eCommerce Analytics

We’ve wrapped up our first look at web analytics market share among online retailers and if you’ve been thinking about ditching your current package for the free Google Analytics, it appears you’re not alone. Google Analytics is now in use by 44% of online retailers with Omniture close behind at 34%. Coremetrics rounds out the big three with 17% market share.

As expected, Google Analytics dominates with smaller e-commerce players while Omniture owns the 5 million uniques/month and above segment. Coremetrics makes a respectable showing in the 500,000+ monthly uniques segment.

33% of mid-sized retailers use two or more analytics packages — nearly double the rate of larger retailers.

Of those, nearly all are using Google Analytics with either Omniture or Coremetrics. Is this a sign that retailers are kicking the tires on Google Analytics or is it just an inexpensive way to validate the figures from the more powerful packages?

Update: The full e-commerce web analytics market share report is now available for download in PDF format.

Fun fact from a study we’re cooking up: almost 2 in 5 retailers who are using Omniture or Coremetrics for their web analytics are also using Google Analytics.

Are they using Google Analytics to verify the numbers from their industrial strength packages or are they contemplating a switch? It certainly makes sense to use a second package if you want some assurance that the numbers you’re seeing are legit, especially if that package is free. But it’s not a stretch to imagine that these retailers might be giving Google Analytics a test drive.

Bonus fun fact: over 1% of online retailers are using both Omniture and Coremetrics. That’s some serious analytics firepower. And while you may have heard of these retailers, they aren’t the household names you might think.

Stay tuned for more web analytics analytics.

I was looking through a Coremetrics white paper today entitled Optimizing Your Marketing Mix in a Down Economy and was struck by the sheer amount of website interaction data the Coremetrics platform tracks:

• Every web page viewed by visitors

• Specific paths that visitors take through key site processes

• Web page point of entry, navigation path, and departure path taken by visitors

• Every banner ad, email campaign, affiliate link, search engine keyword (paid and organic), blog, news article, and any other source that brings visitors to the web site

• Every product, room, flight, or merchandise item that visitors click on, view, or interact with, and reserve, book, buy, or abandon

• Every newsletter signup, customer registration, and opt-in identification action taken by visitors indicating that they wish to be contacted

• Every important attribute of the visitor’s browser, including screen resolution, plug-ins, time zone, language, IP address, and domain name

Read Our Next Challenge: Turning a Firehose of Data into a Trickle? »

I just got finished perusing ReadWriteWeb’s series on recommendation engines since, as a recommendation engine company, we have a keen interest in the subject. One thing that is abundantly clear is that recommendation engines still reside in the realm of technology, as opposed to business. Despite all of the success of Amazon and Netflix in using their recommendation engines to drive revenue and customer satisfaction (both companies are in the top 40 in customer satisfaction), the idea of recommendation engines still hasn’t quite caught on. One of the reasons is that old channels die hard and recommendation engines are still perceived as a purely ecommerce play. But they don’t have to be. And following these four suggestions will make recommendation engines more palatable to multi-channel retailers who need to take more time migrating online.

Read 4 Recommendations for Recommendation Engines »

Omniture’s sales and marketing department are simply world class.  Not only are they beating the pants off of CoreMetrics, but they’ve sold a vision of complete information awareness that online retailers are eagerly buying into.  Almost every single one of our customers has bought Omniture or is about to.  And every single one of them has asked the same question.  Now what?

Read Congratulations! You’ve installed Omniture! Now What? »

We talk a lot about the nuts and bolts of predictive analytics on this blog - how we build customer analytics models, how we interpret them, and how we establish whether or not they’re working in production. However, one item we have yet to tackle - and a very important item at that - is how to integrate data mining and/or predictive models into your organization and your company’s routines. Indeed, the black box in the middle should be the necessary data integration (getting the data into the right format for the models) and running a bevy of models against the data to see which one projects as the most effective. But it’s the before and after this black box that really make or break any attempt to incorporate predictive models into a company’s processes: data collection and incorporating data mining results into your processes.

Do you need to collect more data than you already have?
The main problem that companies run into is the need to collect more data in order to build the necessary predictive models. For example, if a company wants a model that tells them which of their products a specific customer might buy next, does it already collect the data necessary to support a model that does that? Ultimately, the question is: Is it worth the time and cost needed to invest in collecting more data that may not yield any better results when supercrunched? New collection methods take months to set up and sour clients on predictive models before the fun has even begun.

We believe that it’s important to try to work first with the data that our clients have and add new sources of data over time. This is a handy tip for business users who really want data mining to work in their organization: start small by using the data you have and get more sophisticated as you go. I know it may be tempting to put in that clickstream collection database right now so that you can use online behavior to segment and market to your customers. But trust me, get the buy-in from your organization first by proving that predictive customer analytics work. Then ask for the stars.

Are you ready to completely change the way that you do direct marketing?
Some companies would have you subscribe to a whole new way of doing business in order to use their models. Customer-centricity is my favorite new business philosophy. Though I firmly agree with the need for firms to be customer-centric, is it realistic to expect companies that use offers, coupons, and holidays to draw new and existing customers to their websites - and have for years - to change their direct marketing approach to accommodate a new set of tools? Not really. Company culture and routines are slow-developing and even slower-changing.

So the question at the end is really: What are you going to do with all this newfound predictive power? How are you going to fit in customer-centric model results - We’re 99% sure that Brad Pitt will buy two Baby Bjorns with lumbar support - to your next email blast that has a summer theme and features hats and sunscreen? And what about the extra creative necessary to relay that customer-centric message? You’re going to have to make a new email blast featuring the Baby Bjorn with lumbar support, in addition to the summer-themed email.

Well, I’m pretty sure that the summer themed email blast was probably going to draw some business but I’m also positive that luring customers with what they want when they want it is a lucrative way to market. The best practice is to initially skim the cream off of the predictions, to take those that have the highest probabilities of succeeding, and run with them. Collect a group of customers that has the highest probability of buying the Baby Bjord - Mr. Pitt among them - and send an intermittent email to just that group. Now watch your open rates and clickthroughs soar.