ChatGPT answers, unedited

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.