Assessing Chatting Performance

The Problem with “Chatting Ratios”

Last year, I remember being completely glued to my CRM’s dashboard, keeping tabs on each of my model’s chatting ratios to a religious extent. It seemed to be all anyone talked about and the only way I should be assessing my chatter’s performance.

I later realised that it was a completely useless metric to base your judgement on.

It is like analysing how many cars a dealership sells per potential customer, then comparing that ratio to another dealership. There is no consideration of the types of car being sold, the business model the dealership is based on (e.g., low volume of high value cars, or high volume of low value cars), or the quality of their average customers. A Porsche dealership attracts clients with a few more zeros in their bank balance usually when compared to a budget garage. 

Applied to the OnlyFans industry, imagine the quality difference on these two types of fans:

Funnel One: Guy matches with girl on Tinder → moves to Snapchat, where he believes he is talking to the girl, they live in the same city, and sexting has already been initiated → subscribes to her OnlyFans.

Funnel Two: A random model shouts out your client’s OnlyFans page, causing one of their fans to subscribe.

The first funnel will of course deliver fans with a far greater willingness & capacity to spend than the latter. That is why the ‘chatting ratios’ of a dating app agency, and a SFS-focused agency are completely incomparable statistics. This problem is only amplified when we consider differences in sub price, or price after discount, a factor in which your chatting team has no control over; the real kicker is that a good chatting team would have a high rebill subscription rate, ‘worsening’ their chatting ratio. It is absolutely comical that the metric holds any weight to this day. 

What Metric Should I Use?

Any metric taken in isolation will ultimately fail in giving you an accurate representation of chatter performance. There’s just too many factors at play, and siding with one metric over another would be completely arbitrary. 

That being said, as a business we need data on sales in order to make informed decisions, so I rely primarily on three methods of analysis: 

[1] Revenue Per New Subscriber 

This method is extremely useful to analyse the ROI on a model, but it can also be used – alongside other metrics – to gauge the performance of your chatters.

Working out this metric is incredibly simple: 

• We take the amount of new subscribes in the last 30 days
• We then take the total account revenue in the last 30 days
• We then divide the total revenue by total new subscribers in the last 30 days

Whilst this metric avoids some limitations of a chatting ratio, it comes with its own problems, such as: (a) inaccurate if there are any FTLs being sent to the account, and (b) the figure can be skewed on accounts that have a solid history, and an established cohort of high-spending fans. 

[2] LTV For Each Marketing Channel

This obviously requires accurate tracking links for each marketing channel, campaign or platform you use to generate traffic, which is unfortunately easier said than done with the reliability of OnlyFans TLs, but is an effective way to assess both chatter performance & the quality of subscribers from any given funnel. 

This statistic is easily accessible on all CRMs, and with more effort, on an OnlyFans page itself. When coupled with revenue per new subscriber, you get practically the clearest picture possible on chatter performance, if limited only to empirical data. However, fortunately for us, we can go beyond just raw numbers on a screen.

[3] Scroll Their Chats You Lazy Bastard

Now, this is obviously time consuming, but it is an absolutely critical element of developing your chatting team. It is a skill that your chatting managers and supervisors need to be acutely aware of.  

It displays the nuances in chatting that you simply just can’t see from purely looking at the numbers. Empirical studying allows a more holistic approach to analysing your team. It is the method we as an OFM agency but also as a chatting agency place the most weight in. 

You truly can’t beat the old fashioned metric of looking over the quality of chats, their response times between messages, and a chatter’s individual sales for the amount of fans he/she spoke to. 

At LGM we place a tremendous amount of focus into oversight and we have teams of supervisors and quality analysts analysing the actual chats.