pain points, summarising proof points, and identifying common objections from sales and customer data.

For B2B SaaS, the strongest ad copy usually comes from customer insight, not generic AI output. AI should be used to speed up testing and analysis, while the positioning still comes from ICP understanding, product value, and sales feedback.

Budget allocation

Budget allocation should be based on more than CPC, CTR, and CPL. AI can help analyse wasted spend, SQL rate, pipeline quality, opportunity creation, CAC, and revenue influenced across campaigns and channels.

This makes budget decisions more useful. Instead of asking “which campaign got the cheapest leads?”, teams can ask “which campaign created the best pipeline at the most efficient CAC?”

Revenue attribution

Revenue attribution connects campaign activity to pipeline and closed-won revenue. This includes UTMs, campaign naming, CRM tracking, opportunity data, revenue reporting, and closed-loop measurement.

AI can help summarise attribution patterns, identify campaigns that influence pipeline, and highlight where tracking gaps are making reporting unreliable. For this to work, teams need clean B2B marketing attribution and consistent campaign tracking using tools like a UTM builder.

AI for Google Ads performance

AI can review Google Ads campaigns by intent, product category, ICP, geography, funnel stage, or offer. This helps identify where campaigns are too broad, where ad groups mix different intent levels, and where conversion data is difficult to interpret.

A cleaner Google Ads campaign structure makes it easier to optimise budgets, landing pages, ad copy, and reporting around actual buyer intent.

Negative keyword discovery

AI can help find search terms that waste budget. These may include consumer intent, job-seeker intent, free tool searches, student queries, unrelated definitions, broad informational searches, and searches that do not match the SaaS ICP.

Negative keyword discovery is especially useful when campaigns use broad match, automated bidding, or Performance Max. Without regular review, Google Ads can spend budget on traffic that looks active but does not create qualified pipeline.

Budget pacing and wasted spend

AI can support budget pacing by comparing spend across brand, non-brand, competitor, retargeting, and Performance Max campaigns. It can help identify where spend is increasing faster than conversion quality, where CPC is rising, and where campaigns are consuming budget without meaningful pipeline contribution.

For SaaS teams, this is important because the cheapest lead is not always the best lead. Budget should move towards campaigns that create qualified opportunities, not just campaigns that generate low-cost form fills.

Revenue-based reporting

AI can help turn Google Ads reporting into a clearer view of pipeline impact. Instead of only looking at impressions, clicks, CPC, CTR, conversions, and CPL, teams can analyse qualified pipeline, CRM outcomes, opportunity quality, CAC, ROAS, and revenue influenced.

This is where AI PPC reporting becomes useful. SaaS teams can also use educational resources like Google Ads for SaaS and a Google Ads audit to understand where campaign performance may be leaking. If the account already has spend but weak visibility into wasted budget or pipeline quality, a free ads audit can help identify what needs fixing. For users who need channel-specific execution support, the Google Ads agency page should handle that commercial intent.

AI for LinkedIn Ads and B2B audience targeting

LinkedIn Ads is useful for B2B SaaS teams because it allows campaigns to reach specific companies, job titles, industries, seniority levels, functions, and buying committees. But the same strength also makes LinkedIn expensive when audience targeting, messaging, and offer selection are not clear.

AI can help improve LinkedIn Ads by analysing ICP segments, account lists, persona-level messaging, creative performance, lead quality, and pipeline outcomes. The goal is not just to generate more form fills. The goal is to understand which audiences and messages create qualified conversations for sales.

ICP and account segmentation

AI can help organise LinkedIn audiences by account fit, company size, industry, region, revenue stage, product fit, and buying triggers. This is useful when a SaaS team is targeting different segments such as founders, CMOs, demand generation managers, sales leaders, or revenue teams.

Instead of building one broad audience, teams can use AI to identify which segments deserve separate campaigns, separate messaging, or different offers. For companies running account-based campaigns, this can also support a stronger SaaS ABM strategy without mixing too many audience types into one campaign.

Persona-specific messaging

Different B2B buyers care about different outcomes. A founder may care about pipeline efficiency and CAC. A CMO may care about channel performance and revenue visibility. A demand generation manager may care about lead quality, campaign structure, and reporting. A sales leader may care about SQL quality and opportunity creation.

AI can help compare pain points, objections, job titles, and buying triggers across personas. This makes ad copy more specific and reduces the risk of running generic LinkedIn Ads that speak to everyone but convert no one.

Creative testing

AI can support LinkedIn creative testing by generating hook variations, headline options, proof-point angles, founder-led content ideas, case-study ads, demo offers, and audit CTA messaging.

The best use of AI is not to publish generic ad copy directly. It is to create structured testing options based on ICP insight, customer pain points, and campaign learnings. This is especially useful when planning B2B LinkedIn Ads campaigns that need different messages for different buyer roles.

Lead quality analysis

LinkedIn Ads can generate leads, but not every lead is useful for SaaS growth. AI can help analyse form fills, job titles, company sizes, industries, offers, SQL rates, pipeline creation, and sales feedback.

This helps teams understand whether a campaign is creating real buying interest or just collecting low-intent leads. If a campaign has a low CPL but poor SQL quality, the issue may be audience selection, offer mismatch, weak qualification, or disconnected reporting.

Budget control

LinkedIn Ads often has higher CPCs than other paid channels, so budget control matters. AI can help identify weak audience segments, high-cost campaigns, poor lead sources, underperforming offers, and areas where spend is not turning into pipeline.

For teams that need hands-on execution rather than only strategic analysis, the LinkedIn Ads agency page should handle that commercial intent. On this pillar page, the focus should remain on how AI improves targeting, message testing, budget decisions, and lead quality analysis.

AI for Meta Ads and paid social testing

Meta Ads can support B2B SaaS performance marketing through creative testing, retargeting, paid social experiments, and funnel reinforcement. While Meta is not usually the first channel for high-intent B2B demand capture, it can help teams test messages, understand audience response, and bring previous visitors back into the buying journey.

AI can make Meta Ads more useful by analysing creative performance, audience behaviour, engagement quality, retargeting segments, and post-click outcomes. The goal is not just to find cheap traffic. The goal is to identify which messages, offers, and audiences support pipeline creation.

Creative testing

Meta Ads can be useful for testing hooks, headlines, ad copy, visual concepts, CTA angles, and proof points before scaling ideas across other channels. AI can help generate variations, compare engagement patterns, and identify which messages are getting attention from the right audience.

For SaaS teams, this should still be tied to business outcomes. A creative test is only useful if it helps reduce wasted ad spend, improve CAC, support better attribution, or create higher-quality demand. For deeper channel context, the Facebook Ads for SaaS guide can support this section without making the pillar page too channel-specific.

Retargeting and funnel support

Meta Ads can help retarget blog readers, service page visitors, tool users, returning visitors, and users who engaged with educational content. These audiences may not be ready to book a demo immediately, but retargeting can bring them back with proof, case studies, comparison content, or audit CTAs.

AI can help segment these audiences based on behaviour, page intent, funnel stage, and past engagement. This makes retargeting more useful than showing the same generic ad to every visitor.

Audience and message analysis

Meta often has lower CPMs than LinkedIn, but lower media costs do not always mean better performance. AI can help compare CPM, engagement, lead quality, SQL rate, CAC, and pipeline influenced across different audiences and messages.

A CPM calculator can help teams understand media efficiency, but CPM should not be reviewed in isolation. For B2B SaaS, the stronger question is whether low-cost reach is helping create qualified demand.

Paid social testing

Paid social testing can support other channels. A message that performs well on Meta may become a stronger LinkedIn Ads hook, a better landing page headline, or a sharper email follow-up angle. AI can help identify these patterns across paid social, landing pages, and funnel content.

For teams that need execution support across creative testing, retargeting, and campaign optimisation, the Meta Ads agency page can handle that commercial intent. This section should stay focused on how AI improves paid social testing and learning.

AI for Reddit Ads and community-led research

Reddit can support AI performance marketing in two ways: as a paid media channel and as a source of buyer research. For B2B SaaS teams, Reddit is useful because users often discuss problems, tools, alternatives, frustrations, and buying doubts in their own language.

AI can help analyse these discussions to identify recurring pain points, objections, comparison language, and content ideas. This makes Reddit useful even before a campaign is launched because it can improve ad messaging, landing page copy, and offer positioning.

Pain-point mining

AI can help review Reddit discussions to find buyer language around complaints, alternatives, workflow problems, pricing concerns, implementation issues, and recurring questions.

This can be useful for SaaS teams that are too close to their own product messaging. Reddit often shows how users describe problems when they are not reading a landing page or speaking to a sales team.

Subreddit and community research

Not every subreddit is useful for paid media. AI can help review subreddit context, audience fit, community rules, SaaS categories, moderation patterns, and message-market fit before a team tests campaigns.

This matters because Reddit audiences usually respond poorly to generic ads. The message needs to fit the community context and feel specific to the discussion environment.

Message testing

Reddit research can inform ad angles, landing page copy, comparison pages, blog topics, and objection-handling content. AI can group recurring discussion themes and turn them into structured message tests.

For example, if users repeatedly complain about poor reporting, unclear pricing, slow onboarding, or weak integrations in a category, those themes can become sharper ad hooks and landing page sections.

Reddit Ads for SaaS audiences

Reddit Ads can be useful for reaching niche SaaS communities, but they need careful targeting and authentic messaging. AI can help identify audience themes, test non-generic copy, and analyse which discussions are worth turning into campaign ideas.

For teams exploring Reddit as a paid channel, a focused guide on Reddit Ads for SaaS can support deeper learning. If the intent is execution rather than research, the Reddit Ads agency page should handle that commercial handoff.

AI performance marketing metrics that matter

AI performance marketing is only useful when teams measure the right things. For B2B SaaS, that means looking beyond platform metrics and connecting paid media performance to pipeline, CAC, payback, and revenue quality.

CPC, CTR, CPM, CPA, conversion rate, and cost per lead are still useful. They show whether campaigns are getting attention, traffic, and conversions efficiently. But they do not explain whether those conversions are becoming SQLs, opportunities, or customers.

That is why SaaS teams should also measure CAC, ROAS, SQL rate, opportunity rate, pipeline generated, marketing-sourced opportunities, revenue influenced, and payback period. A ROAS calculator can help evaluate return from ad spend, while CAC should be reviewed alongside sales cycle length, deal size, and lead quality.

Metric	What it tells you	Why it matters
CPC	How much each ad click costs	Helps identify rising media costs and expensive keywords or audiences
CTR	How often users click after seeing an ad	Shows whether the message is relevant enough to earn attention
CPM	Cost to reach 1,000 impressions	Useful for comparing awareness and retargeting efficiency
CPA	Cost to generate a defined action	Helps evaluate conversion efficiency beyond clicks
Conversion rate	Percentage of visitors who complete an action	Shows whether landing pages and offers are working
Cost per lead	Cost to generate a lead	Useful, but should not be treated as the final success metric
CAC	Cost to acquire a customer	Shows whether paid media is efficient at a business level
ROAS	Revenue returned from ad spend	Helps evaluate paid media return, especially when revenue tracking is reliable
Pipeline generated	Pipeline value influenced or created by campaigns	Connects campaign performance to sales opportunities
Marketing-sourced opportunities	Opportunities created from marketing activity	Shows whether paid media is contributing to sales pipeline
SQL rate	Percentage of leads accepted or qualified by sales	Helps measure lead quality
Opportunity rate	Percentage of leads or SQLs that become opportunities	Shows whether campaigns are attracting serious buyers
Revenue influenced	Revenue touched by paid media campaigns	Helps understand channel contribution across longer buying journeys
Payback period	Time needed to recover acquisition cost	Important for SaaS capital efficiency and sustainable growth

AI can help monitor these metrics together, identify unusual patterns, and summarise which campaigns are improving business outcomes. But the metrics only become useful when platform data, website data, and CRM data are connected properly.

Connecting ad spend to pipeline and revenue

The biggest gap in performance marketing is often not campaign setup. It is the gap between ad spend and revenue visibility.

A campaign can look successful inside Google Ads, LinkedIn Ads, Meta Ads, or Reddit Ads because it generates clicks, form fills, or low-cost leads. But for B2B SaaS teams, that is not enough. The real question is whether that spend is creating demo bookings, MQLs, SQLs, opportunities, closed-won revenue, and better pipeline efficiency.

To connect ad spend to pipeline, teams need clean UTMs, consistent campaign naming, reliable form tracking, CRM integration, and pipeline reporting. Without this, AI can summarise data faster, but it cannot fix broken measurement.

A strong B2B marketing attribution setup helps SaaS teams understand which channels, campaigns, keywords, audiences, and offers are influencing revenue. This makes budget reallocation more useful because spend can move towards campaigns that create qualified opportunities, not just campaigns that create cheap leads.

A practical process looks like this:

1. Set clear campaign naming rules
2. Use UTMs across all paid channels
3. Track form fills and demo conversions
4. Push source data into the CRM
5. Review SQL and opportunity quality
6. Optimise budget based on pipeline contribution

A UTM builder can help keep campaign tracking consistent across channels. Once tracking is clean, AI can help analyse which campaigns are producing stronger SQL rates, better opportunity quality, and more efficient revenue outcomes.

When should you use AI tools vs hire a performance marketing agency?

AI tools are useful when your team already understands paid media strategy and needs help moving faster. They can support research, reporting summaries, ad copy testing, campaign analysis, search term reviews, and performance pattern detection.

For example, AI can help a demand generation team summarise campaign performance, compare lead quality across channels, identify wasted spend, or generate structured ad testing ideas. But AI tools still need clean inputs, clear goals, strong tracking, and someone who knows how to interpret the results.

Hiring a performance marketing agency makes more sense when spend is rising but pipeline is unclear, CAC is increasing, lead quality is poor, or reporting is disconnected from the CRM. It is also useful when Google Ads, LinkedIn Ads, Meta Ads, and Reddit Ads need a coordinated strategy instead of being managed as separate channels.

Situation	AI tools may be enough	Hire an agency when
Research	You need help summarising audiences, objections, keywords, or ad ideas	You need research turned into a complete channel strategy
Reporting	You need faster campaign summaries	You need reporting connected to CRM, pipeline, and revenue
Ad copy testing	You want more message variations	You need positioning, offer strategy, and creative direction
Campaign analysis	You need help spotting performance patterns	You need someone to restructure campaigns and act on the insights
Budget allocation	You want to compare CPC, CPL, CAC, and ROAS	You need budget decisions tied to pipeline quality and revenue
Channel expertise	Your team already understands the channel	Your internal team lacks depth across Google Ads, LinkedIn Ads, Meta Ads, or Reddit Ads
Execution	Your team can implement changes internally	You need strategy plus hands-on execution

AI can improve performance marketing, but it should not become a substitute for strategy. If your paid media spend is already active and you are unsure where budget is being wasted, a free ads audit can help identify weak campaign structure, poor tracking, low-quality leads, and missed optimisation opportunities.

AI performance marketing by channel

AI performance marketing works best when each paid channel has a clear role. Google Ads, LinkedIn Ads, Meta Ads, and Reddit Ads should not be judged by the same metrics or used for the same job.

Google Ads is stronger for capturing existing demand. LinkedIn Ads is stronger for B2B audience targeting and account-based campaigns. Meta Ads is useful for creative testing and retargeting. Reddit Ads can support community-led research, niche SaaS targeting, and message-market fit.

AI helps by analysing how each channel contributes to the funnel. Instead of comparing every channel only by CPC or CPL, teams can look at channel role, intent level, lead quality, SQL rate, pipeline contribution, and revenue influenced.

Channel	Best use case	AI can help with	Learn more
Google Ads	Capturing high-intent demand	Search intent, keyword grouping, campaign structure, negative keyword discovery, and budget pacing	Google Ads support
LinkedIn Ads	B2B targeting and ABM	Audience segmentation, persona messaging, account targeting, and lead quality analysis	LinkedIn Ads support
Meta Ads	Creative testing and retargeting	Hook testing, audience insights, retargeting segments, and creative analysis	Meta Ads support
Reddit Ads	Community insight and niche SaaS audiences	Pain-point mining, subreddit research, message testing, and audience validation	Reddit Ads support

The important thing is to avoid treating every channel as a direct-response lead generation channel. Some campaigns capture active demand. Some build familiarity. Some support retargeting. Some help test messages before scaling them elsewhere.

AI can help make these channel roles clearer, but the strategy still needs to define what each channel is expected to do.

A practical AI performance marketing workflow

AI performance marketing works best when it follows a clear workflow. Without structure, teams can end up using AI for disconnected tasks such as writing ad copy, summarising reports, or changing budgets without understanding the bigger performance picture.

A practical workflow should connect ICP, funnel stage, channel role, campaign structure, tracking, reporting, and budget decisions.

1. Define the ICP and priority segments
2. Map the funnel stages from awareness to closed-won revenue
3. Choose channels based on buyer intent and audience fit
4. Build campaign structure around intent, offer, and conversion quality
5. Create message and offer variations for each audience segment
6. Launch campaigns with clean tracking, naming rules, and UTMs
7. Monitor platform metrics and CRM metrics together
8. Use AI to analyse patterns across audiences, campaigns, offers, and pipeline
9. Reallocate budget based on SQL quality, opportunity creation, CAC, and revenue influence
10. Feed learnings back into campaigns, landing pages, content, and sales follow-ups

This workflow helps teams avoid one of the biggest paid media mistakes: optimising inside the ad platform without checking whether the campaign is producing quality pipeline.

For SaaS teams building a structured paid media strategy for B2B SaaS, AI should support decision-making at each stage. If campaigns are already running and performance is unclear, a free ads audit can help identify where tracking, structure, targeting, or budget allocation needs improvement.

Common AI performance marketing mistakes

AI can improve performance marketing, but it can also make weak campaigns scale faster if the strategy is unclear. For B2B SaaS teams, the biggest risk is using AI to optimise activity instead of outcomes.

Here are the most common mistakes to avoid:

1. Relying only on platform recommendations
   Platform recommendations can be useful, but they are usually designed around in-platform performance. They may not account for SQL quality, opportunity creation, CAC, or closed-won revenue.
2. Optimising for leads instead of pipeline
   A campaign that generates cheap leads can still be a poor campaign if those leads do not convert into qualified opportunities. AI should help analyse lead quality, not just lead volume.
3. Using AI-generated ad copy without ICP context
   Generic AI copy often sounds polished but vague. Strong ad copy should come from buyer pain points, objections, job roles, use cases, and sales conversations.
4. Running too many campaigns without clean structure
   More campaigns do not always mean better performance. If campaigns are not structured by intent, audience, offer, or funnel stage, reporting becomes difficult and optimisation becomes unreliable.
5. Ignoring CRM data
   Ad platforms can show clicks and conversions, but CRM data shows whether those conversions became MQLs, SQLs, opportunities, or customers. AI performance marketing needs both.
6. Not separating channel roles
   Google Ads, LinkedIn Ads, Meta Ads, and Reddit Ads should not all be judged the same way. Each channel has a different role in demand capture, audience targeting, retargeting, message testing, or community research.
7. Creating reports that do not explain business impact
   A report that only shows impressions, clicks, CPC, CTR, and CPL is not enough for SaaS growth teams. Reporting should explain what is happening to pipeline, CAC, ROAS, and revenue influenced.
8. Scaling budget before proving lead quality
   Increasing spend too early can make CAC rise quickly. Before scaling, teams should check whether campaigns are producing the right accounts, job titles, SQLs, and opportunities.

If your campaigns are already live and these issues sound familiar, a Google Ads audit can help you review one high-intent channel in detail, while a broader free ads audit can identify wasted spend, weak tracking, and poor campaign structure across paid media.

Frequently Asked Questions

Want to understand where your paid media spend is working?

AI performance marketing works best when campaign data, CRM data, and revenue outcomes are reviewed together. If your team is already spending on Google Ads, LinkedIn Ads, Meta Ads, or Reddit Ads, the next step is to understand which campaigns are creating pipeline and which ones are wasting budget.

OneMetrik can review your current ad accounts to identify weak campaign structure, wasted spend, poor tracking, low-quality leads, and missed optimisation opportunities. The focus is not just platform performance. The focus is pipeline clarity, CAC efficiency, ROAS, and revenue impact.

If you want to see where your paid media can be improved, start with a free ads audit. If you need strategy and execution support across channels, explore our paid media agency services.