Better eCommerce performance through intelligent clickstream data analysis

How to remove pricey stumbling blocks from the customer journey with Process Mining

Whether website tracking or eCommerce tracking: as an online marketer you know the golden rule "Set measurable goals that you want to achieve with your website / with your store". Marketing KPIs (e.g. Return on Advertising Spend, Net Promoter Score, Email Open Rate), operational KPIs (e.g. Average Delivery Time, Time to Market) or sales KPIs (e.g. Number of Leads in Sales Funnel, Average Time for Conversion) can be appropriate signposts. Very important eCommerce KPIs are also conversion rates, cost-per-click and cart abandonment rates. All these KPIs are significant factors and give you guidance on how well your website/shop is performing. So far so good. Optimally, you get relatively reliable and meaningful metrics. What you don't know yet, however: HOW and WHY does this analysis result come about? Let's take a look at the Cart Abandonment Rate example ...

Example "Cart Abandonment" | Why do users abandon at the last moment?

Assuming you are not happy with your rate of "shopping cart abandoners": How do you now find out WHY a potential buyer changed his mind after all? Were the shipping costs too high for him? Did the coupon code not work? Did they have to re-enter credit card information or address data?

Many things can lead to a cancellation "at the last moment", which is highly annoying for you – not least technical inconsistencies.

To gain the relevant insights, you need to take a closer look at the user or customer journey. Depending on how good your capabilities are in terms of customer journey analytics, this may be harder or easier for you. If you google for ways to analyze the customer journey, the shopping cart process and/or the payment process, you will find many search results related to Google Analytics or other customer journey mapping tools. What you will not find much in this context is the data analysis methodology and technology Process Mining.

Complete customer journey transparency thanks to Process Mining

Process Mining uses the clickstream data of the website, i.e. the digital traces left by your users, to automatically map user behavior in the form of lived processes and process variants and to provide them with process-related key figures. Process weaknesses and problematic patterns are revealed automatically and user behavior becomes completely transparent. In IT Service Management, for example, Process Mining is used more routinely than in eCommerce to improve service quality, e.g. by uncovering and eliminating reasons for long ticket processing times or ticket ping-pong. In principle, Process Mining is capable of reading out and automatically mapping any user flows and process data – even across multiple IT systems. In eCommerce, for example, a Process Mining analysis could reveal that a particular payment option is causing problems and generating significantly higher abandonment rates.

Since a cancellation in the order/payment process means hard cash, the customer journey analysis of one's own store with the digital analysis and diagnosis professional Process Mining is particularly worthwhile.