Haus
Haus helps you run geo experiments. BlueAlpha proves what every dollar drives, every week, and moves budget to where it works, inside the stack you already run on.
What’s the difference between BlueAlpha and
Haus
?
Haus is an incrementality platform built around geo experiments, with a causal MMM and expert guidance for enterprise brands. BlueAlpha runs a weekly causal MMM that incrementality tests keep calibrated, then carries out the budget changes you approve inside your existing stack. Pick Haus if a dedicated experiment program is the job. Pick BlueAlpha if you need a budget plan that updates every week and gets executed.
Both causal. Haus leads with geo tests. BlueAlpha leads with a weekly MMM that tests calibrate.
BlueAlpha forecasts every channel weekly. Haus plans from test and model readouts.
BlueAlpha executes approved moves in your stack. With Haus, your team makes the changes.
Haus suits experiment-led enterprise teams. BlueAlpha suits teams that want measurement wired to execution.
Should you choose
BlueAlpha or
Haus
?
Both are serious measurement tools. They're built for different jobs, so the right pick depends on the decision you need to make every week.
Choose if
Haus
if
You want a dedicated geo-experiment platform, with randomized test design as the main product.
Your team already owns planning and activation and needs lift reads to feed it.
You run a few large, high-stakes tests a quarter rather than reallocating continuously.
Choose BlueAlpha if
You need a causal read on every channel each week, including the ones you haven't tested.
You want test results to update one model instead of living in separate readouts.
You want approved budget moves pushed into Google, Meta and the rest of your stack.
You want a forward-deployed team to build on your own metric definitions.
BlueAlpha vs
Haus
feature comparison
Included
Not Included/Documented
Partial
Section | Capability | BlueAlpha | Competitor | Competitor Status |
|---|---|---|---|---|
Measurement | Causal marketing mix model | Causal MMM retrained weekly, backtested and forecast-scored | Causal MMM alongside experiments | yes |
Incrementality and geo testing | Causal incrementality tests designed in the platform | Core product: geo experiments with synthetic controls | yes | |
Test results update one model | Each completed test updates the model | Experiments inform the MMM; ask how results combine | partial | |
How often the read updates | Weekly, across every channel | Per experiment; geo tests usually run several weeks | partial | |
Published model accuracy | 92.6% weekly backtesting accuracy | Not published as a single figure | no | |
Planning | Forecasting and scenario planning | Forecast any proposed move before you make it | Planning guidance from test and model results | partial |
Pricing, promo, LTV and churn models | Custom decision models on your data | Not offered | no | |
Action | Executes approved budget changes in your stack | Approved changes pushed to your ad platforms | Recommendations; your team makes the changes | no |
AI agents with guardrails you set | Agents monitor, recommend, execute and automate within your limits | Not publicly documented | no | |
Data in Claude and other assistants (MCP) | BlueAlpha MCP with Google, Meta, LinkedIn, TikTok and MMM plugins | Not publicly documented | no | |
Data and Delivery | One definition per business metric | Semantic layer shared by every model and agent | Not publicly documented | no |
Hands-on expert team | Forward-deployed team works inside your stack | Measurement strategists included | yes | |
Pricing | Custom, by quote | Custom, by quote |
Speed to Decision
Is BlueAlpha or Haus better for budget planning?
BlueAlpha is built to produce a standing plan: the model retrains weekly, forecasts each proposed move, and agents carry out what you approve. Haus is strongest when the question is whether one channel or tactic is incremental, answered with a well-designed geo test. Many teams use experiments to check the model and the model to plan the whole budget.
What to ask any vendor: how many of your channels get a fresh causal read each week, and how many days pass between that read and the budget actually changing?
Run it like a portfolio
What does rebalancing on marginal return look like?
Every channel saturates. Set a monthly mix, then rebalance: the same budget moves until each channel’s next dollar returns about the same.
Illustrative response curves, not customer data. Figures are modeled monthly incremental revenue for a $500K budget. BlueAlpha fits these curves on your own data and retrains them weekly.
Prove it. Then move it.
“Most measurement stops at media. BlueAlpha has helped us build a clearer understanding of media effectiveness and efficiency across channels, then translate those insights into actionable budget decisions. They don’t just hand us a report, they work alongside our team.”
Joshua Egan, VP of Growth Marketing, Wonder
45%
Increase in monthly order volume at Wonder within five months of engagement.
$0
Measured incremental lift from a channel whose dashboard reported 2–16x ROAS at Cann. About $480K a year moved to channels that worked.
Switching
How do you move from Haus to BlueAlpha?
Most teams keep their test history. We bring completed Haus readouts into the model as evidence during diagnosis, so nothing you learned is lost.
Week 1–2
Forward deploy
Our team connects your marketing, sales and product data and agrees one definition for revenue, conversions and CAC.
Week 3–6
Diagnose
We build your causal model, backtest it weekly and bring in results from past lift tests, including Measured readouts.
Week 6 onward
Build
You get a forecasted budget plan, and agents carry out the changes you approve in the platforms you already use.
BlueAlpha vs
Haus
What is the difference between BlueAlpha and Haus?
Haus is an incrementality platform built around geo experiments, with a causal MMM and expert guidance for enterprise brands. BlueAlpha runs a weekly causal MMM that incrementality tests keep calibrated, then carries out the budget changes you approve inside your existing stack. Pick Haus if a dedicated experiment program is the job. Pick BlueAlpha if you need a budget plan that updates every week and gets executed.
Is BlueAlpha or Haus better for budget planning?
BlueAlpha is built to produce a standing plan: the model retrains weekly, forecasts each proposed move, and agents carry out what you approve. Haus is strongest when the question is whether one channel or tactic is incremental, answered with a well-designed geo test. Many teams use experiments to check the model and the model to plan the whole budget.
Can I use BlueAlpha and Haus together?
Yes. Some teams keep Haus for the job it does best and use BlueAlpha to plan the whole budget and execute approved changes. Most teams keep their test history. We bring completed Haus readouts into the model as evidence during diagnosis, so nothing you learned is lost.
How long does it take to switch from Haus to BlueAlpha?
BlueAlpha works in three stages: Forward Deploy, Diagnose and Build. Timing depends on how many data sources you connect; your team sees the first causal read during diagnosis, before any budget moves.
Does BlueAlpha replace incrementality testing?
No. BlueAlpha runs causal incrementality tests and uses each result to calibrate the marketing mix model, so tests sharpen the model where it is least certain.
How much does BlueAlpha cost compared with Haus?
BlueAlpha is priced by scope, by quote. Haus's current pricing: custom, by quote. Ask both for a quote based on the same channels and data sources.
Who is Haus best for?
Haus fits enterprise brands that want a dedicated geo-experimentation platform with strategist support, and that already have a team to turn lift results into budget changes.
How accurate is BlueAlpha's model?
BlueAlpha reports 92.6% weekly backtesting accuracy. The model retrains weekly and is scored against what actually happened, and you can review backtests on your own data before acting on a recommendation.
Ask AI
Bring one quarter of spend data. We’ll show you which channels are driving incremental revenue and where the next dollar should go.
