Sequence pattern mining on continuous dataset
The established procedure is called symbolic aggregate approximation or SAX in which you first first do piecewise aggregate approximation (PAA), namely send non-overlapping mean (boxcar) windows of width $w$ over the data, and then transform the obtained values into symbols by equal-frequency-splitting the resulting data distribution into $k$ letters of the alphabet.

How to determine appropriate $w$ and $k$? They are a tradeoff between dimensionality reduction and information loss. One way to adapt $w$ is to look at window' data points variance; if the variance is too large, reduce the window size, and vice versa. Another information-theoretic is MDL as explained in this question.

Since you only have 12 values in a series, the windowing step may not be necessary.

Alternatively, you could try reshaping the data into three series:

$$Dd_1^{(c_1)}\dots{}d_{12}^{(c_1)}Dd_1^{(c_2)}\dots{}d_{12}^{(c_2)}D\dots$$

$$Uu_1^{(c_1)}\dots{}u_{12}^{(c_1)}Uu_1^{(c_2)}\dots{}u_{12}^{(c_2)}U\dots$$

$$Bb_1^{(c_1)}\dots{}b_{12}^{(c_1)}Bb_1^{(c_2)}\dots{}b_{12}^{(c_2)}B\dots$$

where D, U, and B are string literals (used as start markers) and $d_m^{(c_i)}$, $u_m^{(c_i)}$, and $b_m^{(c_i)}$ are the values of download, upload and billed, for customer $c_i$ in month $m$. Then you could use one of the frequent subsequence mining algorithms on those three sequences (separately) to find any prevaling patterns.