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| When Leonardo DiCaprio Was a Total Badass Posted: 10 Mar 2014 11:33 AM PDT                         |
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How to Set Up Meaningful (Non-Arbitrary) Custom Attribution in Google Analytics |
| How to Set Up Meaningful (Non-Arbitrary) Custom Attribution in Google Analytics Posted: 09 Mar 2014 04:15 PM PDT Posted by Tom.Capper Attribution modeling in Google Analytics (GA) is potentially very powerful in the results it can give us, yet few people use it, and those that do often get misleading results. The built-in models are all fairly useless, and creating your own custom model can easily dissolve into random guesswork. If you’re lucky enough to have access to GA Premium, you can use Data-Driven Attribution, and that’s greatâ€"but if you haven't got the budget to take that route, this post should show you how to get started with the data you already have. If you've read up on attribution modelling in the past, you probably already know what’s wrong with the default models. If you haven’t, I recommend you read this post by Avinash, which outlines the basics of how they all work. In short, they’re all based on arbitrary, oversimplified assumptions about how people use the internet. The time decay modelThe time decay model is probably the most sensible out of the box, and assumes that after I visit your site, the effect of this first visit on the chance of me visiting again halves every X days. The below graph shows this relationship with the default seven-day half-life. It plots "days since visit" against "chance this visit will cause additional visit." If it takes seven days for the repeat visit to come around, the first visit's credit halves to 25%. If it takes 14 days for the repeat visit to come around, the first visit's credit halves again, to 12.5%. Note that the graph is steppedâ€"I'm assuming it uses GA's "days since last visit" dimension, which rounds to a whole number of days. This would mean that, for example, if both visits were on the day of conversion, neither would be discounted and both would get equal credit. There might be some site and userbase out there for which this is an accurate model, but as a starting assumption it’s incredibly bold. As an entire model, it’s incredibly simplisticâ€"surely we don’t really believe that there are no factors relevant in assigning credit to previous visits besides how long ago they occurred? We might consider it relevant if the previous visit bounced, for example. This is why custom models are the only sensible approach to attribution modelling in Google Analyticsâ€"the simple one-size-fits-all models are never going to be appropriate for your business or client, precisely because they’re simple, one-size-fits-all models. Note that in describing the time decay model, I’m talking about the chance of one visit generating anotherâ€"an important and often overlooked aspect of attribution modelling is that it’s about probabilities. When assigning partial credit for a conversion to a previous visit, we are not saying that the conversion happened partly because of the previous visit, and partly because of the converting visit. We simply don’t know whether that was the case. It could be that after their first visit, the user decided that whatever happened they were going to come back at some point and make a purchase. If we knew this, we’d want to assign that first visit 100% credit. Or it might be that after their first visit, the user totally forgot that our website existed, and then by pure coincidence found it in their natural search results a few days later and decided to make a purchase. In this case, if we knew this, we’d want to assign the previous visit 0% credit. But actually, we don’t know what happened. So we make a claim based on probabilities. For example, if we have a conversion that takes place with one previous visit, what we’re saying if we assign 40% credit to that previous visit is that we think that there is a 40% chance that the conversion would not have happened without the first visit. If we did think that there was a 40% chance of a conversion being caused by an initial visit, we’d want to assign 40% credit to “Position in Path†exactly matching “First interaction†(meaning visits that were the user's first visit). If you want to use “Position in Path†as your sole predictor of the chance that a visit generated the conversion, you can. Provided you don’t pull the percentages off the top of your head, it’s better than nothing. If you want to be more accurate, there’s a veritable smorgasbord of additional custom credit rules to choose from, with any default model as your starting point. All we have to do now is figure out what numbers to put in, and realistically, this is where it gets hard. At all costs, do not be tempted to guessâ€"that renders the entire exercise pointless. Tested assumptionsOne tempting approach is simply to create a model based to a greater or lesser extent on assumptions and guesswork, then test the conclusions of that model against your existing marketing strategy and incrementally improve your strategy in this manner. This approach is probably better than nothing for improving your market strategy, and testing improvements to your strategy is always worthwhile, but as a way of creating a realistic attribution model this starting point is going to set you on a long, expensive journey. The ideal solution is to do this process in reverseâ€"run controlled experiments to build your model in the first place. If you can split your users into representative segments, then test, for example,
and so on, you can start filling in your custom credit rules this way. If your tests are done well, you can get really excellent results. But this is expensive, difficult, and time consuming. The next-best alternative is asking users. If users don’t remember having encountered your brand before, that previous visit they had probably didn’t contribute to their conversion. The most sensible way to do this would be an (optional but incentivised) post-conversion questionnaire, where a representative sample of users are asked questions like:
The results from questions like these can start filling in those custom credit rules in a non-arbitrary way. But this is still somewhat expensive, difficult and time-consuming. What if you just want to get going right away? Deconstructing the Data-Driven Attribution modelIn this blog post, Google offers this explanation of the Data-Driven Attribution model in GA Premium: “The Data-Driven Attribution model is enabled through comparing conversion path structures and the associated likelihood of conversion given a certain order of events. The difference in path structure, and the associated difference in conversion probability, are the foundation for the algorithm which computes the channel weights. The more impact the presence of a certain marketing channel has on the conversion probability, the higher the weight of this channel in the attribution model.The underlying probability model has been shown to predict conversion significantly better than a last-click methodology. Data-Driven Attribution seeks to best represent the actual behaviour of customers in the real world, but is an estimate that should be validated as much as possible using controlled experimentation.†(my emphasis) Similarly, this paper recommends a combination of a conditional probability approach and a bagged logistic regression model. Don't worry if this doesn't mean much to youâ€"I’m going to recommend here using a variant of the much simpler conditional probability method. I'd like to look first at the kind of model that seems to be suggested by Google's explanation above of their Data Driven Attribution feature. For example, say we wanted to look at the most basic credit rule: How much credit should be assigned to a single previous visit? The basic logic outlined in the explanation from Google above would suggest an approach something like this:
To me, this model is somewhat flawed (though I’m fairly sure that this flaw lies in my application of Google’s explanation of their Data-Driven Attribution rather than in the model itself). For example, say we had a large group of repeat visitors who were only coming to the site because of a previous visit, but that were converting poorly. We’d want to assign credit for these (few) conversions to the previous visits, but the model outlined above might assign them low or negative credit; this is because even though conversions among this group are caused by previous visits, their conversion rate is lower than that of new visitors. This is just one example of why this model can end up being misleading. My best solutionFiguring out from our data whether a repeat visitor came because of a previous visit or independently of a previous visit is hard. I’ll be honest: I don’t know how Google does it. My best solution is an approximation, but a non-arbitrary one. The idea is using the percentage of traffic that is either branded or direct as an indicator for brand familiarity. Going back again to how much credit should be assigned to a single previous visit, my solution looks like this:
We can use similar logic applied to users with 3+ visits to calculate the credit deserved by “middle interactionsâ€. This method is far from perfectâ€"that’s why I recommended two others above it. But if you want to get started with your existing data in a non-arbitrary way, I think this is a non-ridiculous way to get started. If you’ve made it this far and you have any ideas of your own, please post them in the comments below. Sign up for The Moz Top 10, a semimonthly mailer updating you on the top ten hottest pieces of SEO news, tips, and rad links uncovered by the Moz team. Think of it as your exclusive digest of stuff you don't have time to hunt down but want to read! |
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In fact, most people switch for better.
Without a doubt, there's a slot in every market for the cheap enough, good enough alternative.
But rapid growth and long-term loyalty come from being better instead.
When your product or your service doesn't measure up, the answer probably isn't to lower your price or offer a refund to the disappointed customer. Instead, the alternative is to invest in making it better. So much better that people can't help but talk about it—and so much better that they would truly miss it if it were gone.
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Mish's Global Economic Trend Analysis |
| Posted: 09 Mar 2014 06:43 PM PDT Every day numerous people send links to articles, edit my typos, comment on the blog, monitor comments on my blog, translate articles, etc. It's a community and I appreciate the global involvement and global help. Each day I get between 50 and 200 emails from readers. I respond to most of them. But even though I spend 2 hours or more every day reading and answering emails, I cannot respond to all of them. Frequently, I offer a one word response: thanks. That does not do justice to how I feel, especially to those who send something every day or nearly every day. Unlike other bloggers, I seldom offer "hat tips" and the reason is simple. I tend to read emails LIFO (last in first out), and most often when someone sends me a link, another person already has. So here is my word of thanks. But I also need to do more. For those who contribute every day, I need to offer special thanks. Spell Checkers I have three mainstay spell checkers: Randy, Curt, and Mark. What one of them misses, another one of them will catch. The only problem is how long it takes me to find the email pointing out my error. Typically it's not spelling per se, as I spellcheck myself, but rather stuff like saying "an" when I mean "and" or vice versa. When you proof your own article you do not see such things. Content I am going to get in trouble over this one because I am sure to leave someone out. Apologies offered in advance. Every day I get emails from Bran in Spain. Every day, Richard sends numerous links I consider writing about. So does Mark the spellchecker. Tony, Brisbane Bear, Bigpond, and Hugh send frequent emails about Australia and Asia. Many others, including Dave, Bob, Joe, Tony, Donald, and Mayraj send so much content that it's hard for me to know where to stop listing names. Blog Monitoring I have one person, "fedwatcher", who monitors comments and removes anything inappropriate. I hope I touched all the bases here, but most likely I didn't. If I missed your name, please accept my apology. Thanks! Mike "Mish" Shedlock http://globaleconomicanalysis.blogspot.com |
| Posted: 09 Mar 2014 04:53 PM PDT In the name of saving jobs, the Spanish government has decided to change the rules as to whether or not a business is viable. In bankruptcy proceedings, companies will no longer be free to decide whether they prefer to liquidate the company. Instead, corporations will be obliged to accept debt restructuring offerings from creditors on creditor-proposed terms centered around debt-to-equity conversions. Via translation from El Economista. According to various judicial, legal and bankruptcy administration sources consulted by elEconomista, companies will no longer be free to decide whether they prefer to liquidate the company.Followup post by el Economista The Ministry of Economy approved the bankruptcy reform legislation to save viable businesses including a provision that transforms debt into equity.1.3 Trillion Euro Delinquencies in Play Via translation Libre Mercado discusses the number of potential forced debt-to-equity conversions. Deputy Prime Minister Soraya Saenz de Santamaria, said that the new law completes a package of measures that are aimed at companies that, despite their high debt, "can continue to run their business while maintaining their employment."Think these debt-to-equity terms will be extremely one-sided? Addendum Reader Bran who lives in Spain sent the following pertinent comment: "1.3 trillion is the total debt held by businesses. The article did not stipulate how much of that is delinquent, but it's at least the 10% they hope to save. Registered delinquent business debt has been rising towards 10%. If they talk of saving 10% there is a lot more at stake than the 130 billion euros they think they can save." Mike "Mish" Shedlock http://globaleconomicanalysis.blogspot.com |
| Posted: 09 Mar 2014 12:10 PM PDT The takeover of Crimea by Russia is nearly complete. All that remains is the final vote on March 16. There would not be a vote if the outcome was uncertain. Troops Install Minefields, Border Markers in Crimea Bloomberg reports Pro-Russia Forces Occupy Ukraine as Separatist Vote Looms. Pro-Russian forces advanced in Ukraine's Crimean peninsula, ignoring Western calls to halt a military takeover before the region's separatist referendum. The U.S. estimates Russia now has 20,000 troops confronting a smaller Ukrainian force there. Ukraine has stepped up its eastern border defenses in the worst standoff between it and the West since the Cold War.Russia Calls in $2 Billion Gas Debt, Gazprom Ups Price of Natural Gas 37%. A Bloomberg video claims Russia Calls in $2 Billion Gas Debt. In addition, Ukraine Sees Gazprom Charging 37% More for Gas in Second Quarter. Ukraine faces a 37 percent increase in the price it pays for Russian natural gas after OAO Gazprom canceled a discount and threatened to cut supplies, Ukrainian Energy Minister Yuri Prodan told reporters today.Minimal Sanctions It is likely the US issues some sanctions. They probably will not be meaningful. as noted on March 5, Russia Has Upper Hand in Ukraine, No Meaningful Sanctions Coming. It was easy enough to impose sanctions on Iran because there was no meaningful trade with Iran other than oil. And global oil supply can come from anywhere.Germany and the UK already rejected major sanctions. Europe gets much of its natural gas from Russia and hoarding supplies is now underway. See Natural Gas Hoarding in Europe Thanks to US Sanction Proposals; Boehner vs. McCain; LNG the Solution.  The chart is from Gazprom and Morgan Stanley. It is mathematically impossible to be more than 100% dependent on a supplier, at least over the long-term. Certainly, for short periods of time imports can exceed usage, but over the long haul they cannot. That said, I do not know the timeframe for the chart. It's possible the chart is reflective of recent hoarding. Regardless, the chart shows the huge dependencies some European countries have for Russian natural gas. I feel sorry for those who will soon be living in a nation they do not feel part of. But there is little that can be done about it. Mike "Mish" Shedlock http://globaleconomicanalysis.blogspot.com |
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Here are some laws rarely broken:
As an organization succeeds, it gets bigger.
As it gets bigger, the average amount of passion and initiative of the organization goes down (more people gets you closer to averge, which is another word for mediocre).
More people requires more formal communication, simple instructions to ensure consistent execution. It gets more and more difficult to say, "use your best judgment" and be able to count on the outcome.
Larger still means more bureaucracy, more people who manage and push for comformity, as opposed to do something new.
Success brings with it the fear of blowing it. With more to lose, there's more pressure not to lose it.
Mix all these things together and you discover that going forward, each decision pushes the organization toward do-ability, reliability, risk-proofing and safety.
And, worst of all, like a game of telephone, there will be transcription errors, mistakes in interpreting instructions and general random noise. And most of the time, these mutations don't make things wonderful, they lead to breakage.
Even really good people, really well-intentioned people, then, end up in organizations that plod toward mediocre, interrupted by random errors and dropped balls.
This can be fixed. It can be addressed, but only by a never-ending fight for greatness.
Greatness can't be a policy, and it's hard to delegate to bureaucrats. But yes, greatness is something that people can work for, create an insurgency around and once in a while, actually achieve. It's a commitment, not an event.
It's not easy, which is why it's rare, but it's worth it.
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