**Tags**

FastCompany wrote an article about Slack, which cited some subscriber numbers. This got me wondering what their monthly recurring revenue (MRR) is based on these figures. The MRR is a key metric that helps determine if your company is cashflow positive or not. Knowing the MRR also gives you insight into a SaaS company’s P/E ratio. Since we don’t know if Slack is profitable, we can’t compute the P/E. We can, however, use price-to-revenue as a naive proxy. In this article, I show how to use Panoptez within Slack to calculate the MRR and P/R instead of Excel (or other spreadsheet program).

A spreadsheet (e.g. Excel, Google Sheets) is often the go-to tool when you want to make a quick back-of-the-envelope calculation. In isolation this is sufficient, but when sharing your calculation with others, it becomes more involved. Within a team, it’s also likely that you want to share your methodology or the function you wrote to your colleagues. In a spreadsheet this becomes a bit more challenging since usually it means writing a function in Visual Basic or something comparable and then figure out how to distribute that among your colleagues. For this article, we’ll ignore the sharing aspect and focus on only the calculations. Our baseline will be using Google Sheets to implement these values.

## The Data

First, we need the raw data. In this case, it comes from FastCompany, which says Slack has 370,000 paid subscribers. Slack has two tiers of pricing, but FastCompany doesn’t break this out for us. The pricing itself comes from Slack, where they list the price of the standard and plus plans.

To get a single value for the MRR, we need to know how many people pay for the standard versus the plus tier. We also need to know how many pay month-to-month versus annually. Since these numbers aren’t available, we have to make assumptions for the proportion of subscribers in each plan as well as the ratio of subscribers paying month-to-month versus annually. My hand-waving guess is 70% pay for the standard tier and 30% pay for plus. I also assume that 70% of the standard tier pay month-to-month and 30% pay annually. For the plus tier I assume the opposite. If you have better assumptions, please let me know in the comments!

## Spreadsheet Calculation

In a spreadsheet, the normal procedure is to populate cells with these values and add some labels for the rows and columns. Next we create a formula to hold some intermediate results. In our case this is the weighted monthly value of a user in the standard and plus tiers. The formula bar shows the computed value for the standard tier.

To get the MRR we tally those up and multiply by the number of paid subscribers. This gives us $3.44 million per month, or $41.3 million per year.

That means with a private valuation of $2.8 billion, the P/R is about 68. Remember, this doesn’t equate to the P/E, since we aren’t accounting for expenses, so the P/E will likely be much higher. This is a detail overlooked in the Business Insider article that you shouldn’t ignore.

## Using Panoptez

Now let’s see how to do the same thing in Panoptez. First, we create a nearly identical table. Remember that since this is in Panoptez, once this table is created, any colleague on Slack can access this same table to use as they wish. We’ll create a data frame using `{ }`

notation and assign it to the variable `slack_stats`

. In case you’re wondering, a “data frame” is a fancy way of saying “table”.

Here’s a text version so you can copy and paste into your Panoptez-enabled Slack. Don’t have Panoptez integrated in your Slack group? Request an invite to the beta.

!pez slack_stats = { standard=[.7, .7, .3, 8, 6.67], plus = [.3, .3, .7, 15, 12.5], @index=["p","pmonthly","pannual","monthly","annual"] }

Each list within the data frame represents a column of the table. In our spreadsheet, the first column of data represented the standard pricing tier. To reference it, we would create a range from `B2:B6`

. Our data frame holds the same data, except we reference it as `slack_stats$standard`

. The `@index`

at the end of the data frame sets the row names for the table. If we don’t specify this, the rows will simply be numbered numerically.

To calculate the weighted value of each tier, we’ll create a temporary function. Since Panoptez tracks all variables created in your workspace, it can fill up with a bunch of garbage quickly. To reduce clutter, you can use what’s known as a “let expression” to create temporary variables that will disappear after the expression has been evaluated. The basic structure of a let expression is `let x in y`

. In this example, we create a temporary function `f`

and then apply it to `slack_stats$standard`

. The function itself is doing the same thing as in the spreadsheet formula `=B2 * (B3*B5 + B4*B6)`

, except we use the dot product (the `**`

operator) instead of explicitly summing the two products. The value at `x[1]`

corresponds to `B2`

in the spreadsheet, since that is the range we are passing to the function. If we had used `slack_stats$plus`

instead, then `x[1]`

would correspond to `C2`

.

Putting it all together, we can take our let expression and use it inside a function! That means we can create a temporary function to simplify the overall calculation. This last step creates a function that accepts the number of paying users and calculates the MRR. Notice that the expression following the `in`

is essentially the same as in the spreadsheet, which was `=F2*(B8+C8)`

. The difference is that instead of cell positions, we are using variables and functions. The variable `u`

is equivalent to `F2`

, while `f(slack_stats$standard)`

evaluates to the same value as `B8`

.

This is the code to try in your Panoptez-enabled Slack session.

!pez slack_mrr = fn u: let f = fn x: x[1] * (x[2..3] ** x[4..5]) in u * (f(slack_stats$standard) + f(slack_stats$plus))

To get the final result, we simply call this function like `!pez slack_mrr 370000`

. The nice thing about having a function is that as Slack’s user base changes, we can call this function again to get the latest MRR.

## Conclusion

In this post, I’ve shown how to use Panoptez to calculate an estimate of Slack’s MRR. I’ll leave it to reader to write an expression that calculates the P/R ratio from this. In a subsequent post, we’ll look at changing the assumptions used in this example.

*Panoptez is a collaborative data analysis and visualization platform accessible via chat systems, like Slack. Request an invite to the beta or contact us for preferred access.*