Creating a measurable competitive advantage using data in Ecommerce is nothing new. Companies like Amazon and eBay have built their entire business operations model around leveraging data to best affect and at the heart of optimisation strategy is precision.
So what techniques can you start using to improve site conversion rate today?
Analytics Diplopia (Double Vision)
Visitors are counted more than once when web analytics tools are poorly implemented.
The good news is that this means conversion rate performance is most probably being under-reported by your business. Unique visitor (and visit) metrics used by many companies as the denominator for calculating conversion rate are most probably, being (artificially) inflated. The standard ‘out-of-the-box’ configuration that comes with leading web analytics tools will double-count unique visitors who navigate between domains during their user journey. Why you ask? Well imagine that a copy of the original visitor with slightly different DNA has to be created to ensure tracking continuity (using cookies).
For example, if a visitor comes via Google organic search keyword “shoes” to an online store (http://www.myshop.com) and adds a product to their basket. The visitor is transferred to a different domain for secure payment (https://www.mysecurepayments.com) for check out. The shopper will be counted as two separate “unique” visitors because different tracking cookies for each site (domain) were used. Imagine what the actual conversion rate could be after these people are measured correctly!
Solution: Readers using Google Analytics should learn about how to improve visitor tracking and because every web analytics tool has a slightly a different method, it is best to ask your vendor or alternatively contact us for expert, impartial advice.
Lore and Orders
Traditional web analytics tools have been known to report purchases more than once.
Use the orders metric and group by the dimension that contains a (unique) purchase transaction id. As a rule of thumb, where there is more than one order recorded per transaction id there is a problem. Calculate the impact historically by pulling data for previous periods and remove these orders from future conversion rate reports. Reader using Google Analytics can use this pre-built duplicate orders report.
Solution: The best long-term plan will require a web developer to adjust the confirmation page HTML code or template so that the ecommerce analytics tag only loads once per purchase so as not inflate the orders metric.
Top Tip! While the developer is changing the ecommerce tag, ask them to rename reloaded confirmation pages (e.g. /purchase_confirmation/reload/). This will help to build more accurate purchase conversion funnel reports by using the authentic success page and bring purchase confirmation Pageview numbers into better alignment with the orders metric total.
Preaching To The Converted
Fix cross-domain tracking in order to evaluate marketing activities properly.
Now having read this far, you could be forgiven for thinking that if both components of the conversation rate formula are inflated, why not just leave them and the equation remains balanced? The answer is not quite since there is probably more duplicate visitors than there are duplicate transactions. However, the real reason to improve tracking is because of customer communication and the accurate evaluation of marketing performance.
Analysing Basket Cases
Discover cross-sell and up-sell opportunities through data mining and increase conversion
From a Merchandiser’s perspective, the hardest part about cross-sell is deciding what products go well together and displaying them on site. The starting point for anyone not wishing to rely on gut feel alone is usually the analysis of existing customer order records as the process can yield surprising results. And in case anyone reading this article has heard of a famous supermarket data mining example, here are the real beer and diaper facts via Daniel Power. After completing the next challenge you’ll be left with an actionable report for cross-sell analysis to customise.
How to get started with product affinity (or market basket) analysis.
After the data has been properly mined and analysed, then comes the interesting bit of finding out whether or not the cross-sell works in real life.
What other techniques are you using to improve site conversion?
About the Author
Alex Brown is a Digital Analytics and Site Optimisation expert who works as an independent freelance consultant. The opinions shared in this blog are based on personal experiences gathered over a decade of data crunching and technology evaluation. The author makes no attempt to be grammatically, politically (or otherwise) correct. Spelling was never a strong point and for practical reasons, not all vendors in the market are referenced in the article - no hard feelings.
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|Resources||(In Alphabetical Order)|
|Daniel Power||Beer and Diapers|
|Google Analytics (1)||Cross Domain Tracking|
|Google Analytics (2)||Duplicate Orders Report|
|Google Analytics for Excel||Excellent Analytics|
|Google Analytics Query Builder||Reporting API|
|Pareto Principle||80/20 Rule|
|Statistical Software Package||R-Project|
|Wikipedia (1)||Cross Sell|
|Wikipedia (2)||Market Basket Analysis|