When I run Attribution Simulations, I have to estimate how incremental ad dollars will impact future demand.
In other words, if I increase my email budget by 50%, how much will total demand attributed to email increase by? 5%? 25%? 50%?
Any Attribution Simulation that forecasts five year volume require a series of assumptions.
Look at the blue line in the graph. This curve assumes that each dollar spent yields a linear and consistent increase in attributed demand.
Look at the green line in the graph. This is the most common outcome - each incremental dollar spent yields less volume, in accordance with a law of diminishing returns.
The brown line in the graph is what we typically see in email marketing - one campaign a week gets you most of the benefit, with marginal benefit observed with increased campaigns each week.
I typically use power functions in my Attribution Simulations, though you are welcome to use any curve you like, as long as the curve represents reality.
The magic of any Attribution Simulation comes from the advertising curves built into the simulation. The advertising curves are the secret sauce, folks.
Here's a case where I can measure repurchase rates by year of recency - going back a whopping twenty-two years. Tell me what you ...
RFM is great for targeting one catalog to one customer. However, RFM is tough to manage in a multichannel environment. This becomes clear ...
If you don't like geeky math, please skip this post, because I am about to show you how the sausage is made! I have eight variables in...
As I've mentioned previously ... " Forecasting is the sum of all knowledge possessed by the Professionals working for a compan...