Calculating View-Through attribution: Challenges

After analyzing the Google Analytics "Content Report" for my blog, it became evident that the "Post-Impression" attribution post is the most popular post so far. So, I thought I would expand a bit more on that same topic, some of the common challenges that could be faced during the test set up and also interpreting the results.

If this test is being done for a very large advertiser, delivering in the range of billions of impressions in a month, then it is virtually impossible to create a test and control group as the reach of the campaign would be so high. It is highly likely that all consumers will be exposed to the advertising.

1. If all the ads are not third party served, it is impossible to create a test and control, as if there is a x% of Ads which are site served, creating a control group will be hard across the entire advertising network
   
2. Even with all the ads being served via third party ad servers, consumers could get exposed to the Ads on different computers, example work vs. home
   
3. After the results if the lift is negative i.e. it shows that showing the ads did not increase the conversion rate vs. control group – this indicates that the "Brand" has very high brand awareness
   
4. Interpreting the results could be another tough nut to crack; DoubleClick in one of their studies has shown that for Continental Airlines that there is a 67% post-impression attribution. Lately, it has been seen that the post-impression attribution has been pretty low due to increase in online advertising.
   
   1. One more variable could be seasonality, the post-impression attribution has been seen to change based on the different times of the year. The consumer mindset changes based on the seasonality for example it could be very high during the back to school season for school products like laptops, ipods etc

Tracking DRTV using Site Analytics

With the advances of web analytics technology, it is also possible to track the impact of DRTV using the web analytics tools like Google Analytics, WebTrends, Hitbox etc.

Usually, the DRTV ads have a unique URL associated with the TV ad, where the advertiser intends the consumer to go to. So, tracking the number of visits to this page would help understand how many consumers the campaign is driving. Using web analytics tools, funnels can be created to track the complete path of the consumers if the campaign involves making a purchase or registration.

One more interesting thing can be done at the call center is giving the customer service representatives a unique URL to open in their browser whenever they receive a phone call, and then finish the process online – tracking this funnel would help understand the complete drop-offs and help make process optimizations.

Bought Display and Search Media, Online sales should go up: Really?

A lot of advertisers tend to think in these terms. People think I bought media, people are looking at my ads, they are clicking on them as well, my sales should go up. Really? Why? In most of the cases it will or should go up, but sometimes it will not. Why? Have you checked your website analytics, i.e. how is the consumer flow? Are there are places where people are dropping off a lot? If yes, that thing needs to be fixed. For example: if consumers are not flowing through after step 3 in the process, then there is something wrong for example is the next button not working or is the next step that you intend the consumers to take not clear enough. The funnel below shows it all…

In an ideal world, every advertiser likes to have a straight funnel i.e. all the consumers flowing straight through the checkout process as shown in the image below.

Net net the most important thing is to track as much as you can, to find out where the consumers are bailing in the checkout process and try to fix that. It might not be an easy fix and it can take a few tests to figure out the exact reason and then you can have your Advertising "$" working for you i.e. generating "$$$".

This is a very common problem seen with e-tailers who have a pretty complex checkout process which could involve more than a simple 5 step process. I have personally seen processes which are over 40+ steps and if there is no web analytics is place, then the advertising team is blind to where consumers are dropping off.

The solution is available fairly easily - use tools like Google Analytics, Webtrends, Omniture - you can define the complete consumer flow, look at visually where people are clicking or missing out on clicking, and then act. It's a circle which goes on - track, test, implement, track again :)

Different systems used to measure same metrics

Many times there is more than one system used to track the same metrics. Many companies use their legacy internal systems to track revenue, margin, visits to their site, which they do not want to give up and also use the new systems like DART, Atlas to track the same metrics.

There will never be two systems which will track exactly the same. There will always be a discrepancy among the two, but the two big things are to identify what the discrepancy is and why does it exist.

Once identified the systems should track in the same direction - i.e. if one shows 20% increase in revenue the other should indicate the same.

In my experience, I think the best way to identify these issues is to map the data flows for both the systems. Once the data flow is mapped, then identity how much difference exists at each step. Also, it is possible that one system must have a longer cookie window than the other - this is a very common issue I have seen in the Online Marketing arena. So, these things could impact a lot.

One more thing I have noticed is that - sometimes systems are set to track a different level. For example: System A could be tracking at creative level and System B could be tracking at placement level. System A could be capturing only certain transactions (example - only certain product categories) but System B could be tracking everything. System A could be tracking only post-click data but System B could be tracking both post-click and post-impression.

One of the common example is trying to match Google Analytics Site data to DART Spotlight tag data. Yes, both of them are same company now but they track differently and never track the same. Some of the variables could be: session time, tags not firing correctly, tags not firing every time etc etc.

It can be a tedious process to identify the differences but once identified the reporting/measurement process becomes so much easy....