With upwards of one hundred times the traffic of a standard day, Black Friday should have provided online retailers with an amazing depth of insight over the past few years – but the reality has been very different.  

Not only are retailers failing to mine the volume of data generated during usual trading volumes, but Black Friday is hardly an a-typical day.

So, does data analytics offer any value at all on the busiest shopping day of the year? Most definitely, if you want to deliver the fastest, slickest and most effective shopping experience.

Consumers shop very differently on Black Friday; it is all about speed – finding products fast, ensuring delivery options fit, and checking out smoothly.

There is no tolerance for confusing offers, for convoluted delivery messaging, check-out processes slowed down by add-on offers, or options to sign up for loyalty schemes.

That is a different message for a different day. To make the most of Black Friday, retailers need to get the fundamentals of the experience as slick as possible – and nothing more.

It sounds so straightforward, yet in a market where user experience teams have little insight into problems due to the sheer volume of data, and test based on best practice and intuition at best, ensuring the fundamentals are working perfectly is difficult.

How well prepared, for example, is the mobile site? During the holiday season, the conversion rate more than doubles on mobile, signalling that more users buy this way when they have a feeling of urgency – and it doesn’t get much more urgent than Black Friday.

What is required therefore is a way to gain rapid insight from the existing data resources.

And that is where AI and machine learning are set to play a vital role in transforming the day-to-day activity of e-commerce teams.

In contrast to manual data mining techniques that can barely scratch the surface of e-commerce data, AI can transform speed to insight.

Whether through mining the entire checkout process and then surfacing immediately at both a problem and its location – or looking at different areas of the page to identify those that don’t get clicked on very often but convert well when they do – AI can provide rapid insight into the priority areas that need to be tested.

Essentially, AI can find the issues quickly – enabling organisations to focus on delivering the right Black Friday experience, from reducing journey length to improving signposting and ensuring the guest check-out is easy to find and use.

And with Black Friday in the UK fast evolving from a day to a period to a week-long event, it is becoming increasingly important to understand different trends in behaviour across the longer timeframe and ensure the experience matches up.

Capturing attention

With good processes in place, retailers can turn their attention to the best ways of capturing shoppers’ interest. Shoppers are ready to buy – with conversion rates rising by 89 percent between the first three weeks of November and Black Friday events, retailers have a short opportunity to attract a huge audience.

While Black Friday is not a day to attempt to boost the loyalty programme, with vast numbers of individuals arriving on a site for the first time – often from Google Shopping – how can a retailer make it compelling and reduce bounce rates?

This is where analysis of this year’s Black Friday activity will provide invaluable insight not only for next year but any other peak trading time, including Christmas.

Understanding what worked, what didn’t, and being able to monetise content is incredibly valuable – especially from a merchandising point of view.

Which of the many offers on the homepage generated the most revenue? Where was it located? Did the less prominent offer outperform one located higher up the page, despite not being seen by the majority of shoppers too impatient to scroll down? Of those that did click on the offer, how many went on to make a purchase?

Instead of ditching all this insight into the Black Friday black hole, understanding the way content performs, the segments of traffic new to the site, and their journey, will deliver retailers invaluable insight to ensure they are ready to make the most of the next big trading day.

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About The Author

UK Managing Director at ContentSquare

Duncan has over 10 years experience within software start-ups. One of the initial team at Maxymiser (now part of Oracle Group) Duncan has worked closely with some of the world’s leading ecommerce teams, helping them solve their digital challenges. Duncan currently leads UK operations at ContentSquare, a user experience analytics and optimization platform that helps businesses understand how and why users are interacting with their app, mobile and website. Duncan is equally comfortable discussing UX analytics as he is discussing the fortunes of his beloved (and much maligned) Wolves FC.