Hi everyone. I'm still learning Terraform and AWS, but I wanted to share a small project I built to help myself understand attribution.
I kept reading about last-click, first-click, and linear models, but couldn't visualize the difference. So I made a simple dashboard using a bit of Python (Flask) and Chart.js. It takes a dummy dataset of marketing "touchpoints" and calculates the revenue credit for each channel using the three models side-by-side.
Here's the basic logic I used for the linear model calculation in my script:
```python
def calculate_linear(touchpoints):
# Simple equal weight distribution
total_revenue = 100 # Example fixed revenue
credit_per_touch = total_revenue / len(touchpoints)
return {channel: credit_per_touch for channel in touchpoints}
```
It's super basic, but seeing the charts update in real-time when I change the sample data really helped me get it. Has anyone else built something like this to learn? I'm curious about how you'd even start modeling something more complex like time decay in code.
Nice approach for getting hands-on with the logic! I often find that building a small tool to visualize data helps lock in the concepts.
> modeling something more complex like time decay in code
For time decay, you'd need to assign a weight to each touchpoint based on its position in the sequence relative to the conversion. A simple way is to give each touchpoint a score that decreases exponentially as you go back in time. Then you distribute the revenue based on those weights, not just equal slices. It's a fun next step for your dashboard!
Have you considered adding a fourth model, like position-based (U-shaped), to your comparison? That's another common one in RFPs for marketing analytics platforms.
Ask me about my RFP template
That's a great way to learn. I usually just read docs, but actually building a visual tool makes more sense.
> seeing the charts update in real-time when I change the sample data
That's the key part for me. It turns an abstract concept into something you can manipulate.
How are you managing the dummy dataset? Are you keeping it in a simple JSON file, or is it in your Flask app?
Great approach for internalizing attribution models. I actually did a similar side-by-side comparison a while back, but using simulated datasets and a local LLM as a code generator.
Your linear model snippet is the perfect starting point. For time decay, you'd basically replace the constant `credit_per_touch` with a weight vector. A quick and dirty version I've tested:
```python
def calculate_time_decay(touchpoints):
total_revenue = 100
# Give more recent touchpoints exponentially higher weight
weights = [2**i for i in range(len(touchpoints))] # simple exponential
total_weight = sum(weights)
return {channel: total_revenue * (weight / total_weight) for channel, weight in zip(touchpoints, weights)}
```
It's a fun way to see how drastically channel credit shifts. Have you tried feeding your own touchpoint sequences to an LLM API and asking it to implement these models? I found GPT-4o and Claude 3 Opus produce surprisingly different code quality for this task. The linear model is easy, but they often overcomplicate time decay with unnecessary libraries.
garbage in, garbage out