Why e-businesses need effective category pages

Imagine that you walked into a store and told a salesperson that you want to buy a blender. The salesperson walks into the store room, grabs one off the shelf, comes back and puts it in your hands. “You want it or not?” he says. “Not this one exactly,” you say. He walks away and returns with another blender. “What about this one?”

Any customer would grow tired of this exercise pretty quickly. Online, it’s no exception. Guiding your online customer to the right product and allowing them to browse for a moment before the sale creates a more pleasurable buying experience and will increase conversion rates.

Occasionally, because of various responsibilities, both clients and digital analysts can forget about this very basic premise. Buying online is a process, and walking with your customer through your online store using digital analysis can be a revealing exercise. With Analytics, we can walk with our customers and map the most common paths to a purchase.

Analytics use in the Internet Retailer 500: Interesting Findings

Like any fast growth company, we use cold calling at Napkyn as a way to start off long term relationships (and sometimes get hung up on). We take pains to follow the number one rule of cold calling: Never waste anyone’s time. The best way to follow this golden rule is to do some homework on a company before you call them.

Which leads to today’s blog post. After this recent article profiling the impact we have had at Scentiments (#384 on the IR 500) we have been signing up new customers across the IR 500 who want to better understand their data and grow their sales.

So we are reaching out companies with under $75 million revenue (#s 315 to 500) on the Internet Retailer 500. In the interest of making every sales call useful for us and the companies we’ll be calling I have been profiling analytics tools usage.

I was so intrigued with the results I did some rough analysis to share with readers of this blog. Feel free to ping me with agreement or hate-mail on my findings.

Segmentation and Conversion: Closer to the Heart

In a previous post I referenced the importance of considering only ‘convert-able’ traffic when looking at a goal conversion rate, i.e. only look at US visitor data if you don’t ship or service outside the US.

The reason that you always look at conversion when analyzing web data is because it allows you to always answer the “So What?” questions you receive when talking about data.

Lets use a few made up 'boss conversations' to illustrate:

The best things in life are free....kinda.

One of the most interesting things about working at Napkyn is that every discussion with a prospect or new customer is a fascinating one. The reason is that every serious owner of a web presence, whether is it a store, lead gen site, non-profit, or even blog is running analytics. Because these analytics tools are only 2% utilized, each discussion we have tends to be informative (for both sides), and interesting, aligning different reports with different unique business requirements.

While every business we talk to has individual needs, there are definitely questions that are pretty standard. A few of them have been discussed in the last posts, but today's post deals with a biggie:

Should I use a free analytics tool, and which one should I pick?

This is a topic worth a book, not a blog post. In order to keep this interesting (and under 20,000 words, I am going to do two posts on the issue. This one will be on the biggest points to consider when examining free analytics tools, and the next will be a discussion and comparison of the ‘big two’ of Google and Yahoo’s analytics tools. (before I start getting emails with suggestions of other tools that should have made the list, finish reading the post....explanation to follow)

There are a few high level points that should be considered when thinking about free analytics tools:

Why bounce rates are important...and not important.

There’s nothing like a solidly contrary title to start off a web marketing blog. One of the most fascinating and frustrating things about web analysis is that every metrics is relevant in certain contexts, and irrelevant in others. Depending on your business and the segment of traffic we are examining, metrics like time on site, page depth of visit, even goal conversion are totally different in terms of importance.

The example this blog will be using is bounce rate.

According to the Web Analytics Associations standards document, bounce rate is the ratio calculated by dividing single page visits by entry pages. In plain english this means that a bounce occurs when someone has a single page session. They entered your site on a specific page, and then left the site without going any deeper.

Your overall bounce rate number is the percentage of people who had a one page visit to your website.

I have had discussions with multiple firms in the last few weeks who were concerned about their ‘bounce rate’ numbers, specifically the overall number.

Overall bounce rate numbers are at best a health metrics for your web business. On it’s own it doesn’t really mean anything, but it can be indicative of areas of interest that actually do mean something.

Example 1#: One of the firms I am working with asked me to examine bounce rate issues as they were well in excess of 50%. I put together two quick segments, one for their ‘true conversion’ segment (they only sell in North America) and one for ‘rest of world’. The bounce rate for their true conversion segment was much lower than rest of world.

So when you look at it, high bounce indicated success. They were able to relay information to non-prospects that allowed them to move on and not waste their time. They were also able to relay information to real prospects to let them know they should stick around and engage the site.

Example 2#: An eCommerce company I work with asked me to examine bounce rates as they were super high, in excess of 80%. As bounce rates are a combination of pageviews and first page of session data, a good place to start to see real numbers is under “Landing Page” reports (which is what Google Analytics calls this type of report).

Over half of all visits started on the homepage, and the bounce rate was much better than expected at around thirty percent. The other half of their traffic initiated visits on something like 27,000 other pages within the site...and all of them had bounce rates in the high nineties! It turns out that this site generates a ton of natural search traffic from visitors searching for very generic non-branded terms (think ‘jogging pants‘ instead of ‘Roots Yoga pants’), which implies a lack of buying intent in the visitor.

So in example one, bounce rates showed that the website was doing a good job at speaking to its primary stakeholders. In example two, bounce rates helped identify a significant underperforming segment of traffic in unbranded natural search visitors.

Moral of the story: Bounce rates are important as early warning signals that allow you to start asking questions of your analytics. Bounce rates are not important as a success metrics for your website.

Cheers,

Jim

This week’s takeaway: Look at your overall year to date bounce rate in your analytics tool. Make a list of the top three reasons you think you have this rate, and see if you can prove/disprove with data.