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Exactius Growth

Growth Systems Library

A reference for executives making capital allocation decisions. Each entry answers one growth question — in plain language, with the formula, the benchmark, and the Exactius take.

40entries

5topic clusters

4 minavg. read time

Cluster

Attribution & Measurement

9 entries

What your media is actually causing — vs. what it's only observing.

Cross-Channel Attribution

Cross-channel attribution distributes conversion credit across all marketing touchpoints in a customer's journey — paid social, search, email, organic, and direct — rather than assigning full credit to a single channel. It attempts to answer which combination of channels is driving growth, and how each channel contributes relative to the others.

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DTC Attribution

DTC attribution is the practice of connecting sales to the marketing channels that influenced them in direct-to-consumer businesses. Unlike single-channel attribution, DTC attribution must account for the interaction between paid social, search, email, organic, and direct traffic — all contributing to the same purchase decision across multiple devices and sessions.

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Holdout Testing

Holdout testing is a causal measurement technique that withholds advertising from a defined group — a geographic market, a user segment, or a time period — and compares conversion outcomes against a matched group that received normal advertising. The difference in conversion rates is the incremental lift caused by the advertising.

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Incrementality Testing

Incrementality testing is the method of measuring whether your advertising actually causes conversions — or whether those customers would have bought anyway. Without it, every attribution model — last-click, multi-touch, or platform-reported — tells you how credit was assigned, not whether that spending created any additional revenue.

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Last-Click Attribution

Last-click attribution assigns 100% of conversion credit to the final marketing touchpoint before a purchase — the last ad, search result, email, or link a customer clicked. It is the default attribution model in most ad platforms and the most widely criticised, because it systematically over-credits bottom-of-funnel channels and under-credits every channel that built demand upstream.

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Media Mix Modeling (MMM)

Media mix modeling (MMM) is a statistical technique that measures the contribution of each marketing channel to overall revenue using aggregate data — not user-level tracking. It is the primary measurement method for channels where individual impression-to-conversion tracking is unavailable, and it has seen a significant revival since iOS 14 made user-level attribution less reliable.

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Multi-Touch Attribution (MTA)

Multi-touch attribution (MTA) distributes conversion credit across multiple touchpoints in a customer's journey using rules or machine learning, rather than assigning full credit to a single channel. It is more accurate than last-click attribution but requires user-level tracking data to function — making it increasingly unreliable in a post-iOS 14 measurement environment.

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Signal Loss After iOS 14

Signal loss after iOS 14 refers to the structural reduction in user-level tracking data available to ad platforms following Apple's App Tracking Transparency (ATT) framework, which required users to opt in to cross-app tracking from April 2021. Opt-in rates settled around 25–35%, meaning platforms lost visibility into roughly 65–75% of iOS conversions at the user level.

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View-Through Attribution

View-through attribution credits an ad impression — not a click — with contributing to a conversion, based on the assumption that seeing an ad influenced a purchase even without a direct click. It is the most contentious attribution method in digital advertising, and the primary mechanism through which platform-reported ROAS is inflated beyond what incrementality testing can verify.

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Cluster

Unit Economics

9 entries

The ratios that determine whether growth is efficient or just expensive.

Blended CAC

Blended CAC is total marketing and media spend divided by total customers acquired — including returning customers, organic acquirees, and customers who would have come back without any paid intervention. It is the most commonly reported acquisition cost metric and the most consistently misleading, because it blends the cost of genuinely new customer acquisition with the cost of re-engaging customers who were already in the funnel.

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CAC Payback Period

CAC payback period is the number of months it takes a new customer to generate enough contribution margin to recover the cost of acquiring them. It is the cash-flow counterpart to LTV:CAC — where LTV:CAC tells you the ultimate return on acquisition, payback period tells you how long you have to wait for it.

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Contribution Margin

Contribution margin is revenue minus the costs that vary directly with each sale — cost of goods sold, payment processing, shipping, and in Exactius's framework, the media spend required to acquire that sale. It is the metric that determines whether a growth system is generating real value or just moving revenue through the business at a loss.

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LTV Forecasting

LTV forecasting is the practice of estimating the total future contribution margin a customer will generate, before they have completed their full purchase history. It is the input that makes LTV:CAC actionable at acquisition time — but it is also one of the most commonly overstated metrics in growth, because early cohort behaviour rarely predicts mature customer value accurately.

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LTV:CAC Ratio

The LTV:CAC ratio measures the lifetime value of a customer against the cost to acquire them. It is the single most important efficiency signal in growth — the ratio that determines whether a business can sustain and compound its growth, or whether it is growing at a loss.

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MER vs ROAS

Media efficiency ratio (MER) is total revenue divided by total ad spend, measured at the business level. Return on ad spend (ROAS) is channel-reported revenue divided by channel spend, measured at the platform level. They look similar but answer fundamentally different questions — and optimising for the wrong one is one of the most common ways growth teams misallocate capital.

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New Customer CAC vs Blended CAC

New customer CAC measures the cost of acquiring a first-time buyer using only the media spend dedicated to prospecting and acquisition. Blended CAC averages that acquisition cost with the much cheaper cost of re-engaging returning customers. The gap between them is the most useful diagnostic for understanding whether a growth programme is genuinely expanding the customer base or primarily recycling existing demand.

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Payback Curve

The payback curve is a cohort visualisation that plots cumulative contribution margin per customer over time against the acquisition cost, showing the month at which a customer cohort breaks even and how fast value compounds beyond that point. It is one of the most diagnostic charts in a growth team's toolkit — it simultaneously reveals CAC efficiency, retention quality, and the pace of the business's growth engine.

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iCAC (Incremental CAC)

Incremental CAC (iCAC) is the true cost of acquiring a customer who would not have come to the business without paid advertising — removing the non-incremental conversions that attribution models count as acquired customers but were never actually at risk of not converting. It is consistently higher than standard CAC, and it is the number that determines whether a channel is genuinely creating growth.

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Cluster

Media & Creative

8 entries

How to scale paid creative without eroding efficiency.

Ad Frequency

Ad frequency is the average number of times a person sees your ad within a given time window — and exceeding the optimal range is one of the fastest ways to erode paid media efficiency without the platform telling you.

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Creative Fatigue

Creative fatigue occurs when an ad creative has been shown to the same audience enough times that its ability to generate attention, engagement, and conversion begins to decline. It is the leading cause of efficiency degradation in paid social campaigns — and it happens faster than most growth teams expect.

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Creative Testing at Scale

Creative testing at scale means running structured experiments across ad variations — copy, format, hook, visual treatment — at a volume and velocity that produces statistically valid signals rather than directional guesses.

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Hook Rate vs Hold Rate

Hook rate and hold rate are two complementary video creative metrics that diagnose whether your ad captures attention in the first seconds and whether it retains that attention long enough to communicate the message — both are earlier and more actionable signals than ROAS.

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Platform-specific Creative Principles

Meta, TikTok, and YouTube each have fundamentally different content consumption patterns, algorithmic surfaces, and audience intent states — creative that performs on one platform often fails on another, and understanding these differences is a prerequisite for efficient multi-platform paid media.

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Scaling Creative Without Decaying Efficiency

Scaling creative without decaying efficiency means increasing ad spend and creative volume while maintaining or improving the contribution margin per acquisition — the challenge most brands fail at when they try to grow paid media beyond a narrow range.

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Thumb-stop Rate

Thumb-stop rate measures the percentage of people who stop scrolling when your ad appears — it is the first signal of creative attention and a reliable leading indicator of whether a piece of content will generate spend-efficient conversions.

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UGC vs Produced Creative

User-generated content (UGC) in paid advertising often outperforms polished production because it mimics the native format of the feed — but the decision between UGC and produced creative should be driven by audience stage and message complexity, not by production trend.

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Cluster

Growth Systems & Operations

8 entries

How high-performing growth teams are structured and operated.

Embedded vs Agency Model

The embedded growth model places cross-functional operators inside the business with full access to data, systems, and decision authority — producing fundamentally different accountability, speed, and compounding than an agency retainer, which advises from outside.

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Experimentation Velocity

Experimentation velocity is the rate at which a growth team runs valid, decision-producing experiments — the number of tested hypotheses per month that generate confident, actionable results rather than inconclusive noise.

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Full-funnel Growth

Full-funnel growth is the practice of managing acquisition, conversion, and retention as a single interconnected system — rather than optimising each channel or stage independently — because the interactions between funnel stages determine overall efficiency more than performance within any single stage.

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Growth Debt

Growth debt is the accumulated inefficiency in a business's growth infrastructure — broken attribution, unmeasured channels, misaligned incentives, and technical shortcuts — that compounds over time until it prevents the company from scaling efficiently.

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Growth Governance

Growth governance defines who owns growth decisions, how those decisions are made, and how performance accountability is structured — the absence of clear governance is one of the most common reasons scaling growth investments underperform.

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Growth Loops

A growth loop is a self-reinforcing system where the output of one cycle becomes the input of the next — compounding rather than depleting over time, unlike a funnel which ends when a customer converts.

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Northstar Metric

A northstar metric is the single metric that best captures the core value a product delivers to customers and correlates most strongly with long-term business health — it aligns the entire company around one number that matters.

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The Capital Allocation Loop

The Capital Allocation Loop is the Exactius framework for making media budget decisions empirically — using incrementality data, LTV:CAC signals, and contribution margin to determine where the next dollar of growth spend goes. It is the operational mechanism at the centre of the Growth Operating System.

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Cluster

Data & Signal Quality

6 entries

First-party data, signal loss, and what to trust when the signal is broken.

Cohort Analysis

Cohort analysis groups customers by when or how they were acquired and tracks their behaviour over time — revealing the patterns that aggregate metrics systematically hide, and providing the foundation for every meaningful LTV and retention decision.

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Conversion API (CAPI)

The Conversions API (CAPI) is a server-side integration that sends conversion events directly from your server to ad platforms, bypassing browser tracking restrictions — it is the most impactful technical fix available for the signal loss caused by iOS 14 and browser privacy changes.

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Data Clean Rooms

Data clean rooms are privacy-safe computing environments where two parties can analyse overlapping datasets without either party ever seeing the other's raw data — they are a solution to the signal loss problem, but only for organisations with sufficient data scale.

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First-party Data Strategy

First-party data — information collected directly from your customers with their consent — has become the most valuable marketing asset in a post-iOS 14, post-cookie world, because it is the only data source that cannot be deprecated by platform policy or privacy regulation.

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Modeled Conversions

Modeled conversions are platform estimates of the conversions that occurred but were not directly observed — Google and Meta fill in the signal gap left by iOS privacy restrictions using statistical inference, and understanding what to trust in their reported numbers is essential for accurate capital allocation.

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Predictive LTV

Predictive LTV uses machine learning to forecast a customer's lifetime value at or near the point of acquisition — enabling growth teams to make bidding and budget decisions based on the expected value of a customer rather than their first-order revenue.

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Exactius Growth

Want a team that runs these systems inside your business?

Exactius embeds cross-functional growth operators — not an agency on retainer.