Data, Reporting and doing what’s right

Data is being used to showcase that vaue has been generated. In order to do this, the most beautiful reports have to be eeked out. Now if you are a follower of Avinash Kaushik and don’t like data pukes, then you would be aghast at some of the reports that agencies in India tend to dish out.

I was, and 13 Llama Interactive was born out of that need to do better at both data driven marketing and reporting with transparency.

The road to hell is paved with good intentions

If you’ve been providing paid marketing services to clients for any extended period of time, you know that every person you work with has a different level of online marketing knowledge. Some people might be experienced account managers, others might know basics, while others still might not know the industry at all. It can be easy…

via 5 Agency Reporting Tips to Prove Your Value to Clients — WordStream RSS Feed

Apparently “agency reporting” is a thing. This is where every week or every month, the agency that is handling the brand account (or the performance account if you may) sends across reams of PDFs (or excel sheets) that’s meant to prove that whatever hair brained plan that they had cooked up the last period has worked.

The most common method to justify existence is to keep throwing boatloads of data reports from all tools and then talk about worthless metrics. Each of these tools mentioned in the article that I have shared helps agencies do this at scale, effortlessly.

Is too much data a bad thing?

It can be. If all that data is leading to Analysis Paralysis … or if it leads to falling in love with data analysis itself and forgetting real business outcomes (the reason why you got money for funding the collection of all that data).

If no one is using this mountain of data for solving problems, then it’s better that the data not be collected at all.

Yes, you are letting go of possibilities, but so be it. The damage to the business by wasting resources on gathering more liabilities instead of assets is much worse.

That’s what creates a paradox. Should we or shouldn’t we collect data?

Here’s a great video from Superweek that makes the case pretty well.

Google launches Machine Learning for AdSense

Google AdSense has been around for more than a decade and a half now, this along with DoubleClick for Publishers allows website owners to monetize their traffic.

One of the key challenges in this was to figure out the optimum ad placements without impacting readability and user experience of the site. This trade-off that the publisher had to do was to decide on the different ad slots to create on the web page, and then balance that with the Revenue Per Thousand Impressions (RPM) metric that the digital advertising industry is so familiar with.

In order to help publishers out, AdSense had experiments where you could test different ad layouts and figure out the best layout to monetize the site.

So what has changed now?

Machine Learning.

This is the applications of artificial intelligence which gives programs the ability to discover new rules and learn from experience without additional programming. So that means, for newbie publishers instead of having to figure out by themselves what ad formats work and what ad placements work for them, you can apply machine learning and let the platform learn on its own.

What that means, is that the publisher is now free to focus on content, and let the AdSense platform figure out the best way to monetize that content on the ad network.

Caveat Emptor

With every new feature, comes a series of disclaimers. Machine Learning requires a lot of data to get things right. If you are a small site such as this blog, then it will take a long time for AdSense to optimally figure out the right ad formats and the proper ad placements.

Having said that, here’s a very simple way using which you can get started with Auto Ads in AdSense.

Setting up Auto Ads

Auto Ads in AdSense

In your AdSense console, in the Ads section you will now find a Auto ads menu item. Click on this, and get started with the setup wizard that’s present there. If you want to know how to embed the Auto ads code in your site, Google also has a helpful support article here.

That’s it! Once the code is setup in your website, you choose the formats you want to add (I chose everything) and let it run.

So far, the results haven’t been that great. However, time will tell if applying machine learning gives great benefits for the publisher.

What benefits should one look at?

Ultimately, it boils down to increasing the aggregate Revenues per thousand impressions metric (RPM). That’s what I’d look at, I would also look at the Click through Rates (CTRs) to go up.

Using Intelligence reports in Google Analytics

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.