In the past 5-6 years or so, a lot of online businesses, especially the ones who are hungry for growth have relied on organic traffic as one of their key sources. Now growth could mean an increase in pure numbers (traffic, sessions, users) … or it could mean an increase in more tangible business parameters (revenues, profits). One of the things that I have learnt is that depending on which success metrics we chase, our own identity undergoes a shift.

Search as a major source of traffic

The major contributors to organic traffic are search and social. Wherever there is a site which has great and unique content by the loads, there is a chance for driving organic traffic.

At different points in time, I have been skeptical about Social Media and me-too posting that most brand pages do on platforms such as Facebook. However, Search for me has always been fascinating and I still have faith in Search :).

SEO can’t be a method

Search Engine Optimization (SEO) has evolved over a period of time and I have blogged about it on multiple occasions. Unfortunately, the number of times the algorithm changes and the rate of evolution of what Google (the market leader in this space) construes as quality content ensures that you can’t have a steady SEO “process”.

Having said that, SEO involves a fair amount of design thinking.

The reason behind this statement is because the problem behind search visibility (and the factors that control that) keep changing. It’s a wicked problem. Design thinking can solve such kind of problems because of its test and iterate mechanism.

Data to drive Design Thinking

This is where having the correct data to decide on next steps is crucial. Having a data driven design thinking approach would entail that there are periodical reviews of what kind of data we have available to make the right choices.

Search data has always been plagued with incomplete information. Starting from the 2011 encrypted search announcement, where a bulk of the data in Google Analytics was being reported as (not set). There have been ample approaches to clarify this data, unfortunately, as Google Search goes more towards handhelds and as digital privacy increases, the percentage of data where there is clear visibility will keep going down.

This can’t be helped. What can be done is take these “anomalies” into account and factor those in while doing your analysis.

So what kind of Data anomalies in Search Console do we expect to find?

Google Support has compiled this list. They keep updating their data reporting logic and keep updating this page as well.

One of the major changes that you can see is that last month, they started reporting more data in Google Webmaster Tools. Please bear in mind that this is just a change in the data that is being reported and not the actual search traffic that is on your site.

The link also explains why there is data disparity between Google Analytics and Google Webmaster Tools and any other third party tool that you could be using to generate keyword data.

So, my data is incomplete, what to do?

Don’t panic.

Work with the list of data anomalies and identify which ones are impacting you the most. Having visibility on which parts of data are not available to you is also better than not knowing anything and assuming that the data you have is complete.

In iterations, the first comparison is always your previous state. In both cases the data being made available to you is pretty much the same. Hence, a week on week comparison report is much more valuable as opposed to a comparison report with your closest competitor.

As long as the measures of success is on the same tool, the data anomaly should be cancelled out. Please bear in mind that for most of our data work, we do not need precise data but can work with coarse data.

A simple approach to identify this would be – if you work with charts and graphs more, then you can work with coarse data and absorb the anomalies. If you work with more than 4 decimals, then you might want to add 3-4 lines of disclaimer below your data.

If you have been listening to Red FM channel in Mumbai, then one of the celebrity RJs they have is Malishka. As part of a radio jingle, she made up this video –

Everyone knows that in Mumbai, rains wreck everything. This year, in fact we have yet to see a day where the entire city has come to a stand still. Having said that, there are a lot of gaps that BMC needs to address. The video was made as a satire, since then, it has had more than 3M views.

So what’s the big deal?

Instead of acting on this creative complaint, what BMC officials chose to do was extremely childish. They organized a “raid” on RJ Malishka’s place and supposedly “found” dengue mosquito larvae.

Then the local Shiv Sena team created a spoof video of this song and BMC went on to file a 500 Cr INR defamation case against the RJ.

All this over a silly jingle that was created. Yes, it was aired, and many people heard it and saw the video.

The big deal is that an individual’s freedom of expression, and the freedom of press is being trampled with here. I don’t know whether I would classify this video under press or under entertainment, but what I do know – is that there is more than a grain of truth to the song.

By reacting like a bully, the BMC has shown how it takes feedback. The next time Shiv Sena talks about giving the marathi manus a voice, think again. It’s all talk and no action.

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.

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??

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 up to 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.

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.

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.

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 –