Allocate Multi-Channel Media Spend With Better Evidence

Use the optimizer to turn budget questions into decision-ready scenarios

The Multi-Channel Budget Optimizer is designed for one practical job: help media teams decide how to allocate spend across channels when every channel has different scale, efficiency, uncertainty, and operating constraints.

It is not a magic ROAS predictor. It is a planning and decision-support tool. The output should be used to pressure-test options, identify likely reallocation opportunities, and bring structure to budget conversations across finance, growth, media, analytics, and leadership.

Use this page as a support guide when setting up the tool, reviewing outputs, or explaining recommendations to stakeholders. The goal is to make multi-channel media budget allocation more consistent, transparent, and defensible.

The optimizer is especially useful when you need to answer questions like:

• How should we divide next month’s media budget across TV, CTV, radio, podcast, paid search, paid social, programmatic, and other channels?
• Which channels appear to have room to scale before returns diminish?
• Which channels are likely over-funded relative to their current marginal return?
• What happens if total budget increases, decreases, or stays flat?
• How do minimum commitments, channel caps, pacing needs, or strategic tests change the recommended allocation?
• What is the tradeoff between maximizing revenue, leads, pipeline, contribution margin, or blended CAC?

Simplicity Media’s broader measurement approach includes attribution, media mix modeling, incrementality testing, closed-loop revenue measurement, and scenario planning across channels. The optimizer fits inside that measurement system as a tactical planning layer: it helps translate performance evidence into specific budget moves. For related support, see Simplicity’s measurement and optimization services, including attribution, MMM, incrementality testing, and scenario planning. (measurement and optimization services)

What the budget optimizer does

At a high level, the optimizer compares the expected marginal return of each channel and recommends a budget allocation that best satisfies your selected objective and constraints.

In plain English: it looks for the next best dollar.

Instead of asking only, “Which channel has the highest average ROAS?” the tool asks a more useful planning question:

If we had one more dollar to spend, where would that dollar likely create the most incremental value, given current spend levels and business rules?

That distinction matters. A channel can have strong historical average performance but limited room to scale. Another channel can look less efficient on average but offer better marginal returns at the next spend increment. Effective multi-channel marketing budget planning depends on this marginal view.

The optimizer can support several planning motions:

1. Baseline review Compare the current budget mix against expected marginal returns.
2. Flat-budget reallocation Keep total spend the same but move dollars across channels to improve the objective.
3. Growth scenario planning Model what happens if budget increases and determine which channels should absorb the incremental spend.
4. Efficiency scenario planning Model what happens if budget decreases and identify where cuts may do the least damage.
5. Constraint testing Add minimums, maximums, pacing rules, test budgets, or strategic guardrails to see how recommendations change.
6. Stakeholder alignment Create a clear rationale for why budget should shift, stay protected, or be tested before larger moves are made.

When to use the optimizer

Use the Multi-Channel Budget Optimizer when you already have some channel-level performance signal and need to make allocation decisions.

Good use cases include:

• Monthly or quarterly budget planning
• Scenario planning before board, finance, or leadership reviews
• Budget reallocation after a major performance shift
• Planning spend increases across multiple active channels
• Reducing spend while protecting volume or margin
• Evaluating whether a channel is saturated
• Comparing channel investment options before launching a new flight
• Translating media mix modeling, attribution, or experiment findings into spend recommendations

The optimizer is most useful when paired with ongoing channel performance analysis. That analysis may include platform data, CRM data, call tracking, web analytics, spot-level media data, incrementality tests, or MMM outputs. Simplicity’s measurement stack includes multi-touch attribution, TV and radio spot-level attribution, podcast attribution, geo-holdout and matched-market tests, branded versus non-branded search lift analysis, and CRM-connected closed-loop revenue attribution. (measurement and optimization services)

Use another measurement method first when the core question is causal rather than allocational. For example, if the question is, “Did this channel actually create incremental demand?” you may need an incrementality test or MMM before using the optimizer for reallocation.

Inputs you need before modeling allocation

The optimizer is only as useful as the inputs. Before building scenarios, collect the cleanest available view of channel spend, outcomes, constraints, and business priorities.

1. Channel list

Start by defining the channels to include. Keep the level of detail consistent with how decisions are actually made.

For example, you might model:

• TV
• CTV
• Radio
• Podcast
• Paid search
• Paid social
• Programmatic display
• Programmatic video
• Affiliate
• Direct mail
• Email or CRM media, if it has paid delivery costs

Avoid mixing strategic channels and campaign-level line items unless the team truly reallocates at that level. If paid search is managed as brand, non-brand, shopping, and Performance Max separately, break it out. If TV is planned as a single budget pool, keep it consolidated.

2. Current spend

Enter current or planned spend by channel for the same time period. If you are planning monthly budgets, use monthly spend. If you are planning quarterly budgets, use quarterly spend.

Consistency is more important than precision at this stage. Do not mix monthly search spend with quarterly TV spend or weekly podcast spend. Normalize everything to the planning window.

3. Baseline performance metric

Choose the metric that represents channel output. Depending on the business, that might be:

• Leads
• Qualified leads
• Sales
• Revenue
• Pipeline
• Funded accounts
• App installs
• Trials
• Subscriptions
• Contribution margin
• Customer lifetime value

Whenever possible, use a metric that is closer to business value than platform-reported conversions. A channel that drives low-cost leads may not be the best channel if those leads convert poorly downstream.

4. Marginal return assumptions

The most important input is how each channel’s expected return changes as spend increases or decreases.

This can come from several sources:

• Historical response curves
• MMM output
• Incrementality tests
• Geo experiments
• Matched-market tests
• Lift studies
• Spend-level performance analysis
• Platform experiments
• Agency planning judgment, clearly labeled as an assumption

If you do not have enough data to estimate curves with confidence, start with conservative assumptions and treat the output as directional.

5. Minimum spend constraints

Some channels cannot be reduced below a practical floor. Minimums may exist because of:

• Contracted media commitments
• Learning phase needs
• Always-on brand defense
• Market coverage requirements
• Production or trafficking economics
• Testing commitments
• Sales team coverage needs
• Retail, seasonal, or geographic obligations

Minimums prevent the optimizer from recommending allocations that look mathematically attractive but are operationally unrealistic.

6. Maximum spend constraints

Caps prevent over-allocation into channels that cannot absorb unlimited spend.

Common reasons for caps include:

• Audience saturation
• Inventory availability
• Search volume limits
• Frequency concerns
• Creative fatigue
• Sales capacity
• Landing page capacity
• Budget pacing rules
• Brand safety restrictions

Caps are especially important in multi-channel marketing because some channels can scale faster than others. Paid search may hit demand limits quickly, while TV or programmatic may have broader reach but different response timing.

7. Strategic guardrails

Not every budget decision should be purely short-term. Add guardrails when the business has strategic reasons to maintain or test a channel.

Examples include:

• Protect at least a small budget for a new channel test
• Maintain investment in brand-building channels even if short-term attribution is limited
• Keep spend in a key geographic market
• Avoid cutting a channel during a learning period
• Reserve budget for creative testing
• Support a product launch or seasonal campaign

The optimizer should support strategy, not replace it.

Selecting the right objective

Before running scenarios, choose what the optimizer is solving for. This is one of the most important setup decisions.

Maximize revenue

Use this when the main goal is total revenue growth and the business is comfortable allocating dollars toward the channels most likely to generate incremental revenue.

This objective is useful for ecommerce, subscription, financial services, lead generation, and other businesses where revenue can be tied back to media performance.

Maximize qualified leads or pipeline

Use this when closed revenue data is delayed, incomplete, or not yet stable enough for optimization. For example, a B2B advertiser may optimize toward qualified pipeline because sales cycles are long.

If using leads, define quality carefully. Otherwise, the optimizer may favor channels that generate volume without business value.

Minimize blended CAC or CPA

Use this when efficiency is the priority. This is common during budget tightening, margin pressure, or periods when the business needs to protect payback.

Be careful with pure efficiency objectives. If the optimizer only minimizes CAC, it may over-favor channels that are efficient but cannot scale.

Maximize contribution margin

Use this when revenue quality varies by product, audience, geography, or channel. Contribution margin is often more useful than gross revenue when some conversions are more profitable than others.

Balance growth and efficiency

Many teams need a blended objective. For example:

• Grow revenue while keeping CAC below a threshold
• Maximize qualified pipeline while maintaining channel diversity
• Increase total leads without reducing average lead quality
• Improve contribution margin while preserving minimum reach

In these cases, set the primary objective first, then use constraints to enforce acceptable tradeoffs.

Understanding marginal return curves

Marginal return curves are the engine of the optimizer.

A marginal return curve estimates how much additional value each additional unit of spend is expected to create at different spend levels. It reflects a simple reality: most channels do not scale linearly forever.

At low spend levels, a channel may have attractive returns because it reaches the highest-intent or most responsive audience first. As spend increases, the channel may need to reach broader, colder, more expensive, or more repetitive audiences. The return on the next dollar can decline.

That decline is known as diminishing returns.

Average return vs. marginal return

Average return looks backward across the entire budget.

Marginal return looks at the next spend increment.

Example:

• A channel spent 100,000 and generated 400,000 in attributed revenue.
• Its average return is 4x.
• But if the next 10,000 is expected to generate only 20,000, the marginal return is 2x.

If another channel has a lower average return but a stronger next-dollar return, reallocating budget may make sense.

Saturation and headroom

The optimizer should help identify both saturation and headroom.

A channel may be saturated when incremental spend is expected to produce materially weaker returns than current spend.

A channel may have headroom when incremental spend can still be deployed at acceptable marginal returns.

Channel performance analysis should focus on both. It is not enough to know what worked historically. You need to understand whether each channel can continue to work at the next budget level.

Why curves should be updated

Marginal return curves are not permanent. They can shift because of:

• Seasonality
• Competitive pressure
• Creative fatigue
• Offer changes
• Landing page changes
• Audience saturation
• Inventory costs
• Economic conditions
• Tracking changes
• Sales capacity
• Channel algorithm changes

Simplicity’s measurement approach includes diminishing returns curve analysis, marginal ROI-based reallocation, creative fatigue detection, and scenario planning as part of its optimization and modeling capabilities. (measurement and optimization services)

How constraints shape recommendations

Without constraints, an optimizer may recommend an allocation that is mathematically clean but impossible to execute.

Constraints make the recommendation usable.

Minimum constraints

Minimum constraints protect required spend. Use them when a channel must remain active.

Examples:

• Keep paid search brand coverage always on
• Maintain podcast flights already contracted
• Preserve a minimum TV presence during a campaign window
• Keep retargeting active at a minimum level
• Fund a test until the measurement window is complete

Maximum constraints

Maximum constraints prevent overspending.

Examples:

• Cap paid search based on available query volume
• Cap paid social because creative fatigue is rising
• Cap CTV based on frequency limits
• Cap programmatic because inventory quality declines after a certain level
• Cap total spend in a market because sales coverage is limited

Change constraints

Sometimes the question is not only where the budget should go, but how fast it should move.

Change constraints limit how much a channel can increase or decrease in one planning cycle.

Use change constraints when:

• You want to avoid sudden disruption
• Channel learning periods matter
• Media contracts require notice
• Creative or landing pages need time to catch up
• Finance wants staged reallocation
• Sales teams need predictable lead volume

Portfolio constraints

Portfolio constraints protect the overall media system.

Examples:

• Keep at least a defined share in demand creation channels
• Keep at least a defined share in demand capture channels
• Limit total budget in any single channel
• Reserve a testing budget
• Maintain geographic coverage

These constraints are helpful because a healthy media system often needs both demand creation and demand capture. Simplicity’s service framework describes this connected system as efficient reach creating demand, digital capturing intent, and measurement feeding insights back into the next cycle.

Scenario comparison: the most useful way to use the tool

Do not run only one scenario. The optimizer is most valuable when you compare multiple scenarios side by side.

Recommended scenario set:

1. Current plan Use existing budgets and constraints as the baseline.
2. Unconstrained efficiency scenario Let the optimizer show where it would move dollars if only the objective mattered. This is not usually the final plan, but it reveals pressure points.
3. Operationally constrained scenario Add minimums, caps, and change limits that reflect real execution needs.
4. Growth scenario Increase total spend and see which channels absorb the next dollars most efficiently.
5. Reduction scenario Decrease total spend and see which cuts are least harmful to the objective.
6. Test-and-learn scenario Reserve budget for experiments or new channels before optimizing the remaining budget.

When presenting outputs, lead with the comparison rather than a single recommendation. Stakeholders need to see what changed, why it changed, and which constraints affected the answer.

How to interpret optimizer outputs

The output should help you decide what to do next, not simply provide a static answer.

Look for these signals:

Recommended spend by channel

This is the headline allocation. Compare it with the current budget to see which channels increase, decrease, or stay stable.

Do not treat every small movement as actionable. If the recommended shift is minor, it may fall within normal measurement noise or operational friction.

Dollar change by channel

The change view shows the practical reallocation decision.

Ask:

• Which channels are gaining budget?
• Which channels are losing budget?
• Are the changes operationally possible?
• Do the changes align with strategic priorities?
• Are any changes too large for one cycle?

Expected objective value

This output estimates how the scenario performs against the selected objective. Treat it as a planning estimate, not a promise.

Use it to compare scenarios, not to guarantee future performance.

Marginal return by channel

This is often the most important output.

If marginal returns are much higher in one channel than another, the optimizer may recommend shifting budget. If marginal returns are similar across channels, the current mix may already be reasonably balanced.

Binding constraints

A binding constraint is a rule that prevents the optimizer from moving further.

For example:

• Paid search may hit its maximum cap.
• TV may be held at a minimum spend.
• Paid social may be limited by a change constraint.
• Podcast may be protected because a test is still running.

Binding constraints are useful because they show whether the recommendation is driven by performance or by business rules.

Sensitivity

If small changes in assumptions create large changes in recommendations, the decision is sensitive. Sensitive decisions require caution, additional measurement, or staged testing.

If multiple scenarios point toward the same reallocation, confidence increases.

Worked hypothetical example

The following example is fictional and simplified. It is intended to show how the optimizer supports decision-making, not to represent expected results for any specific advertiser.

Situation

A growth-stage advertiser spends 1,000,000 per month across five channels:

• TV and CTV: 300,000
• Paid search: 250,000
• Paid social: 200,000
• Podcast: 150,000
• Programmatic display and video: 100,000

The business objective is to maximize qualified pipeline while keeping blended CAC within an acceptable range.

Initial channel performance read

The team reviews recent performance and sees:

• Paid search has strong average efficiency but limited search volume.
• Paid social is still efficient at current spend but creative fatigue is beginning to appear.
• TV and CTV appear to be driving branded search lift and assisted conversions, but response varies by market and daypart.
• Podcast has stable performance, but several host-read placements are already contracted.
• Programmatic has lower direct response but supports retargeting and mid-funnel reach.

Constraints entered

The team adds practical constraints:

• Paid search cannot exceed 300,000 because of query volume limits.
• Podcast cannot go below 125,000 because of committed flights.
• Programmatic cannot go below 75,000 because retargeting coverage must remain active.
• Paid social cannot increase by more than 50,000 in one month because new creative is still in production.
• TV and CTV must remain at or above 250,000 to preserve market coverage during the campaign window.

Scenario 1: current plan

The current plan is entered as the baseline. The optimizer shows that paid search and paid social have attractive average performance, but paid search is approaching its cap. TV and CTV have lower short-term direct response but still show potential headroom in selected markets. Programmatic is not the strongest channel on direct response, but it has a strategic support role.

Scenario 2: unconstrained plan

The optimizer moves a larger share into paid search and paid social. That result is useful, but not fully executable because paid search cannot absorb unlimited spend and paid social needs creative capacity.

The takeaway is not “move everything to search and social.” The takeaway is that demand capture is strong, but scaling it requires operational support and may depend on upstream demand creation.

Scenario 3: constrained reallocation

After applying constraints, the optimizer recommends a more realistic shift:

• Increase paid search modestly until it approaches the cap.
• Increase paid social within the monthly change limit.
• Reduce programmatic slightly while preserving retargeting coverage.
• Keep podcast above its committed minimum.
• Shift TV and CTV spend toward markets, networks, or dayparts with stronger measured response rather than cutting the channel broadly.

Decision

The team approves a staged reallocation rather than a dramatic budget move.

For the next month, they:

• Move a modest amount into paid search.
• Increase paid social only after new creative is ready.
• Keep podcast commitments in place until the test window closes.
• Reallocate within TV and CTV before reducing total TV and CTV investment.
• Set up an incrementality read for the channels where attribution is least conclusive.

This is how the optimizer should be used: as a structured recommendation engine combined with measurement judgment and execution reality.

Reallocation cadence: how often to update budgets

The right cadence depends on spend level, sales cycle, channel mix, and data stability.

A practical cadence often looks like this:

Weekly monitoring

Review pacing, anomalies, tracking issues, creative fatigue, and obvious performance breaks. Weekly reviews are useful for catching problems, but not every weekly movement should trigger a budget reallocation.

Monthly reallocation

For many teams, monthly is the best cadence for meaningful budget moves. It allows enough data to accumulate while still keeping the plan responsive.

Use monthly reallocations for:

• Paid search budget shifts
• Paid social budget shifts
• Programmatic adjustments
• Creative testing budgets
• Channel-level pacing changes

Quarterly scenario planning

Use quarterly planning for larger portfolio decisions:

• Expanding or reducing TV, CTV, radio, or podcast investment
• Launching new channels
• Adjusting market coverage
• Changing the balance between brand and performance
• Reviewing MMM or incrementality findings

Event-triggered reallocation

Sometimes budget should be reviewed immediately. Triggers include:

• Major tracking change
• New product launch
• Competitor price or media shift
• Sudden CAC increase
• Creative fatigue
• Inventory cost change
• Sales capacity constraint
• Seasonality inflection
• New experiment result

Avoid overreacting to noise. The optimizer should help create a disciplined reallocation process, not a habit of constant channel switching.

Limitations of the optimizer

The Multi-Channel Budget Optimizer is a planning tool. It has important limitations.

It depends on input quality

If spend, revenue, lead quality, or attribution inputs are incomplete, the output will reflect those gaps.

It does not prove causality by itself

The optimizer can use causal evidence, but it does not create causal evidence automatically. If a channel’s contribution is uncertain, use incrementality testing, MMM, or other measurement methods.

It cannot guarantee future performance

The tool can estimate scenario outcomes based on assumptions and available data. It should not be used to promise projected ROAS accuracy or guaranteed returns.

It may understate long-term effects

Short-term response data can undervalue channels that create demand over longer windows, such as TV, CTV, radio, podcast, and upper-funnel video.

It may overvalue easy-to-measure channels

Channels with cleaner click or conversion tracking can appear stronger than channels that influence demand indirectly. This is why multi-channel media budget allocation should combine attribution, MMM, and experiments rather than relying on one source.

It cannot replace media judgment

Business strategy, creative quality, inventory availability, sales capacity, and brand risk still matter. The optimizer supports decisions. It does not remove the need for experienced media planning.

When to use MMM, attribution, or experiments instead

The optimizer is strongest when the question is, “Given what we know, how should we allocate budget?”

Use other methods when the question is different.

Use media mix modeling when you need strategic allocation evidence

Media mix modeling is useful when you need to understand channel contribution using aggregate data over time, especially when channels interact, sales cycles are longer, or privacy changes limit user-level tracking. Simplicity describes MMM as a strategic allocation method that can account for seasonality, competition, macro factors, and other external variables. (measurement and optimization services)

Use MMM when:

• You need a broader view of channel contribution
• You have enough historical data
• Offline and online channels interact
• Platform attribution is double-counting or incomplete
• Leadership needs strategic budget guidance
• You want response curves for planning

Learn more through Simplicity’s media mix modeling and measurement services. (measurement and optimization services)

Use media attribution when you need tactical performance visibility

Attribution helps connect touchpoints to outcomes and is useful for tactical optimization, especially within digital channels or closed-loop CRM workflows.

Use attribution when:

• You need campaign, creative, keyword, placement, or audience-level detail
• You want to diagnose funnel performance
• You need to connect leads to pipeline or revenue
• You need faster tactical reads than MMM can provide
• You want to reduce platform over-counting and duplication

Simplicity’s measurement capabilities include multi-touch attribution, cross-device and view-through deduplication, and CRM-connected closed-loop revenue attribution. (measurement and optimization services)

Use experiments when you need causal validation

Experiments are best when you need to know what would have happened without the media.

Use incrementality tests when:

• A channel has high spend but uncertain incremental value
• Attribution and MMM disagree
• You are launching or scaling a channel
• A stakeholder questions whether a channel is truly working
• You need to validate a budget shift before scaling it

Simplicity’s measurement services include geo-holdout and matched-market incrementality tests, along with other methods such as branded search lift analysis. (measurement and optimization services)

Setup checklist

Before running the optimizer, confirm the following:

• The planning period is defined.
• All spend inputs use the same time period.
• Channels are grouped at the level where budget decisions are made.
• The primary objective is selected.
• Conversion, revenue, pipeline, or margin metrics are clearly defined.
• Attribution sources are documented.
• MMM or experiment inputs are labeled where used.
• Marginal return assumptions are entered or estimated.
• Minimum spend constraints are added.
• Maximum spend constraints are added.
• Change limits are added if reallocation must be gradual.
• Strategic test budgets are protected.
• Channel caps reflect real inventory, audience, or operational limits.
• Sales or fulfillment capacity is considered.
• Any known tracking gaps are documented.
• Scenarios are named clearly.
• Outputs are reviewed as directional planning estimates, not guarantees.

Key takeaways

• Multi-channel media budget allocation should be based on marginal return, not only average historical ROAS.
• The optimizer is most useful when comparing scenarios, constraints, and tradeoffs.
• Inputs matter. Clean spend, outcome, and constraint data create better recommendations.
• Constraints make the model operationally realistic.
• Marginal return curves help identify saturation and headroom.
• Small output changes should not always trigger action.
• Use MMM for strategic allocation, attribution for tactical visibility, and experiments for causal validation.
• Do not use the optimizer to promise ROAS accuracy or guaranteed outcomes.
• Reallocation should follow a disciplined cadence, with bigger moves supported by stronger evidence.
• The best use of the tool is not “set it and forget it.” It is a recurring decision process that combines analytics, media expertise, and business judgment.

FAQs

What is multi-channel media budget allocation?

Multi-channel media budget allocation is the process of deciding how much budget each marketing channel should receive based on business goals, performance evidence, constraints, and expected incremental return. It is a core planning discipline for multi-channel marketing because channels often work together rather than independently.

How is the budget optimizer different from a spreadsheet?

A spreadsheet can track spend and performance. The optimizer is designed to compare scenarios, apply constraints, and evaluate marginal returns across channels. It helps structure reallocation decisions rather than simply reporting what happened.

Does the optimizer guarantee future ROAS?

No. The optimizer does not guarantee projected ROAS accuracy or future performance. It produces planning estimates based on inputs, assumptions, and selected constraints. Use it to compare scenarios and guide decisions, not to promise outcomes.

How often should we update channel assumptions?

Update assumptions whenever there is a meaningful change in spend, performance, seasonality, creative, tracking, inventory, or business strategy. Many teams review performance weekly, make tactical reallocations monthly, and revisit larger portfolio assumptions quarterly.

What if two channels have similar marginal returns?

If marginal returns are similar, avoid overreacting. Consider operational factors such as scale, reliability, creative needs, customer quality, strategic importance, and measurement confidence. A small modeled difference may not justify a major budget shift.

Should brand channels be included in the optimizer?

Yes, if you have a reasonable way to estimate their contribution or define their strategic role. Brand channels may need different measurement inputs, such as MMM, geo tests, branded search lift, or longer response windows. Do not evaluate them only by last-click conversions.

What should we do if attribution and MMM disagree?

Treat disagreement as a signal to investigate. Attribution may be better for tactical detail, while MMM may be better for strategic channel contribution. If the decision is material, consider an incrementality test before making a large reallocation.

Can we use the optimizer for new channels with little data?

Yes, but use conservative assumptions and protect the budget as a test rather than treating the recommendation as proven. New channels should have defined learning goals, success metrics, and a measurement window.

What is the biggest mistake teams make with budget optimization?

The biggest mistake is optimizing to the easiest metric instead of the most meaningful one. If a channel drives cheap leads that do not convert, the optimizer may over-allocate to it unless the input metric reflects lead quality, revenue, margin, or lifetime value.

How does this connect to Simplicity Media’s measurement services?

The optimizer helps turn measurement into action. Simplicity’s measurement and optimization services include attribution, media mix modeling, incrementality testing, diminishing returns analysis, marginal ROI-based reallocation, and scenario planning. Those inputs can make budget optimization more grounded and easier to defend. (measurement and optimization services)

Ready to pressure-test your media mix?

If you are using the Multi-Channel Budget Optimizer to plan a major budget shift, pair the model with measurement support. Simplicity can help connect channel performance analysis, attribution, media mix modeling, experiments, and scenario planning into a practical reallocation process.

Use the optimizer to frame the decision. Use measurement to strengthen the evidence. Use media judgment to make the move executable.

Explore Simplicity’s measurement services, media attribution support, and media mix modeling capabilities to build a more connected, accountable media system. (measurement and optimization services)