eCommerce Metrics Product Name 
ConversionXL
FEBRUARY 24, 2014
Example: An Ecommerce site that I analyzed recently had a payment page where 84.71% of the traffic proceeded to buy. Metrics to keep in mind here: Average transaction value. Control: it’s easy to make changes (update quantity, remove products). Asos has a good take on the cart page: What they get right: Product photos.
ConversionXL
APRIL 23, 2019
Your ecommerce checkout flow is where the money is at. An ecommerce site that I analyzed recently had a payment page in which 84.7% A framework for thinking about ecommerce checkouts: The Fogg Behavior Model. Metrics to keep in mind: Average transaction value; Average quantity per transaction. Think about it. Oh, please.
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Occam's Razor
JULY 22, 2013
My solution to these problems was to create a simple framework we can use to put our customers first, evaluate our marketing programs, and right-align our view of success (metrics). Why is it that SEO is only focused on our brand terms and product names, when there are 9 million additional intent-identified queries in the Do stage and 14.6
ConversionXL
JULY 29, 2020
From those fundamental differences flow all tactical differences—the ones that require tweaks to titles or affect how you promote your ecommerce products on other sites. That’s why keyword research is paramount—not just for the obvious product name but for high-value descriptors. What doesn’t change.
ConversionXL
AUGUST 5, 2014
Yet, in a site search benchmark study evaluating 50 top eCommerce sites by Baymard Institute – including NewEgg, Gap, Target & more – it was found that half of the sites evaluated did not have a search functionality that was easy to find. Another survey by SLI which interviewed 160 eCommerce found that 62.5%
ConversionXL
AUGUST 14, 2019
Your join key could be a page URL, product name, user ID, or many other things. In this instance, you’d combine data from your ecommerce platform with Google Analytics data. Select dimensions and metrics you want to compare. Combining Google Analytics with CRM and ecommerce platform data. More examples.
Occam's Razor
SEPTEMBER 7, 2010
As if massive data we have is not enough of a problem, we also rely on Averages, Percentages, Ratios and Compound/Calculated Metrics in a profoundly sub optimal way, as a drunken man uses lamp-posts – for support rather than for illumination. You saw this problem when I sorted descending or ascending for the metric bounce rate above.
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