Forvio Alternative

Looking for a Forvio alternative? Consider how much setup you actually want to own

Forvio is a self-serve toolkit combining MMM, multi-touch attribution and GeoLift testing, built for marketers and agencies to configure and interpret themselves. Brands looking for a Forvio alternative are often after the same accessibility to MMM, but as a managed recommendation rather than a toolkit to set up and run.

MANUAL TOOLKIT SETUP Requires continuous technical ownership. Your team must handle multi-method calibration, scenario planning variables, and model interpretation. Robyn / Meridian Setup OverheadInterpretation & Running GeoLifts

➔automated

AUTONOMOUS DIRECT DECISIONS Pangolin automates the data science pipeline end-to-end, serving human-approved, profit-optimized recommendations directly. Managed Profit Allocation

See how Pangolin compares using your own data

The Drivers

Why businesses look for a Forvio alternative

Teams typically seek a managed path over pure self-serve toolkit platforms for a few core reasons:

01 Setup and interpretation still sit with the team Forvio's Scenario Planner produces budget allocation options across different time horizons, but building the model and interpreting the output remains the user's job.

02 Not built specifically for DTC and ecommerce Forvio serves solo marketers, agencies and enterprises across many sectors, so it isn't tailored to the DTC channel mix specifically.

03 ROAS and CPA-oriented, not profit-first Forvio's optimisation centres on standard performance metrics rather than contribution profit directly.

04 No autonomous execution Forvio's scenario output is there for your team to read and action; executing an approved budget change through to the ad platforms isn't a core part of the product.

Landscape Overview

Different types of alternatives

TYPE 1 Attribution-first platforms Platforms like Triple Whale and Northbeam, centred on multi-touch attribution and real-time operational dashboarding.

TYPE 2 Enterprise measurement Platforms like Rockerbox and Sellforte, offering deeper omnichannel support and multi-market enterprise integrations.

TYPE 3 Incrementality platforms Tools like Measured, proving incremental lift through controlled, geo-matched and platform experimentation tracks.

TYPE 4 Self-serve MMM toolkits Platforms like Cassandra, offering a similarly configurable, no-code approach.

TYPE 5 Automated, decision-first MMM Pangolin's category: a managed Bayesian MMM system optimised for contribution profit, generating a specific AI-recommended budget decision, built specifically for DTC and ecommerce brands.

Pangolin Position

What is Pangolin?

Pangolin is a continuos marketing measurement platform built around Bayesian Marketing Mix Modelling. Rather than providing a configurable toolkit, Pangolin models channel contribution, saturation and contribution profit automatically once your data is connected, then generates a specific AI-recommended budget reallocation that a human approves before it goes live. It's built specifically for DTC and ecommerce brands.

Direct Comparison

How Pangolin compares to Forvio

Capability	Pangolin	Forvio
Marketing Mix Modelling	Core, Bayesian, managed	Core, built on frameworks including Meta's Robyn and Google's Meridian
Model ownership	Modelled and maintained by Pangolin	Self-serve setup by the user
Contribution profit optimisation	Core feature	Not a primary focus (ROAS/CPA-oriented)
Autonomous execution (human-approved)	Yes, core to the product	Not a core feature
Vertical focus	DTC and ecommerce specifically	Broad, cross-sector
Agency support	Not a current focus	Dedicated workspaces for agencies and freelancers

The Core Difference

Pangolin vs Forvio

Forvio's breadth - combining MMM, multi-touch attribution and GeoLift testing in one configurable platform - suits teams who want to cross-check results between methods and are comfortable setting that up themselves. Pangolin narrows the scope deliberately: no toolkit to configure, just a managed model that produces a contribution-profit-optimised recommendation automatically. If you want a multi-method toolkit to run yourself, Forvio fits; if you want the recommendation without the setup, Pangolin does.

When Pangolin is the right choice ✓You want a managed system that generates the recommendation, not a toolkit you configure yourself ✓Contribution profit needs to drive the decision, not ROAS or CPA-based scenarios ✓You're a DTC or ecommerce brand and want a platform built specifically around that channel mix ✓You'd rather approve a specific recommendation than build scenarios into a media plan yourself

When another solution may be better ○If you want MMM, MTA and GeoLift testing combined in one self-serve platform, Forvio's breadth is built for that ○If you're a consultant or agency managing measurement across multiple clients, Forvio's workspace structure suits that directly ○If you want visibility into the underlying modelling framework, Forvio's use of open frameworks like Robyn and Meridian offers that ○If your team is comfortable setting up and interpreting models directly, that control may be worth the extra setup

Platform Interface

From data to a better budget decision

Data➔ Measurement➔ Insights➔ Recommendations➔ Better budget decisions

Live Bayesian MMM Output A live view of channel contribution, saturation and contribution profit, with AI-generated budget recommendations ready for approval.

Meta AdsActive saturation limitsStable

Google SearchOver-claiming creditReduce

TikTok VideoHigh marginal profit curveScale

Live Action Recommendations · No dedicated analyst required · Optimised for contribution profit · One click to approve

Common Inquiries

Frequently Asked Questions

Do we need an in-house data science team?

No. Pangolin is a complete software-as-a-service solution. Our pipeline automatically cleans your data, fits the Bayesian algorithms, and presents the output in an intuitive interface. We handle the hard mathematics so you can focus on allocation decisions.

How often do Pangolin's models update?

Models update automatically every single week. We ingest daily transaction data, process baseline adjustments over the weekend, and deliver the final, validated attribution outputs and recommendations on Monday morning.

What data history is required to start modeling?

We recommend at least 12 months (ideally 24 months) of historical daily sales and advertising spend data. This history is crucial to train the model to understand seasonality and baseline organic performance levels.

Does Pangolin replace our existing analytics stack, or sit alongside it?

Pangolin sits alongside your existing tools. We don't ask you to rip out GA4, your ad platform dashboards, or your CRM. We ingest data from them and turn it into a single incremental-revenue view your team can act on.

How long until we see our first output?

Once your data sources are connected, initial model outputs are typically available within days, not the 3 to 6 months a traditional MMM consultancy takes. Full confidence intervals stabilise over the following few weekly refreshes as the model sees more data.

Skip the toolkit setup. Get straight to the budget decision.

See what an automated, profit-optimised recommendation looks like for your own brand.

Book a demo