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Trusted by the Category Leaders Who've Outgrown Their Dashboards

From global consumer brands to High-growth DTC, Liftlab is the measurement system for CMOs and CFOs. They choose Liftlab when platform attribution no longer suffices.

Retail jewelry — global

+9.5% revenue

from a 2% budget shift

Entertainment — enterprise

7 → 13 channels

weekly mROAS reallocation

Financial software

+19% gross revenue

year-end window

DTC apparel

3.4x TikTok spend

at max profit

Ecommerce grocery

13 channels optimised

weekly CAC forecasts

Six Reasons Your Omnichannel Budget Conversations Keep Stalling

Improving dashboards or data quality will not resolve these issues. They stem from measurement systems built for an environment with fewer channels, no retail media, and a functional identity graph, which no longer reflects your current operating landscape.

Six Reasons Your Omnichannel Budget Conversations Keep Stalling

Improving dashboards or data quality will not resolve these issues. They stem from measurement systems built for an environment with fewer channels, no retail media, and a functional identity graph, which no longer reflects your current operating landscape.

01 — Multi Distribution Endpoint

One media investment, four revenue streams — and zero unified attribution

Marketing spend should generate sales across retail, wholesale, direct-to-consumer, third-party marketplaces, and retail media, each with unique unit economics and conversion timelines. For example, an omnichannel brand may see paid social campaigns increase in-store sales, yet attribution models might not reflect this, even as pixel trackers report direct ecommerce sales.

output: posterior_iROAS[channel] + CI_95%, saturation_curve[channel]

02 — Online-to-Offline Attribution Gap

30–50% of your conversions happen in-store. Your model doesn’t know.

BOFIP (Buy Online, Pick Up In-Store) has become standard. While digital campaigns can drive measurable store visits, current attribution systems have not kept pace. As a result, digital spending that increases foot traffic or in-store conversions is often under-credited. This makes digital media appear less efficient than it truly is and distorts performance measurement.

LiftLab models online-to-offline without in-store pixels

03 — Promotions Contaminate the Signal

You can’t measure media effectiveness if promotions are corrupting the baseline

Omnichannel retail relies heavily on promotions such as seasonal discounts, retailer trade promotions, and clearance events. If promotional lift is not modeled separately from media, it can lead to over-attribution to campaigns occurring that week. ROI figures that include promotional noise may therefore be misleading.

LiftLab strips promo lift from digital baseline models

04 — Measurement Infrastructure Lag

Your MMM was built before retail media existed as a strategic channel

Over the past three years, CTV, retail media networks such as Amazon DSP, Walmart Connect, and Kroger Precision Marketing, creator partnerships, and in-store digital screens have become significant growth channels. However, most omnichannel MMMs were designed for a five-channel media mix. Today, brands manage more than 13 channels using measurement systems that are no longer adequate.

LiftLab ingests 70+ platform including retail media

05 — Signal Fragmentation

Five channel teams, five dashboards, five different versions of “what worked”

Paid search, paid social, programmatic, retail media, and CRM each report separate attribution. As a result, Finance receives fragmented ROAS claims that do not align with a single revenue line. There is no unified view of how these channels contribute to total omnichannel revenue; there are only competing claims about budget performance.

LiftLab creates one source truth with audit

06 — Privacy Signal Erosion

iOS and cookie changes didn't just reduce data; they broke the attribution model your budgets were built on.

iOS ATT restrictions have eliminated the mobile identity graph used for cross-device attribution, and cookie deprecation is accelerating. As a result, brands that depend on pixel-based MTA for omnichannel journeys now operate with incomplete data. Many are unaware of the extent of missing information or its impact on channel mix decisions. Budgets previously calibrated with complete data are now based on incomplete signals, which is reflected in CAC.

LiftLab uses no identity graph or user-level tracking

The Three Measurement Systems You’re Running, and Why None of Them Are Enough

Most omnichannel brands are running legacy MMM, platform self-reporting, and MTA simultaneously. Each has a different story. None account for the full omnichannel picture. And they can’t be reconciled.

INCUMBENT TYPE A

Legacy MMM Consultancies

(Nielsen, Analytic Partners, Ipsos MMA)

  • →Quarterly refresh cycles — operationally incompatible with weekly promotional and media decisions
  • →3–6 month onboarding — by first insight, your media mix has already evolved
  • →Retrospective PowerPoint — tells you what happened, not what to do next quarter
  • →Black-box methodology — Finance is told the number, not shown the architecture
  • →National aggregation — obscures regional variance that drives actual allocation decisions
  • →No closed loop — MMM and geo experiments live in separate workstreams, never connected

INCUMBENT TYPE B

Ad Platform Self-Reporting

(Google, Meta, TikTok, Amazon Ads)

  • →Every platform claims the same conversion — double and triple counting is structural, not accidental
  • →Attribution windows favor the platform — set to maximize apparent ROAS, not advertiser incrementality
  • →Walled-garden conflict of interest — Meta measuring Meta is not an independent audit
  • →Incentivized to inflate — overcount by 40–60% on conversions is documented, not disputed
  • →No offline coverage — zero visibility into in-store revenue, wholesale, or marketplace sales
  • →No promotional decomposition — seasonal spikes become “media wins” in platform dashboards

INCUMBENT TYPE C

Multi-Touch Attribution (MTA)

(GA4, Adobe, Northbeam, Triple Whale)

  • →Broken by iOS restrictions — the identity graph that powered cross-device attribution no longer exists
  • →Systematically over-credits bottom-funnel — search and retargeting capture demand that brand and TV already created
  • →No offline channel coverage — TV, OOH, radio, in-store are structurally invisible
  • →No saturation modeling — MTA doesn’t tell you when a channel has peaked
  • →Doesn’t typically give you Incremental Revenue
  • →No scenario capability — shows what happened, cannot model what would happen with a different mix
  • →Single journey view — omnichannel journeys that cross devices and channels are structurally unmeasurable

One Model. Every Channel. The Same Number Finance Can Actually Approve.

A single, weekly-updated model provides consistent data for both your CMO's campaign planning and your CFO's budget approval. It uses the same numbers and methodology across all your channels, not just those tracked digitally.

Layer 01 — Unified Data FoundationAGILE MMM

One model. Every endpoint. Store sales, DTC, marketplace, retail media; simultaneously.

LiftLab’s two-stage Agile MMM consolidates spend, outcomes, and business-context signals such as pricing, promotions, seasonality, and offline store data from all distribution channels into a unified econometric model. For the first time, the impact of paid social on in-store revenue is measured alongside its direct-to-consumer results, with accurate promotional lift decomposition to ensure Finance can rely on the data.

Explore Agile MMM →
Layer 02 — Incrementality Closed LoopTRUST ENGINE™

Geo holdout results don’t sit in a slide deck, they calibrate the model permanently.

The Trust Engine™ feeds causal proof from transparent incrementality testing directly back into the Agile MMM as calibration inputs. For omnichannel brands, this means geo experiments that separate in-store lift from digital conversion are continuously sharpening channel response curves. Every test cycle, the model gets more accurate. Every budget decision compounds on better evidence than the last.

See Incrementality + Calibration →
LAYER 03 — DAILY SIGNAL INTELLIGENCEPLATFORMSENSE

When CPMs spike mid-campaign, your budget shouldn’t keep running on last week’s assumptions.

PlatformSense collects daily data from over 70 ad platforms and applies real-time signals to stable econometric response curves. It detects CPM shifts, creative fatigue, and auction volatility within 24 hours, rather than 90 days. For omnichannel brands with frequent promotions and seasonal events, this enables in-flight reallocation decisions based on current data rather than outdated models.

Explore PlatformSense →
LAYER 04 — FINANCE-GRADE SCENARIO PLANNINGSCENARIO PLANNER

Walk into every budget meeting with Conserve, Maintain, and Accelerate scenarios your CFO can approve.

The Scenario Planner builds constraint-aware budget scenarios that respect pre-committed TV contracts, channel caps, CAC ceilings, and locked media agreements, producing outcome ranges with explicit trade-offs. Marketing and Finance align on the same numbers before a dollar is committed. The budget meeting becomes a decision, not a negotiation between competing channel dashboards.

Explore Scenario Planner →

The Omnichannel Brand Before and After LiftLab

BEFORE LIFTLAB

  • →Quarterly MMM report lands in November for the campaign that ran in July. Budget decisions already made.
  • →Five channel dashboards, five ROAS numbers — none reconcile to Finance’s revenue line.
  • →n-store conversion is invisible paid social looks inefficient because the attribution model can’t see what it drove.
  • →Promotional lift is credited to media holiday ROAS “wins” in the November Black Friday MMM are actually the promo, not the campaign.
  • →Brand spend is first to be cut because no model has ever proven its halo effect on performance channel efficiency.
  • →Retail media is allocated by gut feel no model integrates Amazon DSP alongside TV and paid social.
  • →CAC inflation is discovered 6 months late the saturation point had passed; the model didn’t flag it in time.

AFTER LIFTLAB

  • →Weekly model refresh — current channel coefficients and mROAS scores available every Monday before the planning call.
  • →One unified iROAS number across store, DTC, marketplace, and retail media — Finance audits the same model Marketing uses.
  • →Online-to-offline modeled explicitly — paid social’s true contribution to in-store revenue is visible and credited correctly.
  • →Promotions decomposed cleanly — media ROI is measured net of promotional noise; no more inflated holiday “wins.”
  • →Brand halo quantified on the P&L — long-term multipliers show brand’s compounding effect on future CAC. Budget protected.
  • →Retail media modeled alongside all channels — Amazon DSP, Target sit in the same model as TV and paid social.
  • →Saturation flagged before CAC spikes — PlatformSense detects when a channel has peaked and surfaces the reallocation signal within 24 hours.

Six Capabilities That Close the Measurement Gaps Omnichannel Brands Actually Have

Six purpose-built tools, each addressing a specific measurement failure that omnichannel brands experience. Connected into one compounding system — not six separate workstreams.

Full-Funnel Budget Planning

Align Marketing and Finance with a unified, constraint-aware budget plan that covers all distribution channels. Enter your actual guardrails, such as pre-committed TV, channel caps, and CAC ceilings, to generate a mathematically sound allocation plan that both teams can implement without revisiting the spreadsheet.

Explore Full-Funnel Budget Planning →

Marginal ROI & Diminishing Returns

Do not rely on average ROAS for optimization. LiftLab identifies where each channel’s returns level off at your current spend, rather than using outdated data. For omnichannel brands, this analysis accounts for retail media saturation, in-store promotional effects, and cross-channel diminishing returns that blended ROAS often conceals.

Explore Diminishing Returns →

Incrementality & Calibration

Geo holdout tests are structured to address omnichannel complexity by capturing in-store lift, marketplace halo, and DTC incrementality within a single experiment. Each result is integrated into the Agile MMM via the Trust Engine™, continuously refining response curves. This closed-loop approach offers a level of insight that standalone incrementality vendors cannot match.

Explore Incrementality & Calibration →

Scenario Planning & Forecasting

“What happens to total omnichannel revenue if we shift 15% from paid search to retail media?” LiftLab provides constraint-aware forecasts to answer this question. The Conserve, Maintain, and Accelerate scenarios are presented in terms of revenue outcome ranges and CAC delta, using the same metrics your CFO applies to other capital investments.

Explore Scenario Planning & Forecasting →

Real-Time Budget Optimization

24 hour from platform shift to updated budget recommendation — not 90 days. PlatformSense ingests 70+ platforms including Amazon Ads, Walmart Connect, and all major paid social and search channels. When a promotional window opens or a channel saturates mid-flight, the signal reaches you before the quarter’s P&L already reflects the missed opportunity.

Explore Real-Time Budget Optimization →

Long-Term Brand Value Measurement

For omnichannel brands, the compounding effect of brand advertising on future CAC and customer lifetime value is often the most under-measured asset in the marketing P&L. LiftLab calibrates long-term advertising multipliers to your brand and category, integrating them into scenario planning so brand investment is supported by financial evidence rather than intuition.

Explore Long-Term Brand Value Measurement →

Frequently Asked Questions

LiftLab incorporates store-level transaction data, regional sales outcomes, offline conversion signals, and digital spend data as core model inputs. The Two-Stage Agile MMM quantifies each channel's impact on total omnichannel revenue, including offline conversions, by distinguishing consumer response from marketplace dynamics. No in-store pixel is needed. The model determines online-to-offline lift by analyzing the statistical relationship between digital media spend and store revenue, calibrated using geo-holdout experiments that directly measure in-store lift.

Your Next Budget Decision Spans Every Channel You Operate. It Should Be Measured That Way.

Schedule a 30-minute session with a LiftLab marketing scientist. We will use your data to model your omnichannel revenue decomposition, clearly identifying each distribution endpoint’s contribution and where additional investment will have the greatest impact.

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First scenario within first planning cycle
Marketing scientist included
70+ platforms including retail media
SOC 2 & ISO 27001 certified
Your Next Budget Decision Spans Every Channel You Operate. It Should Be Measured That Way.

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