Tag: metrics

Source & Medium: A Medium Sized Dilemma

Subtitle: Source, Medium, Attribution, Stale Information, and The Future of Data

Here’s our situation – we want to be able to slice reporting and dashboards by a number of dimensions, including source and medium.

MARDAT (the team I’m lucky enough to be working with) is working to make this kind of thing a simple exercise in curiosity and (dare I say) wonder. It’s really interesting to me, and has become more and more clear over the last year or so, how important enabling curiosity is. One of the great things that Google Analytics and other business intelligence tools can do is open the door to exploration and semi-indulgent curiosity fulfillment.

You can imagine, if you’re a somewhat non-technical member of a marketing or business development team, you’re really good at a lot of things. Your experience gives you a sense of intuition and interest in the information collected by and measured by your company’s tools.

If the only way you have access to that information is by placing a request, for another person to go do 30 minutes, two hours, three hours of work, that represents friction in the process, that represents some latency, and you’re going to find yourself disinclined to place that kind of request if you’re not fairly certain that there’s a win there – it pushes back on curiosity. It reduces your ability to access and leverage your expertise.

This is a bad thing!

That’s a little bit of a digression – let’s talk about Source and Medium. Source and Medium are defined pretty readily by most blogs and tools: these are buckets that we place our incoming traffic in. People who arrive at our websites, where ever they were right before they arrived at our websites, that’s Source and Medium.

We assign other things too – campaign name, keyword, all sorts of things. My dilemma here actually applies to the entire category of things we tag our customers with, but it’s quicker to just say, Source and Medium.

Broadly, Source is the origin (Google, another website, Twitter, and so forth) and Medium is the category (organic, referral, etc) – if this is all new to you I recommend taking a spin through this Quora thread for a little more context.

What I am struggling with, is this: for a site like WordPress.com, where folks may come and go many times before signing up, and they may enjoy our free product for a while before making a purchase, at what point do you say, “OK, THIS is the Source and Medium for this person!”

Put another way:  when you make a report, say, for all sales in May, and you say to the report, “Split up all sales by Source and Medium,” what do you want that split to tell you?

Here are some things it might tell you:

  • The source and medium for the very first page view we can attribute back to that customer, regardless of how long ago that page view was.
  • The source and medium for a view of a page we consider an entry page (landing pages, home page, etc), regardless of how long ago that page view was.
  • The source and medium for the very first page view, within a certain window of time (7 days, 30 days, 1 year)
  • The source and medium for the first entry page (landing page, homepage) within a certain window of time (7 days, 30 days, 1 year)
  • The source and medium for the visit that resulted in a signup, rather than the first ever visit.
  • The source and medium for the visit that resulted in a conversion, rather than the first ever visit.
  • The source and medium for an arrival based on some other criteria (first arrival of all time OR first arrival since being idle for 30 days, something like that)

It feels like at some point Source and Medium should go bad, right? If someone came to the site seven years ago, via Friendster or Plurk or something, signed up for a free site, and then came back last week via AdWords, we wouldn’t want to assign Friendster | Referral to that sale, right?

Maybe we have to create more dynamic Source/Medium assignation: have one for “First Arrival,” one for “Signup,” one for “Purchase.” Maybe even something like Source/Medium for “Return After 60+ Days Idle”

In the long run, it feels like having a sense of what sources are driving each of those behaviors more or less effectively would be helpful, and could help build insights – but I also feel a little crazy: does no one else have this problem with Source and Medium?

It’s Good that Data is Man Made

There’s a post from the folks at Highrise that’s been going around Customer Support and Success circles over the last couple of weeks: Data is Man Made, from Chris Gallo.

As someone who writes and speaks about customer support and leveraging data to do customer support better, I’ve had this article dropped to me in at least two Slack channels. Folks get a sense of mirth, I suspect, from needling me with articles and arguments that run contrary to the sorts of things I write about, and try to be persuasive around.

Yes; I will admit that I found this piece hard to swallow at first blush. Opening with…

Here’s a secret from the support team at Highrise. Customer support metrics make us feel icky.

… is a guaranteed burr in my side. Arguing against measurement from emotional premises?

Continue reading “It’s Good that Data is Man Made” →

Metrics, Means, and Maps

As a younger man, I spent a lot of time reading and discussing philosophy.

In the end, I was most attracted to modern moral theorists like Rawls and Nozick, but like all Philosophy majors at the State University of New York at Binghamton, I spent some time with all of the greats: Plato, Aristotle, Kant, Descartes, Marcuse, Arendt, and so forth.

(In fact, in the forward of Anarchy, State and Utopia, Nozick describes what I think is the most perfect description of all professional academia, not just Philosophy. I’m away from my copy, but I’ll post the passage when I get home!) edit: I gave it its own Post!

I’m bringing this up because one of my least favorite philosophers to read was Immanuel Kant. I struggled with Kant, like I suspect many 20 year olds do, as his writing is so incredibly dense, and translated from the original German. One piece of his moral philosophy that stands with me is this: to behave morally, a moral agent must treat other humans always as ends in themselves, and never as means.

To be more philosophically precise, Immanuel says never to treat other humans merely as means, but always as ends as well.  So, it’s not necessarily immoral to treat another human as a means, so long as you keep them in mind as an end also. It’s a tricky bit that’s easy to forget. Kant, he’s dense.

One thing that we need to bear in mind, whatever department we’re working in, is that our metrics are necessarily abstractions, a means to a larger end. In this way our mindset needs to be like Kant’s – some things are ends, some things are means, and we should be intentional about which is which, and remind ourselves that the distinction is important.

A quote that came up a number of times at the Growth Hackers convention this year was this: “Be careful what you optimize for,” and that, too, points at what I’m getting at here.

Our metrics, our measurable indicators of success, must necessarily be abstractions from real life. 

By this, I mean, reducing churn by 10% is only a means to a larger end, and has to be considered in that larger context. What’s the real reason? Why do you, personally and as an organization, want to reduce churn? Maybe it’s because you believe you have a product that can genuinely make peoples’ lives better, so the more folks who use it, longer, the better off they’ll be. That’s great! Maybe it’s to make more money – that’s OK too. 

In either of these cases, churn reduction is itself only a means toward a larger end. Success with this metric points to a larger success, something that you’re maybe not equipped to measure, something like Customer Happiness or Success of the Business. We need to keep this in mind.

Another quote that’s on my mind a lot these days: “The map is not the territory.”

Our metrics are only maps upon which we build our assumptions and beliefs – the underlying terrain, the real territory of your customers and your business, is far more complex, far more nuanced. Remember that we use metrics because they are abstractions, because they take our complex world that is impossible to understand all at once, and break it into easier-to-understand chunks.

Our metrics are by design not the whole truth. They’re reductive because they must be – because only by reducing a complex concept can we hope to make meaningful decisions. If our metric were the whole truth, if the map were a perfectly accurate representation of the whole territory, it would be perfectly useless.

Measuring our work, and our companies, and our success or lack of success, is absolutely vital to the success of any enterprise in 2016. Choosing the right metrics, and bearing in mind that our metrics only represent one part of the truth, is the hard part.