# How to Implement Marketing Mix Modeling for Budget Allocation
In an era where privacy changes like iOS 14.5 have made digital tracking less reliable, Australian small business owners need a more robust way to measure success. Marketing Mix Modeling (MMM) allows you to step back from individual clicks and look at the big picture, helping you understand exactly how much revenue each marketing dollar actually generates.
By using historical data to see how different channels interact, you can stop guessing and start allocating your budget based on statistical proof rather than just gut feeling.
Prerequisites: What You’ll Need
Before you begin, ensure you have the following ready:- Historical Data: At least 2 years of weekly data for sales and marketing spend (3 years is ideal for seasonal trends).
- Channel Breakdown: Detailed spend logs for Google Ads, Meta, TV, Radio, Print, or Local SEO.
- External Variables: Data on factors you can't control, such as Australian interest rates, public holidays, or weather patterns.
- Software: While advanced teams use Python or R, small business owners can start with Excel or Google Sheets using regression analysis tools.
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Step 1: Define Your Business Objective
Before touching any data, you must decide what you are trying to solve. Are you looking to maximise total revenue, or are you trying to lower your Customer Acquisition Cost (CAC)? What you should see: A clear, written goal such as "Optimise our $5,000 monthly spend to increase total sales by 15% over the next financial year."Step 2: Gather Your Marketing "Inputs"
Collect your weekly spend across every channel. In the Australian context, ensure you are accounting for GST correctly—consistency is key here. If you report on net spend in one channel, do it for all. Pro Tip: Don't forget "Always-on" costs like agency fees or software subscriptions, as these impact your overall ROI.Step 3: Collect Your Sales "Outputs"
This is your dependent variable. For most Brisbane businesses, this is total revenue. However, if you have a long sales cycle (like a mortgage broker or solar installer), you might use "Qualified Leads" as your output instead.Step 4: Identify External "Control" Variables
Your sales aren't just driven by ads. They are influenced by the outside world. To make your model accurate, you need to include:- Seasonality: School holidays in QLD, Christmas, or EOFY sales.
- Economy: The RBA cash rate or local unemployment rates.
- Competitors: Did a major competitor open a shop down the road?
Step 5: Clean and Align Your Data
Your data needs to be in a uniform format. Create a spreadsheet where each row represents one week. Screenshot Description: You should see a table where Column A is the 'Week Commencing' date, Column B is 'Revenue', Column C is 'Google Ads Spend', Column D is 'Facebook Spend', and so on.Step 6: Account for "Adstock" (Carry-over Effects)
Marketing doesn't always work instantly. An ad seen on Monday might lead to a purchase on Friday. In MMM, this is called "Adstock."In your spreadsheet, you'll need to apply a decay formula to your spend. For example, if you spend $100 this week, maybe 20% of its value carries over to help next week’s sales.
Step 7: Run a Linear Regression Analysis
If you are using Excel, use the 'Data Analysis' Toolpak. Set your 'Y Range' as your Revenue and your 'X Range' as all your marketing spend columns and control variables. What you should see: A summary output with an "R-Square" value. An R-Square of 0.80 means your model explains 80% of your sales fluctuations—this is a great result for a small business.Step 8: Calculate the Contribution of Each Channel
Look at the coefficients in your regression results. These numbers tell you how much revenue is generated for every $1 spent on that specific channel.- Example: If Google Ads has a coefficient of 5.5, it suggests every $1 spent returns $5.50 in revenue.
Step 9: Determine Diminishing Returns
Every channel has a "saturation point" where spending more money won't result in more sales. Look at your historical data to see where the curve starts to flatten. This is crucial for Brisbane businesses with a capped local audience.Step 10: Create an "Optimised" Budget Scenario
Based on the ROI (coefficients) and the saturation points, reallocate your budget. Common Mistake: Moving 100% of your budget to the highest-performing channel. This usually fails because you'll hit saturation immediately. Move budget in 10-15% increments.Step 11: Validate the Model with a "Holdout" Period
Test your model against the last 4 weeks of data that you didn't use to build the model. If the model predicts what actually happened in those 4 weeks with high accuracy, you can trust it for future planning.Step 12: Implement and Monitor
Apply your new budget allocation for the next quarter. Monitor the results weekly to ensure the real-world performance matches your model's predictions.---
Tips for Success
- Don't ignore the 'Base': Your model will show a "Base Sales" figure—this is what you would sell if you spent $0 on marketing. This represents your brand equity and word-of-mouth.
- Quality over Quantity: It is better to have 1 year of very clean, accurate data than 3 years of messy, guessed data.
- Keep it simple: Start with 3-4 main channels. You can add more complexity as you get comfortable with the process.
Common Mistakes to Avoid
- Correlation vs. Causation: Just because sales went up when you increased TikTok spend doesn't mean TikTok caused it. It could have been an EOFY sale happening at the same time. This is why control variables (Step 4) are vital.
- Ignoring Offline: If you run a local Brisbane shop, don't forget to track when you do letterbox drops or local newspaper ads.
Troubleshooting
- "My R-Square is very low (below 0.5)": This means your marketing spend doesn't seem to correlate with your sales. You might be missing a major factor, like a competitor's massive sale or a tracking error in your revenue data.
- "One channel has a negative coefficient": This suggests that as you spend more, sales go down. This is rarely true; it usually means that channel is highly correlated with another variable you haven't accounted for.
- "The model says my brand spend does nothing": Brand awareness often has a very long Adstock (carry-over). Try increasing the decay rate in Step 6.
Next Steps
Now that you have a basic Marketing Mix Model, you can begin to refine your strategy.- Review your results every 90 days.
- Test new channels with small budgets to see how they impact the model.
- Need a hand? If the statistics feel a bit overwhelming, the team at Local Marketing Group can help you build a professional-grade model for your business. Contact us today to get started.