Feature engineering suggestion required
Well, you want to identify change in usage you could try something like:

$$ f(day_1,day2) = \frac{day_2-day_1 + \delta}{||day_2-day_1+\delta||} \times \Biggr|\Biggr|\frac{day_2+day_1}{(day_2+day_1+1)(day_2-day_1+1)}\Biggl|\Biggl| $$

where $\delta$ is the eps of your machine (minimum value needed to be summed to differ it from other floats)

that will give you 
$$f(100,0) \approx -98.02$$
$$f(0,100) = 100$$
$$f(100,100) \approx 0.995$$
$$f(0,0) = 0$$

You can look at my experiment here

This will map all non-changes from $[0,1]$ where $f(0,0)$ maps to $0$ and $f(\infty,\infty)$ maps to $1$

Where is it from? Just tuned the function manually. But I think this might suffice for your application

Explaining the idea

You want to have a feature that packs a lot of information:
 - Is the usage greater than zero?
 - Is it increasing or decreasing?
 - If it is stalled, how much is the usage?

Well, your usage vary in integer values so you can map the entire non-changing but above 0 case to a previously non-used interval.

The function above will map in $[0,1]$ all non-changing possibilities, in a exponential kind of way ($a^{(-\frac{1}{usage})}$) also you can extract the actual value from positive changes and the approximate value for negative change (been a better approximation when the drop is high)

This is not the perfect scenario but it is the maximum information I could compress into 1 variable with little loss.