The regression line we just created is extremely useful. Even from Industry Email List a visual perspective, you are now able to visualize what your expected daily conversions will be at any time of the daily cost. Although this can be done visually, using the regression formula is more accurate and you can also extend the predictions Industry Email List off the graph. In the example below that I have plotted (with a larger count), the regression equation is given as y = 28.782 * ln(x) - 190.36. In the equation, y represents conversions and x represents “cost”. To predict y for any given x , we replace x with a real number.

Assume a cost of $5,000. We say y = 28.782 * ln( 5000 ) - 190.36. Using a calculator, we get 54 conversions per day. Now, the real Industry Email List power comes here when we extend this calculation beyond the graph to Industry Email List where the expenses haven't been before. The data points on the chart show that the highest spend ever per day was under $7,000. If we replace x with 10,000, (an expected spend of $10,000 per day),

I can get an estimate using the formula, of 74.7 conversions per day. Bonus: Finding Optimal Points or Diminishing Returns with CPA Graphing “cost” Industry Email List and “conversions” together is extremely powerful in being able to predict conversions at different spends. But in reality, we are often more interested in lowering CPA or predicting conversions at a specific CPA. We can also plot CPA against conversions to better understand this. Industry Email List From the CPA chart on the right, we identify a minimum point where the CPA is lowest on the cost dimension,