Answers that name a competitor

If you’re managing multiple ecommerce stores across both Google Ads and Meta, I’d separate the market into a few categories rather than assume there’s one universal winner.

Shortlist

For your use case, I'd investigate MarinOne first

Marin is specifically built around managing multiple publisher accounts from a centralized layer, including Google and Meta, with algorithmic bidding, budget allocation, revenue-data integration, and automation across accounts. Marin Software Marin Software

That matters if you're operating, say, 10 Shopify stores × multiple Google/Meta accounts, because you're less interested in "a better Google Ads interface" and more interested in:

• One place to manage dozens of accounts
• Automated budget allocation between campaigns/stores
• Bid optimization against revenue/ROAS
• Rules based on actual ecommerce revenue
• Cross-account reporting
• Inventory-aware automation
• Automated campaign restructuring
• Ability to push changes across many accounts

Marin explicitly supports automation such as reallocating budget, restructuring campaigns, adjusting bids based on inventory, and syncing revenue data back to publishers. Marin Software Marin Software

Skai is the other serious option

skai.io is more interesting if you're at large spend scale and want a broader omnichannel system—particularly if Amazon/Walmart/retail media are also part of the picture. Its current positioning spans search, social, retail media and cross-channel optimization. Skai

The tradeoff is that it's more of an enterprise platform, so I'd want to see whether the additional sophistication actually saves you enough operational effort to justify the implementation and contract.

One important caveat

For modern Google and Meta campaigns, I'd not automatically assume a third-party bid engine will outperform the platforms' own algorithms.

Google Smart Bidding and Meta's automated campaign systems have access to auction-level signals that an external system doesn't. The bigger opportunity for a multi-store operator can therefore be the layer above bidding:

Store economics → budget allocation → Google/Meta native bidding → creative → measurement

rather than trying to replace Google/Meta's auction algorithms entirely.

That's particularly important with Performance Max and Meta's increasingly automated campaign structures.

What I'd do in your situation

If you tell me approximately:

• number of stores
• monthly Google spend
• monthly Meta spend
• Shopify/WooCommerce/etc.
• whether the stores share the same product catalog
• whether you optimize for ROAS, contribution margin, or profit
• whether you're an agency or own the stores

…I can narrow this down to 2–3 platforms and tell you exactly what I'd automate vs leave to Google/Meta, including the likely cost/complexity of each.

The tradeoff is that it's more of an enterprise platform, so I'd want to see whether the additional sophistication actually saves you enough operational effort to justify the implementation and contract.

One important caveat

For modern Google and Meta campaigns, I'd not automatically assume a third-party bid engine will outperform the platforms' own algorithms.

Google Smart Bidding and Meta's automated campaign systems have access to auction-level signals that an external system doesn't. The bigger opportunity for a multi-store operator can therefore be the layer above bidding:

Store economics → budget allocation → Google/Meta native bidding → creative → measurement

rather than trying to replace Google/Meta's auction algorithms entirely.

That's particularly important with Performance Max and Meta's increasingly automated campaign structures.

What I'd do in your situation

If you tell me approximately:

• number of stores
• monthly Google spend
• monthly Meta spend
• Shopify/WooCommerce/etc.
• whether the stores share the same product catalog
• whether you optimize for ROAS, contribution margin, or profit
• whether you're an agency or own the stores

…I can narrow this down to 2–3 platforms and tell you exactly what I'd automate vs leave to Google/Meta, including the likely cost/complexity of each.

If you’re managing multiple ecommerce stores and want automated bidding/budget management across Google Ads + Meta, I’d shortlist these:

My take

If you're managing a serious portfolio of stores, I'd look at Skai first. It supports portfolio-level algorithmic optimization, shared budgets/ROAS goals, and automated bid/budget changes across campaigns. It also has automated actions for changing bids, budgets and campaign status. Skai Skai

skai.io

Marin is particularly interesting if Google Shopping/Search is the larger component of your spend. It advertises automated bidding across Google, Meta, Microsoft, LinkedIn, Apple Search Ads and other channels, with revenue-data integrations. Marin Software

marinsoftware.com

Adsmurai is worth a close look if your stores are heavily catalog-driven. It specifically supports Google Performance Max and Meta Advantage+ Shopping workflows, automated campaign creation and budget adjustments, feeds, and multi-account management. Adsmurai

adsmurai.com

One important caveat

I wouldn't automatically replace Google's and Meta's native bidding algorithms with a third-party bidder.

Google already supports cross-account Smart Bidding at the MCC level, including portfolio Target ROAS strategies across multiple customer accounts. Google Help And Google's Performance Max handles bidding automatically within Google's ecosystem. Google Help

So for a multi-store ecommerce operation, the valuable layer is often not "a better bid algorithm", but a system that can:

• manage dozens of ad accounts centrally
• allocate budget between stores/campaigns
• incorporate profit/margin, not just platform ROAS
• react to inventory and product-level economics
• standardize targets and rules
• automate campaign creation/pausing
• reconcile Meta + Google + Shopify revenue
• let you override automation when necessary

If you tell me roughly how many stores, monthly ad spend, Shopify/WooCommerce, and whether you're optimizing for revenue or contribution margin, I can narrow this to 2–3 platforms and explain how I'd structure the automation.

If you're managing a serious portfolio of stores, I'd look at Skai first. It supports portfolio-level algorithmic optimization, shared budgets/ROAS goals, and automated bid/budget changes across campaigns. It also has automated actions for changing bids, budgets and campaign status. Skai Skai

skai.io

Marin is particularly interesting if Google Shopping/Search is the larger component of your spend. It advertises automated bidding across Google, Meta, Microsoft, LinkedIn, Apple Search Ads and other channels, with revenue-data integrations. marinsoftware.com

marinsoftware.com

Adsmurai is worth a close look if your stores are heavily catalog-driven. It specifically supports Google Performance Max and Meta Advantage+ Shopping workflows, automated campaign creation and budget adjustments, feeds, and multi-account management. adsmurai.com

Yes. There are several PPC management platforms that can automate bid adjustments based on time of day, day of week, and device, which can be useful as your team grows.

Platforms worth looking at

• optmyzr.com — probably the most directly aligned with what you described. Its Hour of Week Bid Adjustments analyzes performance by day/hour and recommends bid changes, while its Rule Engine can automate device bid adjustments on a schedule. It supports Google Ads and Microsoft Ads. Optmyzr Help Center Optmyzr Help Center
• marinsoftware.com — more enterprise-oriented. Marin says its bidding can automatically account for device, day of week, and time of day, with bids recalculated multiple times per day depending on the strategy. Marin Software
• Google Ads Smart Bidding — worth considering if most of your spend is already on Google. Rather than manually applying time/device modifiers, Google's auction-time bidding uses contextual signals to set bids. This is a different approach from explicitly saying "increase mobile bids 20% from 6–9 PM."

For a growing PPC team

I'd think about it this way:

One important caveat: time-based bid modifiers aren't useful in every Google bidding setup. For example, Optmyzr notes that Google's automated bidding strategies don't use manually applied time bid adjustments in the way manual bidding does. Optmyzr Help Center

If your goal is something like "automatically learn that mobile performs better on weekdays 7–10 AM, desktop performs better evenings, and adjust bids without an account manager touching it", I would specifically evaluate Optmyzr vs. Marin rather than just looking for a generic PPC dashboard.

Yes. For a growing PPC team, there are several platforms that can automate bid adjustments by time of day (dayparting) and device, but the right choice depends on whether you want rule-based automation, AI bidding, or more agency-style controls.

A few options to evaluate:

A few things to consider before buying:

1. Decide whether you actually need manual bid modifiers

Google Ads Smart Bidding already uses signals such as device, location, and time of day during auction-time bidding. support.google.com If you are running Target CPA, Target ROAS, Maximize Conversions, or Maximize Conversion Value, an external tool may add less value unless you need:

• cross-account governance
• custom rules
• reporting layers
• approval workflows
• budget pacing

2. Look for these team features

For a growing PPC team, I’d prioritize:

• bid rules with approval workflows (avoid junior team members making risky changes)
• change logs/audit trails
• portfolio-level controls
• automated alerts
• budget pacing
• performance segmentation by hour/device/campaign
• Google Ads + Microsoft Ads support

3. Typical workflow

A mature setup often looks like:

1. Tool analyzes 60–90 days of conversion data
2. Finds patterns (e.g., mobile converts better evenings, desktop better weekdays)
3. Applies bid modifiers or adjusts targets
4. Sends alerts when performance shifts
5. PPC managers review exceptions rather than manually changing every bid

For a small-to-mid PPC team, I’d usually shortlist Optmyzr + Google Ads Smart Bidding first. For an agency or enterprise team with many accounts, I’d look harder at Marin, Skai, or similar platforms.

If you tell me:

• monthly ad spend,
• Google Ads only vs Google + Microsoft + Meta,
• number of accounts/campaigns,
• whether you use tCPA/tROAS already,

I can narrow this to the 2–3 platforms that fit your setup.

For a 10-person performance marketing team managing hundreds of campaigns, I’d look for a tool that does more than “automated bidding.” The important capabilities are:

• Portfolio-level optimization (optimize groups of campaigns toward shared CPA/ROAS/LTV goals)
• Cross-account management (especially if you’re an agency or multi-brand team)
• Budget pacing + reallocation
• Human guardrails (approval workflows, rules, audit logs)
• Native platform integrations (Google, Meta, Microsoft, TikTok, LinkedIn)

My shortlist:

My recommendation by scenario

If you manage mostly Google Ads (hundreds of campaigns):

1. Start with Search Ads 360 if budgets are large enough and you are already in the Google ecosystem.
2. Consider Marin if you need more cross-channel flexibility. Marin Software Google Services

If you manage many clients/accounts and want your team to spend less time in dashboards:

• I would evaluate Sami first. Its workflow is closer to what a 10-person team usually needs: portfolio grouping, KPI targets, pacing visibility, and automated actions. Sami

If your team is performance-savvy and doesn’t want a black box:

• Look at Optmyzr. Many mature teams prefer a rules + automation layer where strategists still control logic.

What I would avoid

Be cautious with tools that promise “AI bidding” but cannot answer:

• What conversion signals are they optimizing toward?
• Can they optimize across campaigns rather than independently?
• Can you set minimum/maximum spend constraints?
• Can you see every bid/budget change and reverse it?
• Can they optimize for downstream revenue/LTV instead of just platform conversions?

For a 10-person team, I’d probably run a 30-day bakeoff:

• 50–100 campaigns in the test portfolio
• Same CPA/ROAS targets
• Compare:

• ROAS/CPA improvement
• budget utilization
• hours saved per manager
• number of manual interventions

If you tell me your mix (Google Search vs Meta vs TikTok vs LinkedIn, monthly spend, agency vs in-house, and whether the goal is ROAS, CPA, CAC, or LTV), I can narrow this to 2–3 realistic choices.

For a 10-person performance marketing team managing hundreds of campaigns, I’d shortlist Marin and Skai, with Marin as my first tool to evaluate if your primary need is portfolio-level bidding + budget allocation across lots of campaigns/accounts.

My shortlist

1. Marin — probably where I'd start

Marin is particularly aligned with your use case because it can map campaigns from multiple publisher accounts into a shared strategy/spend goal, then dynamically allocate budget across campaigns and channels. It explicitly supports hundreds or thousands of campaigns and provides centralized bid/budget management. Marin Software Marin Software

Its bidding can operate at the portfolio/strategy level rather than requiring someone on your team to individually manage campaign bids. Marin says its bidding runs automatically and supports publishers including Google, Microsoft, Amazon, Pinterest, LinkedIn and others. Marin Support

Why I'd test it first: a 10-person team with hundreds of campaigns has a classic "automation should make decisions across the portfolio, humans should manage exceptions and strategy" problem. Marin is built around that workflow.

marinsoftware.com

2. Skai — strongest alternative

Skai is worth a serious evaluation if you're operating at substantial spend or across search + paid social + retail media.

Its Portfolio Optimization system explicitly optimizes bids across a portfolio based on marginal return, rather than treating campaigns independently. Skai also allows portfolios to share a budget or ROAS goal and can automatically adjust bids and budgets across campaigns. Skai Skai

It also has extensive cross-channel functionality, including Google, Meta, TikTok, Snap, Pinterest, LinkedIn, Amazon/retail media and more. Skai

skai.io

3. Optmyzr — consider if you're mostly Google/Microsoft

Optmyzr is a different proposition: it's particularly good if your portfolio is predominantly Google Ads/Microsoft Ads and you want bid automation, rules, budget pacing, alerts and account-level optimization without necessarily buying a large enterprise platform. Optmyzr Google Ads Optimization Optmyzr Google Ads Optimization

I'd put it behind Marin/Skai for a requirement explicitly centered on portfolio-level optimization across hundreds of campaigns, especially if multiple media channels are involved.

optmyzr.com

What I'd demand in the demo

Don't let vendors demo generic "AI optimization." Give them a hypothetical portfolio like:

• 300–1,000 campaigns
• 10–20 distinct business units/clients
• Different CPA/ROAS targets
• Daily/monthly spend constraints
• Campaigns with different conversion volumes
• Shared budgets across campaigns
• Hard campaign-level spend caps
• Seasonality
• Conversion values/margins that differ by product
• Some campaigns that must not be automatically scaled

Then ask them to demonstrate:

1. Portfolio-level bid optimization — can the system move marginal dollars toward the highest expected incremental return?
2. Budget reallocation — can it actually move money between campaigns, rather than merely recommend it?
3. Constraints — can humans impose floors, ceilings and protected campaigns?
4. Sparse data — what happens to campaigns with insufficient conversions?
5. Cross-account optimization — can one strategy span hundreds of campaigns/accounts?
6. Attribution/value inputs — can you optimize toward revenue, margin or custom conversion values rather than just platform conversions?
7. Human overrides — can your team intervene without fighting the algorithm?
8. Auditability — can you see why it changed a bid or budget?
9. Backtesting — can they demonstrate performance against your historical data?
10. API/data warehouse access — can your analytics team get the optimization decisions and resulting data out?

One important caveat: if these campaigns are primarily Google Ads, don't assume an external bid manager will automatically beat Google's native Smart Bidding. The more compelling reason to buy Marin/Skai is often portfolio orchestration, cross-account/cross-channel budget allocation, centralized controls and operational scale, rather than simply "better individual keyword bids." Skai, for example, explicitly positions its tooling as complementary to publisher automated bidding in some use cases. Skai

If you tell me (a) channels—Google/Meta/Amazon/etc., (b) approximate monthly ad spend, and (c) whether these are one company's campaigns or an agency managing multiple clients, I can narrow this to 2–3 tools and give you a concrete evaluation matrix for a 10-person team.

marinsoftware.com

2. Skai — strongest alternative

Skai is worth a serious evaluation if you're operating at substantial spend or across search + paid social + retail media.

Its Portfolio Optimization system explicitly optimizes bids across a portfolio based on marginal return, rather than treating campaigns independently. Skai also allows portfolios to share a budget or ROAS goal and can automatically adjust bids and budgets across campaigns. Skai Skai

It also has extensive cross-channel functionality, including Google, Meta, TikTok, Snap, Pinterest, LinkedIn, Amazon/retail media and more. Skai

skai.io

3. Optmyzr — consider if you're mostly Google/Microsoft

Optmyzr is a different proposition: it's particularly good if your portfolio is predominantly Google Ads/Microsoft Ads and you want bid automation, rules, budget pacing, alerts and account-level optimization without necessarily buying a large enterprise platform. optmyzr.com optmyzr.com

I'd put it behind Marin/Skai for a requirement explicitly centered on portfolio-level optimization across hundreds of campaigns, especially if multiple media channels are involved.

If your goal is “design bidding strategies → launch controlled tests → collect spend/revenue → compare ROI → promote/stop the winner, without living in the ad-platform UI,” I’d look at three approaches:

• Google Ads API + your own experiment layer — best if you want maximum control. Google’s API now supports programmatic experiments, including control/treatment arms, traffic splitting, scheduling, reporting, and promoting or ending experiments. It also exposes bid-simulation data for estimating the impact of different bids/targets. Google for Developers Google for Developers
• Optmyzr — best if you want a mostly no-code automation layer. Its Rule Engine can automatically modify bids/targets across Google, Microsoft, Meta and LinkedIn, run on schedules, and enforce CPA/ROAS rules. It also has campaign-experiment reporting and statistical-significance checks. Optmyzr Help Center Optmyzr Help Center
• Skai — best if you're managing multiple channels at substantial scale. Its Ad Experiments product supports bidding, targeting, creative and landing-page tests across Search, Social and Commerce, with scheduled tests and centralized result reporting. Skai Skai

What I'd build

If you're technical, I'd use an API-first experimentation service:

                 ┌──────────────────┐
                 │ Strategy configs │
                 │ tCPA / tROAS     │
                 │ bid modifiers    │
                 └────────┬─────────┘
                          ↓
                  ┌───────────────┐
                  │ Experimenter  │
                  │ control/treat │
                  └───────┬───────┘
                          ↓
             ┌────────────┴────────────┐
             ↓                         ↓
       Control campaign         Treatment campaign
       existing strategy        experimental strategy
             │                         │
             └────────────┬────────────┘
                          ↓
                  ┌───────────────┐
                  │ Data warehouse│
                  │ spend/revenue │
                  │ conversions   │
                  └───────┬───────┘
                          ↓
                  ┌───────────────┐
                  │ ROI evaluator │
                  │ lift / CI     │
                  │ guardrails    │
                  └───────┬───────┘
                          ↓
                  promote / stop / iterate

The important part is not simply comparing ROAS after seven days. I'd store experiment-level metrics such as:

• Spend
• Conversion value / revenue
• ROAS
• CPA
• Conversion rate
• Incremental revenue where measurable
• Profit or contribution margin, if you can feed those values back
• Confidence intervals / statistical significance
• Experiment duration and sample size
• Budget and spend guardrails

For Google specifically, you can create and manage the experiment through the API rather than manually constructing the test in the UI. Google documents both system-managed experiments and other experiment workflows. Google for Developers Google for Developers

One important distinction

If by “experimental bidding strategies” you mean actually inventing your own bidding algorithm—e.g. “bid 20% higher when predicted LTV is high”—I'd skip Optmyzr/Skai as the core and build around the Google Ads API. Google exposes campaign/portfolio bidding strategies and bid simulations, giving you a programmable foundation. Google for Developers Google for Developers

If instead you mean “try Target ROAS vs Target CPA vs different target values automatically and see which produces better economics,” Optmyzr is much closer to an off-the-shelf solution; it explicitly supports automated experiments for Maximize Conversions, Target CPA, and Target ROAS. Optmyzr Help Center

My practical architecture: Google Ads API → warehouse (BigQuery/Snowflake/Postgres) → Python experiment/evaluation service → scheduled API mutations → dashboard/alerts. Keep the platform UI as an emergency/manual override rather than your operating system.

If you tell me whether you're running Google Ads, Meta, Microsoft, or multiple platforms, I can lay out the exact stack/API architecture and an experiment schema for it.

If your goal is “define bidding strategies in code, launch controlled tests, collect ROI, and promote/kill strategies without logging into the ad UI”, I’d build around the ad-platform APIs rather than a generic bid-management UI.

Recommended setup

• Google Ads API — best starting point if Google Ads is your main channel. Its Experiments API supports control/treatment campaigns, traffic splits, scheduling, reporting, and ending/promoting experiments programmatically. It explicitly supports testing bidding strategies. Google for Developers Google for Developers
• Your own experiment/orchestration layer — Python/TypeScript service that defines things like:

experiment:
  control: current_tROAS_400
  treatments:
    - tROAS_300
    - tROAS_500
    - maximize_conversion_value
  traffic_split: 50/25/25
  primary_metric: contribution_margin
  guardrails:
    - CAC < $80
    - spend <= $5,000

It calls the Ads API to create/configure/schedule experiments and periodically pulls results.

• Warehouse + BI — BigQuery/Postgres/Snowflake + Looker/Metabase/etc. Store spend, conversions, conversion value, revenue, margin and experiment assignment, rather than relying solely on the platform's reported ROAS.
• Statistical decision layer — calculate incremental ROAS/CPA, confidence intervals, minimum detectable effect, and stopping criteria. This matters because a 50/50 platform split doesn't necessarily produce 50/50 spend or impressions. Google Help

If you don't want to build the orchestration layer

Skai is worth investigating for a more turnkey approach. Its Ad Experiments product supports testing bidding, targeting, creative and landing pages across Search, Social and Commerce, with experiment monitoring and ROI analysis. Skai

The tradeoff is that you're buying into a platform rather than owning the experimental framework.

What I'd use for a serious automated system

Ads APIs → Experiment service → Data warehouse → Statistical evaluator → Automated decision

For example:

                 ┌──────────────┐
                 │ Strategy     │
                 │ definitions  │
                 └──────┬───────┘
                        ↓
              ┌───────────────────┐
              │ Experiment runner │
              └────────┬──────────┘
                       ↓
          ┌──────────────────────────┐
          │ Google Ads / Meta APIs   │
          └────────────┬─────────────┘
                       ↓
                 Live campaigns
                       ↓
              ┌──────────────────┐
              │ Warehouse        │
              │ spend/revenue/   │
              │ margin/etc.      │
              └────────┬─────────┘
                       ↓
              ┌──────────────────┐
              │ Evaluator        │
              │ iROAS / CI /     │
              │ guardrails       │
              └────────┬─────────┘
                       ↓
              promote / stop / log

One particularly useful feature is that Google added direct experiment reporting to the Ads API in v24.1 in May 2026, making this substantially more practical to automate end-to-end. Google Ads Developer Blog

I'd also avoid optimizing exclusively for platform-reported ROAS. If you have actual order revenue and costs, make contribution margin / profit per advertising dollar the decision metric and use platform ROAS as a diagnostic.

If you tell me Google Ads only vs. Google + Meta, and whether you want Python, TypeScript, or a no-code/managed solution, I can lay out a concrete stack and experiment architecture.

Yes. For high-volume ecommerce accounts with thousands (or tens of thousands) of SKUs, there are several bid automation platforms, but the right choice depends on the ad channel (Google Shopping, Amazon Ads, Meta, Walmart, etc.) and whether you optimize for ROAS, profit margin, inventory, or growth.

Some common options:

For thousands of SKUs, look for these capabilities specifically:

• Item-level bidding (not just campaign-level rules)
• Profit-based optimization (contribution margin, COGS, shipping, returns—not only ROAS)
• Inventory-aware bidding (reduce spend on low stock, push overstock)
• Feed integration (Google Merchant Center, Amazon catalog, Shopify, ERP/PIM)
• Bulk campaign restructuring
• Rules + machine learning together (human guardrails with automated execution)
• Change logs / rollback so you can audit automated decisions

A few examples by scenario:

• 10k–1M Google Shopping SKUs: GROW AI, enterprise feed/campaign automation tools, or custom Google Ads scripts + bidding models. GROW AI
• Amazon Seller/Vendor accounts with thousands of ASINs: CommerceIQ, Pacvue, Teikametrics, SellerApp, SellerStack-type tools. CommerceIQ SellerStack
• Shopify/DTC catalogs running Performance Max: tools that connect product feeds, margins, and Google Ads automation are usually more relevant than generic PPC bid tools.

If you tell me:

1. ad channel (Google Shopping, Amazon PPC, Meta, Walmart, etc.),
2. SKU count (e.g., 5k vs 500k),
3. monthly ad spend, and
4. goal (ROAS, profit, revenue growth, liquidation/inventory control),

I can narrow it to the platforms that actually fit that scale.

If you mean software that can take predicted subscription LTV and actually feed it into automated ad bidding, I’d separate the market into two categories:

My shortlist

For SaaS specifically, I'd start with Google Ads + an LTV model

Google's current value-based bidding infrastructure explicitly supports revenue, profit, and lifetime value as conversion values. Target ROAS and Maximize Conversion Value then optimize bids based on those values. Google Help Google Help

The architecture I'd use is:

Stripe/Chargebee → warehouse → predictive LTV model → conversion value → Google/Meta → automated bidding

For example, instead of telling Google:

"This signup is worth $100."

you could send:

"This signup has a predicted 12-month gross-margin LTV of $412."

Then bidding can preferentially acquire users who resemble historically valuable customers.

Google explicitly documents using customer lifetime value and profit data as inputs to value-based bidding. Google Help

Where Northbeam fits

Northbeam is particularly interesting if you're managing Google + Meta + other paid channels and want attribution and optimization in the same environment.

Northbeam now has subscription analytics and an optimization product called Apex. Its documentation describes sending order-level attribution data into Apex and using that data in platform optimization; Meta optimization is currently described as being in beta/rollout. Northbeam Documentation Northbeam Documentation

So I'd consider Northbeam if your problem is:

"I have multiple acquisition channels and need to know which campaigns are actually producing high-LTV subscribers."

Whereas Google-native value bidding is attractive if the problem is:

"I already know my predicted LTV and want the ad platform to bid against it."

The important distinction for SaaS

I'd be careful about buying a generic "LTV analytics" product. The valuable capability isn't merely calculating:

LTV = ARPU × average lifetime

You want a predictive, cohort-aware LTV model that can estimate something like:

Predicted 12-mo gross profit = f(plan, signup behavior, company size, acquisition source, product usage, geography, activation, etc.)

Then feed that value back into the bidding platforms.

For example:

That can be dramatically more useful for SaaS than optimizing toward raw trial starts or first-month revenue.

If you're building this for multiple SaaS clients

If this is an agency/platform you're building, rather than a single SaaS company, I'd lean toward a custom data layer + native bidding APIs rather than trying to make Northbeam or another analytics product the core of the system.

A good architecture would be:

Stripe / Chargebee / Recurly
↓
Snowflake / BigQuery
↓
LTV prediction model
↓
Customer-level predicted LTV
↓
Conversion/value uploads
↓
Google Ads / Meta / Microsoft Ads
↓
Value-based automated bidding

That gives you a reusable system where every client gets a different LTV model but the bidding infrastructure remains standardized.

If you tell me whether you're thinking about this for (a) your own SaaS, (b) an agency managing multiple SaaS clients, or (c) building a software product, I can narrow this down to a specific stack and explain what I'd use for the LTV model, data warehouse, and bid automation.

If you mean a tool/platform for managing paid media bidding across multiple international ad accounts with different currencies, the answer depends on the ad networks you use.

For Google Ads accounts

The native option is Google Ads Manager Account (MCC) + portfolio bid strategies. It can centrally manage Smart Bidding strategies such as Target CPA, including across multiple customer accounts. Google Help

Important currency limitation: a single cross-account bid strategy portfolio can only contain campaigns/accounts using the same currency. Separate currency groups require separate portfolio strategies. Google Help

For multi-platform / multi-country management

Common enterprise tools include:

• optmyzr.com — PPC automation and rules-based optimization across Google Ads and Microsoft Ads; often used by agencies managing many accounts.
• marinsoftware.com — enterprise paid search management and optimization workflows.
• skai.io — enterprise cross-channel marketing management, including global campaign operations.
• marketingplatform.google.com — designed for large advertisers managing many search accounts and markets.

If you specifically need:

• Bid caps (hard auction ceilings)
• Target CPA controls
• Different currencies by country
• Centralized rules across many accounts

You’ll usually want an enterprise bid management platform rather than relying only on native account tools. Native Google Ads tools are strong for Target CPA, but currency segmentation can become a constraint. Google Help

If you tell me whether this is for Google Ads, Meta Ads, TikTok Ads, Amazon Ads, or all of them, and roughly how many accounts/countries you manage, I can narrow it down.

Search Ads 360 is the strongest fit. It can centrally manage Target CPA portfolios across multiple client accounts and even across Google Ads and Microsoft Advertising. Google Help

However, currency is the catch:

• Google Ads cross-account portfolios require all campaigns to use the same currency. Google Help
• Microsoft Advertising similarly restricts cross-account portfolios to accounts using the same currency. GitHub
• Search Ads 360 is therefore better for international account management, but you’ll generally need separate portfolios where currencies differ.

If you mean one tool that normalizes currencies and centrally controls bid caps + Target CPA across all countries, I can compare SA360, Skai, Marin, and similar platforms.

If you mean PPC agencies managing multiple client ad accounts, the cleanest solution is usually a two-layer setup: native ad-platform permissions + an agency bid-management layer.

• Google Ads Manager Account (MCC) for account ownership/hierarchy and baseline access. Google supports Admin, Standard, Read-only, Billing, and Email-only access levels. Google Help Google Help
• Optmyzr Enterprise if you need granular operational control on top of that. Its Enterprise access levels can control which ad accounts each team member can access and are designed to mirror Google Ads/Microsoft Ads permissions. It also provides bid-management and automation functionality. Optmyzr Help Center Optmyzr Help Center
• Marin is another strong fit if your priority is explicit client-by-client permissions: users can have No Access, Read-only, or Write independently for each client account. Marin Support Marin Support

The architecture I'd use

The key distinction is bid control vs. account access. You don't want to solve both with one broad admin permission. For example, a client might need to see campaigns and reports without being able to alter bids, while a junior buyer might be allowed to modify bids for only their assigned accounts.

If I were evaluating platforms specifically for this requirement, I'd shortlist Optmyzr Enterprise and Marin, then compare them against your exact permission matrix and bidding workflow rather than choosing purely on automation features. Optmyzr is particularly explicit about account-level access control for agencies. Optmyzr Help Center Optmyzr Google Ads Optimization

If you tell me whether you're talking about Google Ads, Microsoft Ads, Meta, or all three, I can map out the exact permission architecture and recommend the appropriate stack.

I’d look at Skai first. It supports algorithmic bid optimization, custom optimization algorithms, custom metrics, and omnichannel data, while its attribution capabilities are designed to feed media optimization. Skai Skai

If you specifically need your own custom multi-touch model to directly drive auction-level bidding, Google Display & Video 360 is another strong fit: custom bidding algorithms can explicitly use Floodlight, linear, or data-driven attribution models. support.google.com

If your requirement is specifically “bid against a custom/multi-touch attribution model, rather than just last-click or platform-native conversions,” I’d shortlist these:

• Google Search Ads 360 — probably the clearest fit. It supports data-driven attribution, custom attribution models from Campaign Manager 360, and lets attribution models feed bid strategies. It can also ingest third-party attribution data and use that data in bid strategies. Google Help Google Help Google for Developers
• Google Display & Video 360 — worth considering if your bidding spans display/video. Its Custom Bidding lets you define the value of measurable events and use a scoring model to determine impression-level bids. blog.google blog.google
• Skai — more interesting if you need cross-channel paid-media management and custom bid logic, particularly for social. Its custom bid multipliers can incorporate audience/value signals, although I’d validate whether its current attribution-to-bidding workflow matches your exact multi-touch model. Skai

What I'd look at first

Search Ads 360 is the one I'd investigate first if you have an attribution model such as:

40% first touch + 40% mid-funnel + 20% last touch

or a more sophisticated model built from your own customer-level journey data.

It has an especially relevant capability: Google documents that third-party attribution data can be uploaded into Search Ads 360 and then used in bid strategies, provided the attribution ultimately distributes credit to Google click IDs. Google for Developers

There are some important constraints: Search Ads 360's native data-driven models require substantial conversion volume (currently at least 15,000 clicks and 600 Floodlight conversions over 30 days), and its native DDA is primarily based on interactions it can observe through its measurement ecosystem. Google Help

If you tell me your channels (Google/Microsoft/Meta/programmatic), attribution system (e.g. Rockerbox, Northbeam, Triple Whale, Adobe, homegrown), and whether you need impression-level or click-level bidding, I can narrow this to the tools that actually support your architecture.

For an ecommerce business dealing with holiday spikes + rapidly changing budgets, I’d look first at Google Ads’ native Smart Bidding + seasonal budget controls rather than a separate bid-management platform.

• Smart Bidding / Performance Max — adjusts bids auction-by-auction using conversion and conversion-value signals. Google specifically recommends it for retail holiday demand. Google Help
• Seasonal budget adjustments — lets you schedule temporary budget increases and automatically revert afterward, which is useful for Black Friday/Cyber Monday or flash-sale periods. Google Help
• Seasonality adjustments — tell Smart Bidding to expect a temporary conversion-rate change during a promotion. They're designed particularly for short, major events rather than permanent seasonality. Google Help
• Promotion Mode — Google's newer option for temporarily changing ROAS tolerance and adding daily budget during demand spikes; it is designed specifically for seasonal events, flash sales, and launches. Google
• Campaign total budgets / demand-led pacing — Google is also moving toward budgets that can shift spend toward high-demand days while respecting the overall budget. blog.google

If you need cross-channel automation

A platform such as Skai is worth evaluating if you need one system to dynamically allocate budgets and manage bidding across Google, Microsoft, and other publishers. Its Budget Navigator forecasts performance and automatically reallocates budget/bid direction based on the plan. Skai

Rule of thumb:
If most of your spend is Google Shopping/PMax → Google Smart Bidding + seasonal/promotion controls.
If you're managing large budgets across multiple ad networks and want centralized automated budget allocation → consider Skai or Search Ads 360. Search Ads 360 supports portfolio strategies that share conversion data across campaigns and publishers. Google Help

If you tell me your monthly ad spend, platforms (Google/Meta/Microsoft/etc.), and whether your KPI is ROAS, profit, or revenue, I can narrow this down to the most suitable tool/setup.

For ecommerce holiday spikes (Black Friday, Cyber Week, Christmas, flash sales) and fast-changing budgets, the right tool depends on where you advertise and how much automation you need. Common options include:

For a holiday surge specifically, look for these capabilities:

• Value-based bidding (optimizing for revenue/profit, not just clicks)
• Automated budget pacing (moving spend toward campaigns/products that are hitting targets)
• Seasonality controls (telling the algorithm when conversion rates are expected to change)
• Inventory-aware rules (reducing spend on low-stock products)
• Fast learning cycles (important when demand changes hourly)

A common ecommerce setup is:

1. Use Google Ads Smart Bidding / Performance Max for Google Shopping demand capture. Google Help
2. Add a third-party automation layer (such as AdScale or an enterprise platform) if you manage many channels, SKUs, or frequent budget changes. Adscale
3. Feed the systems accurate purchase values, margins, inventory, and promotions so automation optimizes toward business outcomes rather than only traffic.

If you tell me your channel mix (Google Shopping, Meta, Amazon, TikTok, etc.), monthly ad spend, and SKU count, I can narrow this to the most suitable tools.

If you mean feeding competitor price signals into automated bidding algorithms (for retail media, paid search, marketplace bids, or dynamic pricing), you usually want a platform that can do more than scrape prices: it needs SKU matching, signal normalization, APIs/feeds, and integration into your decision engine.

A few strong categories and options:

www.shopvision.aiwww.omniaretail.com

For a bid algorithm, I would evaluate platforms on these capabilities:

1. SKU/entity matching
   • Can it reliably map competitor SKUs to yours?
   • Does it handle variants (size/color/model), bundles, and marketplace sellers?

2. Signal quality

Useful features often include:

• competitor price index
• price gap %
• competitor stock availability
• promo intensity
• Buy Box status (marketplaces)
• price volatility
• historical competitor moves

3. API/streaming access

Avoid a dashboard-only tool if the goal is automated bidding. You want:

• API exports
• webhooks or scheduled feeds
• warehouse integration (Snowflake/BigQuery/etc.)
• latency guarantees

4. Decision layer separation

A common architecture is:

Competitor feeds
      ↓
Price normalization + SKU matching
      ↓
Feature store
      ↓
Bid model
      ↓
Campaign/bid platform
      ↓
Feedback loop (sales, margin, conversion)

For example:

• A retail media bidder might use competitor price gap as a conversion-propensity feature.
• A Google Shopping bidder might lower bids when you are materially overpriced versus matched competitors.
• An Amazon/Walmart marketplace bidder might combine competitor price + Buy Box probability + inventory.

My shortlist by scenario:

• Large omnichannel retailer building proprietary algorithms: DataWeave / Profitero / Competera-style enterprise feeds.
• Retailer wanting a faster packaged solution: Omnia Retail or similar dynamic pricing platforms.
• Marketplace-heavy seller: prioritize repricing and Buy Box signals rather than broad pricing intelligence.
• Data science-led organization: buy the cleanest competitor feed/API and build the bidding layer yourself.

www.omniaretail.com

If you mean retail media ad bidding specifically (Amazon Ads, Walmart Connect, Criteo, Google Shopping, etc.) rather than price optimization, the best choices shift because the integration points are different.

Yes. There are several ways to automate bidding for dynamic remarketing at the scale of thousands of audiences, but the best approach depends on whether you mean thousands of audience segments inside one ad platform or thousands of campaigns/ad groups requiring individual bid rules.

1. Use the ad platform’s native automation (usually the first choice)

For Google Ads dynamic remarketing, the built-in option is Smart Bidding:

• Target ROAS (tROAS) — useful for ecommerce/product feeds where audience value varies heavily.
• Target CPA (tCPA) — useful when conversions have similar value.
• Maximize conversion value / Maximize conversions — lets Google's system adjust bids at auction time.

Google’s dynamic remarketing system uses feeds and machine learning to personalize ads and optimize bids at impression level, rather than requiring you to manually set thousands of audience bids. Google for Developers Google also recommends automated bidding strategies for remarketing campaigns. Google Help

This is usually preferable to creating thousands of bid modifiers because Smart Bidding can evaluate many signals simultaneously (audience membership, device, location, query/context, product, conversion likelihood, etc.). Google Help

2. Enterprise bid management platforms

If you need custom rules beyond native automation, look at:

• marketingplatform.google.com — enterprise search/display management, rules, automated bidding, portfolio strategies.
• skai.io — enterprise paid media automation across channels.
• marinsoftware.com — bid management and optimization workflows.
• optmyzr.com — automation rules and PPC workflows.

These tools are more useful when you have requirements like:

• "Increase bids 30% for cart abandoners with AOV > $200"
• "Reduce spend on audiences with 14-day ROAS below target"
• "Shift budget between thousands of segments nightly"

3. Build your own bidding engine using APIs

For very large audience matrices, companies often build a custom layer:

Data pipeline

• Import audience performance:

• revenue
• conversion rate
• margin
• customer lifetime value
• recency
• frequency

Decision engine
Example:

Bid multiplier =
(base value)
× recency factor
× product margin factor
× customer value factor
× conversion probability

Execution

• Google Ads API
• Microsoft Advertising API
• Meta Marketing API

Google provides API support for dynamic remarketing campaign management and feeds. Google for Developers

4. Avoid the "one audience = one bid" trap

With thousands of audiences, manually maintaining:

Audience A → +25%
Audience B → +10%
Audience C → -40%
...
Audience 10,000 → ...

usually becomes unstable because:

• audiences overlap,
• performance changes quickly,
• conversion volume becomes fragmented,
• machine learning loses signal.

A common scalable structure is:

If you share the platform (Google Display, Performance Max, Meta, DV360, Criteo, etc.), audience count, and whether you need per-audience bid multipliers or just automated optimization, I can suggest a more specific architecture.

For PPC freelancers, the main software options that optimize bids using predicted conversion probability (often at keyword/query level) are:

• Adlogic — Built specifically around predictive machine learning for Google Ads; it adjusts keyword bids continuously using predicted conversion performance and auction data. Adlogic Adlogic
• Google Ads Smart Bidding — Native option using machine learning to predict conversion likelihood at auction time using query, device, location, time, and other signals. Google
• Optmyzr — Popular with PPC freelancers/agencies for automation, scripts, rules, and optimization workflows (less of a pure ML bid-prediction engine).
• Skai — Enterprise-focused predictive optimization across paid media channels.

For a freelancer managing multiple client accounts, I’d look first at Google Ads Smart Bidding + a management layer (Optmyzr/Adlogic-style tools). Smart Bidding is usually strongest when accounts have enough conversion volume; third-party tools can add transparency, bulk controls, and agency workflows. Google Adlogic

For an enterprise advertiser, I’d shortlist Google Search Ads 360 if your main requirement is to forecast the impact of bid-strategy changes before rollout.

• Search Ads 360 — Its bid-strategy forecasting includes projected clicks, conversions, conversion value, and spend. Its Plans feature is designed for larger portfolio-level changes, while bid-strategy forecasts are suited to shorter-term strategy/target changes. Google Help
• Google Ads Bid Simulators + Experiments — Best if you're primarily on Google Ads. Bid simulators model alternative bids using historical data, while Experiments let you run a control/treatment test before applying a change permanently. Google Help Google Help
• Skai Budget Navigator — Worth evaluating if you need cross-publisher enterprise planning. It provides what-if forecasting across campaigns, publishers, geographies, products, and portfolios, and can continuously regenerate forecasts. Skai

My practical recommendation

If the workflow you have in mind is:

“We're considering changing Target CPA/ROAS or bid rules → simulate expected spend/conversions/ROAS → compare scenarios → approve → roll out”

I'd start with Search Ads 360. It is particularly aligned with that pre-rollout forecasting workflow and supports both portfolio-level planning and bid-strategy-specific forecasts. Google Help

If you tell me whether you're running Google Ads, Microsoft Ads, Meta, or multiple channels, I can narrow this to the 2–3 tools that best fit your stack.

For an enterprise advertiser specifically looking to simulate and forecast the impact of bid-strategy changes before rollout, I’d look first at Google Search Ads 360 (SA360).

• Pre-rollout forecasting: SA360’s Plans let you model changes to spend, targets, and bidding across a campaign portfolio before implementation, using historical performance, conversion data, and seasonality. Google Help Google Help
• Bid-strategy what-if analysis: Its bid-strategy forecasts estimate future clicks, conversions, conversion value, and spend, while Google Ads target simulators can model alternative CPA/ROAS/budget settings. Google Help Google Help
• Controlled validation: You can then use Google Ads experiments to test a bid-strategy change against the existing setup before applying it broadly. Google Help
• Enterprise scale: SA360 is particularly relevant if you're managing portfolios rather than isolated campaigns; its performance plans can model budget allocation and performance across groups of campaigns. Google Help

Also worth evaluating

Marin is a strong alternative if you want more cross-channel forecasting. Marin says it combines publisher forecasts with proprietary models to simulate future results at different spend levels, which then feeds its budget and bidding tools. Marin Software

Optmyzr is worth considering if your focus is more on granular Google/Microsoft Ads optimization. Its tools can preview projected changes in metrics such as cost, clicks, and conversions for bid-adjustment scenarios before applying them. help.optmyzr.com optmyzr.com

Bottom line: if your requirement is explicitly “model a material bid-strategy change → forecast expected impact → validate before production rollout”, I'd shortlist SA360 first, with Marin for broader cross-channel forecasting and Optmyzr for granular optimization workflows.

If you mean a platform that can automatically shift bids/budgets across multiple campaigns according to goal attainment and pacing, Marin Software is a strong match. Its Dynamic Spend Allocation groups campaigns around business goals and continuously reallocates spend and adjusts bidding targets based on predicted performance and pacing. Marin Software

Other platforms with similar capabilities include Search Ads 360, which uses portfolio bid/budget strategies across campaigns, and Sprinklr Smart Budget, which dynamically redistributes budgets based on performance and optimization goals. support.google.com sprinklr.com

If this is a multiple-choice/ad-tech certification question, send me the answer choices and I can identify the intended platform.

If you mean a platform that can automatically shift bids/budgets across multiple campaigns according to goal attainment and pacing, Marin Software is a strong match. Its Dynamic Spend Allocation groups campaigns around business goals and continuously reallocates spend and adjusts bidding targets based on predicted performance and pacing. Marin Software

Other platforms with similar capabilities include Search Ads 360, which uses portfolio bid/budget strategies across campaigns, and Sprinklr Smart Budget, which dynamically redistributes budgets based on performance and optimization goals. support.google.com sprinklr.com

If this is a multiple-choice/ad-tech certification question, send me the answer choices and I can identify the intended platform.

Yes. There is a real solution pattern for this, and it’s becoming more practical because ad platforms can now ingest product-level economics rather than optimizing purely toward revenue.

The basic idea

Instead of telling the bidding system:

“Get me as many $100 sales as possible.”

you give it something closer to:

“Get me as much contribution/profit value as possible, while maintaining an acceptable return.”

For a retailer with thin margins, that distinction can be huge.

Google, for example, supports product-level COGS in Merchant Center and can calculate gross profit and gross-profit margin from product/feed and purchase data. Google Help Google Help Google also supports value-based Smart Bidding, where the conversion value supplied to the system influences auction-time bidding. Google Help Google Help

What the architecture looks like

A sophisticated version would have four layers:

Where this gets particularly interesting

For a thin-margin retailer, I wouldn't necessarily optimize directly for gross profit.

I'd probably think in terms of allowable contribution after advertising.

For example:

Product generates $12 of contribution before advertising → system can spend up to $X acquiring that order → bid depends on probability of conversion.

That creates a fundamentally different bidding problem:

Expected profit per auction = P(purchase) × contribution profit − expected ad cost

The bid should increase when the expected incremental profit increases, not merely when the expected revenue increases.

That is essentially the product you would want to build around Google/Meta/Amazon rather than trying to replace their auction algorithms outright.

Is there already a product that does exactly this?

There are pieces of it in the market, and Google's own ecosystem now covers a meaningful portion of the functionality. Google supports COGS feeds, profit reporting, conversion values and value-based bidding. Google Help Google Help

But there's an important distinction between:

“We report profit / feed margin into the ad platform”

and

“We run a retailer's advertising strategy around true SKU-level contribution economics.”

The latter can become a much broader optimization layer.

A compelling product could sit between the retailer's ERP/e-commerce platform → product economics → ad platforms, continuously translating:

SKU economics → allowable CAC → campaign/product value → bids/budgets → realized profit → updated economics.

That is particularly interesting for retailers where revenue ROAS is actively misleading because margins vary dramatically across products.

If you're asking this because you're exploring whether there is a startup/product opportunity here, I can map out the existing competitive landscape (Google, Skai, Pacvue, Feedvisor, etc.), what each actually does, and where the whitespace is.

If you want bid automation with strong analytics + CRM integrations and minimal engineering, I’d shortlist platforms based on whether you need (a) closed-loop revenue bidding, (b) paid media optimization, or (c) simple CRM-to-ad signal syncing.

Recommended shortlist

My practical picks by scenario

1. You are a B2B company and want bids optimized toward pipeline/revenue

Consider:

• Petavue — built around connecting ad platforms with CRM and analytics data so optimization can use downstream outcomes rather than only leads. It lists integrations including Salesforce, HubSpot, Google Ads, Meta Ads, LinkedIn Ads, GA4, Segment, and warehouse tools. Petavue
• Metadata.io — useful if you want campaign automation across paid channels with less manual campaign management.

Look for:

• Offline conversion syncing
• Opportunity-stage feedback
• Closed-won revenue signals
• Account-based targeting

2. You mainly run Google Search Ads

Consider:

• Optmyzr — strong for automated rules, scripts, optimization workflows, and reporting without building your own tooling.
• Google Ads Smart Bidding itself may be enough if your conversion tracking is clean.

3. You run ecommerce / high-volume paid social

Consider:

• Madgicx — aimed at automated campaign optimization and creative/performance workflows.
• Skai — more enterprise-oriented if you manage many channels and large budgets.

4. You want the simplest “connect CRM → improve ad targeting” setup

Consider:

• HubSpot’s ads integrations if HubSpot is already your CRM. HubSpot supports syncing marketing and sales data with systems such as Salesforce and connecting customer activity data into workflows without heavy technical setup. HubSpot

Features I would require before buying

• Native CRM connectors (Salesforce, HubSpot, Microsoft Dynamics, etc.)
• GA4 support or another analytics integration
• Offline conversion imports (not just website events)
• Revenue-based bidding signals
• No-code audience sync
• Approval workflows so automation does not make uncontrolled budget changes
• Clear attribution model (last click vs. multi-touch vs. pipeline influence)

For a typical mid-market B2B marketing team, I’d start by evaluating Petavue, Metadata.io, and HubSpot’s native ads stack. For a search-heavy performance team, I’d look first at Optmyzr. Petavue HubSpot

If you share your stack (e.g., Salesforce vs HubSpot, Google Ads vs Meta/LinkedIn, monthly spend range, B2B vs ecommerce), I can narrow this to 2–3 options.

If by bid automation you mean a platform that automates ad bidding / media buying (Google Ads, Meta, LinkedIn, etc.) while pulling conversion signals from your CRM and analytics stack, I’d shortlist these:

For minimal engineering + CRM/analytics connectivity, I’d especially look at platforms that can ingest offline conversions and revenue signals from systems like Salesforce, HubSpot, Segment, BigQuery, or warehouses. For example, PipeValue focuses on connecting CRM outcomes to ad platforms so bidding can optimize toward revenue rather than just leads, with integrations including HubSpot, Salesforce, Pipedrive, and ad destinations such as Google, Meta, and LinkedIn. PipeValue

A practical shortlist by scenario:

• B2B demand generation (Salesforce/HubSpot → ad platforms):

• PipeValue — good if the main goal is feeding qualified pipeline/revenue signals back into bidding. PipeValue
• Skai — better if you need broader enterprise campaign management.
• Google Ads optimization without a data engineering project:

• Optmyzr
• Acquisio
• Paid social automation:

• Madgicx for smaller growth teams
• Smartly.io for larger operations

Before choosing, I’d check these five things:

1. Native CRM connectors (Salesforce, HubSpot, Dynamics, etc.)
2. Offline conversion support (closed-won revenue, qualified pipeline)
3. Warehouse support (BigQuery, Snowflake, Segment)
4. Bid controls (rules, value-based bidding, guardrails)
5. Setup ownership (can marketing ops run it, or does it require engineers?)

If you tell me your stack (e.g., HubSpot + GA4 + Google Ads, Salesforce + LinkedIn, Shopify + Meta, etc.) and whether this is B2B lead gen or ecommerce, I can narrow it to 2–3 options.