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Your average revenue per customer is meaningless

24 点作者 matm大约 11 年前

8 条评论

mrkurt大约 11 年前
It depends on your funnel consistency. If you are selling a self service product with transparent pricing, fully realized ARPU can be a solid number. It will change as your customer base changes, and there still could be outliers, but it&#x27;s decent and easy to reason about (and project with).<p>If, however, you are kind of self service, but do an Enterprise sales deal ... you have two funnels with two vastly different customer profiles. Not only is ARPU bad in this scenario, most numbers are.<p>The trick is narrowing the scope of a given metric enough that you can use it to make good decisions, and but not so much that it ignores important parts of a business.
programminggeek大约 11 年前
Well, you can think that averages are meaningless, but they do mean something. If your average sale is $10 and you are spending on average $9 to acquire them, you have a 10% margin on average.<p>Segmenting is great and can actually move the averages wildly, but that doesn&#x27;t mean the averages are pointless. It means that averages are just one view of the data.<p>If I want a really high level view of the data, an average is great. If I want a super low level view of the data, looking at each customer or small groups of customers is great too. It depends on what you want to know and what problem you are trying to solve.<p>Use the right tool for the job.
twic大约 11 年前
So what indicator statistics <i>are</i> good, then? &quot;Take a look at the entire picture, not just the average&quot; is great, but what am i going to put on my dashboard? What am i going to make my goal for the year?<p>On the technical side, when trying to boil down scads of metric datapoints into a single number, the statistics i usually end up using are the median and the 95th centile (or some higher centile). The median gives some sort of rough idea of where the middle of the main mass distribution is, ignoring the extremes, and the high centile gives an indication of where the top edge of the main mass is, ignoring freak outliers.<p>Would those be any use for revenue per customer?<p>Would we be better off with the median and some number that tries to capture the shape of the power law? Something that says &quot;for every dollar you go up in revenue, the number of users drops by X%&quot;, or something like that?
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Mz大约 11 年前
I am vaguely reminded of a recent discussion* about negotiating with Steve Jobs where kind of the reverse point was made: The author was advised to make the number fit the scenario Steve claimed he wanted. The author did so, creatively, without lying.<p>A lot of people do not understand the substance behind the numbers and this leads to garbage in, garbage out. That&#x27;s what this article is about: Understanding what&#x27;s behind the numbers and not being fooled by them. The previous piece was also about understanding the substance and knowing how to work the numbers to make other people happy with the proposed deal.<p>A good read on similar topics: How to Lie with Statistics.<p>* <a href="https://news.ycombinator.com/item?id=7451018" rel="nofollow">https:&#x2F;&#x2F;news.ycombinator.com&#x2F;item?id=7451018</a>
brockf大约 11 年前
I&#x27;m not sure I follow this, or buy into the suggestion of the post.<p>First, I don&#x27;t see how it&#x27;s true that data with a relatively large amount of variance will tend to be power law distributed. Defining what a &quot;large amount&quot; of variance is is tough (it depends on your intuition and choice of variance metric) but there are lots of distributions with considerable variance that are, for example, normally distributed (many more than are power law distributed, as far as I can tell).<p>Second, if you find that this is misleading your projections, why not just use a different kind of average? For example, if you just want to know, &quot;How much is the next customer likely to spend?&quot;, you might use the mode. Or, if you want a more robust average (i.e., less likely to be seriously thrown off by outliers), why not use the median? You can even complement these with confidence intervals if you want to get a sense of their precision.<p>Like twic already said, you need some indicator to understand what&#x27;s going on with your business. I think that in many cases, this will be the mean. But if you want something more robust or more practical, perhaps the median or mode might suit you better.
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daemonk大约 11 年前
Anscombe&#x27;s quartet is relevant. <a href="http://en.wikipedia.org/wiki/Anscombe&#x27;s_quartet" rel="nofollow">http:&#x2F;&#x2F;en.wikipedia.org&#x2F;wiki&#x2F;Anscombe&#x27;s_quartet</a>
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moments大约 11 年前
While I agree with the spirit of this, the conclusion is not necessarily correct. The mean can be highly informative, but should never be used alone.<p>Assume you know only the mean revenue and the maximum revenue (but forgot to measure variance). You could make an extreme scenario with the maximum possible variance to generate a &quot;worst case&quot; distribution. In this scenario, all customers either provide zero revenue or the maximum. This distribution has the maximum possible variance for a given mean and maximum.<p>Will you be profitable this year? Your chances will be better than the worst case scenario described above! If higher moments are known (variance, skew, etc.), more accurate bounds can be found.<p>In conclusion, the mean can be very useful, especially if higher moments are known.
mildtrepidation大约 11 年前
It&#x27;s trivially easy to say &quot;metric x is meaningless&quot; if you assume the person interpreting it doesn&#x27;t actually understand the context of that metric. You might as well say &quot;metrics that aren&#x27;t based on a solid understanding of your business model and user behavior are meaningless.&quot; Of course they are; this is not useful information.<p>If you run analytics on bad or incomplete information or information that doesn&#x27;t realistically relate to something important to your bottom line, or if you try to interpret useful data without knowing what you should actually be looking at in the context of the service you&#x27;re providing, it&#x27;ll obviously be meaningless.