Marketing mix modeling in Digital Marketing - Step-by-Step Execution

Concept Flow - Marketing mix modeling

Collect Sales & Marketing Data

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Identify Marketing Channels

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Build Statistical Model

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Analyze Impact of Each Channel

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Optimize Marketing Budget

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Implement Changes

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Measure Results

↩Back to Collect Data

Marketing mix modeling uses data to find how different marketing channels affect sales, then helps decide the best budget allocation.

Execution Sample

Digital Marketing

1. Gather sales and marketing spend data
2. Choose channels (TV, online, print)
3. Use stats to link spend to sales
4. Find each channel's effect
5. Suggest budget changes
6. Apply and track results

This process shows how marketing mix modeling uses data and stats to improve marketing decisions.

Analysis Table

Step	Action	Data/Input	Output/Result	Next Step
1	Collect data	Sales figures, marketing spends	Dataset ready	Identify channels
2	Identify channels	Dataset	Channels: TV, Online, Print	Build model
3	Build model	Channels and data	Statistical model linking spend to sales	Analyze impact
4	Analyze impact	Model	Effect size per channel (e.g., TV=40%, Online=35%, Print=25%)	Optimize budget
5	Optimize budget	Channel effects	Recommended spend allocation	Implement changes
6	Implement changes	Budget plan	New marketing plan launched	Measure results
7	Measure results	Sales after changes	Sales increase or decrease measured	Loop back to collect data
8	End	If data insufficient or model unstable	Stop or refine model	End process

💡 Process repeats continuously to improve marketing effectiveness or stops if data/model is insufficient.

State Tracker

Variable	Start	After Step 2	After Step 4	After Step 5	Final
Sales Data	Raw sales numbers	Collected and organized	Used in model	Used for budget optimization	Measured post-implementation
Marketing Channels	Unknown	Identified (TV, Online, Print)	Analyzed impact	Budget allocated	Monitored for results
Model	None	None	Statistical model built	Model guides budget	Model updated with new data
Budget Allocation	Current spend	Current spend	Not changed yet	New recommended spend	Applied spend

Key Insights - 3 Insights

Why do we need to identify marketing channels before building the model?

Because the model needs to know which channels to analyze separately; without this, it can't link spend to sales per channel (see execution_table step 2 and 3).

What happens if the data is insufficient or the model is unstable?

The process stops or requires refinement to avoid wrong conclusions, as shown in execution_table step 8.

Why do we measure results after implementing budget changes?

To check if the changes improved sales and to provide new data for the next cycle (execution_table step 7).

Visual Quiz - 3 Questions

According to the execution_table, at which step is the statistical model created?

A Step 5

B Step 3

C Step 2

D Step 7

💡 Hint

Look at the 'Build model' action in the execution_table.

In the variable_tracker, what is the state of 'Budget Allocation' after Step 5?

A Current spend

B Not changed yet

C New recommended spend

D Applied spend

💡 Hint

Check the 'Budget Allocation' row under 'After Step 5' in variable_tracker.

If the sales data is not collected properly, what will likely happen according to the execution_table?

A The process will stop or need refinement

B The model will be built successfully

C Budget will be optimized correctly

D Marketing channels will be identified

💡 Hint

Refer to the exit_note and step 8 in execution_table.

Concept Snapshot

Marketing mix modeling:
- Collect sales and marketing spend data
- Identify marketing channels
- Build a statistical model linking spend to sales
- Analyze each channel's impact
- Optimize budget based on model
- Implement changes and measure results
- Repeat cycle for continuous improvement

Full Transcript

Marketing mix modeling is a process that uses sales and marketing data to understand how different marketing channels affect sales. First, data is collected and channels like TV, online, and print are identified. Then, a statistical model is built to link marketing spend to sales results. This model shows the impact of each channel. Based on this, marketers optimize their budget to spend more on effective channels. After implementing the new budget, results are measured to see if sales improved. This process repeats to keep improving marketing effectiveness. If data is poor or the model is unstable, the process stops or needs refinement to avoid wrong decisions.