When we first wrote about the Six Foundations, we approached them largely as requirements for building a model properly. But if you lead a brand, the more important question is: why does this matter to me?

Each foundation exists for a business reason. In developing the Six Foundations, if we could not explain what better decision a foundation enabled and how that decision could improve a business outcome, we dropped it from consideration.

Seen this way, the Six Foundations aren’t simply characteristics of a good model. They are requirements for turning better understanding into better business performance.

1. Executive Relevance: Solve Something Worth Solving

We start with what the business is trying to accomplish, rather than with the data we happen to have or the marketing metrics we traditionally measure.

What does management want to change? Revenue? Profit? Customer growth? And what decisions could change that outcome?

This puts marketing into a more useful capital-allocation conversation. Instead of asking how much budget marketing should be allowed, we can ask:

How much should we invest given the incremental returns available?

That’s a question the CMO, CFO and CEO can answer together.

2. Holistic Design: Find All the Levers

Advertising doesn’t operate in isolation. Sales also move because of price, distribution, competitors, economic conditions, customer behaviour, geography and many other factors.

Accounting for these improves attribution. But the larger benefit is that we learn more about what actually moves the business.

One example is baseline dynamics. We can examine the underlying trajectory of the business and how it varies across markets and over time. We have seen advertising perform better in times and places where the baseline is strengthening than where it is weakening.

That gives us another lever for growth.

The objective isn’t simply better attribution. It is better understanding of the business and more ways to improve it.

3. Predictive Accuracy: Know Before You Go

Explaining what happened is useful. Predicting what will happen is much more valuable.

If management is considering another large investment, we want to predict the incremental outcome, where that investment should go, and whether another course of action would produce more.

We want to know before we go.

The more accurately we can predict the consequences of alternative actions, the less uncertainty surrounds the investment decision.

Accuracy creates confidence. Confidence moves money. Outcomes provide proof.

This is especially important when the problem is underinvestment. If management doesn’t know what another dollar will produce, being conservative is rational. Better prediction can change that calculation.

4. Actionability: Capture the Opportunity

Finding an opportunity isn’t the same as capturing it.

A model needs to operate where decisions can actually be changed — by geography, audience, channel, timing, investment level or other controllable variables.

It also needs to recognize that decisions interact. A media opportunity doesn’t have a fixed value. Its value depends partly on what else the advertiser is doing.

This means we can have individually optimized search, television and CRM programs and still have a suboptimal overall plan.

The objective isn’t to optimize every component independently. It is to find the combination of decisions that produces the best business outcome.

This also has implications for model design. We need to understand how each channel is actually planned and bought, and build the model at a level that allows the best combination of actions we identify to actually be executed.

5. Independent Testing: Earn Confidence

We shouldn’t trust a model simply because it explains historical data well.

Confidence needs to be earned.

That means testing against data the model wasn’t built on, testing predictions forward and, ultimately, comparing predicted incremental outcomes with what happens when the recommended action is actually taken.

Predict. Act. Observe. Test. Learn.

That also creates continuous improvement. Every action produces new evidence. Where prediction and outcome differ, we learn and improve.

For management, this creates a sensible path to scale: start, test, learn and put more capital behind the system as the evidence earns greater confidence.

6. Leverage: How Much Better Can We Make the Business?

This may be the most important question of the six.

Imagine an extremely accurate model that passes every test but discovers the business is already operating close to its potential.

It is a good model. But it hasn’t uncovered a particularly valuable opportunity.

Now imagine another model that identifies a substantial difference between what the organization is doing and what it could be doing.

Perhaps money is allocated to the wrong places. Geographic variation isn’t being exploited. Activities that work particularly well together aren’t coordinated. Timing could be improved.

Or perhaps the biggest opportunity is that the business should simply invest more.

That difference between the expected outcome from continuing as we are and what could be achieved through better decisions is leverage.

Marketing analytics has traditionally emphasized efficiency: How can we make the existing budget work harder?

That’s important, but it isn’t necessarily the most valuable question.

Suppose a business spends $50 million and we find a way to make that $50 million produce more. We have created value.

But suppose the evidence shows it should spend $70 million because the next $20 million can produce an attractive incremental return.

Not investing that money also leaves value on the table.

The constraint shouldn’t automatically be today’s budget.

The objective should be the best business outcome.

The Question That Ultimately Matters

The Six Foundations can therefore be reduced to six questions:

Are we solving something important?

Do we understand what is really driving the business?

Can we accurately predict what will happen?

Can we act on what we learn?

Have we independently tested our predictions?

And finally:

How much better can we make the business?

The first five establish our ability to answer the sixth.

Because the objective isn’t to build the most sophisticated model, or even simply to measure marketing more accurately.

It is to use better understanding, prediction and decision-making to find, value, prove and capture opportunities to improve the business.

That is what a Six Foundations model is designed to do.

Background

The “Advertising:Who Cares” movement seeks to improve the practice of advertising, reversing recent trends that have led to distrust, dissatisfaction, diminishing pride in creativity, and a decline in the appeal of the industry to new graduates.   

One dimension of improvement is with the measurement of advertising’s effect on sales (or other KPIs of fundamental importance to the health of the brand and business)

Deficiencies in these methods has blinded advertisers to the consequences of their decisions leading to abuses such as digital ad fraud.  Advertising is too often viewed with suspicion or even outright hostility in some C-suites in part because the evidence of contribution is either non-existent or couched in terms that are meaningless to CFO’s or CEO’s. Short-termism leads some brands to under-invest and miss market opportunities.

Measurement and accountability go hand in hand. For advertisers to earn the trust of their C suite colleagues they must be able to both measure their contribution and employ methods that help them reliably and consistently improve that contribution.  Stronger measurement methods will set a solid foundation from which marketers can better contribute to the development of business strategy.

We seek to set a high standard for the conduct of practitioners and help business decision makers recognize and reward the value created as a result.

For companies seeking better business performance, one sure path goes through excellence in the practice of measurement.

Setting a high standard for the measurement of advertising effect.

1. C Suite Goals
   1. Begin by aligning the measurement methods with the goals pursued by the C suite; sales, profit margins, new customers acquired or other metrics considered vital to brand and business health.
   1. Use optimization and simulation technology to connect models of these KPIs to prescriptive analytics.
   1. Measure both the opportunity and risk of plans being considered; use these measures to build C suite consensus around strategic choices.
2. Hold a Holistic view of cause and effect; develop models and source data accordingly.
   1. Recognize that once a goal is chosen, we need to explain what drives cause and effect for that goal measure.
   1. Include all forms of advertising and marketing communications efforts including PR, DM, Social (including consumer-generated media), Direct, Digital, Promotions, Sponsorships.  Owned and earned as well as paid media.
   1. Models should use audience measurement data of high quality.  Reference the Who Cares Measurement and Accountability manifesto covering this topic in depth.  (link)
   1. The measurement of business lift due to the creative used should be quantified. At the same time, it may be that creative executions used in market do not show sufficient variation to be measured in these kinds of models; in which case other techniques designed specifically for the evaluation of creative need to be used in parallel.
   1. Consider both short and long term effects of advertising; avoid over-focusing on the short term.  Recognize the asset value media spend and creative together generate.
   1. Enable balance points to be weighed through prescriptive analytics:
      1. Short vs long term; varying planning windows
      1. Offline vs online
      1. Brand vs promotion
      1. Channel mix design
   1. Certainly, advertising will contribute, but so will factors that are not under the control of the advertisers but that can affect outcomes. 
   1. All, or at least the most consequential, of these factors should be taken into account.  These could include, beyond advertising itself:
      1. The economy; local, national and international
      1. Category dynamics such as technology developments
      1. Competitor moves e.g., new product launches, pricing dynamics
      1. Distribution decisions; including the type and quality of sales outlets
      1. Operations decisions such as credit, manufacturing capacity and supply chain management
      1. Weather, where the category is sensitive to variation
      1. Other, as relevant to each brand
3. Build models to a high standard of accuracy
   1. To measure business effect, models should be built to a high standard of accuracy. 
   1. Aim to explain at least 90% of the variation in outcomes over the calibration period
   1. Test the model against hold-out periods and again when implemented, to predict outcomes over a time period relevant to decision making while maintaining high levels of accuracy
4. Models should be designed to be as actionable as possible
   1. grade proposed solutions by the ability and ease of translation into buying
   1. avoid overly-theoretical models that are impossible or at least difficult to translate into buying guidance
   1. if required, link a multi-channel model to channel-specific models to improve predictive accuracy, prescriptive relevance and buying support
5. Ensure a measurement solution can be tested independently of the developer.
   1. while testing should involve the developer, the results should be transparent to all
   1. build testing over time to prove the model can be trusted and prescriptions derived from the model achieve effects along the order of those predicted
6. Models should be judged on the basis of the leverage they bring to decision-making that effects business outcomes.  What lift in incremental business can the model help us create?  At what risk levels?  Think of the model as an asset, and its use as analogous to that of a lever being used to increase the force applied to an object.  As Archimedes famously said “give me a place to stand and a lever long enough and I will move the world”. Properly applied through simulation and optimization, models can be the lever that can move business dynamics in the right direction and at scale.

Moving from Advertising to Marketing

Our group endorses an ambitious role for the measurement of effect, evolving from a focus on advertising/marketing communications alone to the broader topic of the management of marketing. 

Measurements and then prescriptive analytics can support:

• pricing decisions
• distribution dynamics
• creative evolution
• product development
• customer experience design

Advanced solutions should allow the expression of a conceptual model of ad or marketing effectiveness to be translated into intermediate KPIs and then linked to the C suite outcome KPIs discussed above.  Avoid solutions that end with intermediate KPI action plans only.

As with advertising solutions, we recommend vendors and developers be graded for accuracy, actionability and support of sound decision-making that in turn delivers consistent positive outcomes.

Many companies have no systematic capture of their external environment and activities (economy, category, competition, distribution, operations) and marketing investments (resources including but beyond paid media). Marketing should partner with Financial Accounting standards to develop processes. Companies could then organize their internal systems to feed the modeling and reporting data in near real-time.

The industry could exert its wits and collective intelligence to build a standardized non-profit tracker of consumer brand perceptions available to all. Brand metrics can be tied to financial valuation, another outcome of marketing/accounting collaboration. An ambition we hold is to see the development of an ISO standard for ad effectiveness measurement, similar to ISO 10668 standard for brand valuation.

Marketing and advertising effectiveness should in 2020’s be measured by their contribution to the triple bottom line of ‘people’ and ‘planet’ as well as ‘profit’.

Stewart Pearson

David Beaton

Most acquisition programs optimize for one number: Customer Acquisition Cost (CAC).

That’s a problem.

A “cheap” customer isn’t necessarily a good customer. If they churn at a high rate, spend little, or cost more to service than they generate, low CAC can quietly destroy business value.

Yet many dashboards still celebrate volume + efficiency:
✔️ Lower CAC
✔️ More conversions
✔️ Higher click-through rates

…but say nothing about customer quality.

Two customers acquired at the same CAC can have radically different outcomes:

• One becomes high-value, long-term, profitable
• The other churns fast and erodes margin

If we treat them as equal, we’re optimizing blind.

The shift that matters

From:
“How cheaply can we acquire customers?”

to:
“Which customers should we acquire more of? And at what price?”

That means bringing predictive economics into acquisition decisions:

• Expected lifetime value
• Retention / tenure
• Margin contribution
• Cost to serve
• Repeat / upsell potential

What this changes

• Media optimization shifts from conversions → future value
• Channels are compared on profitability, not just CAC
• Offers are judged by the quality of customers they attract
• Growth aligns with real business outcomes, not just activity

Often, the “best” campaign isn’t the cheapest—it’s the one that delivers the most valuable customers.

Bottom line

CAC is necessary.

But CAC alone is dangerous.

The future of acquisition is quality-adjusted growth—building relationships with better customers who drive real enterprise value.

That’s where high accuracy predictive modeling changes the game.

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.