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New eCommerce KPIs to Take into the Boardroom

19 January 2018 by Al McCloud

eCommerce professionals like you need a pat on the back. The average consumer might not appreciate that your industry often forces you to throw out everything you’ve ever learned and start from scratch.

Capture 1It’s a symptom of the internet cycle: we adapt to our users’ needs only for those needs to change again with the introduction of new tech.

In a much simpler time, users only had one device (usually a bulky beige PC) and the idea of content marketing would have been deemed a distraction. Back then just one KPI mattered: conversion rate.

But users got used to ordering things on their laptop, and then on their smartphone, and now they’re speaking their desires into thin air via a voice assistant or having a friendly chat with your AI.

In 2018 the average user has at least three devices along with unique quirky browsing habits. Equally, eCommerce businesses have a ton of new statistics to sift through: reliable and measurable customer satisfaction analytics, engagement from a finessed multi-channel content marketing plan and complex funnels.

With all this data to sift through, can you slice your 2017 numbers and find new meaning to present? How can you plan for the next quarter in a modern and meaningful way?

Conversion probability

This new feature is one of Google Analytic’s first ventures into machine learning with ‘Analytics Intelligence’. Whilst the exact calculations remain characteristically hidden, this metric is – in effect – a new way to define behaviour by working backwards from all other conversions. In doing so, it should be possible to find similarities in behaviour between your converting users which good old common sense alone might have missed.

In Google’s version, converting users are given a score from 0-100; a probability that they will buy again in the next 30 days. This score is based on the shared characteristics of your existing converters.

So, whilst you may be obsessing over the age or source of your most valued customers, it might, in fact, be the strange quality of a particular journey through your site which is responsible for visitors converting.

There is a caveat – you’ll need a minimum of 1,000 eCommerce transactions per month and have GA eCommerce Tracking on too.

Still, even without the tool itself, there is still inspiration to be taken from the approach – we often categorise converting users by human factors like time of day, demographic qualities, or a particular channel when trying to understand our converters – its time to consider any and all similarities, however strange initially, if it will lead to a sale.

Lifetime metrics

Google has introduced a second new view for your data in the last few months: lifetime user metrics. Whilst your customers are logged-in you’ll have all of their buying histories – that’s no doubt part of your bread-and-butter, but how about before they sign-in? What’s making them sign-in to buy?

With lifetime metrics you have a second layer of long-term user analytics, so you can look back through user-level cookies and see the total amount of time individual, anonymous users spent or the total number of transactions an individual user has made on your website, or when a user made their first visit to your site and which channel acquired them before they even sign in.

Lifetime Value is a concept you’ll understand well, but as the eCommerce industry matures and you retain your customers – potentially for a lifetime – being able to predict how your visitors’ behaviour evolves and matures could give you the edge as the next generation of visitor lands.

Time on site vs. Average time on page

In a massive study by digital marketing specialist Woflgang Digital, which included 143 million website sessions and $531 million in online revenue, the most convincing correlation in the entire study was between conversion rate and the overall time spent on the site. By increasing time on site by 16%, the study showed that conversion rates went up 10%. What’s more, the pages per session also correlated solidly with revenue growth (0.25).

Put simply, create ‘sit forward’ content; hold on to visitors with sticky and engaging content but don’t let them sit back for too long; ensure that their session includes lots of interaction and next steps.

Conclusion

We’re on the dawn of a machine learning age in which customer data can just as easily help us predict the future as it has helped us analyse the past. We can now use machine learning to make more sense of cold hard data without the stumbling block of human common sense, whilst at the same time ensuring that content appeals to very human needs – keeping us not only entertained but actively engaged.

In reply to a tweet from UKFast on KPIs, Al Mackin, CEO of market-leading form analytics business Formisimo said: “work backwards from the most important number of all (most likely revenue) and create a hierarchy of metrics that have an impact on the one above. Focus on these, and these alone.”

Whilst there might never be one golden KPI, there will always be a new way of interpreting your raw data to find new angles. Every single one of these is worth exploring – even if that does mean once again throwing out your rule book!

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