ChatGPT answers, unedited

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.

If your requirement is specifically “bid optimization driven by a custom attribution model across multi-touch journeys,” I’d look first at Google Search Ads 360 (SA360).

Why SA360 fits

• Custom attribution: You can create data-driven attribution (DDA) models or import custom attribution models from Campaign Manager 360. Google Help Google Help
• Multi-touch paths: DDA distributes conversion credit across interactions in the conversion path rather than simply assigning everything to the last click. Google Help
• Attribution → bidding: SA360 can use attribution-model conversion data directly in bid strategies, including DDA and certain imported/predefined models. Google Help
• Cross-channel data: Floodlight provides deduplicated conversion data, and SA360 can incorporate paid social, display, and other interactions into its attribution framework, subject to the model's supported data. Google Help
• Offline conversions: It can incorporate appropriately uploaded offline conversions into attribution and bidding, which is particularly useful for B2B or longer sales cycles. Google Help Google Help

support.google.com

One important caveat

If by custom attribution model you mean your own externally calculated model—for example, a Markov, Shapley, or proprietary MTA model whose conversion weights you calculate in Snowflake/BigQuery—SA360 isn't necessarily the cleanest solution. Its native custom-model functionality is centered around Search Ads 360 DDA and Campaign Manager 360 attribution models, with specific compatibility limitations. Google Help

In that case, I'd also evaluate Skai. It supports bringing first- and third-party data into its optimization layer and building custom algorithmic optimization rules, which can be more flexible for an externally defined bidding signal. Skai Skai

Short version:

• Need native attribution-to-bidding integration: SA360
• Need a highly customized external data/algorithm layer across multiple publishers: Skai
• Primarily Google Ads and don't need an external MTA model: Google Ads Smart Bidding may be sufficient, since its conversion attribution setting directly affects automated bidding. Google Help

If you tell me which channels you need to bid across (Google, Meta, Microsoft, programmatic, etc.) and where your MTA data lives (GA4, Snowflake, BigQuery, Segment, etc.), I can narrow this down to 2–3 tools that actually support your architecture.

In that case, I'd also evaluate Skai. It supports bringing first- and third-party data into its optimization layer and building custom algorithmic optimization rules, which can be more flexible for an externally defined bidding signal.

Short version:

If you mean a paid-media bidding/optimization platform that can actually use your own multi-touch attribution output as the optimization signal, I’d shortlist these:

• Exactag + your bid manager — probably the closest architectural fit. Exactag says it can use custom metrics, update attribution models daily, and push granular attribution data into bid-management systems at touchpoint, order, campaign, or tactic level. Exactag
• Skai — strong if you want bidding and attribution/measurement in a broader enterprise media platform. Skai supports bringing first- and third-party data into algorithmic optimization and lets advertisers build their own optimization algorithms. Skai Skai
• Breezy PPC — interesting if your primary use case is Google/Bing-style search bidding tied to closed-won revenue. Its enterprise offering describes a portfolio bidding agent targeting a margin target and an attribution agent connecting closed deals back through multi-touch attribution to campaigns/keywords. Breezy PPC
• Cometly — worth considering as the attribution layer rather than assuming it's a full replacement for a sophisticated bid-management platform. It captures multi-touch journeys, supports multiple attribution models, and can send closed-won data back to Meta and Google. Cometly Cometly

What I'd look for technically

The key distinction is between:

“The platform reports on custom attribution”
vs.
“The bidding algorithm actually optimizes toward custom-attributed value.”

For your requirement, you want something capable of this loop:

Ad/touch data → custom MTA model → attributed revenue/value → bid signal → campaign/API → next optimization cycle

For example, if your model says a conversion is worth $400 under your custom multi-touch model, rather than the platform's default last-click $250, that $400 needs to become the value signal driving bids—not merely appear in a reporting dashboard.

My initial recommendation: Exactag if you already have a bid-management stack; Skai if you want a more integrated enterprise platform. The exact choice depends heavily on whether you're optimizing Google/Microsoft Search, paid social, programmatic, or all of them, and whether your custom attribution model lives in a warehouse/CDP, CRM, or attribution platform.

If you tell me your channels (e.g. Google + Meta + LinkedIn) and where the MTA data lives (Snowflake/BigQuery, Salesforce, Adobe, custom model, etc.), I can narrow this to 2–3 platforms and compare their actual ability to feed custom attributed value into bidding, rather than just their attribution reporting.

If your key requirement is “the bidding engine should actually optimize against a custom multi-touch attribution model,” rather than merely report MTA, I’d shortlist these:

• Skai — best enterprise bidding/optimization choice. Skai lets you bring in first- and third-party data and build custom optimization algorithms, then algorithmically optimize bids and budgets around your business goals. It also supports omnichannel paid search, social, retail media, display, and app campaigns. Skai Skai
• Attribution — best if your attribution model is the differentiator. It offers customizable user-level MTA and can push the resulting conversion/value signals back into ad platforms, so Google/Meta bidding can optimize against your attribution rather than the platform's default view. Attribution Attribution
• Google DV360 + Campaign Manager 360 — best native Google stack. You can create custom attribution models in CM360 and use attribution models, including custom/data-driven models, in DV360 custom bidding algorithms. This is probably the cleanest option if most of your spend is Google/programmatic. Google Help Google Help
• Tracyn — interesting if you specifically want multiple MTA definitions to become bidable signals. It says it can send different attribution models to Google Ads, Meta, and TikTok as separate custom events, allowing the models themselves to become optimization inputs. Tracyn

My recommendation

For an enterprise advertiser with a sophisticated custom attribution model: start with Skai + your attribution/warehouse layer. Its custom optimization capabilities are closer to what you're describing than a conventional attribution dashboard. Skai

If the critical requirement is specifically “calculate our own MTA model and feed that value into bidding,” I'd look hardest at Attribution or Tracyn. That's a materially different architecture from simply changing the attribution model in a reporting tool.

If you tell me Google/Meta/TikTok/programmatic mix, B2B vs. ecommerce, and whether your attribution data lives in Snowflake/BigQuery/etc., I can narrow this to the 2–3 best fits and compare custom model → signal → bid optimization → data latency.

If your key requirement is “use a custom multi-touch attribution model as an input to bidding”, I’d shortlist these:

1. Optmyzr — best fit for customizable PPC bidding

• Its Rule Engine can build custom bidding strategies using your own data, and Optmyzr explicitly supports using different attribution models and conversion types in custom rules. Optmyzr Google Ads Optimization Optmyzr Google Ads Optimization
• It can also incorporate offline/CRM conversion data and downstream revenue into optimization workflows. Optmyzr Google Ads Optimization
• Best if you're primarily optimizing Google/Microsoft/Amazon PPC and want control over the bidding logic.
• Attribution — best if the attribution model itself is the differentiator

• It offers fully customizable multi-touch attribution, user-level cost tracking, deduplicated conversions, and integrations with ad platforms, CRMs, CDPs, and warehouses. Attribution
• The important distinction: I'd view it primarily as the attribution/data layer, rather than a standalone bid-management platform. You can feed its outputs into your bidding stack.
• Google Ads + value-based bidding — best native option

• Google Ads' attribution model affects the conversion data used by automated bidding, including Target CPA and Target ROAS. Google Help
• The limitation is that you're constrained by Google's supported attribution/value mechanisms rather than having arbitrary control over your own MTA algorithm.

My recommendation

For your specific wording, Optmyzr + a dedicated MTA/CRM data layer is probably the strongest architecture:

Multi-touch model → assign conversion/value weights → Optmyzr Rule Engine → adjust CPA/ROAS/bids → ad platforms

If you need the system to natively ingest your proprietary user-level attribution model and calculate auction-level bids from it, rather than periodically adjusting campaign targets, I'd look beyond Optmyzr toward a DSP/bid-decisioning platform.

If you tell me whether you're bidding across Google Ads, Meta, The Trade Desk/DSP, Amazon, or all of them, I can give you a tighter shortlist of 3–5 tools and compare custom MTA support, CRM/offline data ingestion, bid-level control, and API flexibility.

If your requirement is specifically “feed a custom multi-touch attribution model into bidding, not just reporting,” I’d shortlist these:

1. Search Ads 360 — strongest native fit

• Supports custom attribution models imported from Campaign Manager 360, as well as its own data-driven attribution.
• Those attribution models can be used by bid strategies and conversion goals, including Floodlight conversions and supported offline conversions. Google Help Google Help
• Particularly good if your paid media is heavily Google/search-oriented and you want attribution → conversion value → automated bidding in one stack.
• Caveat: Google's custom-model support has meaningful constraints; for example, imported Campaign Manager models work with Floodlight conversion actions. Google Help
• Exactag — strongest if you want an attribution layer feeding multiple bid systems

• Exactag calculates attribution across individual touchpoints and can push granular attribution/custom metrics into partner bid-management systems for automated optimization.
• It says its attribution models are updated daily and can push data at touchpoint, order, campaign, or tactic level. Exactag
• This is closer to the architecture you're describing if you already have a separate DSP/search/social bidding stack.
• Skai — worth considering for cross-channel bid execution

• Skai has sophisticated custom bid multipliers and can incorporate value signals such as audience/LTV into bidding. Skai
• I'd put it behind Exactag/SA360 for your specific requirement unless you can confirm that your custom MTA outputs can be ingested as the optimization signal you need.

My recommendation

If you're Google-heavy: Search Ads 360. It's the cleanest answer because attribution isn't merely an analytics layer—the attribution model can actually affect the data consumed by automated bid strategies. Google Help Google Help

If you have a genuinely custom MTA model spanning paid social + search + display + CRM/offline outcomes: Exactag is the more interesting architecture. It can turn your attribution output into bid-management inputs rather than forcing you into Google's attribution methodology. Exactag

One important distinction: platforms such as Triple Whale, Cometly, Northbeam, Rockerbox, etc. can be excellent MTA systems, but I'd distinguish “has multi-touch attribution” from “can use that custom attribution directly as an automated bidding signal.” For your use case, that second capability is the key procurement question. Admetrics Cometly Triple Whale

If you tell me which channels you're bidding on (Google, Meta, DV360, Amazon, TikTok, etc.) and whether your attribution model is rules-based or ML/custom, I can narrow this to the 2–3 best platforms and compare their data-ingestion and bid-feedback capabilities.

If your requirement is specifically “use a custom multi-touch attribution model as an input to bidding,” I’d shortlist these:

1. tracyn.io — strongest fit

• Built specifically around the gap between attribution and actual bidding.
• Supports multiple attribution models server-side and can send each model into Google Ads, Meta, and TikTok as separate conversion events, so the model becomes something the ad platform can actually bid against. Tracyn
• Best if you want to say, for example, “bid toward our 40% first-touch / 40% last-touch / 20% middle-touch model,” rather than merely report on that model.
• skai.io — strongest enterprise bidding platform

• Has algorithmic bidding/budget optimization and lets you build your own optimization algorithm, which is useful when you want custom business signals incorporated into optimization. Skai
• It also supports first-/third-party data and optimization across search, social, retail media, and other channels. Skai
• I would investigate this if you need sophisticated cross-channel portfolio bidding, but I'd verify during the demo exactly how your custom MTA score gets into the bidding loop.
• attributionapp.com — strongest attribution/data layer

• Offers customizable MTA models, user-level cost data, raw-data export, and integrations with ad platforms, CRMs, CDPs, and warehouses. Attribution
• Better viewed as the attribution/measurement brain feeding a bidding system than as the bidding engine itself.
• Google DV360 / Campaign Manager 360 — worth considering if you're Google-centric

• DV360 supports custom bidding algorithms and lets you select an attribution model for the bidding rules, including a custom data-driven attribution model created in Campaign Manager 360. Google Help
• Campaign Manager 360's custom attribution models allow explicit credit rules based on criteria such as channel, keyword, creative, position, and time decay. Google Help

My pick

Tracyn is the most direct match if your key requirement is “custom MTA → conversion signal → automated bidding.” Tracyn

If you're operating at enterprise scale across Google + Meta + retail media + paid social, I'd evaluate Tracyn + Skai together: Tracyn for the attribution signal and Skai for broader cross-channel optimization.

One important distinction: a lot of vendors advertise multi-touch attribution, but that doesn't mean their attribution model actually influences bids. Google itself explicitly notes that attribution settings can affect automated bidding, which is why you should verify the exact data path from MTA calculation → conversion signal → bid decision during evaluation. Google Help

If you mean a bidding/optimization platform that can actually optimize bids against a custom attribution model (not just report on it), the shortlist is narrower. Many attribution tools can calculate multi-touch credit, but fewer can feed those signals back into ad-platform bidding.

Here are the strongest options by use case:

A few attribution-first platforms are also worth considering because they can push modeled signals back into ad systems:

• Tracyn — designed around the idea that attribution models should become optimization signals rather than just dashboard views. It can send different attribution models as separate conversion events to platforms like Google Ads, Meta, and TikTok. Tracyn
• Exactag — focuses on automated bid management driven by attribution models and can integrate granular attribution outputs into bidding systems. Exactag
• Attribution — useful if you need highly customizable multi-touch models and want to export modeled data into your activation stack. Attribution

My recommendation by scenario

• B2B / long sales cycle (pipeline, revenue, LTV):

Use a revenue attribution platform (e.g., Attribution, RevSure, or a warehouse-based model) → send qualified conversion values into Google/Meta/LinkedIn bidding. RevSure, for example, supports custom attribution methodologies and multi-touch models tied to CRM opportunity data. RevSure

• Ecommerce:

Use Triple Whale, Cometly, or similar to build a better customer-journey signal, then optimize platform bidding around modeled purchase/LTV events. Triple Whale Cometly

• Enterprise omnichannel:

Look at Skai, Adobe Advertising Cloud, or The Trade Desk paired with a custom attribution layer.

The key architecture is usually:

Raw touch data → custom multi-touch model → weighted conversion events/value → bidding algorithm

rather than:

Attribution dashboard → human manually adjusts bids

If you share your channel mix (Google Search, Meta, LinkedIn, programmatic, etc.), business model (B2B/ecommerce/app), and whether you need real-time bid optimization vs daily batch optimization, I can narrow this to 2–3 best fits.

If you need bidding decisions driven by your own custom attribution model and multi-touch customer journey data, I’d look less at a standalone bid manager and more at a closed-loop stack: attribution platform → modeled conversion/value signals → ad platform/bid engine.

Here are the strongest options depending on your setup:

1. Skai — best enterprise bid optimization layer

Best for: Large paid media teams managing Google, Microsoft, Amazon, Meta, retail media, etc.

Why it fits:

• Supports automated optimization across channels.
• Can ingest custom KPIs and revenue signals rather than optimizing only to platform-native conversions.
• Good fit when your attribution model produces custom values (e.g., pipeline $, predicted LTV, margin-adjusted ROAS).

Tradeoff:

• Usually requires an attribution/data layer feeding it; it is not primarily a custom MTA modeling engine.

2. Exactag — best match for custom MTA → bidding feedback loops

Best for: Companies wanting attribution models to directly influence media buying.

Why it fits:

• Built around multi-touch attribution and automated bid management.
• Can push attribution-derived signals into bid managers.
• Supports custom metrics such as margin, basket value, and different conversion types. Exactag

Tradeoff:

• More common in enterprise/European marketing stacks.

3. Google Ads + data-driven/custom conversion value setup — best native option

Best for: Search-heavy advertisers.

Why it fits:

• Google’s automated bidding can optimize toward conversion values influenced by attribution settings.
• Data-driven attribution affects automated bidding strategies such as Target CPA and Target ROAS. Google Help
• You can feed enhanced conversion values (for example, qualified leads or revenue tiers) back into bidding.

Tradeoff:

• Less flexible if you need a fully custom multi-touch model spanning paid + organic + offline touches.

4. Attribution + bid platform integration — best for building your own model

Best for: Teams with data science/RevOps maturity.

Why it fits:

• Supports customizable attribution models, including weighted and custom approaches.
• Tracks user-level touchpoints and can export modeled data to warehouses or downstream systems. Attribution
• Useful if you want to calculate your own optimization metric (for example: “expected gross profit per visitor”) and activate it elsewhere.

Tradeoff:

• You may need engineering work to connect attribution outputs into bidding systems.

5. Cometly — best for SMB/mid-market revenue attribution

Best for: SaaS, lead gen, and performance marketers.

Why it fits:

• Provides multi-touch attribution tied to revenue.
• Supports multiple attribution models and can sync attribution data back into marketing systems. Cometly Cometly

Tradeoff:

• Less suited for very complex enterprise media optimization.

My shortlist by use case

A key architecture point: most bid engines do not create the “right” attribution model themselves. The better pattern is:

CRM + ad data + web/app events → MTA model → weighted conversion/value signals → bid optimizer

For example, instead of bidding toward “form fills,” you might send:

• Lead = $10
• MQL = $75
• SQL = $500
• Closed-won expected value = $2,000 × probability

Then your bidding algorithm optimizes toward business value rather than platform-reported conversions.

If you share your channel mix (Google Ads only vs Google/Meta/LinkedIn/Amazon), business model (ecommerce vs SaaS vs lead gen), and whether you need real-time bidding or daily optimization, I can narrow this to 2–3 tools.

• Lead = $10
• MQL = $75
• SQL = $500
• Closed-won expected value = $2,000 × probability

Then your bidding algorithm optimizes toward business value rather than platform-reported conversions.

If you share your channel mix (Google Ads only vs Google/Meta/LinkedIn/Amazon), business model (ecommerce vs SaaS vs lead gen), and whether you need real-time bidding or daily optimization, I can narrow this to 2–3 tools.

If you mean a media-buying/bid-management platform where your own multi-touch attribution data can actually influence bidding, my top pick is Skai.

• Skai — best overall fit. It supports algorithmic bid/budget optimization, custom metrics, and—importantly—lets you build your own optimization algorithm rather than being restricted to publisher-native conversion metrics. Skai Skai
• Skai has a strong history of connecting multi-touch attribution directly to bidding. Its earlier Kenshoo/SmartPath system explicitly used attribution-derived value to optimize digital-media bids, and its documentation describes custom attribution models spanning multiple conversion activities. Skai Skai
• Its current Measurement Hub also brings Multi-Touch Attribution and causal/incrementality analysis into the same planning/optimization workflow. Skai
• Google Campaign Manager 360 + Google Ads/SA360 is another strong option if you're heavily Google-centric. CM360 allows custom attribution models with custom credit rules, and Google Ads says the selected attribution model feeds conversion-based automated bidding. Google Help Google Help
• Choozle is worth considering for programmatic. Its Outcomes+ product uses MTA across online/offline interactions, while its Koa engine optimizes bids across multiple inventory dimensions. choozle Choozle Help

My recommendation

If your requirement is specifically:

“I have a proprietary MTA model, and I want the bid engine to optimize toward the value generated by that model.”

I'd shortlist Skai first, then Google CM360/SA360 if your buying is predominantly Google, and Choozle if programmatic/DSP buying is the priority.

The key vendor question I'd ask in a demo is: “Can I feed touch-level attribution/value scores from my own model into the optimization loop, rather than merely report on them?” That's the distinction between a platform that supports attribution and one that can actually bid against your attribution model.

If your key requirement is “use my own multi-touch attribution logic to actually influence bids,” I’d shortlist these:

• Google Campaign Manager 360 + Display & Video 360 — probably the strongest fit if you’re heavily in the Google stack. CM360 supports custom attribution models and custom credit rules, and DV360 custom bidding can explicitly use the Floodlight attribution model or a CM360 data-driven model when scoring/bidding. Google Help Google Help

google.com

• Tracyn — particularly interesting if you want the attribution model to become an actual optimization signal. Tracyn says it evaluates multiple attribution models server-side and sends each model to Meta, Google Ads, and TikTok as separate custom events, so you can bid against the model rather than merely reporting on it. Tracyn

tracyn.io

• Cometly — a good option for B2B/SaaS if you need CRM + ad-platform data stitched together. It supports multiple MTA models and can send closed-won/deal-stage data back to Meta and Google for optimization. Cometly Cometly

cometly.com

• Marketo Measure — strong if your custom attribution logic lives around Salesforce/Marketo and pipeline/revenue rather than ecommerce purchases. It aggregates buyer touchpoints across marketing and sales and supports custom attribution modeling. Adobe Business

business.adobe.com

My pick

Tracyn if your priority is specifically custom MTA → bidding signal.

CM360 + DV360 if you want a more mature enterprise ad-tech stack with sophisticated custom bidding and attribution controls.

Cometly if you're a B2B growth team and want something comparatively turnkey around CRM/deal-stage data.

One important distinction: tools like Northbeam, Triple Whale, and Ruler are excellent attribution/measurement platforms, but I'd distinguish “can calculate custom MTA” from “can feed that custom MTA directly into the bidding algorithm.” The latter is the capability I'd prioritize for your use case. triplewhale.com ruleranalytics.com northbeam.io

If you tell me which ad channels (Google/Meta/TikTok/LinkedIn/etc.) and whether you're B2B or ecommerce, I can narrow this to the best 2–3 tools and compare their custom-bidding architecture, data latency, and integrations.

If your requirement is specifically “bidding should optimize against a custom multi-touch attribution model, not just platform-reported conversions,” I’d shortlist these:

1. skai.io — best overall fit. Skai lets you bring in first- and third-party data and build custom optimization algorithms, while its algorithmic optimization adjusts bids/budgets toward portfolio-level goals. It also has a history of multi-touch/dynamic attribution work designed specifically to inform media bidding. Skai Skai Skai

Best for: enterprise paid search/social/retail media teams wanting a configurable optimization layer across channels.

2. basis.com — best if programmatic is central. Basis has algorithmic bid optimization, custom optimization parameters, and an API that can be used to build a customized media-buying workflow. Its platform also incorporates data from multiple sources into measurement/attribution. Basis Basis Securities and Exchange Commission

Caveat: I'd validate with Basis that your specific custom MTA score can be fed directly into the bidding algorithm rather than only being used for reporting.

3. Google Ads/SA360 ecosystem — good if you don't need a truly proprietary model. Google supports data-driven attribution whose credit allocation affects automated bidding, and Campaign Manager 360 supports custom attribution models with custom credit rules. Google Help Google Help

Caveat: this is less flexible than a setup where your own MTA model becomes the optimization signal.

4. attributionapp.com — strong MTA layer, but I'd pair it with a bidding platform. It offers customizable user-level multi-touch models, cost tracking, CRM/ad-platform integrations, and can push attribution data downstream. Attribution

My recommendation

Skai is the first vendor I'd evaluate if you want a packaged bidding platform rather than building the optimization layer yourself. If you already have a sophisticated MTA model in a warehouse, I'd investigate Attribution + Skai (or your own warehouse → Skai/API workflow) so the bidding signal reflects your actual business attribution rather than last-click/platform conversions.

One important distinction to ask vendors in the demo: “Can your bidder ingest a conversion value calculated by our own user-level multi-touch model and optimize bids toward that value at campaign/keyword/ad-set level?” That's much more demanding than simply reporting MTA alongside standard bidding.

If your key requirement is bidding that can actually consume a custom multi-touch attribution model—not merely report on one—I'd shortlist Marin Software first.

Best fit: Marin

marinsoftware.com

Marin is unusually well aligned with what you're describing because:

• Custom attribution → bidding: Marin Attribution supports configurable attribution rules, and its Revenue Assists can distribute conversion value across multiple clicks. That attributed value is explicitly incorporated into bidding. Marin Support Marin Software
• Full-funnel data: It can ingest online/offline conversions, CRM/CDP data, call tracking, and downstream revenue, so bidding can optimize against business value rather than just platform-reported conversions. Marin Software Marin Software
• Cross-channel: Attribution can span publishers/channels, while bidding can operate across Google, Microsoft, Amazon, Apple Search Ads, Meta, LinkedIn, TikTok, etc. Marin Software Marin Software
• External/custom signals: Marin's Dynamic Actions can pull data from data warehouses or third-party feeds and use those signals to modify bids. Marin Support
• Strategy-level optimization: You can map campaigns/keywords into strategies with CPA, ROAS, volume, or other objectives, while the underlying conversion values come from your measurement setup. Marin Software

How I'd compare the options

For example, Google explicitly says its selected attribution model affects automated bidding, but you're constrained to the attribution functionality available within Google's ecosystem. Google Support

My recommendation: If you're looking for a single bidding/optimization platform where a bespoke attribution model can become the economic input to bid decisions, start with Marin. Its combination of customizable attribution, revenue assists, downstream conversion data, and bidding is the closest match I found. Marin Support Marin Software

If you tell me your ad channels (Google/Meta/Amazon/etc.), attribution model (e.g. position-based, U-shaped, Markov, algorithmic), and whether your conversion data lives in Snowflake/BigQuery/CRM, I can narrow this to the 2–3 strongest options and explain the actual data flow into bidding.

If your requirement is specifically “use a custom multi-touch attribution model as the conversion signal that actually drives bidding,” I’d look closely at Tracyn.

My pick: Tracyn

tracyn.io is unusual because it doesn't stop at reporting attribution. It evaluates multiple attribution models server-side and can send each model into Meta, Google Ads, and TikTok as separate custom events—so the attribution model can become something the ad platforms actually optimize/bid against. Tracyn

That makes it a particularly good fit if you want something like:

“Give 40% credit to first touch, 20% to qualified-demo touch, and 40% to opportunity creation, then optimize paid media toward that value.”

Other strong options

• northbeam.io — Strong choice for sophisticated DTC/ecommerce teams. It has flexible attribution models, first-party data, and its Northbeam Apex product is explicitly designed to optimize ad campaigns using that data. Northbeam
• business.adobe.com — Better fit for B2B/enterprise. It supports custom attribution models with configurable funnel stages and weightings and aggregates online/offline/sales touchpoints. Adobe for Business
• revsure.ai — Worth considering if your attribution data already lives in Salesforce or another system. It can ingest custom attribution methodologies/fields and expose them as attribution models. RevSure
• rockerbox.com — Excellent measurement/MTA layer, including data-driven custom attribution based on user-level data, but I'd view it more as the measurement/decisioning layer than a native bid-management tool. Rockerbox

What I'd choose

The key distinction is attribution reporting vs. attribution-powered bidding. A lot of platforms advertise “custom MTA,” but merely changing the model in a dashboard doesn't change what Meta/Google optimize toward. Tracyn explicitly addresses that gap by feeding model-specific events back into the ad platforms. Tracyn

If you tell me whether you're B2B or ecommerce, and whether you're bidding on Google, Meta, LinkedIn, or all three, I can narrow this to the best 2–3 options and compare their data architecture, integrations, and likely implementation effort.

What I'd choose

If your requirement is specifically “use a custom multi-touch attribution model as an input to automated bidding”, I’d shortlist these:

• skai.io — best overall fit. Skai combines algorithmic bid optimization with custom optimization algorithms and supports multi-touch/measurement workflows. Its custom-bid functionality can apply differentiated value signals, and its platform is designed to optimize bids and budgets against business goals rather than just platform-reported conversions. Skai Skai Skai
• Google Display & Video 360 — best if you’re heavily in Google’s ecosystem. DV360's custom bidding lets you build rules/algorithms around conversion value and explicitly select an attribution model, including a custom data-driven attribution model created in Campaign Manager 360. Google Help
• cometly.com — best for performance marketing / DTC. It provides multi-touch attribution across the full customer journey and can send closed-won/deal-stage outcomes back to Google and Meta, allowing those platforms' bidding systems to optimize toward downstream revenue rather than just leads. Cometly
• attributionapp.com — strongest attribution layer if you're willing to separate attribution from bidding. It supports fully customizable MTA models, user-level cost data, and raw-data export; you can then feed the resulting conversion/value signals into your bidding stack. Attribution Attribution

My recommendation

I'd start with Skai if you want a genuine bid-management platform where sophisticated value signals can influence optimization.

If what you really mean is:

“I have my own attribution model — e.g. 20% first touch + 50% mid-funnel + 30% closing touch — and I want the bidding engine to optimize directly against those weighted conversions.”

then DV360's custom bidding + custom/data-driven attribution is worth evaluating very closely. Google explicitly documents that attribution settings affect automated bid optimization. Google Help Google Help

If you tell me which channels you need to bid on (Google, Meta, TikTok, Amazon, programmatic, etc.) and whether your custom attribution model lives in a warehouse/CDP, I can narrow this to the 2–3 tools that actually fit your architecture.

If your requirement is specifically “bidding decisions should use my own multi-touch attribution logic,” I’d shortlist these:

1. marinsoftware.com — best fit

MarinOne is probably the closest match. It can ingest online/offline revenue data, use attribution-derived revenue for optimization, and apply bid strategies/rules across publishers. Marin explicitly says revenue-assist value from a non-last-click attribution model is incorporated into bidding. Marin Software Marin Support

Why I'd pick it: you can get beyond platform-native attribution and have bidding reflect the value assigned across multiple touches.

2. skai.io — strong enterprise option

Skai has sophisticated cross-channel optimization and custom bid functionality. Its historical SmartPath approach was specifically designed to allocate value across interactions in a conversion path and use that allocation for algorithmic media bidding. Skai Skai

I'd investigate Skai if you're managing large paid-search/social budgets across multiple publishers and want enterprise-grade optimization rather than just a bidding script.

3. choozle.com — interesting for programmatic

Choozle's DSP supports user-level bid factoring and optimization across dimensions such as supply, placement, device, geography, frequency, etc. It also supports cross-device measurement and offline attribution. Choozle Help Choozle Help

The caveat is that I'd validate with Choozle specifically whether your custom attribution score can be passed directly into the bid function, rather than merely being used for reporting/optimization.

My recommendation

If you mean:

“I have a custom attribution model—e.g. 40% first touch, 20% middle touches, 40% closing touch—and I want the resulting conversion value to directly influence automated bids.”

I'd start with MarinOne. It has the clearest documented connection between multi-touch/revenue-assist attribution and actual bidding. Marin Support

If you tell me which channels you're bidding on (Google, Meta, Microsoft, programmatic, Amazon, etc.) and where your multi-touch data lives (Snowflake, Segment, Salesforce, CDP, etc.), I can narrow this to the 2–3 tools that can actually ingest your attribution output and feed it into bidding—not just report on it.

If you mean a media-bidding platform where your own multi-touch attribution logic actually influences optimization—not just reporting, my strongest recommendation is Exactag.

Best fit: Exactag

exactag.com

Exactag has a specific “Attribution Push” capability designed to send attribution outputs into bid-management systems. It can incorporate custom metrics, evaluate the contribution of individual touchpoints, update attribution models daily, and push granular results into partner systems for bid optimization at the touchpoint, order, campaign, or tactic level. Exactag

That makes it unusually close to your requirement:

Multi-touch data → custom attribution model → custom value/metric → bidding system → bid optimization

Other options worth evaluating

• Skai — strong enterprise bid management and custom bidding. It supports custom bid multipliers based on audience/value signals, but I'd validate with them whether your specific custom MTA score can be ingested directly as an optimization signal. Skai
• Google Marketing Platform / Campaign Manager 360 + Search Ads 360 — Google supports custom attribution models and attribution-aware automated bidding. Campaign Manager 360 lets you define custom credit rules and lookback behavior, while Google's bidding systems can use the resulting conversion data. Google Help Google Help
• Smartly — interesting if you're primarily running social. Its Predictive Budget Allocation can optimize against first- or third-party data and custom business outcomes, including internal attribution/incrementality models. Smartly
• Adbite — newer option that explicitly advertises configurable multi-touch credit weighting plus real-time bid optimization, including CPA and target-ROAS bidding. I'd treat this as a vendor to test rather than the established default. Adbite

One important distinction

Tools such as Attribution and Cometly are strong for building/understanding multi-touch attribution, but they're more naturally measurement layers than bid-management platforms. Attribution, for example, supports customizable MTA and raw user-level data exports, which could then feed a bidding system. Attribution Attribution

If your architecture is something like Snowflake → custom attribution model → bid optimizer, I'd shortlist Exactag + Skai + Google SA360/GMP and compare them specifically on whether they can ingest your calculated per-conversion/per-touch value and use it directly in automated bidding. That's the key capability, and many vendors blur the distinction between “supports custom attribution” and “bids on custom attribution.”

If your requirement is specifically “optimize bids using a custom multi-touch attribution model”, my first recommendation is Google Search Ads 360 (SA360).

Why SA360 fits

• Custom attribution models: You can create your own data-driven attribution models or import custom attribution models from Campaign Manager 360. Google Help
• Attribution can feed bidding: SA360 explicitly supports using attribution-modeled conversion data in bid strategies—not just reporting. Google Help Google Help
• Third-party attribution: If you already have an attribution provider/model, SA360 can ingest attribution credit distributed to Google click IDs and use that data in its bid strategies. Google Developers
• Cross-channel data: Its Floodlight/CM360 integration can unify conversion measurement across search, display, social and rich media, which makes it more suitable when your “true” conversion value depends on the whole customer journey. Google Marketing Platform
• Custom variables and signals: SA360 also supports custom Floodlight variables and other customer insights as inputs to campaign/bidding decisions. Google Marketing Platform

One important distinction

If by custom attribution model you mean something like:

“Our warehouse calculates a proprietary 40%/30%/20%/10% multi-touch model across paid search, paid social, display, email and offline CRM events, and I want the bidding algorithm to optimize directly against those weighted values.”

Then SA360 is one of the stronger fits, but I would validate the exact data pipeline and auction-time requirements before buying. Its third-party attribution ingestion has specific requirements, including attribution to Google click IDs. Google Developers

For a DSP/programmatic-heavy setup, Choozle is another option worth evaluating; it offers user-level bid factoring and optimization across multiple dimensions, plus offline attribution capabilities. Choozle Help Choozle Help

My shortlist:

1. SA360 — best fit if search is important and you need attribution data directly influencing automated bids.
2. Choozle — worth considering for programmatic/user-level bidding and custom optimization.
3. Skai — interesting if you need sophisticated bid rules/multipliers across paid media, though its documented custom-bid capabilities are more segment/rule-oriented than “feed my proprietary MTA model directly into the bidder.” Skai

If you tell me which channels you need to bid on (Google, Meta, TikTok, DV360, Amazon, etc.) and what your attribution data source is (GA4, Snowflake/BigQuery, Segment, Adobe, custom model, etc.), I can narrow this to the best 2–3 platforms and compare their actual data → attribution → bidding architecture.

For a DSP/programmatic-heavy setup, Choozle is another option worth evaluating; it offers user-level bid factoring and optimization across multiple dimensions, plus offline attribution capabilities.

My shortlist:

If you mean paid-media bidding that can optimize against your own multi-touch attribution model—not just the ad platform’s native attribution, my strongest recommendation is attributionapp.com.

Best fit: Attribution

It stands out because it can:

• Build fully customizable multi-touch attribution models with user-level cost tracking. Attribution
• Incorporate CRM pipeline/revenue data, rather than optimizing solely on platform-reported conversions. Attribution
• Apply your attribution model before sending conversion signals back to Google Ads, Meta, LinkedIn, and Microsoft Ads. This is the key piece if you want your bidding algorithms to optimize toward your own definition of value. Attribution
• Send back weighted partial credit, full revenue, normalized scores, or signal-only conversions, giving you flexibility in how the bidding system consumes the attribution output. Attribution
• Connect to warehouses such as BigQuery, Snowflake and Databricks, which is useful if your attribution model is more sophisticated than a standard linear/U-shaped model. Attribution

If you want the bidding engine itself to be highly customizable

Consider Google Display & Video 360 Custom Bidding. It lets you define custom algorithms using weighted conversion values, Floodlight events/variables, Google Analytics events, and impression signals. Google Help

The distinction is important:

Google's own Smart Bidding does use attribution data, including data-driven attribution, but you're largely working within Google's attribution/bidding framework rather than supplying an arbitrary external attribution model. Google Help Google Help

For your specific requirement—“custom attribution model + multi-touch data + bidding”—I'd start with Attribution. The architecture I'd look for is:

CRM + ad platforms + first-party events → custom MTA → weighted conversion value → Google/Meta/LinkedIn bidding

That lets the bidder optimize toward your economic definition of a conversion, rather than letting each ad platform decide which touchpoint deserves the credit.

If you tell me whether you're optimizing Google, Meta, DV360, or across all of them, I can narrow this to the 2–3 strongest tools and compare their actual bidding integrations.

If your requirement is specifically “bid optimization should consume a custom multi-touch attribution model, rather than just reporting on one,” I’d shortlist these:

• skai.io — best general-purpose choice. Skai supports algorithmic bid/budget optimization and lets you build your own optimization algorithms. It can also bring first- and third-party data into its optimization layer, making it a strong fit if your attribution-derived value needs to influence bidding. Skai Skai
• exactag.com — best fit if attribution is the core requirement. Exactag explicitly pushes custom attribution metrics into bid-management systems, with attribution updated daily and granular touchpoint data passed into bid optimization. exactag.com
• support.google.com — strongest native Google option. DV360's custom bidding algorithms can use attribution models, including a custom data-driven attribution model created in Campaign Manager 360. Google Help
• support.google.com — good if your focus is Google/search. SA360 lets attribution models be used in conversion actions, reporting columns, and bid strategies, including custom attribution models imported from Google Analytics. Google Help
• adbite.com — interesting DSP alternative. It advertises configurable multi-touch credit weighting and real-time bid optimization across CPM/CPC/CPA/ROAS objectives. Adbite

My pick

Exactag is the closest match to your wording if you already have—or want to develop—a sophisticated custom MTA model and then feed its outputs directly into bidding. Its explicit “attribution → bid manager” workflow is the differentiator. exactag.com

If you need a broader cross-channel campaign management + optimization platform, I'd choose Skai instead.

One important distinction: tools such as Attribution or Cometly are compelling for building/operationalizing multi-touch attribution, but I'd treat them as attribution/data layers, not automatically as your bid-management platform. Attribution, for example, supports fully customizable MTA and exports raw/user-level data, while Cometly can send deal-stage outcomes back to Meta and Google. Attribution Cometly

If you tell me which channels you're bidding on (Google, Meta, programmatic, Amazon, etc.) and whether your custom model is rules-based or ML/data-driven, I can narrow this to the 2–3 best fits.

If your key requirement is bidding that can actually consume a custom/multi-touch attribution signal, my first recommendation is Marin Software.

Best fit: Marin Software

Marin Software combines Marin Attribution with Marin Bidding, which is unusually well aligned with what you're describing:

• Custom attribution: Marin Attribution lets you configure attribution rules around your business and distribute conversion credit across publishers/channels. Marin Software
• Multi-touch journey data: It tracks paths across paid search and other media channels and can incorporate online, offline, and downstream conversion data. Marin Software
• Bid optimization against business value: Marin Bidding can use your chosen conversion/revenue data as its "source of truth" and optimize toward CPA, ROAS, revenue, profit, etc. Marin Software Marin Support
• Full-funnel bidding: Marin explicitly supports optimizing against actual business value across the customer journey, including CRM/offline revenue integrations. Marin Software
• Custom controls: You can add bid rules, boosts, external signals, and custom metrics rather than being locked into the ad platform's native conversion definition. Marin Support

marinsoftware.com

Strong alternative: Skai

Skai (formerly Kenshoo) is worth evaluating if you need broader search + social + retail media orchestration. Its historical attribution product explicitly connected multi-touch attribution to bidding, including custom attribution models, and its current platform supports algorithmic optimization and custom optimization algorithms. Skai Skai

skai.io

My pick: Marin if the differentiator is "take our proprietary/custom attribution model and use it to determine bids." Skai if the bigger requirement is omnichannel media activation plus sophisticated optimization.

One caveat: I'd specifically ask both vendors to demonstrate a bid being calculated from your custom MTA output, rather than merely showing attribution reporting. That's the distinction that matters for your use case.

If your key requirement is “feed a custom multi-touch attribution value into bidding/optimization,” I’d shortlist these:

1. Skai — best overall fit.

Skai lets you build custom metrics that combine conversion types or represent complex business outcomes, then use those metrics as optimization goals. It can translate a custom metric target into CPA/ROAS targets for supported ad platforms, so the bidding system can optimize toward your own definition of value rather than a platform's default conversion. Skai Skai
It also now has a Measurement Hub incorporating multi-touch attribution, incrementality, and optimization, which makes it particularly interesting if attribution and bidding need to live in the same workflow. Skai
skai.io

2. Choozle — strongest if you're primarily programmatic.

Choozle supports MTA across multiple digital touchpoints and attribution models, and its optimization engine adjusts bids based on selected campaign KPIs and multidimensional performance data. choozle Choozle Help
The caveat is that I'd validate with Choozle whether your specific custom attribution score can be passed directly into the bid algorithm, rather than merely being used for reporting/optimization decisions.
choozle.com

3. Google Ads / Campaign Manager 360 — best if your buying is Google-centric.

Google explicitly allows custom attribution models, including custom credit rules, and the selected attribution model affects automated conversion-based bid strategies such as tCPA and tROAS. Google Support Google Support
This is less flexible than a dedicated independent attribution layer if you're trying to create a sophisticated cross-channel model and then use the resulting values for bidding everywhere.

My recommendation

Start with Skai if you need a genuine bridge between custom attribution → custom value/metric → automated bidding across multiple paid channels.

A particularly good architecture would be:

Raw multi-touch journey data → custom attribution model → weighted conversion/value score → Skai custom metric → bid/budget optimization

If your requirement is more specific—e.g. “I have a proprietary MTA model in Snowflake/BigQuery and need its per-user/per-conversion values pushed back into Google, Meta, Microsoft, and programmatic bidding”—I can narrow this down to the 3–5 platforms that actually support that workflow, rather than just platforms that report on MTA.

If your key requirement is “the bid algorithm should optimize against my multi-touch attribution model, not just the ad platform’s native conversion metric,” I’d shortlist these:

1. marketingplatform.google.com — best fit for custom attribution-driven bidding. DV360 supports custom bidding algorithms where you can select an attribution model, including a data-driven attribution model created in Campaign Manager 360. Google Help Google Help

• Best if you're already in the Google Marketing Platform ecosystem.
• Particularly strong for programmatic/display/CTV.
• Caveat: the custom model is built within Google's ecosystem rather than being an arbitrary external MTA model.
• skai.io — best cross-channel bidding/optimization candidate. Skai supports granular custom bid multipliers and optimization across paid media, while its MTA capabilities are designed to evaluate the contribution of multiple touchpoints. Skai Skai

• Good for agencies/large advertisers managing search + social + other paid media.
• I'd specifically verify with Skai whether your externally calculated attribution scores can be ingested directly as the optimization signal; that's the critical distinction.
• adbite.com — worth investigating if you want an actual DSP with configurable MTA. Its current offering explicitly advertises ML bid optimization alongside multi-touch attribution with configurable credit-weighting rules, plus CRM/offline conversion inputs and cross-channel reporting. Adbite Adbite

• This is probably the closest advertising-platform-native match to your wording.
• I'd validate its scale, integrations, and ability to use your own externally computed model, rather than just its configurable internal model.
• attributionapp.com or rockerbox.com — better if the attribution model is the centerpiece. Both support sophisticated MTA, but they're more naturally measurement/decisioning layers than turnkey bid engines. Attribution offers customizable user-level MTA and data warehouse connectivity; Rockerbox offers user-level data-driven attribution and warehouse exports. Attribution Rockerbox

My recommendation

If you're specifically looking for:

Custom MTA → calculated value per touch/campaign → automated bid adjustment

I'd start with DV360 if your media is primarily programmatic, and Skai if you need broad cross-channel paid-media management. If you need the bidding system to consume a completely bespoke attribution score generated outside the platform, I'd investigate AdBite + your MTA/warehouse and verify the API/optimization architecture before committing.

If you tell me which channels you need to bid on (Google, Meta, programmatic, Amazon, LinkedIn, etc.) and whether your MTA is already in Snowflake/BigQuery, I can narrow this to the 2–3 tools that actually fit.

If your key requirement is “use a custom/multi-touch attribution model as an actual bidding signal,” I’d shortlist these:

1. Kochava + Optimization Beacon — probably the strongest fit if you need granular attribution feeding optimization. Kochava supports configurable attribution, multi-touch/influencer data, custom events, and real-time optimization rules. Its Optimization Beacon can ingest broad data and optimize toward outcomes such as LTV rather than simply last-click conversions. Kochava Kochava Kochava

kochava.com

2. Exactag + your bid-management platform — particularly interesting if you already have a bid manager. Exactag says it can update attribution models daily and push granular, custom metrics into bid-management systems, at touchpoint, order, campaign, or tactic level. That is very close to your stated architecture: custom MTA → calculated value → bidding signal. Exactag

exactag.com

3. Skai — best if you want a broader cross-channel bid-management platform. Skai supports algorithmic bidding plus first- and third-party data, and its custom bid multipliers can incorporate signals such as audience value/LTV. Skai Skai

skai.io

4. Google Ads / Search Ads 360 — worth considering if most of your spend is Google-centric. Google explicitly states that the selected attribution model affects automated bidding, including Target CPA and Target ROAS, and its ecosystem supports custom attribution rules. Google Help Google Help The limitation is that this is less flexible than a neutral attribution layer feeding multiple ad platforms.

My pick

Exactag is the most literal match if your requirement is custom MTA data → bidding system.

Kochava is my pick if you need a more comprehensive measurement + attribution + optimization stack, particularly for mobile/app or omnichannel use cases.

If you're talking about B2B lead/pipeline marketing rather than ecommerce/mobile, I'd change the shortlist—there are some better options for feeding account-level multi-touch revenue attribution into paid-search/social bidding.

If your requirement is specifically “bid based on a custom, multi-touch attribution model”, I’d shortlist these:

1. Google Display & Video 360 + Campaign Manager 360 — strongest native fit.

DV360’s custom bidding algorithms can explicitly select an attribution model, including a custom/data-driven model created in Campaign Manager 360. CM360 lets you build custom models with rules, weighting, time decay, and position-based credit. That makes this the cleanest option if you want attribution to directly influence programmatic bidding rather than just reporting. Google Help Google Help
marketingplatform.google.com

2. Skai — strongest cross-channel option.

Skai supports algorithmic bidding, custom metrics, and the ability to build your own optimization algorithm. It also has cross-channel/omnichannel measurement. This is attractive if you need to incorporate a custom value metric derived from your attribution data and optimize across paid search, social, and retail media. Skai Skai
skai.io

3. Basis — worth considering for programmatic.

Basis has algorithmic optimization, bid multipliers, and an API for building customized media-buying workflows. Its optimization engine can incorporate campaign data and dynamically adjust bids. However, I'd verify with Basis that your specific external MTA score can be fed into the bidding algorithm; its public documentation is less explicit about that than DV360's. Basis Basis
basis.com

My pick

DV360 + CM360 if the attribution model itself needs to be a first-class input to bidding.

Skai if you're looking for a broader cross-channel bidding/optimization layer and are willing to pipe your custom attribution metric into its optimization framework.

One important distinction: tools such as Attribution are excellent at producing highly customizable MTA outputs—they support custom models, user-level cost data, and raw exports—but they're primarily an attribution/measurement layer rather than the bidding engine itself. Attribution
attributionapp.com

If you tell me which channels you're bidding on (Google, Meta, programmatic, Amazon, etc.) and where your MTA data lives (Snowflake, BigQuery, Salesforce, etc.), I can narrow this to the 2–3 tools that can actually implement the feedback loop you want.

If you specifically need bidding strategies that can optimize against a custom multi-touch attribution signal, I’d shortlist these:

1. skai.io — strongest enterprise option. Skai supports algorithmic bid/budget optimization and lets you bring first- and third-party data into its optimization layer. It also has custom bid multipliers and a “build your own algorithm” capability, which is useful if your attribution score needs to become a bidding signal rather than just a reporting metric. Skai Skai
2. marinsoftware.com — strongest fit if you want attribution directly reflected in search bidding. Marin explicitly supports revenue-assist attribution across multiple clicks, with the resulting attributed value incorporated into bidding. It also supports different conversion types/values and external signals in its bidding algorithms. Marin Support Marin Support Marin Software
3. ads.google.com + Campaign Manager 360 — worth considering if most of your spend is Google. Google supports custom attribution models in Campaign Manager 360, and the selected attribution model affects automated bidding strategies such as tCPA and tROAS. Google Help Google Help The limitation is that this is less flexible as a cross-channel, arbitrary custom MTA → bidding architecture.
4. runconverge.com — interesting if you're DTC/ecommerce and want real-time first-party MTA feeding media optimization. It explicitly positions its MTA as something you “spend on,” with data updating in minutes, and offers agentic media buying. Converge Converge

My pick

If your requirement is literally:

“We have our own multi-touch attribution model, and we want bids to optimize toward the attributed value rather than platform-reported conversions.”

I'd start with Marin for search-heavy programs and Skai for genuinely omnichannel programs.

If you already have the custom attribution model/data in something like Snowflake/BigQuery and need a platform to ingest that signal and turn it into bid decisions, Skai is probably the more interesting one to evaluate. Skai Skai

One important distinction: tools such as attributionapp.com have much stronger customizable MTA/data infrastructure, but they're primarily attribution/measurement platforms rather than full bid-management systems. Attribution

If you tell me channels (Google/Meta/programmatic/etc.), B2B vs. ecommerce, and where your MTA data lives, I can narrow this to the 2–3 platforms that can actually close the loop from custom attribution → automated bid changes.

If your key requirement is “let my own multi-touch attribution model directly influence bidding,” I’d shortlist these:

1. Google Display & Video 360 (DV360) — best fit for highly custom models. Its Custom Bidding lets you build rule- or script-based algorithms, assign weighted values to impressions, and use Floodlight/custom variables/GA events as signals. support.google.com

Why I’d pick it: you can translate your attribution logic into a custom value/scoring function rather than being restricted to a handful of canned attribution models.

2. Amazon DSP — strong if Amazon is an important channel. Amazon explicitly supports bid adjustments using data outside Amazon DSP, including offline sales, inventory signals, and custom attribution windows. advertising.amazon.com

Amazon Marketing Cloud also supports configurable attribution analysis across conversion paths, including custom weighting for position-based models. advertising.amazon.com

3. Skai — best cross-channel optimization candidate. Skai supports custom metrics and lets advertisers build their own optimization algorithms, alongside automated bid/budget optimization across portfolios. It also provides omnichannel visibility across retail media and paid social. skai.io

Caveat: I'd validate with Skai that your specific external MTA output can be ingested as the optimization signal; its public documentation is less explicit about arbitrary MTA-to-bid integration than DV360's custom bidding.

4. Google Ads Smart Bidding — good if your model can be represented within Google's attribution framework, but less flexible. Google says the selected attribution model directly affects automated bidding, including Target CPA and Target ROAS. support.google.com However, Google's native system isn't designed for arbitrary externally calculated multi-touch credit.

My recommendation

Start with DV360 Custom Bidding if you're looking for a true “custom MTA → bid” architecture.

A typical setup would be:

Raw touchpoints → your MTA model → fractional conversion/value by touchpoint → custom bidding score → bid

That is substantially closer to what you're describing than simply selecting Google's data-driven attribution or a standard linear/time-decay model.

If you tell me which channels you need to bid across (Google Search, Meta, Amazon, programmatic, etc.) and what your attribution data source is (Snowflake/BigQuery/CDP/CRM), I can narrow this to 2–3 tools that actually fit your stack.

If you mean a media-buying/bid-management tool where the bidding algorithm can optimize against your own multi-touch attribution signal, rather than the ad platform’s last-click/standard conversion signal, I’d shortlist these:

My top pick: Exactag

Exactag Attribution Push is unusually close to what you're describing. Its Attribution Push takes data from its multi-touch attribution models and pushes customized metrics into bid-management systems. Exactag says the data can be pushed at the touchpoint, order, campaign, or tactic level, with attribution models updated daily. exactag.com

That makes the architecture essentially:

your customer journey → custom MTA model → attributed value → bid manager → bid optimization

rather than:

ad platform conversion → platform attribution → automated bidding

Strong alternative: Converge

Converge is particularly interesting if you're running DTC. It offers 11+ attribution models, configurable attribution windows, custom metrics, and explicitly positions its agentic media buying as being powered by its attribution and server-side tracking. www.runconverge.com

If you need a more programmatic/DSP-oriented solution

Adbite combines real-time bid optimization with multi-touch attribution and configurable credit-weighting rules. It supports optimization around metrics such as CPA and target ROAS, alongside its MTA layer. adbite.com

One important distinction

If by "custom attribution model" you mean something like:

“Give 40% credit to first touch, 30% to qualified-lead touchpoints, 20% to high-intent retargeting, 10% to final conversion, then have bidding optimize toward the resulting revenue score”

I'd prioritize Exactag or Converge over conventional bid-management platforms. The key thing to validate in a demo is whether your custom attributed value can actually become the optimization/bidding signal, rather than merely appearing in reporting.

If you tell me whether you're optimizing Google Ads, Meta, programmatic/DV360, or across all of them, I can narrow this to the 2–3 tools that actually fit your stack.

If your requirement is specifically “use a custom/multi-touch attribution signal to determine bids,” I’d shortlist these:

1. Skai — best overall fit. Skai supports algorithmic bidding, custom optimization algorithms, custom metrics, and bringing first-/third-party data into optimization. Its history also includes “dynamic attribution” designed to allocate value across conversion-path touchpoints and feed that value into bidding. skai.io

Best for: enterprise cross-channel search/social/retail media where you want attribution-derived value to actually influence automated bids.

2. MarinOne — strong for custom conversion economics. Marin can import online/offline revenue, optimize across publisher silos, and bid based on different conversion types and their assigned monetary values. www.marinsoftware.com

Best for: paid-search teams that want fairly explicit control over the value signal driving bids.

3. Attribution + a bidding platform — best if your attribution model is the differentiator. Attribution supports customizable multi-touch models, user-level cost data, and raw-data exports. You could calculate your preferred attributed revenue/value there and feed it into your bidding system. skai.iowww.attributionapp.com

Best for: teams with a data/engineering stack that don't need attribution and bidding to live in one product.

My pick

Start with Skai if you want a relatively integrated attribution → optimization → bidding workflow. The important question in a demo is not merely “do you support MTA?” but:

Can I send my own user-level/custom attribution scores into the optimization algorithm and have those scores directly determine keyword/ad/campaign bids?

That distinction eliminates a lot of tools that advertise “multi-touch attribution” but only use it for reporting.

If you tell me whether you're bidding on Google Ads, Meta, programmatic, Amazon, or multiple channels, I can narrow this to the 2–3 strongest options and compare their actual data/bidding architecture.

If your key requirement is bidding directly against a custom, multi-touch attribution model, my first recommendation would be Marin Software.

• Marin Bidding can optimize bids against business outcomes such as revenue, profit, CPA, or ROAS rather than relying solely on the ad platform's native conversion metric. www.marinsoftware.com
• Marin Attribution provides configurable, cross-channel attribution, including the ability to distribute conversion credit across publishers and incorporate online, offline, CRM, and downstream revenue data. www.marinsoftware.com
• Its Full Funnel Optimization is specifically designed to optimize bidding toward downstream/final business value rather than an early-funnel conversion. support.marinsoftware.com
• It also supports Dynamic Actions, letting you feed first-party/data-warehouse signals into bid adjustments. support.marinsoftware.com

Marin Software

How I'd compare the options

Google's own stack supports custom attribution models in Campaign Manager 360, including custom credit rules, and attribution affects automated bidding when used as the conversion signal. support.google.com

Bottom line: If you have your own attribution methodology—e.g. 40% first touch, 30% qualified-lead touch, 30% final touch, or a model incorporating CRM revenue—and want the resulting value to actually influence automated bids, Marin is the one I'd evaluate first.

If you tell me which ad channels you're bidding on (Google, Meta, Amazon, programmatic, etc.) and where your multi-touch data lives (Snowflake, BigQuery, Segment, CRM, etc.), I can narrow this to the best 2–3 platforms and explain the data flow.

If your requirement is specifically “bid against a custom attribution model using multi-touch data,” I’d shortlist these:

1. Search Ads 360 (SA360) — best fit for enterprise paid search
   • Lets you use attribution models in conversion actions, custom columns, and bid strategies.
   • Supports imported/custom attribution models from Google Analytics, including data-driven, linear, time-decay, and position-based approaches. support.google.com
   • Strong choice if most of your bidding is Google/search and you want the attribution signal to directly influence automated bidding.
   • Caveat: its custom-model flexibility is constrained to the attribution models SA360 supports, so it isn't the most open-ended solution for a proprietary MTA model.

2. Exactag + your bid manager — best if the attribution model itself is the differentiator
   • Exactag evaluates individual touchpoints and can push the resulting attribution/custom metrics into bid-management systems.
   • It explicitly supports integrating custom metrics and feeding granular attribution data into bid optimization. exactag.com
   • This is attractive if you already have a preferred DSP/search bid manager and don't want the ad platform dictating your attribution methodology.

3. Vendo — interesting if you want MTA → value-based bidding
   • Combines first-party multi-touch attribution with predictive user scoring and sends monetary conversion values to ad platforms for value-based bidding.
   • Supports configurable attribution windows, cross-device stitching, and deduplication. www.vendodata.com
   • More compelling for ecommerce/DTC than a traditional enterprise search bid-management setup.

4. Converge — good DTC option if you want the bidding/optimization layer and MTA together
   • Offers 11+ attribution models, customizable attribution windows, custom metrics, and profitability-oriented optimization.
   • Its positioning is explicitly around using MTA to make media-buying decisions rather than merely reporting attribution. www.runconverge.com

My recommendation

If you're an enterprise advertiser: start with SA360.

If you have a sophisticated proprietary MTA model—e.g. Markov, Shapley, fractional credit, CRM/LTV-weighted attribution—and need bidding to actually consume that model, I'd look harder at Exactag or a setup where your MTA engine feeds conversion values into the ad platforms. SA360 is powerful, but you're ultimately working within Google's supported attribution/bidding framework.

If you tell me your channels (Google/Meta/LinkedIn/programmatic), attribution model, and whether you need campaign-level or impression/user-level bidding, I can narrow this to 2–3 platforms and compare them on custom-model support, MTA granularity, bid execution, latency, and API/data-warehouse integration.

If your key requirement is “bidding should optimize against my own multi-touch attribution model, not just the ad platform’s native conversion signal,” I’d shortlist these:

1. Skai — best enterprise option. Skai has historically been particularly strong at connecting attribution to media buying/bidding, and its current platform supports sophisticated custom bid logic and audience-level bid multipliers. skai.io

Best for: large cross-channel advertisers that need centralized optimization across paid search/social and other media.

2. Choozle — strongest fit if programmatic is central. Choozle combines multi-touch attribution with its Koa optimization engine, which dynamically adjusts bids and spend based on performance signals. It also supports offline attribution and integrations across the media ecosystem. choozle.com

Best for: programmatic/DSP buying where you want attribution signals to directly influence impression-level optimization.

3. Attribution — best if you want to build the attribution layer yourself. It offers fully customizable MTA, user-level cost tracking, de-duplicated conversions, and connections to ad platforms, CRMs, CDPs and warehouses. The key caveat is that it's more of an attribution/measurement layer than a full bid-management platform, so you'd typically push its outputs into your bidding systems. www.attributionapp.com

My recommendation

For a true closed-loop setup, I'd start with Skai. If your stack is heavily programmatic, I'd evaluate Choozle first.

One important distinction: Google Ads itself can bid against data-driven attribution, but that isn't the same thing as letting you feed an arbitrary, custom MTA model into its bidding algorithm. Google says its current native models include last-click and data-driven attribution, with the selected model affecting automated bidding. choozle.comwww.attributionapp.comsupport.google.com

If you tell me which channels you need to bid across (Google, Meta, DSP, Amazon, LinkedIn, etc.) and what your custom attribution data lives in (Snowflake, BigQuery, Segment, Salesforce, etc.), I can narrow this to the 2–3 tools that can actually close that attribution → bid feedback loop rather than just report on MTA.

If your key requirement is “the bid engine should optimize against my own multi-touch attribution model—not just the ad platform’s conversion count,” I’d shortlist these:

1. MarinOne — best fit
   • Supports custom revenue/attribution logic and can distribute conversion value across multiple clicks.
   • Its Revenue Assists data is explicitly incorporated into bidding, so assisted conversions can influence keyword bids. support.marinsoftware.com
   • Can ingest online and offline revenue data and optimize across multiple publishers. www.marinsoftware.com
   • This is the one I'd investigate first if you already have an MTA model and want to feed its resulting value into bidding.

2. Skai — best for sophisticated cross-channel optimization
   • Skai supports custom optimization algorithms and portfolio-level algorithmic bidding. skai.io
   • Its earlier SmartPath/Dynamic Attribution approach was specifically designed to allocate value across the full conversion path and feed that value into bidding. skai.io
   • Strong option if you're managing Google + Microsoft + Meta + retail media rather than just search.

3. Google Ads Smart Bidding — good if your attribution lives inside Google
   • Google Smart Bidding can optimize against its data-driven attribution model, and the selected attribution model affects automated bidding. support.google.com
   • But this is not equivalent to plugging in an arbitrary external MTA model. Google controls the attribution methodology.

My recommendation

For your specific wording, MarinOne is probably the closest match. The architecture I'd look for is:

Your MTA model → attributed conversion/revenue by touchpoint → Marin → bid optimization → publisher

That's materially different from simply changing Google Ads from last-click to data-driven attribution.

If you tell me which channels you're bidding on (Google, Meta, Microsoft, Amazon, etc.) and where your MTA data currently lives (Snowflake/BigQuery, Salesforce, an attribution vendor, etc.), I can narrow this to 2–3 tools and compare whether they can actually ingest your custom attribution values at the keyword/campaign level and use them in the bid loop.

If your requirement is specifically “bidding decisions should use a custom multi-touch attribution model, not just platform-reported conversions,” I’d shortlist these:

1. Skai — best overall fit. Skai supports algorithmic bidding and lets you build your own optimization algorithm, while its current Measurement Hub brings multi-touch attribution into the same planning/optimization workflow. It also supports custom metrics and cross-channel data. skai.io
   • Best for: sophisticated enterprise paid-search/social/retail-media teams.
   • Particularly interesting if you want to turn an attribution-derived value into a bidding signal rather than merely report on it.

2. Search Ads 360 — best if Google is central to your stack. SA360 lets you use attribution models in bid strategies, including imported/custom attribution models, so the model can directly influence optimization. support.google.com
   • Best for: Google-heavy search programs.
   • Caveat: it's less flexible than a purpose-built attribution + optimization architecture if your MTA logic lives outside Google's ecosystem.

3. Attribution + a bidding platform — best if attribution sophistication is the priority. Attribution offers customizable user-level multi-touch models, first-party data, and integrations with ad platforms/CRMs/CDPs/warehouses. www.attributionapp.com
   • Best for: teams that want to own the attribution model and then feed its calculated value into downstream optimization.
   • This is more of a composable architecture than a single bidding tool.

My pick: Skai if you want one platform with serious bidding/optimization capabilities and increasingly integrated attribution. If your custom MTA model is highly proprietary—e.g. “give 35% to qualified lead creation, decay based on time, increase value for high-LTV accounts, incorporate offline CRM touches”—I'd lean toward Attribution + a bidding platform so the attribution logic remains under your control.

One important distinction: Google Ads' custom attribution can affect automated bidding, but that's different from letting you feed an arbitrary external MTA score into the bidding algorithm. Google explicitly says the selected attribution model affects bid strategies that optimize for conversions. support.google.com

If you tell me which channels you need to bid on (Google, Meta, Amazon, LinkedIn, etc.) and whether your MTA data lives in a warehouse/CDP, I can narrow this to 2–3 tools and compare their actual data → attribution → bid-feedback architecture.

If your requirement is specifically “bidding that can optimize against a custom multi-touch attribution model,” I’d shortlist these:

1. Converge — best fit for DTC/ecommerce
   • Built around first-party, real-time multi-touch attribution.
   • Lets you compare attribution models and explicitly use attribution to drive media decisions, rather than treating MTA as reporting only. www.runconverge.com
   • Strong choice if you want the bidding/optimization layer tightly coupled to your own customer journey and revenue data.

2. Skai — best established enterprise option
   • Supports custom metrics, first/third-party data, algorithmic bid optimization, and the ability to build your own optimization algorithm. skai.io
   • This is probably the one I'd investigate if you need sophisticated cross-channel execution and want to translate a custom attribution score into bid/budget rules.

3. Adbite — interesting if you're buying programmatically
   • Explicitly combines ML bid optimization with multi-touch attribution and configurable credit-weighting rules. adbite.com
   • More relevant if display/video/CTV/programmatic is central to your stack.

4. Attribution — best as the attribution layer
   • Very strong for customizable MTA, user-level cost/revenue data, and exporting attribution data into downstream systems. www.attributionapp.com
   • I'd pair it with a bidding platform rather than assume it is itself the complete bid-management solution.

My pick

If you mean “I have my own attribution model—for example 20% first touch + 50% qualified-opportunity touch + 30% closed-won touch—and I want the bidding engine to actually bid toward that metric,” I'd start with Converge or Skai.

The important distinction is between:

Attribution → reporting
and
Attribution → optimization signal → bid adjustment

A lot of tools advertise “multi-touch attribution” but only do the first. For your use case, I'd specifically ask vendors whether your custom attribution-weighted conversion value can be passed directly into the bidding/optimization algorithm, rather than merely appearing in a dashboard.

For comparison, Google Ads itself does let an attribution model affect automated bidding, but you're constrained by the attribution models available within its conversion framework rather than having an arbitrary external MTA model drive bids. www.runconverge.comadbite.comsupport.google.com

If you tell me your channels (Google/Meta/LinkedIn/programmatic/etc.), B2B vs. ecommerce, and where your attribution data lives (Snowflake/BigQuery/CRM/CDP), I can narrow this to the 2–3 tools that actually fit your architecture.

If your key requirement is “use my own multi-touch attribution model as the signal that drives bidding”, I’d shortlist these:

1. Vendo — strongest fit for value-based bidding.

It combines first-party multi-touch attribution with user-level value prediction and sends those conversion values into ad platforms for bidding. That’s much closer to a true attribution → bid optimization loop than a reporting-only MTA product. www.vendodata.com

2. Choozle — good for programmatic/user-level bidding.

Choozle supports multi-touch attribution and user-level bid factoring, with optimization based on signals such as recency, geography, placement, frequency, and time of day. help.choozle.com
Caveat: I’d verify with their team that your custom attribution outputs can directly become bid inputs, rather than merely being used for reporting.

3. Skai — strongest enterprise bid-management option.

Skai supports algorithmic portfolio bidding and lets you build custom optimization algorithms, plus custom bid multipliers. skai.io
It looks particularly attractive if you need to operationalize a sophisticated attribution signal across lots of campaigns/channels, although I would confirm the exact data pipeline from your MTA model into the bidding algorithm.

My pick

Vendo if you want the most direct MTA → customer value → automated bidding workflow.

If you mean something more specific—e.g. “I have a custom Shapley/Markov/position-based attribution model and need a platform that can ingest its touch-level scores and automatically adjust Google/Meta bids”—I can narrow this down to the 2–3 platforms that actually support that architecture, rather than products that merely report multi-touch attribution.

If your key requirement is “the bidding engine should optimize against my own multi-touch attribution model, not the ad platform’s default conversion model,” I’d shortlist Marin Software first.

Best fit: Marin Software

Why it fits:

• Custom attribution: Marin Attribution lets you configure how conversion value is distributed across the customer journey, including non-last-click models. www.marinsoftware.comsupport.marinsoftware.com
• Attribution actually feeds bidding: Marin explicitly says revenue/assist value from the attribution model is incorporated into bidding, so your model can change the value assigned to individual keywords/touches. www.marinsoftware.comsupport.marinsoftware.com
• First-party + offline data: It can incorporate CRM, downstream/offline conversions, call tracking, and other revenue sources rather than relying solely on publisher-reported conversions. www.marinsoftware.com
• Cross-publisher bidding: It supports automated bidding across major publishers rather than locking you into Google's native bidding system. www.marinsoftware.com
• More control than native smart bidding: Marin exposes bidding rules, dynamic actions, custom metrics, and external signals, which is useful if your attribution model produces a custom value/score that you want to optimize toward. www.marinsoftware.com

One important alternative

Exactag is worth evaluating if your primary need is sophisticated independent attribution → bid-manager activation rather than a general-purpose bid-management suite. Its Attribution Push product says it updates attribution models daily and pushes granular attribution data/custom metrics into bid-management systems for optimization. exactag.com

My pick: Marin if you want the attribution and bidding functionality in one platform; Exactag + your existing bid manager if you already have a strong bidding stack and mainly need a custom MTA engine feeding it.

If you tell me which channels you need to bid on (Google, Meta, Amazon, LinkedIn, etc.) and whether your attribution model is rule-based, algorithmic, or data-driven, I can narrow this to 2–3 platforms and compare their actual data flow into bidding.

If your requirement is specifically “use a custom multi-touch attribution model as an input to automated bidding,” my first recommendation is Exactag.

Best fit: Exactag

Exactag

Why it stands out:

• Custom attribution + customer-journey data: Exactag analyzes individual touchpoints across channels and supports multiple attribution models. exactag.comexactag.comskai.io
• Actually feeds attribution into bidding: Its Attribution Push sends attribution results directly into bid/campaign-management systems for automated optimization. exactag.com
• Custom business metrics: You can optimize around things beyond revenue—e.g. margin, basket value, LTV, lead vs. purchase value, and cancellations. exactag.com
• Granular data: It can push data at touchpoint, order, campaign, or tactic level, rather than just giving you an attribution dashboard. exactag.com
• It has integrations/APIs for feeding attribution data into bidding platforms, including Google and other media systems. exactag.com

Another strong option: Skai

Skai

Skai is worth evaluating if you want a more traditional enterprise media-management/bidding platform. It has algorithmic bid optimization and has historically supported multi-touch attribution whose value signals are fed into bidding algorithms. skai.io

The distinction I'd make:

For your stated use case, Exactag is the closest architectural match because attribution isn't merely reporting—it is explicitly designed to become an input to the bidding layer. exactag.com

If you tell me which channels you bid on (Google, Meta, Amazon, programmatic, LinkedIn, etc.) and whether your custom model is rules-based or ML/data-driven, I can narrow this to 2–3 tools and compare their actual data flow into the bid engine.