You are doing Google Analytics all wrong. Here is why

I have dealt with hundreds of Google Analytics accounts in my career and have seen lot of issues from incorrect tracking code, selecting wrong KPIs to analyzing data without custom reports & advanced segments. All of these issues prompted me to write the post Google Analytics Account Setup Checklist. But this post doesn’t solve the biggest problem of all in web analytics i.e.  ‘Misinterpretation of Analytics data’.

Everyone seems to be making this mistake of crediting conversions and e-commerce transactions to wrong acquisition channel and that too again and again. Many marketers can’t help themselves because they believe that the reports provided by Google Analytics (& other web analytics softwares) are ‘what you see is what you get’. But it is actually what you interpret is what you get. For years analytics software vendors knowingly or unknowingly have been hiding the holy grail of web analytics:

In Analytics, conversions and ecommerce transactions are credited to marketing channels that referred the visitor when he/she was converted.

This one sentence alone is so important that if for some reason, all of the web analytics knowledge were to be destroyed and only one sentence passed on to the next generation of web analysts then this sentence will contain the most information in the fewest words.

Majority of businesses and marketers even today give credit for conversions and ecommerce transactions to the last campaign, ad or search that referred the visitor when he/she was converted. This has resulted in marketers taking wrong business decisions and losing money.  All the data you see in Google Analytics Reports today lie to you unless you know exactly how to interpret it correctly.  Let us consider three different scenarios:

Scenario-1:

Majority of marketers looking at this standard ‘All Traffic’ report in Google Analytics of the last 3 month will draw following conclusion:

Organic traffic is playing a second fiddle to direct traffic. Majority of traffic and revenue is coming through direct traffic. We need to speed up content development and link building.

Scenario-2:

One look at this monthly PPC report and many of you will declare this whole campaign a total failure. Look at the first campaign, just one conversion in the whole month and cost per conversion is whooping $531. You must be kidding, right.

Scenario-3:

Do you really think your brand name generated revenue of $9566?

Welcome to the Real World

Let us analyse the three different scenarios once again but this time in the real world.

Scenario-1:

Truth about direct traffic

Google Analytics treats all the traffic that comes from untagged shortened ULRs on social media networks like Facebook and twitter as direct traffic. So if someone clicks on one of your untagged tweet and made a purchase on your website, Google will credit the conversion to direct traffic instead of the poor twitter. No wonder why businesses fail to calculate the ROI of social media marketing campaigns.

All untagged or improperly tagged marketing campaigns from display ads to emails are treated as direct traffic by Google.  Whenever a referrer is not passed, the traffic is treated as direct traffic by Google. Mobile applications don’t send a referrer, word/PDF documents don’t send a referrer. 302 redirects sometimes caused the referrer to be dropped. Sometimes browsers don’t pass the referrer. During http to https redirect (or vice versa) the referrer is not passed because of security reasons. All such traffic is treated as direct traffic by Google.

So on the surface it looks like 85000 visits were direct, but it may actually be only 25000 visits which were from direct traffic and the rest were from display ads, email, organic,  social media and applications/campaigns in which the referrers were not passed.

But this analysis does not end here because you are still not looking at the complete picture. 

Visitors do not always access your website directly before they make a purchase. They are generally exposed to multiple acquisition/marketing channels (like display ads, social media, paid search, organic search, referral websites, email etc) before they access your website directly and make a purchase.  So if you are unaware of the role played by prior acquisition channels you will credit conversions and e-commerce transactions to wrong acquisition channels like in the present case to direct traffic.

 If you look at the chart above organic search is playing a key role in driving direct traffic to the website which eventually resulted in conversions and ecommerce transactions. So the conclusion that organic traffic is playing a second fiddle to direct traffic is incorrect.

Scenario-2:

Visitors do not always click on your paid search ads before they make a purchase. They are generally exposed to multiple acquisition channels before they click on your ad and make a purchase. Sometimes visitors click on your ads but make a purchase through different acquisition channel or medium. For example a person may click on your paid search ad through laptop at work. Then later make a purchase via his home desktop PC through a branded keyword. Sometimes your paid search ads play a bigger role in assisting conversions than directly resulting in conversions.

The ‘conversions’ and ‘cost per conversions’ (cost/conv) reported by Google Adwords in scenario-2 above, are all based on last touch attribution (people click on ad and buy) and hence are providing poor analytical insight. When I paused these campaigns, I saw a huge decline in revenue. These campaigns are in fact very profitable and there assisted conversion value is also very high.

Scenario-3:

This scenario is not any different from scenario 1 and scenario 2. Here too you don’t see the complete picture. Visitors do not always search for your brand name before they make a purchase. They generally start their search with a non branded and generic search term then they refine their search queries as they get better understanding of what exactly they are looking for. Sometimes they make a purchase right after making a search but often they come back later to your site via a branded search term. Since website/brand name is easiest to remember among all branded search terms, it often ends up being attributed lot of conversions and transactions.

Takeaways

1. Web analytics reports are not ‘what you see is what you get’. It is ‘what you interpret is what you get’.

2. Direct traffic is polluted. So find ways to clean it. The first step should be to correctly tag all of your campaigns URLs. Use Google Analytics URL Builder.

3. Visitors do not always access your website directly before they make a purchase

4.Visitors do not always click on your paid search ads before they make a purchase.

5. Visitors do not always search for your brand name before they make a purchase.

6. Understand the role various website referrals, social media, display, email, paid/organic search etc played prior to your conversions via Multi Channel Funnel Reports before you discard/label any marketing channel as ineffective or over invest in any particular channel.

7. Understand how different acquisition channels work together to create conversions and transactions. No one acquisition channel is solely responsible for sales.  So do not overestimate or underestimate the impact of other marketing channels.

8. Understand that when you change the budget of one acquisition channel it will have impact on the performance of other acquisition channels. Nothing is black and white in the world of analytics.

Attribution modeling is programmatically very difficult to implement at present but Google has taken one right step in this direction through ‘Multi Channel Funnel Reports’. If you wish to learn more about attribution modeling and attributing conversions and ecommerce transactions to the right marketing channel then check out this post: Selecting the Right Attribution Model for Inbound Marketing . It will prove you why first touch and last touch attribution models are flawed and also introduce you to a new revolutionary and unchallenged attribution model called Proportion Multi Touch Attribution Model.

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