Cross-channel marketing measurement and budget planning workspace

For

Marketing leads and performance teams running paid campaigns across several channels

Solves

Campaign results sit in separate ad platforms and analytics tools, so budget decisions rely on last-click numbers and manual spreadsheet joins.

Delivers

Reviewed attribution, incrementality and budget-plan outputs linked to source evidence

Built in

about 5 weeks of creation time, MVP in 6 days

Investment

$13,500 for the MVP, $46,000 for the full product

Run it

Inside your business, or as part of your offer to clients

Reduce manual reporting effort while making budget shifts traceable to measured channel impact.

1. Connect and map ad platform, analytics and CRM data.
2. Attribute conversions across multiple touchpoints.
3. Model channel impact on sales or conversions.
4. Design and read out incrementality tests.
5. Apply causal inference to channel performance.
6. Centralize scattered marketing data into one source of truth.
7. Recommend scaling, reducing or holding spend.
8. Build and compare budget plans.
9. Forecast revenue impact of budget scenarios.
10. Visualize complex datasets for non-specialists.
11. Track performance in near real time.
12. Anticipate trends and outcomes from historical patterns.
13. Adjust campaign settings from reviewed signals.
14. Guide budget allocation using saturation analysis.
15. Draft and test ad creative variants.
16. Support pixel-based lifetime attribution and synthetic conversions.
17. Present a holistic metrics dashboard.
18. Run routine daily marketing actions.
19. Generate custom audience presets.
20. Compare the reviewed result with the recorded baseline and value assumptions.
21. Capture corrections and named-owner approval before consequential use.
22. Export a versioned reviewed attribution, incrementality and budget-plan outputs linked to source evidence with source references and unresolved questions.

Everything these tools do, in one app

• Multi-Touch Attribution Attributes conversions to multiple marketing touchpoints across channels.Found in Lifesight, Forvio
• Marketing Mix Modeling Models the impact of various marketing channels on sales or conversions.Found in Lifesight, Forvio
• Incrementality Testing Tests the incremental effect of marketing activities to validate true impact.Found in Lifesight, Forvio
• Causal AI Uses causal inference to provide granular insights into marketing performance.Found in Lifesight
• Unified Data Integration Centralizes scattered marketing data from various channels into a single source of truth.Found in Lifesight
• Actionable Insights Delivers real-time recommendations on scaling, reducing, or maintaining campaign spend.Found in Lifesight
• Budget Planning and Forecasting Allows marketers to create and compare budget plans and forecast their impact on revenue.Found in Lifesight
• Scenario Planning Tools Enables quick creation and comparison of budget plans to forecast revenue impact.Found in Lifesight
• Advanced Data Visualization Facilitates easy interpretation of complex datasets through visual tools.Found in Forvio
• Real-Time Performance Tracking Tracks marketing performance in real time.Found in Forvio
• Predictive Analytics Helps businesses anticipate trends and outcomes.Found in Forvio
• Real-Time Optimization Adjusts campaigns in real-time based on customer interactions.Found in Forvio
• Budget Allocation Guidance Analyzes saturation to guide budget allocation.Found in Forvio
• AI-Powered Creative Assistant Streamlines the creation and testing of ad creatives.Found in Markopolo
• PIXEL Integration Enables lifetime user attribution and synthetic conversion tracking across ad platforms.Found in Markopolo
• Advanced Dashboard Provides a holistic view of all key metrics in one place.Found in Markopolo
• Automated Daily Actions Simplifies routine marketing tasks.Found in Markopolo
• Targeting AI Generates custom audience presets to optimize ad reach and engagement.Found in Markopolo

What goes in, what comes out

The workflow

1. In
   Start with

   Licensed ad platform exports, analytics events, CRM outcomes and spend records

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect licensed ad platform exports

4. 3

   Analytics events

5. 4

   CRM outcomes and spend records

6. 5

   Then follow this sequence: 1

7. Out
   Finish with

   Reviewed attribution, incrementality and budget-plan outputs linked to source evidence

AI does the heavy lifting, people stay in charge

Use AI to interpret permitted inputs, suggest structured mappings and generate candidate outputs for the stated task modules. Use deterministic code for arithmetic, schema validation, hard constraints and reproducible tests. Review source-linked explanations and uncertainty before accepting results. One fixed attribution window and licensed data set; final budget approval and causal claims remain with the marketing owner. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Data connections and mapping, Measurement workspace, Budget plan and client report. Use a channel overview gallery, a large central analysis canvas, and a right-hand panel for data sources, model settings and comments. Let users compare attribution models and budget scenarios side by side. Display draft, reviewed and approved states. Provide a client preview link with comments anchored to the relevant chart or plan. Make the task-specific outcome reviewed attribution, incrementality and budget-plan outputs linked to source evidence visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, data source versions, client comments, approval states, usage allowances, revision limits, download history and a rights record for supplied material. Add organization access boundaries, named reviewers, usage caps, data retention controls, export logs and explicit approval for external actions.

Integrations and data access

Ad platform APIs, analytics platforms, CRM systems and spend records. Cloud data storage, BI export and reporting destinations. Start with file exchange and validate destination specifications before promising direct publishing. Start with authorized file exchange. Validate current provider access, usage rights and schema behavior before promising a connector.

We build with our own AI software development factory, so most implementations take days to a few weeks of creation time, not months. You see working software at every step, and exact timing depends on availability.

1. 1

   Scoping call

   Day 1

   Thirty minutes on your process, your data and how you want to run it: for your own team, or for your clients. You get a fixed scope and price for the MVP.

2. 2

   MVP

   6 days

   One buyer segment, one recurring use case; first modules: connect and map ad platform, analytics and CRM data; attribute conversions across multiple touchpoints. Manual review in the loop. Built by our AI software factory.

3. 3

   Paid pilot

   7 days

   Accounts, roles, review states, audit trail and the first integration, hardened for two to three paying pilot customers.

4. 4

   Full product

   2 weeks

   Self-serve onboarding, billing, monitoring and the wider integration set.

5. 5

   Run and improve

   Monthly

   We host, monitor and improve it for a fixed monthly fee, or hand it over to your team. How the retainer works.

Why we start with an MVP

An MVP, or minimum viable product, is the smallest version that your users can actually work with. It is not a cheap version of the full solution. It is a test, built to answer the questions that decide whether the rest is worth building.

1. Pick the riskiest assumption. Here: will marketing leads and performance teams running paid campaigns across several channels use it to solve "campaign results sit in separate ad platforms and analytics tools, so budget decisions rely on last-click numbers and manual spreadsheet joins"?
2. Build only what tests it. One team, one use case, a few core modules. People do the rest by hand for now.
3. Run a paid pilot. Agree quality and outcome thresholds before the pilot using this measure: Accepted budget decisions per reporting cycle and variance between forecast and actual channel outcomes.
4. Measure, then decide. Track accepted budget decisions per reporting cycle and variance between forecast and actual channel outcomes; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase. Then expand, change course or stop, with evidence instead of opinions.

MVP scope for this solution. Pilot scope: One fixed attribution window and licensed data set; final budget approval and causal claims remain with the marketing owner. Implement one approved input format, a bounded representative case set and the first two task modules: connect and map ad platform, analytics and CRM data; attribute conversions across multiple touchpoints. Support the third module with operator review: model channel impact on sales or conversions. Include source references, corrections, basic organization access, approval states, export and value measurement. Use managed operator assistance for unresolved exceptions. The cost estimate covers this narrow prototype, not unrestricted multi-tenant scale, complex production integrations, specialist certification or physical operations.

After the MVP. Once paid pilots prove usefulness, automate repeatable reviewed steps and add one verified source integration. Expand supported inputs and case volume only after new evaluation cases pass. Build reusable customer configurations and recurring value reports around reviewed attribution, incrementality and budget-plan outputs linked to source evidence. Retain the explicit scope boundary: One fixed attribution window and licensed data set; final budget approval and causal claims remain with the marketing owner.

What the build depends on. Data upload and preview, asynchronous processing jobs, editable version history, reviewer access and tested export formats. High-fidelity measurement requires specialist analytics QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One fixed attribution window and licensed data set; final budget approval and causal claims remain with the marketing owner.

A planning range to start the conversation, not a quote. You pay per phase, so you can stop after the MVP.

Running costs per month

A rough indication of monthly hosting and AI model costs once it is live, not tested. Real costs depend on usage, file sizes and the models chosen.

Your brand, or this one

Run it under your own brand, or start from this concept style.

Selling it to your own clients: the go-to-market playbook

Pricing to test

Test a USD 300-1,500 fixed pilot for one defined data package. Offer a monthly measurement allowance after repeat demand. Quote complex multi-brand or cross-region work separately. These are test prices, not market benchmarks. Package the initial sale as one bounded reviewed attribution, incrementality and budget-plan outputs linked to source evidence. Recurring fees must specify volume, review depth and integration support. For exchanges, test a disclosed coordination or successful-service fee rather than holding customer funds. Reprice only after measuring real delivery labor; platform-build cost is separate from a commercial pilot fee.

Message to test

Reduce manual reporting effort while making budget shifts traceable to measured channel impact. Demonstrate a concrete reviewed attribution, incrementality and budget-plan outputs linked to source evidence using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Marketing leads and performance teams running paid campaigns across several channels professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample reviewed attribution, incrementality and budget-plan outputs linked to source evidence from a small authorized input set, with a transparent calculation of accepted budget decisions per reporting cycle and variance between forecast and actual channel outcomes and no promised savings.

The first 30 days

1. Week 1: interview five marketing leads and performance teams running paid campaigns across several channels and inspect a recent example of campaign results sit in separate ad platforms and analytics tools, so budget decisions rely on last-click numbers and manual spreadsheet joins.
2. Week 2: prepare a consented or synthetic demonstration of the three task modules.
3. Week 3: seek one bounded paid pilot with agreed baseline and acceptance criteria.
4. Week 4: measure accepted budget decisions per reporting cycle and variance between forecast and actual channel outcomes, reviewer effort and repeat-purchase interest. This is a demand-validation plan, not a thirty-day full-product delivery promise.

Paid pilot

Agree quality and outcome thresholds before the pilot using this measure: Accepted budget decisions per reporting cycle and variance between forecast and actual channel outcomes. Continue only if the buyer accepts the actual output, the intended job outcome improves without unacceptable errors, and measured delivery cost fits willingness to pay. Revise or stop if access is unavailable, qualified review cannot be provided, or apparent savings disappear after corrections and support. Use held-out cases when comparing model quality; use a properly reviewed comparison design before making causal claims. Record missing cases and negative results alongside successful outputs.

Success metrics

Accepted budget decisions per reporting cycle and variance between forecast and actual channel outcomes; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs reviewed attribution, incrementality and budget-plan outputs linked to source evidence. Retain permissioned settings and reviewed examples, report realized value honestly, and sell increased volume or adjacent approved workflows only after contribution margin and quality remain acceptable.

Why clients would pick it

A reusable library of approved measurement setups, channel constraints and review examples, together with reliable delivery for a narrow marketing niche. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for marketing leads and performance teams running paid campaigns across several channels. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Lifesight, Forvio and Markopolo, plus spreadsheets and native ad platform reports. Compare this product with the buyer's present method on accepted budget decisions per reporting cycle and variance between forecast and actual channel outcomes. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Data processing, storage, reviewer hours, client revision rounds and licensed source data. Additional initial validation requires representative authorized sample preparation, buyer interviews, buyer-side evaluation and bounded validation of reviewed attribution, incrementality and budget-plan outputs linked to source evidence. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve data permissions, source attribution, measurement accuracy and usage rights. Marketing owners approve budget changes and external reporting. One fixed attribution window and licensed data set; final budget approval and causal claims remain with the marketing owner. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.