Lifesight vs Other MMM Vendors

Fully Automated Data Ingestion

Seamless onboarding of data from hundreds of media, commerce, CRM, and offline platforms with automated pipelines, validation, and schema alignment.

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Time to First Model

First model in under 3 weeks (including data onboarding, transformation, data qa, Eda & model presentation)

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Open Modeling Platform

A fully transparent modeling platform giving marketers and marketing scientists visibility into every assumption, transformation, constraint, and causal relationship used in the model.

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Causal Inference over Correlation

Model structure is powered by an explicit causal graph that acts as a digital twin of the business’ data-generating process. Models are designed for causal reasoning, not just correlation fitting.

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Bayesian or Frequentist Choice

A hybrid Bayesian–frequentist approach. Lifesight uses an ensemble of inference and predictive methods, allowing flexibility in uncertainty estimation, regularization, and forecasting performance.

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Support for Priors

Supports weak, informative, and strong priors for both model training and re-training, as well as calibration using domain knowledge and experimental evidence.

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Calibration (Multi-Source)

Supports calibration using multiple external signals including priors, geo experiments, lift studies, and incrementality tests to correct bias and improve causal validity.

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Robust Demand Forecasting

Beyond media optimisation, Lifesight enables demand forecasting by jointly modeling media, price, promotions, seasonality, macro factors, and structural demand drivers.
Lifesight uses Ensemble Forecasting technique for robust forecasting.

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Cross-Dimensional Modeling

Goes beyond hierarchical models to support true multi-dimensional modeling (channel × tactic × geography × funnel stage) with statistical rigor and scalable planning workflows.

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Mediation Analysis

Causal mediation analysis quantifies how upper- and mid-funnel interventions propagate to lower-funnel outcomes, enabling true full-funnel impact measurement.

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Interaction Analysis

Media variables do not act in isolation. Lifesight explicitly models synergies and cannibalisation effects through interaction terms to learn second-order effects.

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Halo Effect Modeling

Extends interaction modeling to capture third-order effects, measuring how investments in one channel influence outcomes across other channels, brands, or products over time.

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Trend Analysis

Lifesight decomposed trend to Category Momentum and Brand Momentum. Category momentum is inferred from Additive Auto-regressive Time Series Decomposition processes, whereas Brand Momentum is inferred from proprietary volume of search and share of search data.

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Custom Dashboard Builder

Fully customizable dashboards enabling teams to build role-specific views for executives, planners, and analysts without being constrained by fixed vendor templates.

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LLM-Powered Chart Explanations

Marketer-friendly, natural-language explanations that translate complex model outputs into clear business insights and decision guidance.

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Knowledge Agent

An embedded knowledge agent that understands the business context, model logic, and historical decisions, enabling guided exploration and faster insight discovery.

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Supports Granular Modeling

Layered modeling architecture supports high-granularity analysis while maintaining coherence with top-level demand and budget constraints.

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Calibrates Attribution Systems

Uses MMM and experimental evidence to calibrate and correct biased attribution outputs, aligning tactical attribution with true incremental impact.

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Includes Experiment Calibration

Native support for ingesting and operationalizing experiment results directly into the modeling process rather than treating experiments as standalone reports.

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Falsifiable Results

Models are designed to be challenged through holdouts, experiments, and re-estimation—making assumptions explicit and results empirically testable.

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Reports Model Coefficients & Hyper-parameters

Full access to estimated coefficients, transformations, and uncertainty intervals for transparency, auditability, and deeper scientific review.

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Reports Accuracy & Goodness of Fit

Reports multiple accuracy and fit metrics (in-sample, out-of-sample, predictive performance) rather than a single headline statistic.

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Holdout & Backtesting Accuracy

Systematic use of temporal holdouts and backtesting to validate stability, forecast accuracy, and robustness under changing market conditions.

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Model monitoring & refresh

Model is refreshed weekly. Model is monitored for drift & right alerts are raised when the model drift

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Incrementality adjusted forecasting

Lifesight follows an approach of "algorithmic fit" for our inference and prediction - we adjust prediction from inference

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