E129 – Why your Analytics Conversion Data is Trash
Analytics

 
 
00:00 / 19:43
 
1X
 

Analytics Conversion Data Gap

Mat kicks the episode off with a great example to set the tone and topic of what the guys dig into. Visits from Google, Facebook, Twitter, Bing, etc. can change from day to day and month to month but what really matters is your leads and sales.

Rankings for keywords come and go but what you need to focus on is the value of that traffic.

Why Your Conversion Rate is Wrong

This episode was inspired by Unbounce’s post The Simple Reason Why Your B2B Lead Gen Conversion Rates Are Completely Wrong. To quote the post as it gives a great example:

If you spend $1,000 on a lead gen campaign and get 250 leads, that’s a cost per lead of $4, right? So wrong.

– Unbounce

What are Issues with Analytics Conversion Data?

Oh so many things. But to quickly answer that Dave digs in with some examples:

  • Someone fills out the form multiple times in a visit.
  • Someone fills out multiple forms on a visit.
  • Someone fills out a form with junk data.
  • Bad phone numbers.
  • Fake email addresses.
  • Duplicate leads – they filled it out last week, month, year, etc.
  • Fake names or other information
  • QA leads showing up in Analytics

Your analytics will say that all of these leads and form submissions are valid but your CRM will likely scrub of filter these and drop most into the junk box. So while your Analytics may tell you that you rocked a day with 25 form submissions but in reality you had 25 – X. If X is 12 your conversion rate is 50% of what your Analytics tells you.

Paid Data Conversion Data Issues

The real downside can be when the AI and Google think that a Campaign/Ad Group/Keyword is crushing it when those conversions it is seeing are really all garbage or are significantly less. (Yes there are ways around this but in general…).

Analytics is Great but Not Perfect

For those that ever have dug into Omniture, Google Analytics or any solution you know that there are issues with the data at some level. One example that Dave mentions is Social Media related data and trying to bucket it. Annie covered this some years ago with Why You Should Keep Social and Referral Data Separate in Google Analytics in great detail so check it out.

Junk Data In, Junk Data Out

Few of us are Google Analytics experts (or Omniture experts, or name your tool) and that often results in bad setups. Dave digs into a number of things commonly seen or missed rather in setups he sees.

  • Not filtering internal IPs
  • Not filtering Dev/Staging
  • Not filtering bots
  • Only one profile
  • Events poorly setup
  • Conversions poorly setup
  • So many other things that can throw off your data.

How to Fix Bad Conversion Data

Ask questions. Work with experts and test your UTM codes and setup. Test and QA updates and your site and remember to also test your Analytics when you test your code. Internally come together to make sure that everyone is using the same data source for leads, sales and conversions.

Use your Analytics conversion data as directional. Use your CRM and internal data for actual decisions and any reports or dashboards.

In wrapping up the episode Mat walked through a great example of how people internally saw 47 conversions but the real number was 2. So what happened? Listen and see!!!

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