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Thoughts on Social Media

February 23, 2018 Prasad AjinkyaLeave a comment

I wrote this note out for a discussion on Social Media sites and how their relationship with publishers has evolved over a period of time. It goes to show that too much of reliance on any one channel may not be such a good thing after all!

Can we as digital marketers and analysts create a measurement model that can reliably help us to identify whether our social media investments are justified?

Social Media and Creators

One of the problems that new Social Media websites face is generating enough content that users want to consume. This they do by welcoming publishers to come and register on their websites. This is the main fuel for their growth.

The social media site in question (including Facebook) does all it can to attract publishers and creators. The focus is on getting more creators and therefore more users. Users get to follow their favorite brands and celebrities on these sites. Brands and celebrities get a scalable way to engage with their fans. A win-win on paper.

A platform is born

As more users sign-up and start using the site, it soon starts being recognized as a platform. This platform now is independently known and now, creators are attracted to the platform not because its easy to publish their content or its easy to create their content … but because that platform already has their potential target audience.

So, from engagement at scale, the reason why the platform is being used shifts to reach and discovery. The very publisher who used to get throngs of crowds flocking around them now is looking at the platform as the source of that crowd. The shift of behavior due to the change in thinking is not amiss to platform owners.

From Win-Win to Monopoly

The platform owner now knows the dependence of the publisher upon the platform. E.g Facebook single-handedly crippled the stock prices of Zynga (famous for Farmville app on Facebook) by taking it off their Featured apps page.

Take the organic reach that Facebook now provides. Some years back (circa 2012), a single post on your Facebook page would be shown to 10-12% of your followers. This has slowly trickled down to 1% now (3%-4% if you have high engagement on the page). The reason behind this is because every brand out there is pushing out more and more content than what the platform was designed for, and every brand / celebrity out there wants to create content that goes viral.

Pursuit of Viral

Publishers in the pursuit of this holy grail tend to create a Sea of Crappy Content. This is loads and loads of content which does not drive engagement. Platform owners now are scared by the very publishers they used to chase. Not because they don’t need them … but because they are not clearly able to differentiate the good ones from the bad ones. The definition of quality becomes more blurred.

Zero Organic Reach

In the end, the platform owner plays the one card that they can control. Throttle the impressions and reach of the publishers. Quality is then replaced with budgets, with the underlying assumption – if you can create great content, most likely you have enough budgets to buy the impressions required to go viral.

Another example to highlight this is to look at any Facebook page which has over 10,000 likes, the last post of that page won’t even have an engagement rate of 1%. The problem may not with the page or the post in itself, it stems from the throttling down of organic reach.

So what can be done?

Do we pay the piper and buy our followers? Or do we dance to the tune of the platforms and keep pushing more content in the hopes of getting that one beautiful post that gets shared by the millions.

Can we instead, arrive at a scientific method of identifying what platform works and what doesn’t in furthering our objectives?

2018, the year of numbers

January 10, 2018January 11, 2018 Prasad Ajinkya1 Comment

I have been talking about data and analytics for quite some time now. So much so that, I have shifted from doing development as a service (at 13 Llama Studio), to agency as a service (at 13 Llama Interactive). The reason behind this was to capitalise on my love for data analysis and build an organisation that works with data instead of opinions.

From Full Service to Data Analysis

One of the main things that I have been doing, is never say no to anything that lands on my work desk. This is a good thing, since you can pretty much get started as a service based business and do a variety of things.

This, however, is a bad thing since it takes you away from your chosen area of work. In my case, that’s analytics.

We started off as a Full Service Digital Agency and did everything under the sun. Websites, logos, app development … product development, incubation even. Whereas, it’s a fantastic way to keep busy, it did not sate my need to work with numbers.

Saying No

The year 2017 was the year of No. I have been steadfastly refusing to engage with anything which did not involve numbers. So much so that, the organisation that I had so loving built has become an empty shell, almost.

While, this lean attitude is good for companies where there is a lot of waste, taking this to near starvation levels also does not help. Unfortunately, I keep getting such insights only as hindsight :)

What 2017 did offer was a massive consolidation of business interests, which was a good sign. It also taught me the value of human engagement and how business engagements were closely related to the simple human interactions.

Focus on Measurements

I had been going on and on about measurements for some time. I realised that without getting into this completely in your system, you cannot really appreciate this thought. Here’s a quote from Swami Vivekananda –

Take up one idea. Make that one idea your life — think of it, dream of it, live on that idea. Let the brain, muscles, nerves, every part of your body, be full of that idea, and just leave every other idea alone. This is the way to success…

To fully understand and appreciate what this means, do go through this interpretation by Srinivas Venkatram.

It took me some time to fully get this, and for me that meant focusing on analytics. It did not really mean saying No to different engagements. It means applying my love for data and analysis in whichever engagement to drive value.

2018 for me, represents just this. A year where using measurements I would drive value. Be it in product development, be it in promotions.

Using Intelligence reports in Google Analytics

December 7, 2017 Prasad AjinkyaLeave a comment

It’s always a pleasure to use a product that keeps evolving. The possibility of discovering a new feature that’s been recently launched, and the happiness of seeing the applications of that new feature is what keeps me coming back to the product. Google Analytics is one such product for me. Slowly and steadily, they have evolved the product so as to give the free tier users a taste of what Google Analytics Premium (GAP) offers.

Intelligence reports have been around for quite some time now. However, what GA has done in the recent times, is give the user the ability to articulate their question in natural language, and use natural language parsing to understand the question and present meaningful answers back to the user.

Smart and Intelligent reports

Here’s an example of how these intelligent reports work. Suppose, I see a spike in traffic yesterday, and I want to know the reason why.

Normally, I would go to the Source/Medium report in the Acquisition section and see which of the sources have had an increase in traffic since yesterday. However, what intelligent reports does is this –

So what’s the big deal?

The big deal is this. If you are not comfortable with the analytics interface or are not savvy with using the right set of reports for fetching your data, then the intelligent reports are a rather user friendly way for getting access to perhaps the right data.

Notice, in my example, the segments that intelligent reports ended up reporting was a rather advanced segment (Organic traffic, Country-wise).

To reach there, I’d have to go through atleast two separate iterations. This was given to me rather quickly.

Cool, are there any disadvantages?

There is one huge disadvantage. The data given is prescriptive in nature.

You are relying on Google Analytics to give you the right data.

While, for most use cases, the data may not be that important, but for someone whose living runs on getting the right numbers, this may not be enough. It’s good enough to get you started in the right direction though.

Why do I still like it?

The nature of querying is also pretty great. Now, business teams can directly dive into Google Analytics instead of having to wait for an agency or an analyst to make sense of this data. That’s power to the people!

This means, a lot more people can now engage with analytics and take the right data driven steps for improvement.

Shiny tools don’t make a purpose

November 10, 2017 Prasad AjinkyaLeave a comment

Recently, I bought a Fitbit. It’s a fantastic tool. Now, I can rave more about the features and go on and on. However, a friend and a colleague asked me an interesting question.

Has it changed you?
No, it did not.

Before I go on, I have to tell you that I am on the heavier side of the weighing scale. Those of you who know me personally would be surprised at the sudden interest in all things health. Yeah, I roll like that.

It’s not about the Fitbit

Like any other measurement tool, the Fitbit is doing a marvelous job at letting me know certain metrics that I need to care about.

They have even gamified the steps by putting in cute little badges and built in peer support (and also peer pressure) to keep me motivated. All this is good as it should be.

At the core of it, it’s a measurement tool. Just like any of the billion other tools we use in Analytics.

Targets and Measurements

On very similar lines, we as marketers or as businessmen often deploy shiny new tools because we think they will help us do more.

Unfortunately, like me in this case, how many of us forget on defining the purpose?

I implicitly assumed that the Fitbit would automatically by some magic give me the purpose of losing weight and leading a more healthy life. Without this purpose, here’s what would happen —

I will wear it to work, and dutifully report the steps taken and life would go on as usual. Some of the badges would come in as time goes by, and it would not really matter to me if I took 2000 steps a day (which is a walk in the park) or 10000 steps a day (I haven’t achieved this yet).

How would I change, if let’s say I choose to give myself a target of say, 10000 steps a day.

Without Purpose, there’s no Change

I would for one have to make time to walk those 10000 steps. I could try walking in the office or doing a much more rigorous transit than an Uber. However, I would have to commit to making the time for those steps.

Thus, this choice of making a change in my routine should be addressed. At the heart of it, the shiny new tool is not at the center. Yes, you have bought Google Analytics Premium and all of that is great … but that’s not really at the center.

At the center, is the purpose. Has this been defined? Has this been clarified and articulated so that the team knows about this?

A tool doesn’t give us Purpose

It does give us a sense of progress towards our purpose. A Measure of Success, if you will. The shiny new tool that we just acquired is useful, but only as long as we keep the purpose at the center.

As people who know how to use a tool, if we do not understand the purpose, the tool will end up regurgitating meaningless data.

TL;DR — When setting up measures, don’t keep the tool at the center. Keep the purpose at the center. The rest should follow.

Data anomalies in Search Console

August 8, 2017 Prasad AjinkyaLeave a comment

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.

ḷ.com, one more shenanigan in Referral Spam

July 28, 2017May 10, 2018 Prasad AjinkyaLeave a comment

Spamming the Analytics data of websites is now an old practice. It’s better known as Referral Spam, and I have written about this in the past at multiple times. Purely a black hat practice, I doubt whether it would give great returns.

Yes, it would give traffic to the spammer, but how does that really translate into revenue. Or is the tactic hoping to drive gullible folks by the hordes?

The referral spam industry for some reason also loves to send the geographical position as Samara. For those of you who are noticing this now, here’s how the tactic works.

How Referral Spam works

The bot hits a particular site for multiple times in the day
The analyst sees his Google Analytics account, and gets surprised by a spike in traffic. Who wouldn’t mind seeing such a spike :)
The obvious report to check this out would be the Source / Medium in the Acquisition section.
There staring at you in all glory is the spamming domain
The analyst gets curious, and visits the site

The rest, would not be history, it would be a scam.

How should I combat this?

Raven Tools has a comprehensive article on combating Referral spam. They have listed multiple methods to ensure that this spammy data is not accounted for in your analytics data.

Personally, I allow the data to reside in my Master Data View. The reason behind that is – since I do not look at aggregate data anyways (I prefer lots and lots of custom segments), I am not too bothered with that data! I do however, mark it as a annotation on my GA. That’s the advice I would give to anyone.

Category: Analytics