Friday, March 26, 2010

Notice anything different?

A lot of changes and progress have occurred at Napykn over the past few months. The biggest change? Me. I'm Ben. Happy to be on board as the new digital analyst (and blogging enthusiast). But that's not all.

We've re-vamped the blog, as you can see. And along with this spiffy new template, we're re-committing ourselves to posting new and interesting articles on a regular basis. Jim's got a great one lined up for April 1 that's going to open some eyes, and I'll also be contributing a few anecdotes during April.

The best part about our blog is that it's the result of our everyday interactions with our digital analysis clients and their data. When we notice something cool, we'll be throwing it up here for you to pick apart. Also, expect to see news and notes from around the office and the internet as a whole. 

So don't forget to subscribe to our RSS feed before you bounce, because there's a lot of good stuff coming your way.

Wednesday, December 16, 2009

Magnifying Glasses, Microscopes and Web Analytics.

We have been talking to and working with a lot of companies this year for web analytics, and an interesting trend has been emerging with the executives that we are dealing with. Most digital decision makers we talk to either want to have Napkyn deliver ‘magnifying glass’ focused consulting on a few critical business metrics every month, or they want us to pull out the microscopes and look for new revenue potential in the specifics of their data.

In understanding these two types of executives, their motivations and ultimate goals, we can quickly see what value a good web analyst can immediately bring to an organization.

Executives who are responsible for a digital channel tend to fall into one of two types:

Analytics for performance management (macro level analysis) : Macro executives view WA data as a set of health metrics that can be used to understand the digital business. Their ultimate goal is to have a small set of business critical metrics that they can monitor to assess their online success. An example of this would be Patrick Byrne at Overstock, who says that he continually monitors their net promoter score as an operational success metric.

Friday, December 4, 2009

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.

Tuesday, October 6, 2009

Napkyn Featured in Internet Retailer


It has been a crazy few months of growth, and today Napkyn was written up by Internet Retailer, the leader in eCommerce news.

One of our clients, Scentiments.com (number 384 on the IR500) was featured in a story where they discussed the revenue impact that ongoing analysis has for an eCommerce business.

Thursday, July 30, 2009

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: