Leventar: Only Pay for Proven Results

Marketing leaders are under constant pressure to prove the impact of every dollar they spend.

CMOs must justify budgets to CFOs and boards. Agencies must demonstrate their value to increasingly skeptical clients. Yet the core question remains difficult to answer:

What business results would not have happened without this marketing investment?

That question—true incrementality—is where most marketing measurement still falls short.

And when incrementality cannot be confidently measured, two problems appear.

First, brands underspend their real growth opportunity. When leaders lack confidence in measurement, they naturally become stingy in allocating budgets.

Second, at the same time, marketing waste persists. Suboptimal media mix, weak targeting, timing mistakes and platform-driven optimization often divert investment away from the activities that actually drive business outcomes.

The result is a credibility problem for marketing itself.

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A Different Approach

Leventar was created to address this challenge.

Instead of selling marketing services or analytics reports, Leventar focuses on one objective:

Delivering measurable incremental business outcomes.

Using advanced predictive modeling and AI-driven optimization, Leventar identifies the marketing actions most likely to generate incremental revenue, customers, or profit.

But the most important difference is the business model.

Brands pay only for proven incremental outcomes.

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How It Works

The Leventar system operates in three stages.

1. Predict the incremental impact.
Using high-accuracy predictive models developed over hundreds of marketing campaigns, Leventar estimates how marketing investments will translate into real business outcomes.

2. Optimize the marketing plan.
AI-driven optimization identifies the best mix of channels, targeting strategies, and timing to maximize incremental growth.

3. Measure real outcomes.
Results are validated through deterministic business metrics such as new customers, revenue, or profit. Leventar’s compensation is tied directly to those outcomes.

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Benefits for Brands and Agencies

For brands, Leventar provides a new level of confidence in marketing investment. Leaders can pursue growth opportunities knowing that decisions are backed by predictive analytics and validated by real results.

For agencies, the model restores trust. Independent incrementality analytics help agencies demonstrate the real impact of their work while allowing them to focus on what they do best: strategy, creativity, and execution.

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A New Standard for Marketing Performance

Global advertising spending exceeds $1 trillion annually, yet many investment decisions are still guided by imperfect measurement.

Leventar introduces a simpler principle:

Marketing should be paid for the business value it creates.

When incremental outcomes can be predicted, optimized, and verified, marketing moves from a cost center to a measurable driver of growth.

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Interested in learning more about how Leventar works?

We welcome conversations with brand leaders, agencies, and platform partners exploring the next generation of marketing accountability.

Why Planners Must Understand Single-Period vs Multi-Period Optimization

A long-established principle in marketing science is that advertising works over time—not just in the moment it appears. An impression served today rarely produces all of its sales lift today. Instead, it generates a decay curve of effects that lasts for days, weeks, months, or even years depending on the channel, message, and category. These delayed effects interact with the lift created by subsequent ads, sometimes reinforcing them, sometimes diminishing them. What we observe as the “total effect of advertising” is really the accumulation of all these time-based interactions. This idea is supported by decades of research in advertising response modeling.

Models That Detect, Rather Than Assume, Time Effects

A well-designed model does not impose a time effect on the data. Instead, it identifies it empirically.
For example:

• A brand TV campaign may show sales impact that persists for many months.
• A short-term digital promotion may decay in days.

In our experience, these general patterns are surprisingly consistent across brands and categories, although the scale of the effects may vary. But the model must be allowed to discover them. That is why we test multiple lag structures for every channel/content combination until we reach a highly accurate model that also performs well in field validation.

Such a model forms the foundation for optimization algorithms used in planning.

But this is precisely where marketing leaders must exercise caution.

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Where Planning Goes Wrong: The Hidden Assumption in Many Optimizers

Most advertising optimization tools optimize one period at a time.
A “period” might be a week, a month, or a quarter—but the optimizer assumes your goal is to maximize results inside that window only.

This is a critical limitation.

Why it matters

Imagine a marketer planning the next quarter. The underlying model correctly predicts that certain channels—say, brand TV—will continue generating lift well into the following quarter.

But a single-period optimizer ignores all lift that happens after the planning window.

As a result:

• Channels with long-term effects appear undervalued.
• Channels with short-term effects appear more attractive.
• Budgets unintentionally shift toward digital, promotion-heavy, or lower-funnel tactics.

Nothing in the model is wrong—the issue is the optimizer’s time horizon.
Many planners do not realize this trade-off is happening under the hood.

This exact problem appears in the academic literature on dynamic advertising optimization, where single-period models are shown to bias allocations toward short-lived effects.

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The Better Path: Multi-Period Optimization

A superior approach is a multi-period optimization algorithm—one that understands that advertising effects naturally spill over time.

Multi-period optimizers:

• Value both in-period and future-period lift.
• Produce allocations that reflect the true economics of advertising effects.
• Better match how real brands actually grow value across fiscal cycles.

When using these tools, it is essential to examine both:

1. In-period lift
2. Total lift across all relevant periods

The difference between these two can be substantial—and strategically meaningful.

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A Practical Example: The End of the Fiscal Year

This dynamic becomes most apparent in Q4 planning.

Most marketers work within an annual budget and face a familiar dilemma:

• Maximize Q4 results (and hit internal targets)
• Or
• Invest in Q4 activity that also drives Q1 of next year

A single-period optimizer will aggressively favor Q4-only lift.
A multi-period optimizer presents a more realistic picture:

• The true total return on each allocation
• The degree to which Q4 decisions influence Q1
• Where a marketer can “have their cake and eat it too” by hitting short-term targets and priming the next fiscal cycle

Accurate, lag-sensitive models empower better trade-off decisions.

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Key Takeaways for Senior Decision-Makers

• Advertising works over time. Optimizers must reflect this.
• Many tools silently impose a single-period view of the world, skewing budget decisions.
• Multi-period optimization more accurately represents how brands grow and how advertising creates value.
• Reviewing both in-period and total lift supports better strategic judgment.
• This becomes crucial in high-stakes situations such as year-end planning.

In over 20 years of helping brands measure and improve their advertising impact, we’ve found a striking pattern: nearly every brand we’ve worked with—bar one—was underinvesting in advertising. In every case, increasing spend would have driven incremental sales at a positive ROI (including time-discounted future cash flows).

And we’re not alone. Other seasoned practitioners report similar findings.

The only analytical approach that reliably reveals this underfunding—and prescribes how to fix it—is a properly built Marketing Mix Model (MMM). Not just any MMM, but one grounded in these five principles:

• C-suite alignment – built to support executive-level goals
• Accuracy – explains over 90% of historical sales variation and maintains predictive strength
• Actionability – directly informs media buying across all channels
• Holistic scope – incorporates paid, owned, and earned media, plus baseline drivers
• Validated – tested and proven in forward-looking campaigns

At a time when brands face margin pressure and agencies are stretched thin, underfunding remains a silent killer of growth. It’s a shared problem for CMOs, CFOs, and CEOs—and a symptom of flawed budget-setting without robust analytics.

Want to unlock low-risk, high-return growth? Start by asking: Are we spending too little on advertising? Finding out pays big dividends, and if you need to upgrade your MMM to find out, that may be the best investment you can make.

There’s a wave of social posts lately touting the ROI of one media channel over another. Perhaps you’ve seen them: “Channel X delivers an ROI of 5.60 vs. Channel Y at 3.50.” The implication is obvious — shift budget to Channel X, right?

Media owners are, of course, right to promote their offerings. But I wish they’d do so more carefully.  Maybe these posts should come with a footnote: * your results may vary.

The truth is, ROI is a function of many decisions — and highly sensitive to context. These values aren’t stable enough to support bold claims without major caveats. The fine print matters: such results are valid only within the scope of the study, and may not hold true in your business environment.

ROI for any channel can vary based on:

• Spend level: In most channels, increasing spend decreases ROI (and vice versa), due to diminishing returns.
• Mix effects: ROI is not a fixed channel property. Change the mix, and each channel’s ROI shifts.
• In-channel tactics: Targeting, creative, timing — all affect outcomes. Some are captured in MMM models; some are not.

Even more importantly: ROI should not be your sole metric. A high-ROI channel that delivers little incremental volume may not help your business grow. Smart media planning balances financial with volume impacts.

ROI is best viewed not as a goal, but a constraint. The right question is: How do I maximize total incremental volume while staying within a viable ROI threshold? That answer usually lies in the mix — not a single channel.

And all of this assumes the underlying model is robust, validated, and comprehensive enough to rule out confounding effects.

So when you see bold ROI claims, take them with a generous pinch of salt. Don’t reallocate your budget based on someone else’s results. Instead, Find Out For Yourself (FOFY). Run simulations, explore marginal impacts, test different mixes. That’s how you make confident, context-specific decisions — and help ensure your results can vary in your favor.

There is a class of marketer whose business model gives them a distinct advantage. The group includes banks, telcos, publishers, ecommerce sites, some companies in travel and health. Their difference? They know their customers directly; they can transact and communicate without going through intermediaries.  In recent years these businesses have been joined by new hybrid models, for example, retailers whose loyalty programs allows them to see individual customer behavior, not just sales baskets and aggregate unit volumes.

When it comes to building a marketing mix model for these brands, there are significant opportunities to improve returns on investment by combining an understanding of the impact of public facing media (eg TV, Print or OOH), as well as personal, (eg 1:1, CRM media such as email.

To achieve this, the data design needs to embrace all CRM media along with above-the-line media into the MMM (and all other forms of marketing communication!), so that you can understand the total business dynamic more coherently.  The model can then incorporate the effects of efforts aimed at cross-sell, up-sell, retention, and winback.

Whether you see this as an exciting opportunity probably depends on whether you’re a marketer who sees things as glass half full, or glass half empty.

The good news is this kind of integration offers the opportunity to increase the incremental effect of all efforts….CRM, brand building, etc…by double digit percentages.  On the CRM side, contact frequency, channel choice and customer prioritization all benefit from this broader view of what impacts buying.  

For the MMM view of marketing effect, we can meaningfully distinguish effect on current customers from effectiveness in converting prospects to customers.  We can also take advantage of segmentation data on the customer database to better understand how all forms of marketing communication affects customers from those segments.  For example, what marketing mix best supports the relationship with the minority of customers who generate the majority of profits?

The bad news is that by NOT doing this your budget is almost certainly wasting money on poor attribution, inappropriate allocation, and misleading evaluation of effect. And yet, many marketers make that same mistake.

Why does this happen?

• Data and Decision Silos
  • In some companies CRM planning and decision-making are located in a different part of the company from media planning
  • There should be a point of integration of the two, but sometimes this is not the case or if there is, the processes function poorly
• Attribution Bias
  • Thinking of attribution as analysis of only digital data or of MMM as only mass media blinds analysts and decision makers
• Analytical Complexity
  • There is no doubt that this integration increases the complexity of both CRM and MMM modeling
  • In some organizations the perceived complexity is enough to turn marketing leadership away from the effort
• Bad Incentive Design
  • Media teams may focus on MMM and only for media as their responsibility; same with CRM teams. 
  • Synergy between the two won’t happen unless they both support it, along with the CMO

How to overcome these barriers?

Consider what a double digit percentage improvement in incremental sales would mean to your company and your brand.  Compared to that, realigning incentives, improving analytical work and creating bridges between marketing teams is far less costly. 

So let’s say the glass is half full, and get to work on filling it to the brim.

Looking for help in making MMM + CRM = ROI work for you?  Contact me at dbeaton@navigationme.com