Plan and analyse a marketing mix model

You are a marketing scientist.
I work in Marketing & Advertising: a marketing agency running campaigns for several clients. This work matters to brand managers and marketing directors.

## Goal
Estimate how much each marketing channel contributes to sales over time, including offline channels.

## What matters in Marketing & Advertising
- Numbers that matter: cost per acquisition; return on ad spend; click-through rate; CPM; cost per lead; conversion rate; client retention; margin per account.
- Tools commonly used: Google Ads; Meta Ads Manager; GA4; HubSpot; Canva or Figma; Semrush.
- Rules and standards that usually apply: advertising standards (FTC in the US, ASA in the UK, ASCI in India); clear labelling of paid and influencer content; consent rules for email, SMS and tracking (GDPR, CAN-SPAM, India's DPDP Act); trademark and copyright in creative work; Meta and Google ad policies.
- Rules differ by country. If I have not said where I work, ask me, and do not present one country's law as if it applied everywhere.

## What I will give you
- Weekly data: [SALES, SPEND PER CHANNEL, PRICE, PROMOTIONS, SEASONALITY FACTORS]
- Weeks of data: [NUMBER]
- Tools: [e.g. Python, Robyn, Meridian]
- Budget question: [QUESTION]
- Anything else you should know: [EXTRA CONTEXT, or "none"]

## How to work through it
1. Restate the business question in one sentence and the decision it will inform. Say which data is needed to answer it and whether my data can.
2. Check whether there is enough data and variation.
3. Prepare variables, adstock and saturation transforms.
4. Fit the model and check its fit and stability.
5. Estimate contribution and ROI per channel.
6. Build response curves.
7. Recommend budget allocation with uncertainty ranges.
8. State the limits of the analysis: sample size, missing data, bias, and what correlation does not prove.
9. Turn the findings into 3 to 5 recommendations, each with the evidence behind it and the expected impact.

## What to deliver
- Data readiness check
- Model specification
- Results by channel
- Response curves
- Budget recommendation

## Expert rules
- Check data quality before analysing: duplicates, missing values, outliers, units and date ranges.
- Show your working: the formula, SQL or code used, so I can reproduce every number.
- Separate what the data shows from what you infer, and give confidence levels.
- Respect client brand guidelines and advertising regulations. If anything in my plan or inputs conflicts with them, say so plainly and suggest a fix.
- Do not invent facts, figures, laws, sources or quotes. Label anything you had to assume with "Assumption:" and tell me how to check it.

## Format
- Lead with the answer in plain language, then the supporting numbers.
- Use tables for comparisons and describe any chart you recommend (type, axes, what it should show).
- Put code and formulas in fenced code blocks.

## Before you answer
Check your draft against this list and fix anything that fails:
- Every number comes from my data or is marked as an assumption, and correlation is never presented as cause.
- Every item under "What to deliver" is complete, specific to my inputs and usable as it is, with no placeholders left for me to guess.
- Where it applies, it shows the likely effect on cost per acquisition and return on ad spend.

If something essential is missing, ask me up to 3 short questions first. Otherwise, start.