A Success/Failure method for Analytics

Prasad Ajinkya4 Comments

When identifying the Key Performance Indicators (KPI) of your business, it makes sense to choose the proper measures of success. I have written about choosing the proper measures of success in the past. Since most of the work that I do is in the realm of the web, the principles via which we operate and do reports are more or less the same.

The only thing that changes is the conversion … or the success metric. In other words, the reason for which the website is built, the purpose of that site. Hence, the measure of success approach works.

Designing for new paradigms

However, what would happen if the product being built is not meant for the web, or was not based on the same principles? How would we go about identifying metrics and actionable reports.

For that we would have to go to the very reason why we need analytics.

The Purpose of Analytics

If I were to define the reason why we use analytics in any product, it would be to –

  1. Identify the wins, celebrate them and try to find the rules which get us more wins
  2. Identify the failures, and figure out ways to fix those failures so that we can improve

This view helps us do two things primarily, one to find out and scale the good things, and the other to find out and weed out the bad things in our product.

To do this, we would need metrics (or KPIs) that would indicate a success or a failure.

Measures of Success

The measure of success metric help in identifying the clear wins and celebrating them within the team. These also help in figuring out what worked for you in the past and on how to re-create those wins. One definitive thing that needs to be done (and I have learnt this the hard way), is that wins or measures of success metrics need to shared in a broader audience to give a sense of purpose to the entire team on what they are working on.

A good measure of success is task completion rate, or conversion rate, or profitability.

Measures of Failure

The measure of failure metric help in identifying failures within a certain activity. These are also metrics which help in identifying opportunities of improvement. Measure of Failure metrics should help us root out problems within our current design/product. I say root out, because once you identify the failure, you have to act and ensure that the failure does not happen again.

An example of measure of failure could be bounce rate.

Unlike measures of success, measures of failure may not be shared with large teams. Rather I feel (and I am want your opinion on this), that they are much more effective when communicated to the right localized teams.

Importance of Context in Analyzing data

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Recently, I was analyzing some user generated data in a mobile app. The app was sending content on specific categories to a niche audience, and at the end of each content piece, there was a simple 5 star rating feedback for users to rate the piece.

The assumption that the design team who thought of this was that the feedback data was an objective metric.

Objective metric for Subjective behavior

Unfortunately, the behavior of users and how they understood the content piece is a very subjective topic. By subjective, I mean to say that for two different users, the value they would associate to the usefulness of the same piece varies.

We could always say ceterus paribus, but I would say – “Let’s not fool ourselves here”.

In the world of subjectivity, ceterus paribus doesn’t exist

There could be so many factors that are associated to my giving a 5/5 to a piece v/s 4/5 to the same piece, that in the end, I’d be forced to say it depends, and then list out of a whole new set of variables.

Slicing the Data with new variables

This is a problem. Since, my existing data set does not have these new variable. So, from analyzing – now I am back to collecting data. To be frank, there’s no end to this cycle … collect data, realize that you might want more data and rinse, repeat.

Where do we divine the new rules and new variables? We start from the context.

Ergo, the simple and freeing approach of the answer to the questions we were looking for in the data, sometimes lies partially in the data points, and partially in the context.

Let me illustrate this

Let’s take a fairly popular metric – Bounce rate.

Now, if I were to say that my website’s bounce rate is 100%, what would you say?

Sucks, right??

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Now, if I were to tell you that my website is a single page website where I want my users to watch a product launch video. That bounce rate suddenly pales and aren’t you itching to ask me about the number of users who played the video upto a certain point?

If you have been working with Google Analytics, then some of you might even suggest that adding a non-interaction event in GA when the play button is hit.

One more example

Let’s take one more metric. Pages/Session to measure how much content the user is consuming on a site.

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Let’s see this in a different spiel. A user is on your site, searching for content and is not able to find what he wants, and keeps visiting different pages. After going through 8-9 pages, he finally gives up and leaves the site. That 8.5 as pages/session now doesn’t seem that sexy now does it?

 

Understand the context

Therefore staring at a pure data puke may not help. Understanding the context under which that data was collected is as important as going through excel sheets or powerpoint presentations.

TL;DR – Data without context is open to too many interpretations and is a waste of time.

Shifting to _utmz to _ga

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Some years back I had written about the __utmz cookie that Google Analytics uses to identify source attribution for visitors. If you are interested in reading that post, click here on Understanding the __utmz cookie.

Google evolves beyond Urchin

Google Analytics is based on the Urchin tracking management system and has been improving on that system over a period of time. As I have seen this product evolve, and many more features that were not there in Urchin … one of the major changes has been in the usage of cookies.

That makes my earlier post defunct.

The utmz Cookie

The utmz cookie used to contain the information about where the user has come from, which campaign, source and medium did the user react to arrive at the site. This information could be read and stored in a separate system (such as a CRM whenever a lead is captured). This could help in attribution of paying customers, and bring in all the crunchy goodness that you wanted.

Unfortunately, the utmz cookie no longer exists. The cookies have changed, if you are interested in know which cookies Google Analytics uses now, you can read this support article.

Where does that leave us?

So how do we go about finding more information about the user. This information is now not readable. However, what information we have on hand is a unique identifier of the cookie. That much still hasn’t changed.

So let’s take a look under the hood shall we,

The _ga cookie contains a value. This is the client id of the user. If you see the cookies collection, there are multiple _ga cookies, however, when you match it with the domain column, for every user – domain combination, there is a single _ga cookie.

This cookie is accessible by your server side script as well as your client side JavaScript. Therefore, we can get access to the _ga cookie value and store the client id within.

What is a client id?

To understand this, let’s go to Google Analytics. In GA, under the Audience section, we have a User Explorer report. Here’s a screenshot from my GA –

Check the value – 129754452.1496423206

This is available in the _ga cookie as well as in the user explorer. I can now identify specific users and leads in my CRM based on their client ids.

Therefore, I can even start checking their user behavior on the site, like so –

This is how the user has been visiting the site over a period of time. Notice the source is changing for different visits.

In a world where I would have been storing just the final source in the CRM, now I have a much more detailed view of how the user keeps coming to my site. This allows me to explore other attribution models and share the credit of the user’s conversion across channels.

This brings me one step closer to the World of And.

The World of And

In case if you haven’t already watched this, you need to watch this –

 

Game Theory in Dating, more towards understanding Nash’s Equilibrium

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Game Theory is a fascinating subject. Especially when you take it out of theoretical economics and start applying it to human collectives.

I had written about applying Game Theory to SEO, a competitive field, where the more important point was to have a strategy and keep evolving instead of having a static winning strategy.

Game Theory in Dating

Do we really need to do this?

Yes, because applying the concepts helps us to understand some of the product features (Superlike for instance).

More importantly, in a space where the one currency both the members of the dating app has, is attention. That’s the time spent with your significant other. In an ideal scenario this would be equal. This is the Nash Equilibrium state.

Nash Equilibrium in Dating

However, the more men you have in a dating app (Tinder has 60% men global, in India this is all the more skewed), the more dynamic would be the state of the Nash equilibrium. Data from Tinder has shown that men are twice as more active on such apps.

The reason behind this is simple, men are spending more currency (attention and time) to find that ideal person on Tinder. Unfortunately, because the number of men is more on the app, the amount of attention an average man would have to spend will keep going up (since the Equilibrium is unbalanced).

Nash’s equilibrium is a simple concept that helps economists predict how competing companies will set prices, how much to pay a much-in-demand employee and even how to design auctions so as to squeeze the most out of bidders. It was developed by John Nash, the Nobel Prize-winning economist and mathematician, whose life story was told…

via Why we need a dating app that understands Nash’s equilibrium — Quartz

So what?

The next time you are on such an app and if you are a woman, don’t be surprised if you are hounded by men. The equilibrium will never be reached unless you have the same amount of men and women on the app.

Take this concept and apply in real life.

In a country such as India, where sons are preferred (there I said it, and it’s not politically correct), the gender ratio in population is skewed. The Nash Equilibrium is also getting badly skewed.

You have to woo and court your significant other, not just because it’s romantic, but because it is required!

How to Clean an Infected Site — WordPress.tv

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If you have been playing with WordPress themes or providing WordPress based web builds as part of your business, then you would have installed a nulled theme in your life.

What’s a Nulled theme?

A nulled theme is a premium theme that’s released by someone in the wild. There are multiple such sites.

Wait, isn’t that piracy?

I consider it so. But this is where two different ideals are conflicting. That’s space for another post.

So what happens when you do install a nulled theme … chances are it might contain a malware.

An infected site

This is a nightmare to handle. The worry is not at the technical front, the worry is the grief the publishing team feels … as someone who regularly writes – I would feel bad if my blog were to get compromised.

Here’s a methodical way to sort yourself out.

https://videopress.com/embed/4vjvbhOr?hd=0

Immensely passionate about technology, Owen has built his career on his innate ability to understand and dissect organisational challenges and apply timely and effective solutions, typically focusing on emerging techniques and systems. Owen has been using WordPress since version 2 and runs a number of sites for himself and his clients. He is a Certified Ethical Hacker (CEH) and tries to learn everything about the WordPress security scene. His talk is on ‘Keeping WordPress secure, how sites get infected and how to clean them when they do.’ He decided to talk about malware in WordPress, because it’s a problem that effects a lot of people. he explained malware is just code, code in the same type of code that WordPress is, if you understand what it does and how it does it then there are steps you can take to avoid it.

via Owen Cutajar: How WordPress Malware Works and How to Clean an Infected Site — WordPress.tv

Game Theory and SEO

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This blog has been my place to articulate my thoughts, to propose experiments and my views on multiple topics. Having said that, this is one such piece.

I would love to hear your views about this and feel free to scroll down to that comment box and leave a line (or two).

What is Game Theory?

Taking the excerpt from Wikipedia –

Game theory is “the study of mathematical models of conflict and cooperation between intelligent rational decision-makers.” Game theory is mainly used in economics, political science, and psychology, as well as logic, computer science and biology.

In this piece, I am proposing that we can use the basic precepts of Game Theory and apply them to SEO strategies as well.

Originally, it addressed zero-sum games, in which one person’s gains result in losses for the other participants. Today, game theory applies to a wide range of behavioral relations, and is now an umbrella term for the science of logical decision making in humans, animals, and computers.

In Search Engine Optimization, for a particular query search, only one site can be at the top. At the cost of the search visibility of other sites.

Ergo, SEO is clearly a zero-sum scenario.

Wait, isn’t this between two players?

That’s what we construe of Game Theory … and more importantly with Prisoner’s Dilemma. However, in the real world, and in almost any market driven environment, there are always multiple players.

Such scenarios are referred to as n-person games, or in Gaming parlance – multi-player games. This gives way to something we define as Evolutionary game theory.

What is Evolutionary Game Theory?

Evolutionary game theory considers games involving a population of decision makers, where the frequency with which a particular decision is made can change over time in response to the decisions made by all individuals in the population.

So, in SEO the strategy that I can adopt at any point of time is suspect to change, and over a period of time, most players who are working on their SEO would tend to change their strategy and evolve their approach.

In economics, the same theory is intended to capture population changes because people play the game many times within their lifetime, and consciously (and perhaps rationally) switch strategies.

Ditto about SEO again. In textbook style, I could say don’t do Black Hat. However, you know it and I know it … that at some point of time in our lives we have done Black Hat. Yes yes yes, it doesn’t work and you have to pay the price, but we still have gone ahead, haven’t we?

This change in tactics, resulting in evolution of market dynamics effectively ends up changing the winning strategies of the game. A research article that talks about how the competing strategies change within a network of decision makers is available here.

To read more on Evolutionary Game Theory, here is the wiki link.

Rituals and Evolutionary Game Theory

One more interesting characteristic that mathematical biologist John Maynard Smith realized when studying the behavior of game theory in communities was that in biological communities (his research was based on Darwinian concepts and survival of the fittest) most of the players did not focus on their strategy as a winning one, but treated their strategies as at a ritualistic level.

Ergo, for most members of the population it was not important whether they were engaged in a competitive and winning strategy, but rather that they were engaged in a strategy in the first place.

Wait, what?

Let me rephrase that statement.

Players involved in playing a multi-player game, where the game itself was changing constantly, the winning strategy was not important for players.

So much, as having a strategy in the first place.

Uh, I thought this was going to be on SEO

It is.

In a game of lets-get-on-top (on Google), all of us marketers are running circles trying to figure out the best SEO strategy.

We have seen many of the oft-quoted paradigms here –

  1. Content is king
  2. Great Link profiling
  3. Black Hat

What I am proposing is that it really does not matter which step you take … as long as you decide to take a step as per a strategy and then choosing to evolve your stance after you find out the result.

 

Taking a look at Jetpack Stats

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Let me state upfront that I love Google Analytics. I use it at work in 13 Llama Interactive to measure the effectiveness of the campaigns that my team runs.

That being said, I will try and not be too biased about comparing Jetpack Stats to Google Analytics. As a marketer, the way I look at an analytics package is from an ability to extract a fair amount of data.

However, Jetpack Stats is on top of WordPress and available to all WordPress based sites which are connected to the WordPress.com site. This makes Jetpack Stats primary user base as bloggers.

Let’s see what Jetpack Stats has to offer.

The wp-admin Dashboard Integration

Jetpack Stats puts a nice pretty looking graph on the wp-admin Dashboard. This is how it looks like for my site –

Jetpack-Stats-on-wp-admin-Dashboard

Now, this is fairly similar to the Audience Overview you get when you check out Google Analytics.

Google-Analytics-Dashboard

Straight off the bat, I prefer Jetpack Stats overview as opposed to the one given by Google Analytics. Jetpack Stats also provides me with how my posts have performed this day, this report would be available in GA witin the Behavior section, the Site Content report.

The Top Searches that you see in the screenshot would have been helpful had it been accurate. Unfortunately, Google accounts for the majority of organic traffic on my site, and most of that traffic is encrypted. Thus, these keywords that you see (really, I rank for ‘big ass girl dunes’) are not a complete set!

Jetpack Stats does not talk to Google Webmaster Tools, which now is the only source of this keyword data.

Jetpack Stats Posting Activity

One awesome feature about Jetpack Stats is the posting activity screen –

Jetpack-Posting_Activity

This data is shown with a correlation of average traffic per day as well as traffic per month. You could always get this data in Google Analytics (here is a useful post I had written some time back – Google Analytics for Content Marketers).

It’s just this kind of insights that makes me keep Jetpack around for my measurement requirements.

Jetpack Stats vs Google Analytics

Jetpack Stats is a very lightweight tool and it would be useful for a simple blog. However the minute we enter the realm of finding user engagement and performance marketing, Jetpack simply does not have those features yet.

This is where Google Analytics shines through with its Event tracking.

Having said that, Jetpack Stats is an apt solution for a user who is more focused on the publishing process.