Web Analytics Deep Dive

Are you ready for some more analytics? Earlier, I covered an intro to analytics from Matt Bailey. This session takes a deeper look into the data, and how to make sense of it all. Thom Craver (@ThomCraver) and Garry Przyklenk (@GPrzyklenk) help us dive deep into the world of Web analytics …

Up first is Thom Craver. He is talking about data vs. information. Data is seemingly disconnected numbers. What turns data into information is context. A system does this, like Google Analytics.

Know your objective. Why are you measuring? You have to tie everything back towards your business objectives.

Look at visitor segmentation, you can approach it this way:

1. Origin of visitor
2. On-site behavior
3. Goals and conversion

For more segmentation, look at your in-site search in Google Analytics to track what people are searching for in your site. What are they trying to do?

Now, microconversions. You know people are not going to buy every time. What can they or did they do on your site besides buying that is still valuable?

For example, offer a newsletter and measure signups against the cost of buying leads. Or put a value on a download versus printing and mailing a piece of collateral. Measure this by showing how much money you saved. Maybe it’s not about making money; maybe it’s about saving money.

Next is Gary Przyklenk [pronounced shoo-klenk? Yep.]. He is talking tried-and-true approaches first.

• Filter, group, segment
• If-then analysis
• Derive business value
• Get closer to the customer
• Integrate data sources

No Web analytics platform is perfect. Filtering is key and cleans up the data integrity issues. The top 10 and top 50 entries don’t change from month-to-month. So how can you tell a story?

Then, group. Simplify the granular data. Build complex segments. Groups change, segments shouldn’t. For example, referring URL values can be grouped to go beyond other types like internal referrer and external affiliate.

Extreme segmentation can help tell the story better. At his company (a bank environment), they look at:

• Visitor type: Prospect/new, customer, affiliate, employee.
• Source: PPC, SEO, social media, external display ad, internal display ad, affiliate.
• Visitor intention: Research, purchase/broker, renew, transact.
• Product engagement: Core accounts, credit cards, lending, investing, insurance, business banking

Isn’t all of this really hard? Yes, yes it is. Use pivot tables to help.

Let’s look at the If-Then analysis. People don’t care what the numbers are, they want to know what the opportunities are. This is where microconversions come into play. For example, what happens if you send more traffic to your product landing pages that are more optimized for conversions?

Now, deriving business value. Take paperless statements for example. To the bank, it’s saving money each month on stamps. This adds up if you have a million customers. There is a customer benefit, too.

So, where does the greatest opportunity exist in this example?

• Total customers?
• Total online customers?
• Products eligible for statements?
• Internal banner ads served?
• Going paperless?

The greatest opp is the total online customer option. If more people log in, more people can sign up for paperless.

Finally, getting closer to the customer. Web analytics can be complex. Data can only tell us so much. What can the customer give us? Start communicating with them, start asking them what they think about the marketing that you’re doing and the things you are offering.

Jessica Lee is the founder and chief creative for bizbuzzcontent Inc., a marketing boutique that focuses on digital content strategy and professional writing services for businesses.

See Jessica's author page for links to connect on social media.